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Tag: customer service

How Customer Service AI Can Change Your Business

Posted on Max Fortis

AI is one of the most exciting new developments in customer service. But how does customer service AI work and what it makes possible? In this piece, we’ll offer the context you need to make good decisions about this groundbreaking technology. Let’s dive in!

What is AI in Customer Service?

AI in customer service means deploying innovative technology–generative AI, custom predictive models, etc.–to foster support interactions that are quick, effective, and tailored to the individual needs of your customers. When organizations utilize AI-based tools, they can automate processes, optimize self-service options, and support their agents, all of which lead to significant time and cost savings.

What are the Benefits of Using AI in Customer Service?

There are myriad advantages to using customer support AI, including (but not limited to):

1. AI will automate routine work.

As with so many jobs, a lot of what customer service agents do day-to-day is fairly repetitive, as little imagination is required to do things like order tracking, balance checking, or password resetting. These days, customers are obsessed with quick and convenient service, so utilizing customer service AI to automate and speed up routine tasks benefits both customers (who want answers now) and agents (who don’t have to do the same thing all the time).

2. Scalability and Cost Savings

A related point concerns the fact that AI automating routine tasks and supporting agents with data-driven insights allows businesses to scale customer service without a proportional increase in costs. This lowers operating expenses, increases capacity to handle peak volumes, and frees-up human agents for high-value tasks.

3. Customer service AI can help make ‘smart’ documentation.

Many customers will begin by checking out your documentation to see if they can’t solve a problem first, so it’s important for yours to be top-notch. You can use large language models (LLMs) to draft or update documentation, of course, or you can go a step further. Modern AI agents can use documentation to answer questions directly, and can also guide customers to use the documentation themselves.

4. Customer Support AI Supercharges Chat.

Customer service leaders have long recognized that AI-powered agents for chat support are a cost-effective (and often preferred) alternative to traditional phone or email support. AI agents for chat are rapidly becoming a mainstay for contact centers because they can deliver personalized, round-the-clock support across any channel while seamlessly integrating with other tools in the CX, eCommerce, and marketing tech stacks.

5. AI contributes to a better customer experience.

All of the above ultimately adds up to a much better customer experience. When customers can immediately get their questions answered (whether at 2 p.m. or 2 a.m.), with details relevant to their specific situation, or even in their native language, that’s going to leave an impression!

6. Use customer service AI to learn about your customers.

Before moving on, let’s discuss the amazing capacity customer service AI has to help you discover trends in your customers preferences, predict customer needs, identify patterns, and proactively address issues before they become major problems. This can significantly speed up your response time, reduce churn, improve resource allocation, and establish your reputation for anticipating customer desires.

Where to Get Started with AI in Customer Service

If you’re looking to get started with customer support AI, this section will contain some pointers on where to begin.

Deploy AI Agents for Maximum Efficiency

The next frontier in customer service AI is ‘agents,’ which have evolved from the AI chatbot and are capable of much more flexible and open-ended behavior. Whereas a baseline large language model can generate a wide variety of outputs, agents are built to be able to pull information, hit APIs, and complete various tasks from start to finish.

Use Customer Service AI to Guide Humans and Optimize your Business Processes

AI-powered tools in customer service are changing how support teams operate by enhancing the productivity of human agents, as well as the efficiency of workflows. By providing agents with response suggestions specifically tailored to each customer’s unique needs, for instance, these tools enable agents to burn through issues more swiftly and confidently. This can be especially helpful during onboarding, where agents benefit a great deal from additional guidance as they learn the ropes.

More broadly, AI can automate many aspects of customer service and thereby streamline the support process. To take just one example, intelligent, AI-powered ticket routing can use sentiment analysis and customer intent to direct inquiries to the agent best able to resolve them.

As mentioned above, AI can also participate more directly by suggesting changes to responses and summarizing long conversations, all of which saves time. In addition to speeding up the overall support process, in other words, these optimizations make agents more efficient.

Use Voice AI for Customer Calls

Another exciting development is the rise of ‘multimodal models’ able to adroitly carry on voice-based interactions. For a long time now there have been very simple models able to generate speech, but they were tinny and artificial. No longer.

Today, these voice AI applications can quickly answer questions, are available 24/7, and are almost infinitely scalable. They have the added advantage of being able to translate between different natural languages on the fly.

Effectively use AI in Emails

In customer service, email automation involves leveraging technologies such as generative AI to automate and customize email interactions. This enhances your agents’ response speeds, increases customer satisfaction, and improves overall business efficiency. It enables businesses to handle a large number of inquiries while maintaining a high quality of customer interactions.

Given the email channel’s enduring importance, this is a prime spot to be looking at deploying AI.

Make the Most Out of Digital Channels

For a while now, people have been moving to communicating over digital channels like Facebook messenger, WhatsApp, and Apple Messages for Business, to name a few.

As with email, AI can help you automate and personalize the communications you have with customers over these digital channels, fully leveraging rich messaging and text messaging to meet your customers where they’re at.

AI can Transform your E-Commerce Operations

When you integrate AI with backend systems – like CRM or e-commerce platforms – it becomes easier to enhance upsells and cross-sells during customer support sessions (an AI agent might suggest products tailored to a customer’s previous purchases, items currently in their shopping cart, or aspects of the current conversation, for instance).

Moreover, AI can proactively deliver notifications featuring customized messages based on user activity and historical interactions, which can also increase sales and conversion rates. All of this allows you to boost profits while helping customers–everyone wins!

Things to Consider When Using AI in Customer Service

Now that we’ve covered some necessary ground about what customer support AI is and why it’s awesome, let’s talk about a few things you should be aware of when weighing different solutions and deciding on how to proceed.

Augmenting Human Agents

Against the backdrop of concerns over technological unemployment, it’s worth stressing that generative AI, AI agents, and everything else we’ve discussed are ways to supplement your human workforce.

So far, the evidence from studies done on the adoption of generative AI in contact centers have demonstrated unalloyed benefits for everyone involved, including both senior and junior agents. We believe that for a long time yet, the human touch will be a requirement for running a good contact center operation.

CX Expertise

Though a major benefit of customer service AI service is its proficiency in accurately grasping customer inquiries and requirements, obviously, not all AI systems are equally adept at this. It’s crucial to choose AI specifically trained on customer experience (CX) dialogues. It’s possible to do this yourself or fine-tune an existing model, but this will prove as expensive as it is time-intensive.

When selecting a partner for AI implementation, ensure they are not just experts in AI technology, but also have deep knowledge of and experience in the customer service and CX domains.

Time to Value

When integrating AI into your customer experience (CX) strategy, adopt a “crawl, walk, run” approach. This method not only clarifies your direction but also allows you to quickly realize value by first applying AI to high-leverage, low-risk repetitive tasks, before tackling more complex challenges that require deeper integration and more resources. Choosing the right partner is an important part of finding a strategy that is effective and will enable you to move swiftly.

Channel Enablement

These days, there’s a big focus on cultivating ‘omnichannel’ support, and it’s not hard to see why. There are tons of different channels, many boasting billions of users each. From email automation for customer service and Voice AI to digital business messaging channels, you need to think through which customer communication channels you’ll apply AI to first. You might eventually want to have AI integrated into all of them, but it’s best to start with a few that are especially important to your business, master them, and branch out from there.

Security and Privacy

Data security and customer privacy have always been important, but as breaches and ransomware attacks have grown in scope and power, people have become much more concerned with these issues.

That’s why LLM security and privacy are so important. You should look for a platform that prioritizes transparency in their AI systems—meaning there is clear documentation of these systems’ purpose, capabilities, and limitations. Ideally, you’d also want the ability to view and customize AI behaviors, so you can tweak it to work well in your particular context.

Then, you want to work with a vendor that is as committed to high ethical standards and the protection of user privacy as you are; this means, at minimum, only collecting the data necessary to facilitate conversations.

Finally, there are the ‘nuts and bolts’ to look out for. Your preferred platform should have strong encryption to protect all data (both in transit and at rest), regular vulnerability scans, and penetration testing safeguard against cyber threats.

Observability

Related to the transparency point discussed above, there’s also the issue of LLM observability. When deploying Large Language Models (LLMs) into applications, it’s crucial not to regard them as opaque “black boxes.” As your LLM deployment grows in complexity, it becomes all the more important to monitor, troubleshoot, and comprehend the LLM’s influence on your application.

There’s a lot to be said about this, but here are some basic insights you should bear in mind:

  • Do what you can to incentivize users to participate in testing and refining the application.
  • Try to simplify the process of exploring the application across a variety of contexts and scenarios.
  • Be sure you transparently display how the model functions within your application, by elucidating decision-making pathways, system integrations, and validation of outputs. This makes it easier to model how it functions and catch any errors.
  • Speaking of errors, put systems in place to actively detect and address deviations or mistakes.
  • Display key performance metrics such as response times, token consumption, and error rates.

Brands that do this correctly will have the advantage of being established as genuine leaders, with everyone else relegated to status as followers. Large language models are going to become a clear differentiator for CX enterprises, but they can’t fulfill that promise if they’re seen as mysterious and inscrutable. Observability is the solution.

Risk Mitigation

You should look for a platform that adopts a thorough risk management strategy. A great way to do this is by setting up guardrails that operate both before and after an answer has been generated, ensuring that the AI sticks to delivering answers from verified sources.

Another thing to check is whether the platform is filtering both inbound and outbound messages, so as to block harmful content that might otherwise taint a reply. These precautions enable brands to implement AI solutions confidently, while also effectively managing concomitant risks.

AI Model Flexibility

Finally, in the interest of maintaining your ability to adapt, we suggest looking at a vendor that is model-agnostic, facilitating integration with a range of different AI offerings. Quiq’s AI Studio, for example, is compatible with leading-edge models like OpenAI’s GPT3.5 and GPT4, as well as Anthropic’s Claude models, in addition to supporting bespoke AI models. This is the kind of versatility you should be on the look out for.

What is the Future of AI in Customer Service?

This has been a high-level overview of the ways in which customer support AI can make you stand out in a crowded market. AI can help you automate routine tasks and free up agent time, personalize responses, gather insights about customers, and thoroughly optimize your internal procedures. However, you must also make sure your models are observable, your offering is flexible and dynamic, and you’re being careful with sensitive customer data.

For more context, check out our in-depth Guide to Evaluating AI for Customer Service Leaders.

10 Examples of AI Customer Service Solutions That Could Change Your Business

Posted on Michael Hartsog

From full-bore automation to multilingual speech and more, AI is already changing the way customer service works. We’ve written a lot about this over the past year, drawing on both industry statistics and our insider knowledge.

But it always helps to ground these claims with specific, real-world examples, which will be our focus today. In our judgment, there are ten domains where AI customer service helps businesses better serve their customers and improve their own profits, all of which we’ll walk through below.

Let’s get started!

What are the Ways AI can be used for Customer Service?

The following ten examples of AI in customer service are especially compelling. Read through them to see how you might use AI in your enterprise.

1. Instant, Multilingual, 24/7 Replies

Your customer service agents might require downtime, but an AI agent can operate round-the-clock. Capable of handling inquiries and resolving issues at any time of the day or night, AI agents ensure that matters are either resolved or neatly handed over to your human agents the next morning. This not only means that service is continuous, it also reduces the workload waiting for your agents while they’re getting their coffee.

Additionally, with the evolution of machine translation, it’s now possible to automatically translate between most languages, which is essential for serving a diverse, global customer base. Offering support in a customer’s native language not only improves key performance metrics like average handling time and resolution rates, but also significantly enhances customer satisfaction, demonstrating your commitment to providing a comforting, personalized experience (all this without the need to open up contact centers in each new region you operate in).

2. Automatically Sorting Emails and Tickets

Properly resolving an issue requires many steps, but one big one that must be handled up front is sorting and prioritizing incoming emails or tickets. A person could easily spend most of their day combing through communications with customers and deciding what should be handled first.

But, with AI, this is no longer required! Clever email automation can parse these incoming messages and route them to the relevant agents, boosting retention and improving your bottom line. Even better, AI agents are often good enough to solve problems directly (more on this later). Just make sure you’re following best practices when looking for an email automation for customer service platform.

3. Voice AI

Throughout this piece, we speak in general of AI agents, but one specific kind of AI worth singling out is the multimodal models able to parse voice commands and respond in kind.

On their own voice models aren’t new, but they’re now good enough to form a pillar of your customer experience strategy. Voice AI for customer experience can increase the efficiency of your operation, enhance the customer experience, and facilitate multilingual support.

4. Personalized Responses

Enhancing the customer experience by tailoring your interactions to their circumstances is easy enough to understand, but it’s far from trivial. However, a combination of advances in artificial intelligence and the vast amounts of data generated from our increasingly online lives is starting to change this picture.

However you’re reaching out to customers, people will respond better if you successfully draw on their specific data–purchase history, past interactions with your contact center, or whatever–to make it clear you’re talking to them.

Check out our guide to retrieval-augmented generation for more information.

5. Automating Content Generation

So, AI can help you personalize responses, but this is a special case of the more general fact that they can help you with content generation. Modern generative AI can create product information, summaries of calls or interactions, and even blog posts. These are huge time savers, reducing the workload for customer service teams and enabling them to handle more complex tasks.

6. AI Agents Are Handling Issues on Their Own

AI agents are great at automating routine tasks and asks, such as answering common questions or troubleshooting straightforward technical problems.

You’ll have to experiment with these tools to figure out which ones they can sort out alone, but we recommend maintaining robust checks to ensure they don’t behave in unexpected ways.

7. AI Systems are Letting Customers Solve Issues on Their Own

A related but distinct fact is that modern agents can help customers solve their own issues without the need for much oversight from agents.

AI agents can make your troubleshooting guides conversational, helping customers get their software working and guiding them through key steps while answering whatever questions come up along the way.

8. Natural Language Processing and Sentiment Analysis are Making Customers’ Feelings Clearer

There are different kinds of data, but two of the biggest categories are ‘structured’ and ‘unstructured.’ The former is the kind of thing you might find in clearly labeled columns in an Excel spreadsheet, while the latter consists of things like feedback from social media, product reviews, call transcriptions, etc.

Though unstructured data is profoundly informative, not much could be done with it until natural language processing in general – and sentiment analysis in particular – made significant strides. These techniques allow you to extract insights around how customers feel, at a much larger scale. AI agents can now automatically search for recurring issues or customer sentiment, while customer service teams can quickly respond to emerging problems and adapt in real time.

9. Predictive Analytics and Machine Learning are Helping Us Better Serve Customers

A similar idea is to use predictive analytics and machine learning. With these tools, enterprises can craft AI agents to analyze customer data to identify trends, preferences, and pain points. With enough data, for example, they might see cyclical patterns in customer buying behaviors or realize that a product feature consistently causes a certain kind of frustration.

This gives customer service teams and AI agents the ability to proactively address common issues and personalize responses (discussed above), thereby improving CSAT and other metrics by making interactions more helpful.

10. AI is Making Human Agents More Effective

We’ve made a few allusions to this already, but here we’ll make it explicit: AI agents can make your human agents much more effective.

Human agents can now use generative AI tools to help them craft responses, coach them to do their jobs better, and facilitate the automation of parts of their work. For example, Quiq’s Agent Assist offers suggested responses, agent coaching and process automation (see how it works here).

These are just a few examples, of course, but they’re enough to demonstrate the profound ways AI agents can augment (and improve) your workflows!

Leverage AI Customer Service Solutions Today

AI for customer service is among the most promising frontiers for our industry. To learn more about Quiq’s approach to customer-facing AI, click here.

How the AI Chatbot for Customer Service Became the AI Agent (And How It Actually Works)

Posted on Max Fortis

Chatbots have become a staple in customer service for brands across the world. This is why eight out of ten businesses have some kind of chatbot on their website to help customers in their journey.

And it’s not hard to see why, as there are myriad benefits to using such AI support chatbots. They’re available 24/7, without having to take breaks or sick days; they’re able to handle multiple conversations simultaneously; they’re cost efficient and scalable; they can personalize queries to each individual (more on this shortly); and they boost customer satisfaction.

Perhaps this is why the chatbot market was thought to be worth nearly $5 billion in 2022, a figure estimated to triple before the end of this decade.

But having said that, there’s a lot of diversity hidden under the ‘chatbot’ label. There are many techniques for building chatbots, these techniques have changed over time, and today, there are ‘AI agents’ which need to be distinguished from the older chatbots they replaced.

This is what we’re here to discuss today. We’ll first define AI chatbots in the context of customer service, provide an overview of their history, and how they’re different from the agents rapidly changing the contact center industry.

What is an AI Chatbot for Customer Service?

An AI chatbot for customer service refers to a program, platform, or machine-learning model that can perform some fraction of the work done by customer service agents.

Chatbots vary widely in complexity. First, there were the simple rule-based systems of yesteryear that attempted to understand the intent of a query and match it to an appropriate, pre-defined response. Over time, advances in machine learning, natural language processing, and data storage led to the billion-parameter large language models we use now, which can respond flexibly and dynamically under a range of circumstances, even to questions that are ambiguous or contradictory.

These are incredibly different offerings, but we won’t do more than point that out for now, as this will be our focus for most of the rest of the piece.

Regardless, an AI customer service chatbot generally lives on a company’s website, where it can answer questions. It has also become common to integrate them into various communication channels, such as Apple Messages for Business, WhatsApp, Voice, and email.

Though there are critical aspects of human interactions that are still not outsourceable to algorithms, customers have gradually become more willing to talk directly to AI chatbots to resolve their customer service issues. Surveys have shown that almost everyone has heard of chatbots and understands in general terms what they are. And nearly three-quarters of respondents prefer chatbots over humans to quickly get simple questions answered. When asked whether they were satisfied with their last interaction with a chatbot, 69% said ‘yes,’ and a little over half cited long wait times as one of their chief frustrations.

How AI Chatbots Have Evolved

As promised, let’s now discuss some of the ways in which the customer service AI chatbot has changed over time.

Here’s a broad overview, taken from TechTarget:

The Evolution of Chatbots

What are the Kinds of AI Chatbots?

You’ll notice the chart above tracks three broad types of chatbots, which is a categorization we more or less agree with—though we think there’s an important distinction between chatbots and agents, which isn’t reflected here. (We discuss that more in the next section).

The first kind of chatbot to be developed was by far the simplest, and it emerged from research done in the 1960s. These were based on a primitive model known as a ‘decision tree,’ and were only suitable for basic, formulaic interactions where there were clear rules and virtually no room for either ambiguity or creativity. In contact centers, robust AI agents are replacing these, but you might still see them answering the most common questions.

Although there was a lot of research into methods like neural networks, these ‘scripted chatbots’ were more or less the standard for the next four decades, until the field of natural language processing made enough progress to power a different approach.

Once it became possible to use sentiment analysis to detect emotional tones in writing, and entity extraction to automatically detect information like product names and formal titles (to pick just two examples), the road was paved to create more powerful ‘conversational chatbots.’

Unlike their predecessors, conversational chatbots could carry on much longer-range, multi-turn interactions, and help in a much broader variety of circumstances. Common examples of these tools are Siri and Alexa, both of which can process voice commands, look things up, fetch information, and even perform simple tasks (like scheduling a meeting or adding a reminder to your calendar).

Then, we come to the modern crop of AI chatbots, which are so much more powerful and far-reaching it’s better to call them ‘agents’ instead of ‘chatbots.’ These ‘generative AI agents’ are built around large language models, made famous by the release of ChatGPT in November of 2022.

For the most part, AI agents aren’t actually a new kind of technology, as neural networks have been around for a while and have been used in chatbots for a while, too. No, the single biggest distinguishing feature is that the networks are so big and are trained on such a bewildering variety of data that they can do things prior iterations couldn’t do.

No doubt you’ve spent some time playing with these models, or you’ve seen demonstrations of them, and you know what we mean. They can write code and poems, translate near-instantaneously between dozens of languages, describe (and generate) images (and videos), and take on all sorts of subtle postures in their interactions with humans. They can be instructed to act like a kindly grandma, for example, a stern teacher from fifth-grade, or an exceptionally polite and deferential friend.

This is precisely the reason that generative AI is having such a profound impact on contact centers. It can do so many things, and there are so many ways to fine-tune and tweak it, that people are finding dozens of places to use it. It’s not so much replacing human agents as it is dramatically simplifying and accelerating their workflows in hundreds of little ways.

AI Chatbots vs. AI Agents

Okay, now let’s get to the main distinction we want to draw out in this piece, the one between ‘AI chatbots’ and ‘AI agents’. In doing so, we’ll provide CX leaders with the valuable context they need to make the best decisions about the technological tools they deploy and invest in.

First, we’ve already written a lot about chatbots, so let’s define an ‘agent.’ Broadly speaking, an agent is an entity that can take one or more actions in pursuit of an overarching goal. Some agents are very simple, like single-celled organisms that just sort of float around looking for food, while some are very complex, such as the humans working out ways to terraform Mars.

But what they all have in common is a goal.

An AI agent is the same thing, it’s an artificial entity that can usually achieve a goal, like ‘download these data files and create a line chart with them’ or ‘check these six sources on quantum computing and summarize your findings.’

As with chatbots more generally, agents aren’t exactly new. We’ve been working with reinforcement learning agents for years, for example. But generative AI has opened up a whole new frontier, and the agent projects being built on top of it are really exciting.

A full discussion of this frontier is outside the scope of this article, but you can check out our piece on the future of generative AI for a discussion of specific agent projects.

7 Best Practices for Using AI Agents

What does concern us here is the impact this will have on CX leaders and the contact centers they manage, which is why we’ll cover some of the best practices of successfully using AI agents in this section.

1. Be clear about your intended use of AI agents.

As we’ve already mentioned, generative AI is great at many tasks, but the way to get the most out of it is to identify which KPIs you’re trying to drive and what changes you want to see, then implementing an agent that can help get you there. In other words, don’t be overwhelmed by its possibilities; start by drilling down into a few promising use cases and expand as appropriate.

2. Focus on design and access.

You want to be sure that the interface customers use to interact with your chatbot is sleek, intuitive, and easy to find. You can have the most powerful AI agent in the world, but it won’t do you much good if people hate using it or they can’t locate it in the first place.

3. Use full personalization.

One of the reasons modern chatbots are so powerful is that they can use retrieval-augmented generation to ‘ground’ their generations in sources of information–knowledge bases, product feeds, CRMs, Notion pages, etc. This makes replies more useful, while also making your customers feel more heard. So make sure your AI agent has access to the systems or data it needs to take action and be helpful (as you would with a new employee).

4. Gather feedback and improve.

AI agents are capable of being improved in a bevy of different ways. You should implement systems to gather your users’ impressions and use them to update your systems.

5. Let AI and humans play their strongest roles.

AI agents are great at many tasks, but others need a human’s superior flexibility and insight. The key here is to craft a system that can seamlessly switch between humans and agents.

6. Have your AI agents be proactive.

AI agents can be configured to reach out on their own if a user engages in certain behaviors or otherwise seems confused. For example, one well-known furniture brand and Quiq customer implemented Proactive AI and a Product Recommendation engine, which led to the largest sales day in the company’s history through increased chat sales.

7. Ensure transparency.

Most of us are really excited about the promise of generative AI, but one thing that has many concerned is the way data is used by these models, and their broader implications for privacy. Make your policies clear, and make sure you are being responsible with the data your customers trust you with.

You can use these best practices when designing your own AI agent system, but the easier way forward is to treat them as a checklist when you’re shopping around for third-party platforms.

AI Agents and You

Large language models, and the AI agents they make possible, will be a key part of the future of contact centers. If you want to learn more about this technology and the ways to harness it to redefine CX success, check out our latest guide.

How to Automate Email for Customer Service – Complete Guide

Posted on Mark Kowal

In our last article, we made the case for email automation for customer experience by pointing out that it’s easy to use, reduces work for your agents, and allows you to utilize the personalization that customers today have come to expect.

But that still leaves the ‘how’ of choosing and setting up a platform to automate email for customer service, which is what we’ll focus on today. By the end, you’ll have a much clearer picture of what to look for and how to make the overall process run as smoothly as possible.

Let’s dive in!

An Overview of Email Automation for Customer Service

First, let’s briefly recap what email automation for CX actually is. Email automation in customer service refers to using technologies like generative AI to automate and personalize email communications, positively impacting the speed of your agents’ response times, how satisfied your customers are, and how efficiently your business runs. It helps businesses manage large volumes of inquiries while still retaining high standards for the quality of customer interactions.

Chances are, you’re already using email to communicate with customers, but by leveraging AI for email responses, you can rapidly address queries about warranties or recent purchases, thus dramatically decreasing the inbound issues requiring your agents’ attention. This is a net win for everyone–you, your team, and your customers.

How to Automate Email for Customer Service

Now that you’re sufficiently excited by the possibilities represented by robust email AI assistants, let’s walk through what’s actually involved in using them.

In the next few sections, we’ll cover two big topics: how to select an email automation for CX platform, and what to do with it afterward.

Decide on a Good Email Automation Solution

Any attempt to implement email automation for customer experience has to revolve around a certain set of features. When you’re shopping around, check that your preferred tool has them.

1. One of the first things you’ll want to check is that a platform supports email AI assistants based on large language models (LLMs).

We’ve alluded to these assistants a few times already, and that’s because they play a major role in effective automation. AI for email responses is what drives the ability to personalize each email to the recipient, for example, but that’s hardly all. These days, customer-facing AI assistants can often answer questions directly (in text and voice), without the need of hassling a human agent, freeing them up to laser in on those interactions requiring more judgment.

Moreover, they can intelligently classify and route issues by topic, urgency, etc. You will want to ensure your software provider is using the latest generation of AI and large language models because it is much better at reasoning over the nature of your customer inquiry and generating a response.

Plus, in some cases, email automation for customer experience platforms support native data analytics (even if they don’t support it currently, it’s important to make sure it’s on the roadmap since analytics is an important feature). There’s a reason they’re calling data the new oil, and that’s because it can unlock dramatic insights into your customers’ preferences and how your business is operating. When a platform makes it easy to collect and utilize this data, you can make far more informed decisions.

2. Next, you want to make sure that your preferred email automation solution integrates well with other software.

Obviously, it’ll need to play well with your email client, but at minimum, you’ll also want to verify that it’ll plug into your CRM and any knowledge management systems you use (such as databases of customer purchases).

A related advantage is to pick a tool that allows you to deploy once and run everywhere. Integration is part of this, because it requires the platform to be built on business logic that allows it to utilize the same data as your other applications (i.e., to fit seamlessly into your data layer.)

3. Then, you’ll want to assess its adaptability and channel support.

In the beginning, you may only need the barest functionality—in fact, it’s often best to test these tools with simple proofs of concept that don’t make use of any fancy bells and whistles. But, as you grow, your needs will change, and you want a platform that will grow along with you.

Migrating later is always an option, but that will require a certain expense in time and money that could be avoided by making sure you’re using the right platform in the first place.

In a similar vein, when you automate email for customer service you want something with support for multiple channels. Even when a conversation begins with a customer emailing you it might quickly move to voice or text, and you want something that can move seamlessly over all these channels with the least friction possible.

“Ground” Your Email Automation Software

Once you’ve got this lined out, you’ll need to ensure that the AI email assistant lying at the heart of the software only references your company’s trusted knowledge and data. The most recent large language models work quite well out of the box, but even the best of them will still need to be programmed to your specific circumstances.

There are a few parts of this cycle. First, you must gather the relevant data, with a particular focus on two things:

  1. Company-specific data that helps capture your brand, vision, and approach;
  2. User-specific data that helps your email AI assistant to generate better replies.

The former is necessary to strike the right tone (are you going for ‘playful’ or ‘professional’, for example) and converse with customers in a way that reflects your basic values. One way to do this is to offer a model brand voice examples in instructions or prompts.

Less abstractly, the second is needed to make sure models are responding in the most helpful possible ways, by drawing on your data. This could involve using techniques such as retrieval augmented generation (RAG) or fine-tuning. RAG involves pointing a model to a CRM, a Notion table, etc., so that it can utilize this data in answering a customer inquiry.

Fine-tuning is another technique that could involve something like showing a large language model many instances of your best agents helping a customer, then using that to have the model make suggestions to more junior agents as they resolve an issue or respond directly on its own.

3 Best Practices for Using Automated Email for Customer Service

Now, we’ll spend a few sections explaining some of the best ways to successfully automate email for customer service, in a way that makes your life easier and pleases your customers.

1. Personalize your Emails

The first is straightforward but very important: make sure you’re personalizing your communications to your customers. This is one of the great use cases for modern AI email assistants. Unlike prior generations of AI tools that relied entirely on templates and brittle rules, good email AI assistants use powerful language models to grasp the context of each email and generate responses customized to a given customer.

This means that every customer feels seen in a way that’s not possible with generic boilerplate responses. For you, this drives positive outcomes like reducing response times, increasing issue resolution, and boosting customer satisfaction.

2. Make Privacy and Safety a Priority

Another thing that’s important is emphasizing customer privacy and safety. On the one hand, consumers today are accustomed to providing credit card information or their social security online (to make purchases or to file taxes), but they also see endless examples of data breaches compromising millions of people and putting them at risk of identity theft.

There’s no easy way to guard against this, but our purpose here is to highlight how crucial this part of your enterprise is. Your customers will trust you more if they believe that you’re doing everything you can to keep their confidential information from prying eyes. Learn more about security and compliance in the context of email automation for CX here.

3. Always be Testing and Monitoring

Finally, settling on a platform to automate email for customer service and deploying it are the start of your journey, not its end.

One reason for this is that the underlying language model technology that powers AI for email responses is stochastic. There are successful methods which make the models much more reliable. With prompt chaining, for example, you might check an incoming user email, reformat it for better results, generate an answer (making sure it’s included citations to its sources), then fact check it.

Nevertheless, you still have to monitor its output over time to make sure it’s still providing timely, helpful responses to your customers. A good platform will handle a lot of this monitoring while providing you a way to provide feedback if you’re seeing outputs that are problematic in some way.

Make The Most Use of Email Automation for Customer Service

Automating the workflows involved in addressing customer queries is one of the exciting new frontiers in customer service—and it’s already supercharging agents in contact centers.

You can learn more about the unique benefits of Quiq’s email AI right here!

7 Reasons Why Customers Want to Text You

Posted on The Quiq Team

If you’re old enough, you may remember a time when everyone wasn’t on their phones every waking moment of their lives. We’ve left those days long behind, and surveys indicate that many of us check our phones more than 300 times daily.

This isn’t surprising, given that we’re using our phones to buy tickets, order groceries, argue with relatives (and everyone else), talk to coworkers, and even find a date.

But one thing we’re not doing very often with phones is using them to make calls. This is where texting comes in. In 2020, mobile business messaging traffic climbed to 2.7 trillion, up a full 10% from just one year prior. More directly relevant to CX directors, text-based customer service requests increased by more than a quarter in 2021 alone.

What does this ultimately mean for your team? Simple: customers prefer texting.

The emergence of this crucial channel is one of the major recent trends in customer communication, and this article will enumerate the 7 biggest reasons why.

1. Customers Want Options

Too many options may lead to feeling overwhelmed—but that doesn’t stop customers from wanting them anyway.

There are countless ways to reach out to businesses—including phone, email, texting, and talking to AI assistants—but people generally pick the method of communication that fits their preferences.

Make sure to offer texting as one of those options; it’s become extremely popular, and may become more popular still as the years go on.

2. Your Customers are Busy. Texting is Asynchronous.

The pace of life has quickened these days. Your present and future customers will abhor the thought of wasting a single moment, and this is one of the biggest reasons customers prefer texting.

Suppose a customer starts a return or warranty flow at their office, but won’t be able to take a picture of the damaged item they bought until they got home. With text messaging, this becomes one seamless, continuous conversation that can be picked up or paused as needed, making it a great way to resolve issues that are important but can be handled in multiple independent steps. And with the right vendor, agents don’t have to close conversations while waiting for customer responses.

For its part, email support still feels like a more formal medium. People spend longer drafting messages, which means getting help will take more of their day, and no one enjoys not knowing when they’ll receive a reply.

And even though generative AI has begun to change this picture for the better, it’s still the case that a text message tends to fit better into your customers’ lives, allowing you to reach them where they are.

3. Texting Isn’t Limited to Millennials (or GenZ). It’s Generation Agnostic.

Before going further, it’s worth busting the myth that all the benefits of texting for customer service appeal to younger crowds. That may have been the case when The Matrix was released (25 years ago–ouch!), but it’s not the case any longer.

No, our parents and grandparents can text along with the best of us, and some surveys indicate that 90% of adults over 50 use their smartphones to send instant messages, texts, or emails (a number that’s likely gone up since.)

Part of what’s driving this trend in customer communication is that texting is both ubiquitous (almost everyone uses text messaging to communicate with someone), and it’s also a less technical way for people of any age to reach customer service.

Other messaging channels may require downloading an app or using unfamiliar social media. Text messaging, however, is ready to go on everyone’s device—even if they remember the Nixon administration.

4. Customers Want Better Experiences.

One of the main goals of customer service is to give patrons an experience they’ll remember, and continue to want to pay for.

And pay for it they are; research conducted by PwC found that almost three-quarters of people indicated that customer experience is one of the main things they consider when making a purchase.

Of course, text messaging is hardly the only thing that goes into creating a superlative customer experience, but when done correctly, it’ll probably get you further than you think.

Some of the benefits of texting for customer service have already been discussed (it’s quicker, it’s more convenient), but you may not realize that texting is a medium that can complete your overall customer experience strategy. Everything from sending support messages and getting order updates to checking out can all be handled right within SMS text messaging.

As we alluded to earlier, many think email is a fairly formal way of communicating, while texting is almost always more conversational. If it doesn’t feel contrived, the greater warmth and friendliness that comes through in texting can help build trust with customers, which is becoming all the more important in an age of data breaches and deepfakes.

5. Customers Want More than Words: They Want Rich Messaging, Too.

Building on this theme, one of the other major trends in customer communication is a move away from stiff, formal diction and towards talking to people more like they’re friends or acquaintances (with emojis, GIFs, the works).

When you think about business text messages, “Haha, yeah, sounds great! 😂” may not be the first thing that comes to mind. But perhaps a similar conversational tone should be, especially if using text messaging as a channel is a core part of your CX strategy.

Much of this is powered by “rich messaging,” which was developed to support emojis, in-message buttons and cards, audio messages, high-quality pictures, videos, and all the other staples of modern communication. Rich messaging can help you connect to your customers in a newly engaging way, and also helps them complete tasks quickly because they can use buttons or product cards to quickly input their information. This not only makes their lives easier, it increases the fidelity at which brands can communicate with customers.

Learn more about why your business should use rich messaging here.

6. They Just Prefer Texting.

The previous two sections discussed how texting leads to better customer experience and how rich messaging does a lot to increase the information and the conversationalism of communicating over text.

Taken together, these facts point to a broader one, which is that, well, many customers just prefer texting.

We’re all guilty of hitting ignore on a well-intentioned phone call or putting off making an appointment when we have to dial a number. More and more, people perceive a phone call as invasive and time-consuming.

This contention is backed up by data: 75% of millennials avoid phone calls as they’re time-consuming, and 81% experience anxiety before summoning up the courage to make a call. Millennials have a reputation for being phone-averse, but it doesn’t stop with them.

One of the basic issues with phone calls is that they’re unpredictable. A customer service call can take a few minutes or half an hour, so customers don’t know how to prepare.

Business texts are quick and efficient, and they can happen on the customer’s terms.

This is reflected in the fact that texts are more likely to be opened and responded to, by a large margin. The vast majority of texts (95%) receive replies within three minutes, and the average open rate is close to 100% (and just shy of five times better than similar metrics for emails).

To sum up (and reiterate): customers prefer texting, so your business should seriously consider adding it to your channel mix if you haven’t already.

7. Customers Want Texting to be Responsive and Personalized. Generative AI Makes That Easier On Businesses.

We’d be remiss if we didn’t include a section on the remarkable advances of large language models and what they mean for broader trends in customer communication—particularly the generative components.

This is a huge topic, but what they mean for customers is the availability of responses that are contextual, personalized, and always on.

The formulaic, stilted chatbots of yesteryear have been replaced by models that are dynamic, able to use techniques like retrieval-augmented generation to reply in ways that are tailored to that customer. It might generate answers from a knowledge base, for example, or provide personalized recommendations from a product catalog.

Moreover, language models don’t take holidays, breaks, or time off, and can therefore reply whenever and wherever they’re needed.

This doesn’t mean they’re a full-on replacement for human contact center agents, of course. But they’re a remarkable supplement. In addition, when you partner with a conversational AI platform for CX, you can utilize AI-driven benefits of texting for customer service with a minimum of hassle!

When It Comes Down to It: Yes, Your Customers Still Want to Text You.

Whether you’re looking at hard data or just vibes, it’s clear that more and more customers prefer texting. It’s easy, convenient, and fits better into a busy life while also affording the opportunity for personalization that drives higher levels of customer satisfaction.

If you’d like to learn more about how Quiq supports enterprise CX companies that want to make texting a centerpiece of their customer outreach strategy, learn more here about text messaging vs calling your customer.

How To Encourage More Customers To Use your Live Chat Service

Posted on Michael Hartsog

When customer experience directors float the idea of investing more heavily in live chat for customer service, it’s not uncommon for them to get pushback. One of the biggest motivations for such reticence is uncertainty over whether anyone will actually want to use such support channels—and whether investing in them will ultimately prove worth it.

An additional headwind comes from the fact that many CX directors are laboring under the misapprehension that they need an elaborate plan to push customers into a new channel. But one thing we consistently hear from our enterprise customers is that it’s surprising how naturally customers start using a new channel when they realize it exists. To borrow a famous phrase from Field of Dreams, “If you build it, they will come.” Or, to paraphrase a bit, “If you build it (and make it easy for them to engage with you), they will come.” You don’t have to create a process that diverts them to the new channel.

The article below fleshes out and defends this claim. We’ll first sketch the big-picture case for why live chat with customers remains as important as ever, then finish with some tips for boosting customer engagement with your live chat service.

Why is Live Chat Important for Contact Centers?

Before we talk about how to get people to use your live chat for customer service features, let’s discuss why such channels continue to be an important factor in the success of customer experience companies.

The simplest way to do this is with data: 60% of customers indicate that they’re more likely to visit a website again if it has live chat for customer service, and a few more (63%) say that a live chat widget will increase their willingness to make a purchase.

But that still leaves the question of how live chat stacks up against other possible communication channels. Well, nearly three-quarters (73%) are more comfortable using live chat for customer service issues than email or phone—and a high fraction (61%) are especially annoyed by the prospect of being put on hold.

If this isn’t enough, there are customer satisfaction (CSAT) scores to think about as well. This is perhaps the strongest data point in support of customer live chat, as 87% of customers give a positive rating to their live chat conversations.

Agents also prefer live chat over the phone because regularly dealing with angry and upset customers via phone can take an emotional toll. Live chat contributes to agent job retention—a big, expensive issue that many CX leaders are constantly trying to grapple with.

So, the data is clear and it makes sense for all the reasons we’ve discussed: Live chat for customer service shows every indication of being a worthwhile communication channel, both now and in the future.

6 Tips for Encouraging Customers to Use Live Chat

With that having been said, the next few sections will detail some of the most promising strategies for getting more of your customers to use your live chat features.

1. Make Sure People Know You have Live Chat Services

The first (and probably easiest) way to get more customers to use your live chat is to take every step possible to make sure they know it’s something you offer. Above, we argued that little special effort is required to get potential customers to use a new channel, but that shouldn’t be taken to mean that there’s no use in broadcasting its existence.

You can get a lot of mileage out of promoting live chat through your normal marketing channels–a mention on your support page, on your social feeds, and at the bottom of your order confirmation emails, for example. In the rest of this section, we’ll outline a few other low-cost ways to boost engagement with live chat for customer service.

First, use your IVR to move callers from phone to messaging. You can also mention that you support live chat for customer service during the phone hold message. We noted above that people tend to hate being put on hold. You can use that to your advantage by offering them the more attractive alternative of hopping onto a digital messaging channel instead—including WhatsApp, Apple Messages for Business, and SMS. For example, this might sound as simple as: “Press 2 to chat with an agent over SMS text messaging, or get faster support over live web chat on our website.”

From your perspective, an added benefit is that your agents can easily shuffle between several different live chat conversations, whereas that isn’t possible on the phone. This means faster resolutions, a higher volume of questions answered, and more satisfaction all the way around.

Similarly, include plenty of links to live chat when communicating with your customers. After they make a purchase, for example, you could include a message suggesting they utilize live chat to resolve any questions they have. If you’re sending them other emails, that’s a good place to highlight live chat as well. Don’t neglect hero pages and product pages; being able to answer questions while talking directly to current and future buyers is a great way to boost sales.

BODi® (formerly Beach Body) is a California-based nutrition and fitness company that pursued exactly this strategy when they ditched their older menu-based support system in favor of “Ask BODi AI.”

Bodi-Customer

This eventually became a cost-effective support channel that was able to answer a variety of free-form questions from customers, leading to happier buyers and better financial performance.

2. Minimize the Hassle of Using Live Chat

One of the better ways of boosting engagement with any feature, including live chat, is to make it as pain-free as possible.

Take contact forms, for example, which can speed up time to resolution by organizing all the basic information a service agent needs. This is great when a customer has a complex issue, but if they only have a quick question, filling out even a simple contact form may be onerous enough to prevent them from asking it.

There’s a bit of a balancing act here, but, in general, the fewer fields a contact form has, the more likely someone is to fill it out.

The emergence of large language models (LLMs) has made it possible to use an AI assistant to collect information about customers’ specific orders or requests. When such an assistant detects that a request is complex and needs human attention, it can ask for the necessary information to pass along to an agent. This turns the traditional contact form into a conversation, placing it further along in the customer service journey so only those customers who need to fill it out will have to use it.

Or take something as prosaic as the location and prominence of your ‘live chat’ button. Is it easy to find, or is it buried so deep you’d need Indiana Jones to dig it out? Does it pop out proactively to engage potential or returning customers with contextual messaging based on what they’re browsing?

It’s also worth briefly mentioning that the main value prop of rich messaging content– like carousel cards, buttons, and quick replies–results in much less friction for the consumer. We have a dedicated section on rich messaging below that spells this out in more detail.

Though they may seem minor in isolation, there’s an important truth here: if you want to get more people to use your live chat for customer service, make it easy and pain-free for them to do so. Every additional second of searching or fiddling means another lost opportunity.

3. Personalize Your Chat

Another way to make live chat for customer service more attractive is to personalize your interactions. Personalization can be anything from including an agent’s name and picture in the chat interface displayed on your webpage to leveraging an LLM to craft a whole bespoke context for each conversation.

For our purposes, the two big categories of personalization are brand-specific personalization and customer-specific personalization. Let’s discuss each.

Brand-specific personalization

For the former, marketing and contact teams should collaborate to craft notifications, greetings, etc., to fit their brand’s personality. Chat icons often feature an introductory message such as “How can I help you?” to let browsers know their questions are welcome. This is a place for you to set the tone for the rest of a conversation, and such friendly wording can encourage people to take the next step and type out a message.

More broadly, these departments should also develop a general tone of voice for their service agents. While there may be some scripted language in customer service interactions, most customers expect human support specialists to act like humans. And, since every request or concern is a little different, agents often need to change what they say or how they say it.

This is no less true for buyers on different parts of your site. Customer questions will be different depending on whether they’re on a checkout page, a product page, or the help center because they are at very distinct points in their buying journey. It’s important to contextualize any proactive messaging and the conversational flow itself to accommodate this (i.e., “Need help checking out? We’ve got live agents standing by.” versus “Have questions about this product? Try asking me”).

Setting rules for tone of voice and word choice ensure the messaging experience is consistent no matter which agent helps a customer or what the conversation is about.

Customer-specific personalization

Then, there’s customer-specific personalization, which might involve something as simple as using their name, or extend to drawing from their purchase history to include the specifics of the order they’re asking about.

Once upon a time, this work fell almost entirely to human contact centers, but no more! Among the many things that today’s LLMs excel at is personalization. Machine learning has long been used to personalize recommendations (think: Netflix learning what kinds of shows you like), but when LLMs are turbo-charged with a technique like retrieval-augmented generation (which allows them to use validated data sources to inform their replies to questions), the results can be astonishing.

Machine-based personalization and retrieval-augmented generation are both big subjects, and you can read through the links for more context. But the high-level takeaway is that, together, they facilitate the creation of a seamless and highly personalized experience across your communication channels using the latest advances in AI. Customers will feel more comfortable using your live chat feature, and will grow to feel a connection with your brand over time.

4. Include Privacy and Data Usage Messages

As of this writing, news recently broke that a data breach may have resulted in close to three billion – billion! – people having their social security numbers compromised. You’re no doubt familiar with a bevy of similar stories, which have been pouring forth since more or less the moment people started storing their private data online.

And yet, for the savvy customer experience director, this is an opportunity; by taking privacy very seriously, you can distinguish yourself and thereby build trust.

Customers visiting your website want an assurance that you will take every precaution with their private information, and this can be provided through easy-to-understand data privacy policies and customizable cookie preferences.

Live messaging tools can add a wrinkle because they are often powered by third-party software. Customer service messaging can also require a lot of personal information, making some users hesitant to use these tools.

You can quell these concerns by elucidating how you handle private customer data. When a message like this appears at the start of a new chat, is always accessible via the header, or persists in your chat menu, customers can see how their data is safeguarded and feel secure while entering personal details.

An additional wrinkle comes from the increasing ubiquity of tools based on generative AI. Many worry that any information provided to a model might be used to “train” that model, thus increasing the chances that it’ll be leaked in the future. The best way to avoid this calamity is to partner with a conversational AI for CX platform that works tirelessly to ensure that your customers’ data is never used in this way.

That said, whatever you do, make sure your AI assistants have messages designed to handle requests about privacy and security. Someone will ask eventually, and it’s good to be prepared.

5. Use Rich Messages

Smartphones have become a central hub for browsing the internet, shopping, socializing, and managing daily activities. As text messaging gradually supplemented most of our other ways of communicating, it became obvious that an upgrade was needed.

This led to the development of rich messaging applications and protocols such as Apple Messages for Business and WhatsApp, which use Rich Communication Services (RCS). RCS features enhancements like buttons, quick replies, and carousel cards—all designed to make interactions easier and faster for the customer.

For all these reasons, using rich messaging in live chat with customers will likely help boost engagement. Customers are accustomed to seeing emojis now, and you can include them as a way of humanizing and personalizing your interactions. There might be contexts in which they need to see graphics or images, which is very difficult with the old Short Messaging Service (SMS).

In the final analysis, rich messaging offers another powerful opportunity to create the kind of seamless experience that makes interacting with your support enjoyable and productive.

6. Separating Chat and Agent Availability

Once upon a time, ‘chat availability’ simply meant the same thing as ‘agent availability,’ but today’s language models are rapidly becoming capable enough to resolve a wide variety of issues on their own. In fact, one of the major selling points of AI assistants is that they provide round-the-clock service because they don’t need to eat, sleep, or take bathroom breaks.

This doesn’t mean that they can be left totally alone, of course. Humans still need to monitor their interactions to make sure they’re not being rude or hallucinating false information. But this is also something that becomes much easier when you pair with an industry-leading conversational AI for CX platform that has robust safeguards, monitoring tools, and the ability to switch between different underlying models (in case one starts to act up).

Having said that, there are still a wide variety of tasks for which a living agent is still the best choice. For this reason, many companies have specific time windows when live chat for customer service is available. When it’s not, some choose to let customers know when live chat is an option by communicating the next availability window.

In practice, users will often simply close their tabs if they can’t talk to a person, cutting the interaction off before it begins. In our view, the best course is usually to shift the conversation to an asynchronous channel where it can be handled by an AI assistant able to hand the chat off to an agent when one becomes available.

Employing these two strategies, means that your ability to service customers is decoupled from operational constraints of agent availability, and you are always ready to seize the opportunity to serve customers when they are eager to engage with your brand

Creating Greater CX Outcomes with Live Web Chat is Just the Start.

Live web chat with customers remains an excellent way to resolve issues while building trust and boosting the overall customer experience. The best strategies for increasing engagement with your live chat is to make sure people know it’s an option, make it easy to use, personalize interactions where possible—and make the most out of AI to automatically resolve routine inquiries while filling in live agent availability gaps.

If you’re interested in taking additional steps to resolve common customer service pain points, check out our ebook on the subject. It features a number of straightforward, actionable strategies to help you keep your customers as happy as possible!

Current Large Language Models and How They Compare

Posted on J.R. Rettenmeyer

From ChatGPT and Bard to BLOOM and Claude, there is a veritable ocean of current LLMs (large language models) for you to choose from. Some are specialized for specific use cases, some are open-source, and there’s a huge variance in the number of parameters they contain.

If you’re a CX leader and find yourself fascinated by the potential of using this technology in your contact center, it can be hard to know how to run proper LLM comparisons.

Today, we’re going to tackle this issue head-on by talking about specific criteria you can use to compare LLMs, sources of additional information, and some of the better-known options.

But always remember that the point of using an LLM is to deliver a world-class customer experience, and the best option is usually the one that delivers multi-model functionality with a minimum of technical overhead.

With that in mind, let’s get started!

What is Generative AI?

While it may seem like large language models (LLMs) and generative AI have only recently emerged, the work they’re based on goes back decades. The journey began in the 1940s with Walter Pitts and Warren McCulloch, who designed artificial neurons based on early brain research. However, practical applications became feasible only after the development of the backpropagation algorithm in 1985, which enabled effective training of larger neural networks.

By 1989, researchers had developed a convolutional system capable of recognizing handwritten numbers. Innovations such as long short-term memory networks further enhanced machine learning capabilities during this period, setting the stage for more complex applications.

The 2000s ushered in the era of big data, crucial for training generative pre-trained models like ChatGPT. This combination of decades of foundational research and vast datasets culminated in the sophisticated generative AI and current LLMs we see transforming contact centers and related industries today.

What’s the Best Way to do a Large Language Models Comparison?

If you’re shopping around for a current LLM for a particular application, it makes sense to first clarify the evaluation criteria you should be using. We’ll cover that in the sections below.

Large Language Models Comparison By Industry Use Case

One of the more remarkable aspects of current LLMs is that they’re good at so many things. Out of the box, most can do very well at answering questions, summarizing text, translating between natural languages, and much more.

But there might be situations in which you’d want to boost the performance of one of the current LLMs on certain tasks. The two most popular ways of doing this are retrieval-augmented generation (RAG) and fine-tuning a pre-trained model.

Here’s a quick recap of what both of these are:

  • Retrieval-augmented generation refers to getting one of the general-purpose, current LLMs to perform better by giving them access to additional resources they can use to improve their outputs. You might hook it up to a contact-center CRM so that it can provide specific details about orders, for example.
  • Fine-tuning refers to taking a pre-trained model and honing it for specific tasks by continuing its training on data related to that task. A generic model might be shown hundreds of polite interactions between customers and CX agents, for example, so that it’s more courteous and helpful.

So, if you’re considering using one of the current LLMs in your business, there are a few questions you should ask yourself. First, are any of them perfectly adequate as-is? If they’re not, the next question is how “adaptable” they are. It’s possible to use RAG or fine-tuning with most of the current LLMs, the question is how easy they make it.

Of course, by far the easiest option would be to leverage a model-agnostic conversational AI platform for CX. These can switch seamlessly between different models, and some support RAG out of the box, meaning you aren’t locked into one current LLM and can always reach for the right tool when needed.

What’s a Good Way To Think About an Open-Source or Closed-Source Large Language Models Comparison?

You’ve probably heard of “open-source,” which refers to the practice of releasing source code to the public so that it can be forked, modified, and scrutinized.

The open-source approach has become incredibly popular, and this enthusiasm has partially bled over into artificial intelligence and machine learning. It is now fairly common to open-source software, datasets, and training frameworks like TensorFlow.

How does this translate to the realm of large language models? In truth, it’s a bit of a mixture. Some models are proudly open-sourced, while others jealously guard their model’s weights, training data, and source code.

This is one thing you might want to consider as you carry out your LLM comparisons. Some of the very best models, like ChatGPT, are closed-source. The downside of using such a model is that you’re entirely beholden to the team that built it. If they make updates or go bankrupt, you could be left scrambling at the last minute to find an alternative solution.

There’s no one-size-fits-all approach here, but it’s worth pointing out that a high-quality enterprise solution will support customization by allowing you to choose between different models (both close-source and open-source). This way, you needn’t concern yourself with forking repos or fret over looming updates, you can just use whichever model performs the best for your particular application.

Getting A Large Language Models Comparison Through Leaderboards and Websites

Instead of doing your LLM comparisons yourself, you could avail yourself of a service built for this purpose.

Whatever rumors you may have heard, programmers are human beings, and human beings have a fondness for ranking and categorizing pretty much everything – sports teams, guitar solos, classic video games, you name it.

Naturally, as current LLMs have become better known, leaderboards and websites have popped up comparing them along all sorts of different dimensions. Here are a few you can use as you search around for the best current LLMs.

Leaderboards for Comparing LLMs

In the past couple of months, leaderboards have emerged which directly compare various current LLMs.

One is AlpacaEval, which uses a custom dataset to compare ChatGPT, Claude, Cohere, and other LLMs on how well they can follow instructions. AlpacaEval boasts high agreement with human evaluators, so in our estimation, it’s probably a suitable way of initially comparing LLMs, though more extensive checks might be required to settle on a final list.

Another good choice is Chatbot Arena, which pits two anonymous models side-by-side, has you rank which one is better, then aggregates all the scores into a leaderboard.

Finally, there is Hugging Face’s Open LLM Leaderboard, which is similar. Anyone can submit a new model for evaluation, which is then assessed based on a small set of key benchmarks from the Eleuther AI Language Model Evaluation Harness. These capture how well the models do in answering simple science questions, common-sense queries, and more, which will be of interest to CX leaders.

When combined with the criteria we discussed earlier, these leaderboards and comparison websites ought to give you everything you need to execute a constructive large language models comparison.

What are the Currently-Available Large Language Models?

Okay! Now that we’ve worked through all this background material, let’s turn to discussing some of the major LLMs that are available today. We make no promises about these entries being comprehensive (and even if they were, there’d be new models out next week), but they should be sufficient to give you an idea as to the range of options you have.

ChatGPT and GPT

Obviously, the titan in the field is OpenAI’s ChatGPT, which is really just a version of GPT that has been fine-tuned through reinforcement learning from human feedback to be especially good at sustained dialogue.

ChatGPT and GPT have been used in many domains, including customer service, question answering, and many others. As of this writing, the most recent GPT is version 4o (note: that’s the letter ‘o’, not the number ‘0’).

LLaMA

In April 2024, Facebook’s AI team released version three of its Large Language Model Meta AI (LLaMa 3). At 70 billion parameters it is not quite as big as GPT; this is intentional, as its purpose is to aid researchers who may not have the budget or expertise required to provision a behemoth LLM.

Gemini

Like GPT-4, Google’s Gemini is aimed squarely at dialogue. It is able to converse on a nearly infinite number of subjects, and from the beginning, the Google team has focused on having Gemini produce interesting responses that are nevertheless absent of abuse and harmful language.

StableLM

StableLM is a lightweight, open-source language model built by Stability AI. It’s trained on a new dataset called “The Pile”, which is itself made up of over 20 smaller, high-quality datasets which together amount to over 825 GB of natural language.

GPT4All

What would you get if you trained an LLM on “…on a massive curated corpus of assistant interactions, which included word problems, multi-turn dialogue, code, poems, songs, and stories,” and then released it on an Apache 2.0 license? The answer is GPT4All, an open-source model whose purpose is to encourage research into what these technologies can accomplish.

BLOOM

The BigScience Large Open-Science Open-Access Multilingual Language Model (BLOOM) was released in late 2022. The team that put it together consisted of more than a thousand researchers from all over the worlds, and unlike the other models on this list, it’s specifically meant to be interpretable.

Pathways Language Model (PaLM)

PaLM is from Google, and is also enormous (540 billion parameters). It excels in many language-related tasks, and became famous when it produced really high-level explanations of tricky jokes. The most recent version is PaLM 2.

Claude

Anthropic’s Claude is billed as a “next-generation AI assistant.” The recent release of Claude 3.5 Sonnet “sets new industry benchmarks” in speed and intelligence, according to materials put out by the company. We haven’t looked at all the data ourselves, but we have played with the model and we know it’s very high-quality.

Command and Command R+

These are models created by Cohere, one of the major commercial platforms for current LLMs. They are comparable to most of the other big models, but Cohere has placed a special focus on enterprise applications, like agents, tools, and RAG.

What are the Best Ways of Overcoming the Limitations of Large Language Models?

Large language models are remarkable tools, but they nevertheless suffer from some well-known limitations. They tend to hallucinate facts, for example, sometimes fail at basic arithmetic, and can get lost in the course of lengthy conversations.

Overcoming the limitations of large language models is mostly a matter of either monitoring them and building scaffolding to enable RAG, or partnering with a conversational AI platform for CX that handles this tedium for you.

An additional wrinkle involves tradeoffs between different models. As we discuss below, sometimes models may outperform the competition on a task like code generation while being notably worse at a task like faithfully following instructions; in such cases, many opt to have an ensemble of models so they can pick and choose which to deploy in a given scenario. (It’s worth pointing out that even if you want to use one model for everything, you’ll absolutely need to swap in an upgraded version of that model eventually, so you still have the same model-management problem.)

This, too, is a place where a conversational AI platform for CX will make your life easier. The best such platforms are model-agnostic, meaning that they can use ChatGPT, Claude, Gemini, or whatever makes sense in a particular situation. This removes yet another headache, smoothing the way for you to use generative AI in your contact center with little fuss.

What are the Best Large Language Models?

Having read the foregoing, it’s natural to wonder if there’s a single model that best suits your enterprise. The answer is “it depends on the specifics of your use case.” You’ll have to think about whether you want an open-source model you control or you’re comfortable hitting an API, whether your use case is outside the scope of ChatGPT and better handled with a bespoke model, etc.

Speaking of use cases, in the next few sections, we’ll offer some advice on which current LLMs are best suited for which applications. However, this advice is based mostly on personal experience and other people’s reports of their experiences. This should be good enough to get you started, but bear in mind that these claims haven’t been born out by rigorous testing and hard evidence—the field is too young for most of that to exist yet.

What’s the Best LLM if I’m on a Budget?

Pretty much any open-source model is given away for free, by definition. You can just Google “free open-source LLMs”, but one of the more frequently recommended open-source models is LLaMA 2 (there’s also the new LLaMA 3), both of which are free.

But many LLMs (both free and paid) also use the data you feed them for training purposes, which means you could be exposing proprietary or sensitive data if you’re not careful. Your best bet is to find a cost-effective platform that has an explicit promise not to use your data for training.

When you deal with an open-source model, you also have to pay for hosting, either your own or through a cloud service like Amazon Bedrock.

What’s the Best LLM for a Large Context Window?

The context window is the amount of text an LLM can handle at a time. When ChatGPT was released, it had a context window of around 4,000 tokens. (A “token” isn’t exactly a word, but it’s close enough for our purposes.)

Generally (and up to a point), the longer the context window the better the model is able to perform. Today’s models generally have context windows of at least a few tens of thousands, and some getting into the lower 100,000 range.

But, at a staggering 1 million tokens–equivalent to an hour-long video or the full text of a long novel–Google’s Gemini simply towers over the others like Hagrid in the Shire.

That having been said, this space moves quickly, and context window length is an active area of research and development. These figures will likely be different next month, so be sure to check the latest information as you begin shopping for a model.

Choosing Among the Current Large Language Models

With all the different LLMs on offer, it’s hard to narrow the search down to the one that’s best for you. By carefully weighing the different metrics we’ve discussed in this article, you can choose an LLM that meets your needs with as little hassle as possible.

Pulling back a bit, let’s close by recalling that the whole purpose of choosing among current LLMs in the first place is to better meet the needs of our customers.

For this reason, you might want to consider working with a conversational AI platform for CX, like Quiq, that puts a plethora of LLMs at your fingertips through one simple interface.

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Google Business Messaging is Ending – Here’s How You Should Adapt

Posted on Max Fortis

Google Business Messaging (GBM) has long been one of the primary rich messaging channels for Android, but it’s now in the process of being phased out.

GBM is being sunsetted, but that doesn’t mean your customer experience has to suffer. This piece will walk you through the main alternatives to GBM, ensuring you have everything you need to keep your organization running smoothly.

What’s Happening with Google Business Messaging Exactly?

According to an announcement from Google, Google Business Messaging will be phased out on the following schedule. First, starting July 15, 2024, GBM entry points will disappear from Google’s Maps and Search properties, and it will no longer be possible to start GBM conversations from entry points on your website. Existing conversations will be able to continue until July 31, 2024, when the GBM service will be shut down entirely.

What are the Alternatives to Google Business Messaging?

If you’re wondering which communication channel you should switch to now that GBM is going away, here are some you should consider. They’re divided into two groups. Group one consists of the channels we personally recommend, based on our years of experience in customer service and contact center management. Section two deals with communication channels that we still support but which, in our view, are not as promising as alternatives to GBM.

Recommended Alternatives to Google Business Messaging

Here are the best channels to serve as replacements to GBM

  • WhatsApp: WhatsApp enables text, voice, and video communications for over two billion global users. The platform includes several built-in features that appeal to businesses looking to forge deeper, more personal connections with their customers. Most importantly, it is a cross-platform messaging app, meaning it will allow you to chat with both Android and Apple users.
  • Text Messaging or Short Message Service (SMS): SMS is a long-standing staple for a reason, and with a conversational AI platform like Quiq, you can put large language models to work automating substantial parts of your SMS-based customer interactions.

Other Alternatives to Google Business Messaging

Here are the other channels you might look into.

  • Live web chat: When wondering about whether to invest in live chat support, customer experience directors may encounter skepticism about how useful customers will find it. But with nearly a third of female users of the internet indicating that they prefer contacting support via live chat, it’s clearly worth the time. This is especially true when live chat is used to provide an interactive experience, readily available, helpful agents, and swift responses. There are plenty of ways to encourage your customers to actually use your live chat offering, including mentioning it during phone calls, linking to it in blog posts or emails, and promoting it on social media.
  • Apple Messages for Business: Unlike standard text messaging available on mobile phones, Apple Messages is a specialized service designed for businesses to engage with customers. It facilitates easy setup of touchpoints such as QR codes, apps, or email messages, enabling appointments, issue resolution, and payments, among other things.
  • Facebook Messenger: Facebook Messenger for Business enables brands to handle incoming queries efficiently, providing immediate responses through AI assistants or routing complex issues to human agents. Clients integrating with a tool like Quiq have seen a massive ROI – a 95% customer satisfaction (CSAT), a 70-80% resolution rate for incoming customer inquiries automatically, and more. Like WhatsApp, FB messenger is a cross-platform messaging app, meaning it can help you reach users on both Android and Apple devices.
  • Instagram: Instagram isn’t just for posting pictures anymore – your target audience is likely using it to discover brands, shop, and make purchases. They’re reaching out through direct messages (DM), responding to stories, and commenting on posts. Instagram’s messaging API simplifies the handling of these customer interactions; it has automated features that help initiate conversations, such as Ice Breakers, as well as features that facilitate automated responses, such Quick Replies. Integrating Quiq’s conversational AI with Instagram’s messaging API makes it easier to automate responses to frequently asked questions, thereby reducing the workload on your human agents.
  • X (formerly Twitter): With nearly 400 million registered users and native, secure payment options, X is not a platform you can ignore. And the data supports this – 50% of surveyed X users mentioned brands in their posts more than 15 times in seven months, 80% of surveyed X users referred to a brand in their posts, and 99% of X users encountered a brand-related post in just over a month. By utilizing X business messaging, you can connect with your customers directly, providing them with excellent service experiences. Over time, this approach helps you build strong relationships and positive brand perceptions. Remember, posts—even those related to customer service—occur publicly. Thus, a positive interaction satisfies your customer and showcases your company’s engagement quality to others. Even better, the X API enables you to send detailed messages while keeping the conversation within X’s platform. This avoids the need for customers to switch platforms, enhancing their overall satisfaction.

How to Switch Away From Google Business Messaging

Even though GBM is going the way of the Dodo, the good news is that you have tons of other options. Check out our dedicated pages to learn more about SMS, WhatsApp, and Facebook Messenger, and you’re warmly invited to consult with our team if you are currently using GBM with another managed service provider and are not sure what the best direction forward is!

8 Strategies to Improve Customer Retention

Posted on Cristina Bravo-Olmo

Recruiting new customers costs seven to nine times as much as required to keep current customers from leaving. Besides the obvious foregone revenue, dissatisfied customers are not going to recommend you to the people they know, and they might even go out of their way to tell their friends and family about their negative experiences.

For all these reasons, it’s imperative not to let customers slip away – and one of the best ways of doing that is to implement an effective customer retention strategy.

Even a small increase in customer retention could substantially improve your bottom line, but customer retention can be extremely challenging. Having said that, enhancing customer retention can be challenging and generally requires an intentional strategy that many companies don’t choose to prioritize.

In this post, we will examine the big picture of why improving customer retention is important and offer advice that any customer experience team can use to keep its customers happy and loyal.

What Is Customer Retention?

“Customer retention” refers to any effort to keep a customer satisfied enough with you to keep them using your product or service.

Customer retention is an important aspect of business strategy and, done correctly, can help you gain a competitive advantage. Tragically, many businesses don’t invest enough in it – they spend vast amounts of time and money trying to bring in new customers while neglecting the ones they’ve already worked so hard to get.

But with the right approach and high-quality service, there’s no reason that excellent customer retention can’t be one of the things setting you apart.

Why Is Customer Retention Important?

We’ve already established that getting new customers is more expensive than keeping old ones, but it’s also worth pointing out that existing customers spend an average of almost 70% more than new customers.

Even better, loyal customers are far more likely to share their experiences with their social circles and purchase from your company again.

These customers are not only your best cheerleaders, they also help you better understand your brand in various other ways, like via CSAT and NPS (Net Promoter Score®) surveys. If you ask them, they will provide honest feedback about your product and customer service, allowing you to make the course corrections required to succeed. We’ll have more to say about all of this in the section on improving customer retention.

Calculating Customer Retention

Determining your current customer retention rate (CRR) is an important first step in improving customer retention.

The CRR measures how many customers are retained over a particular period (usually one year) and allows you to gauge the long-term profitability of your marketing and sales efforts. The math is pretty straightforward: we just need to divide the number of repeat customers by the total number of active customers over the same time period.

So, if we have 50 return customers and 200 active customers for the year 2023, our CRR would be 25%.

A related metric worth tracking is the cost per acquisition (CPA). The CPA measures the cost a company incurs to acquire one new customer (ideally, a new customer who becomes loyal to the company’s brand).

If you have both the CRR and the CPA, you should have a good chunk of the context needed to make smart, data-driven decisions. If you want to increase your retention rate, read the next section.

8 Customer Retention Strategies That Work

Now that we’ve made a strong case for trying to enhance customer retention, let’s discuss specific strategies that’ll help you actually do it.

1. Good Values Build Good Relationships

Many companies have “mission” or “vision” statements that explicitly state the values they live by. Though these statements are sometimes viewed as hot air that only serves to give the marketing team something to put on the company website, the truth is that your processes, the quality of your products, and the way you treat your customers are all a reflection of them.

This is a long way to say that values are important, but you don’t have to take our word for it. When asked, many customers who stated they had a relationship with a brand indicated that it was due to shared values. This isn’t surprising – customers will naturally be attracted to brands that mirror their beliefs while enhancing their lifestyles, especially when they’re younger.

Building a brand that your customers can easily relate to will foster trust. This is key to creating strong relationships and, by extension, a successful business. Let your customers know what you stand for, and be sure to act on these convictions (by donating to worthy causes, for example). Having common values with your customers makes it easier to attract and retain them.

2. Empower Your Customer Service Team to Build Trust

As a CX leader tasked with building, operationalizing, and scaling your contact center, you undoubtedly think about human agents’ interactions with customers. An important element in that equation is how you empower your team of customer service representatives to build trust with your customers.

To achieve this, focus on comprehensive training programs that emphasize empathy, active listening, and effective problem-solving. For instance, role-playing scenarios can prepare agents to handle various customer concerns with confidence and care. Keep your team up to date on best practices and emerging trends with regular workshops and continuous learning opportunities, too.

Implementing a customer feedback loop can help your team understand and respond to customer needs more effectively. Encourage your agents to ask for feedback after interactions and use this information to improve service delivery. Monitoring key performance indicators (KPIs) such as customer satisfaction scores (CSAT), Net Promoter Scores® (NPS), and first-call resolution rates can provide valuable insights into how well your team is building trust.

That validation helps to reinforce your team’s hard work, deepening a healthy internal culture. Speaking of culture, creating an internal culture centered around customer love, advocacy, and even “customer obsession” is foundational to trust building.

But as a CX leader, you must also factor in ways to highlight your team members’ success in putting customers first. This could involve recognizing and rewarding team members who exemplify customer-first values. For example, a monthly “Customer Hero” award can highlight and incentivize exceptional service.

3. Make Yourself Transparent and Easy to Work With

A great way to stand out is by making it as easy as possible for customers to find what they need. If your documentation or website is complex or confusing, this is certain to become a problem at one point or another. Clear, concise information, on the other hand, can help enhance customer retention.

Take the issue of refunds. If a customer is looking for a refund, they’re obviously dissatisfied. How much worse will they feel if they must then struggle to find a way to contact you, only to be faced with a maze of robotic voices endlessly repeating a menu of options?

If your agents are sympathetic and your information is easy to navigate, a refund needn’t be the end of a professional relationship. More broadly, it pays to invest the time required to make your content easy to follow and your agents easy to contact.

4. Meet Your Customers Where They Are

Customers love great offers and discounts, but they also love when they can get help solving problems with as little friction as possible.

A good way to do this (and improve customer retention simultaneously) is to provide support through the channels that make the most sense for your customers. There are a few other advantages to this omnichannel approach:

  • It enables you to respond very quickly to incoming queries, which can be a huge advantage for reasons already discussed above.
  • By integrating with technology like large language models, you can personalize your replies at scale and even offer services like real-time translation.
  • You can drive faster resolution times, contributing to customer satisfaction and retention.

5. Prioritize Quick Turnarounds

As a general rule, people have never enjoyed waiting around. But now that we’ve grown accustomed to 30-minute DoorDash deliveries and same-day shipping from Amazon, it’s only gotten worse.

For this reason, it pays to focus on replying to issues as quickly as possible.

Note, however, that this doesn’t necessarily mean you have to resolve an issue right off the bat. Many customers will feel less anxious and frustrated simply by knowing they’ve been heard and someone is working on a solution. Respond immediately, even if it’s just to say, “We’re sorry you’re running into issues, and we’re committed to getting you up and running again as soon as possible.”

You can also take this initial message as an opportunity to manage expectations about how long it will take to find a solution. Obviously, some problems are relatively straightforward, while others are more substantial, and you can communicate that to the customer (assuming it’s appropriate to do so). It’s never fun to hear that you’ll have to wait a week to get some issue sorted out, but it’s far worse to find that out after you’ve already made a bunch of plans that are difficult to change.

6. Be Sure to Personalize Your Communications

Artificial intelligence has a long history of delivering personalized content. You’re probably familiar with Spotify, which can discover patterns in the music and podcasts you enjoy and use algorithms to recommend songs and artists that align with your tastes.

With the power of generative AI, platforms like Quiq are elevating this to unprecedented levels.

Once upon a time, only human agents could analyze a customer’s profile and tailor their responses with relevant information. Now, a well-optimized generative language model can achieve this almost instantaneously – and on a much larger scale.

For a contact center manager focused on enhancing customer experience, this is a significant step forward.

7. Let Customer Data Work for You

Customer data can help determine your customers’ needs, and surveys are an effective way to gather that data — including NPS (Net Promoter Score®) surveys. Some of the benefits of conducting customer surveys include:

  • They’re a great way to interact with your customers
  • Customers tend to give honest and open feedback
  • These customers will be more likely to give feedback in the future if they see changes implemented based on prior concerns
  • Survey feedback can result in positive adjustments to your products, services, or processes
  • Surveys show your customers that you value their opinions and are willing to do whatever it takes to make them happy.
  • It can help ensure you’re pursuing the right targeting strategy
  • They can help you identify dissatisfied customers before they leave and create campaigns or offers to win them back

Of course, surveys aren’t the only way to do this; you can also treat customer complaints that come through other feedback channels in a similar manner.

Regardless of how you choose to proceed, interacting with your customers in this productive, proactive way is a great opportunity. Seventy percent of customers who complain will purchase your product again if their complaints are favorably resolved.

8. Reward Loyalty

Though nothing beats exceptional customer service, thoughtful gestures go a long way. In addition to standard discounts and other offers, think of things that will make your customers feel good about using your product.

A thank you note or any positive acknowledgment can keep your customers coming back, thus enhancing your customer retention rate.

Key Benefits of Customer Retention

Retaining customers is one of the most effective ways to drive business growth and ensure long-term stability. Here are the key benefits:

Cost Efficiency

Acquiring new customers is typically much more expensive than retaining existing ones. By fostering relationships with your current customers, you can lower marketing and acquisition costs while maximizing the value of customers who already know and trust your brand. This cost efficiency allows you to allocate resources more strategically, ensuring a higher return on investment.

Increased Revenue

Loyal customers are more likely to make repeat purchases and spend more over time. As trust in your business grows, they may explore additional products or services, increasing their lifetime value and boosting overall revenue. Retained customers also offer a more predictable revenue stream, helping stabilize cash flow and support sustainable growth.

Stronger Customer Relationships

Retaining customers allows businesses to develop deeper, more meaningful relationships. Over time, these connections lead to a greater understanding of customer needs, resulting in more personalized experiences. This not only enhances satisfaction and loyalty but also positions your business as a trusted partner, fostering long-term advocacy.

Positive Word-of-Mouth

Satisfied, loyal customers are natural brand advocates. They share positive experiences with friends, family, and colleagues, generating valuable word-of-mouth marketing that helps attract new customers without additional spending. Word-of-mouth is particularly impactful because it builds trust quickly, as recommendations from peers are often more credible than traditional advertising.

Competitive Advantage

Strong customer retention differentiates your business from competitors. When customers consistently choose your brand, it signals reliability, quality, and a superior experience. This loyalty gives you a competitive edge in crowded markets, making it harder for competitors to sway your customers with lower prices or flashy campaigns.

Common Challenges with Customer  Retention and How to Avoid Them

Retaining customers is essential for sustainable business growth, yet many organizations need help keeping their customers engaged and loyal. By understanding and addressing common challenges, businesses can create stronger relationships, reduce churn, and foster long-term customer satisfaction. Here are some of those main challenges:

Lack of Personalized Solutions

Problem: Customers today expect businesses to understand their unique needs and preferences. When businesses rely on generic communication, it can make customers feel undervalued and disengaged.

Solution: Use customer data and segmentation to create personalized experiences. Tailor messaging, product recommendations, and offers to align with individual needs. Personalization helps build stronger emotional connections, enhancing customer satisfaction and loyalty.

Poor Customer Service

Problem: Slow response times, unresolved issues, or impersonal service experiences can frustrate customers and drive them to competitors.

Solution: Implement omnichannel support systems that allow customers to connect seamlessly across platforms (email, chat, phone, social). Equip your customer service team with thorough training to deliver quick, empathetic, and effective solutions. Exceptional support reduces churn and turns customers into loyal advocates.

No Clear Value After Purchase

Problem: If customers don’t see ongoing value from your product or service after the initial purchase, they are less likely to stay engaged or continue their subscription.

Solution: Consistently communicate the value of your product through customer education, feature updates, and exclusive benefits. Offer webinars, newsletters, or loyalty programs to showcase how your product continues to solve their challenges. Keeping customers aware of your product’s impact ensures long-term engagement.

Failure to Build Trust

Problem: Inconsistent quality, transparency issues, or broken promises can damage the trust customers have in your brand, making retention difficult.

Solution: Focus on transparency and delivering on commitments. Clearly communicate timelines, policies, and product changes. Regularly gather and act on feedback to address concerns. A trustworthy brand builds credibility, leading to stronger relationships and increased retention.

Neglecting Customer Feedback

Problem: Ignoring customer feedback makes customers feel unheard, leading to frustration and disengagement.

Solution: Proactively gather feedback through surveys, reviews, or direct conversations. Analyze the data to identify trends and make improvements. Most importantly, communicate to customers how their input influenced changes. Demonstrating that you value their opinions builds loyalty and encourages advocacy

Building Customer Relationships

Customers are the foundation of any business. But it’s not enough to just get customers, you must also ensure that you invest in improving customer retention. You can do this by using the strategies presented in this post to build world-class relationships with your customers.

To find even more such strategies, check out our free ebook on resolving common customer-service pain points. It’s got excellent advice on dealing with angry or frustrated customers, elucidating their expectations, and more. With it, you’ll have everything you need to send your customer retention rates into the stratosphere!

9 Top Customer Service Challenges — and How to Overcome Them

Posted on Michael Hartsog

It’s a shame that customer service doesn’t always get the respect and attention it deserves because it’s among the most important ingredients in any business’s success. There’s no better marketing than an enthusiastic user base, so every organization should strive to excel at making customers happy.

Alas, this is easier said than done. When someone comes to you with a problem, they can be angry, stubborn, mercurial, and—let’s be honest—extremely frustrating. Some of this just comes with the territory, but some stems from the fact that many customer service professionals simply don’t have a detailed, high-level view of customer service challenges or how to overcome them.

That’s what we’re going to remedy in this post. Let’s jump right in!

What are The Top Customer Service Challenges?

After years of running a generative AI platform for contact centers and interacting with leaders in this space, we have discovered that the top customer service challenges are:

  1. Understanding Customer Expectations
  2. Next Step: Exceeding Customer Expectations
  3. Dealing with Unreasonable Customer Demands
  4. Improving Your Internal Operations
  5. Not Offering a Preferred Communication Channel
  6. Not Offering Real-Time Options
  7. Handling Angry Customers
  8. Dealing With a Service Outage Crisis
  9. Retaining, Hiring, and Training Service Professionals

In the sections below, we’ll break each of these down and offer strategies for addressing them.

1. Understanding Customer Expectations

No matter how specialized a business is, it will inevitably cater to a wide variety of customers. Every customer has different desires, expectations, and needs regarding a product or service, which means you need to put real effort into meeting them where they are.

One of the best ways to foster this understanding is to remain in consistent contact with your customers. Deciding which communication channels to offer customers depends a great deal on the kinds of customers you’re serving. That said, in our experience, text messaging is a universally successful method of communication because it mimics how people communicate in their personal lives. The same goes for web chat and WhatsApp.

Beyond this, setting the right expectations upfront is another good way to address common customer service challenges. For example, if you are not available 24/7, only provide support via email, or don’t have dedicated account managers , you should  make that clear right at the beginning.

Nothing will make a customer angrier than thinking they can text you only to realize that’s not an option in the middle of a crisis.

2. Next Step: Exceed Customer Expectations

Once you understand what your customers want and need, the next step is to go above and beyond to make them happy. Everyone wants to stand out in a fiercely competitive market, and going the extra mile is a great way to do that. One of the major customer service challenges is knowing how to do this proactively, but there are many ways you can succeed without a huge amount of effort.

Consider a few examples, such as:

  • Treating the customer as you would a friend in your personal life, i.e. by apologizing for any negative experiences and empathizing with how they feel;
  • Offering a credit or discount for a future purchase;
  • Sending them a card referencing their experience and thanking them for being a loyal customer;

The key is making sure they feel seen and heard. If you do this consistently, you’ll exceed your customers’ expectations, and the chances of them becoming active promoters of your company will increase dramatically.

3. Dealing with Unreasonable Demands

Of course, sometimes a customer has expectations that simply can’t be met, and this, too, counts as one of the serious customer service challenges. Customer service professionals often find themselves in situations where someone wants a discount that can’t be given, a feature that can’t be built, or a bespoke customization that can’t be done, and they wonder what they should do.

The only thing to do in this situation is to gently let the customer down, using respectful and diplomatic language. Something like, “We’re really sorry we’re not able to fulfill your request, but we’d be happy to help you choose an option that we currently have available” should do the trick.

4. Improving Your Internal Operations

Customer service teams face the constant pressure to improve efficiency, maintain high CSAT scores, drive revenue, and keep costs to service customers low. This matters a lot; slow response times and being kicked from one department to another are two of the more common complaints contact centers get from irate customers, and both are fixable with appropriate changes to your procedures.

Improving contact center performance is among the thorniest customer service challenges, but there’s no reason to give up hope!

One thing you can do is gather and utilize better data regarding your internal workflows. Data has been called “the new oil,” and with good reason—when used correctly, it’s unbelievably powerful.

What might this look like?

Well, you are probably already tracking metrics like first contact resolution (FCR) and (AHT), but this is easier when you have a unified, comprehensive dashboard that gives you quick insight into what’s happening across your organization.

You might also consider leveraging the power of generative AI, which has led to AI assistants that can boost agent performance in a variety of different tasks. You have to tread lightly here because too much bad automation will also drive customers away. But when you use technology like large language models according to best practices, you can get more done and make your customers happier while still reducing the burden on your agents.

5. Not Offering a Preferred Communication Channel

In general, contact centers often deal with customer service challenges stemming from new technologies. One way this can manifest is the need to cultivate new channels in line with changing patterns in the way we all communicate.

You can probably see where this is going – something like 96% of Americans have some kind of cell phone, and if you’ve looked up from your own phone recently, you’ve probably noticed everyone else glued to theirs.

It isn’t just that customers now want to be able to text you instead of calling or emailing; the ubiquity of cell phones has changed their basic expectations. They now take it for granted that your agents will be available round the clock, that they can chat with an agent asynchronously as they go about other tasks, etc.

We can’t tell you whether it’s worth investing in multiple communication channels for your industry. But based on our research, we can tell you that having multiple channels—and text messaging in particular—is something most people want and expect.

6. Not Offering Real-Time Options

When customers reach out asking for help, their customer service problems likely feel unique to them. But since you have so much more context, you’re aware that a very high percentage of inquiries fall into a few common buckets, like “Where is my order?”, “How do I handle a return?”, “My item arrived damaged, how can I exchange it for a new one?”, etc.

These and similar inquiries can easily be resolved instantly using AI, leaving customers and agents happier and more productive.

7. Handling Angry Customers

A common story in the customer service world involves an interaction going south and a customer getting angry.

Gracefully handling angry customers is one of those perennial customer service challenges; the very first merchants had to deal with angry customers, and our robot descendants will be dealing with angry customers long after the sun has burned out.

Whenever you find yourself dealing with a customer who has become irate, there are two main things you have to do:

  1. Empathize with them
  2. Do not lose your cool

It can be hard to remember, but the customer isn’t frustrated with you, they’re frustrated with the company and products. If you always keep your responses calm and rooted in the facts of the situation, you’ll always be moving toward providing a solution.

8. Dealing With a Service Outage Crisis

Sometimes, our technology fails us. The wifi isn’t working on the airplane, a cell phone tower is down following a lightning storm, or that printer from Office Space jams so often it starts to drive people insane.

As a customer service professional, you might find yourself facing the wrath of your customers if your service is down. Unfortunately, in a situation like this, there’s not much you can do except honestly convey to your customers that your team is putting all their effort into getting things back on track. You should go into these conversations expecting frustrated customers, but make sure you avoid the temptation to overpromise.

Talk with your tech team and give customers a realistic timeline, don’t assure them it’ll be back in three hours if you have no way to back that up. Though Elon Musk seems to get away with it, the worst thing the rest of us can do is repeatedly promise unrealistic timelines and miss the mark.

9. Retaining, Hiring, and Training Service Professionals

You may have seen this famous Maya Angelou quote, which succinctly captures what the customer service business is all about:

“I’ve learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

Learning how to comfort a person or reassure them is high on the list of customer service challenges, and it’s something that is certainly covered in your training for new agents.

But training is also important because it eases the strain on agents and reduces turnover. For customer service professionals, the median time to stick with one company is less than a year, and every time someone leaves, that means finding a replacement, training them, and hoping they don’t head for the exits before your investment has paid off.

Keeping your agents happy will save you more money than you imagine, so invest in a proper training program. Ensure they know what’s expected of them, how to ask for help when needed, and how to handle challenging customers.

Final Thoughts on the Top Customer Service Challenges

Customer service challenges abound, but with the right approach, there’s no reason you shouldn’t be able to meet them head-on!

Check out our report for a more detailed treatment of three major customer service challenges and how to resolve them. Between the report and this post, you should be armed with enough information to identify your own internal challenges, fix them, and rise to new heights.

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Google Business Messages: Meet Your Customers Where They’re At

Posted on Max Fortis

The world is a distracted and distracting place; between all the alerts, the celebrity drama on Twitter, and the fact that there are more hilarious animal videos on YouTube than you could ever hope to watch even if it were your full-time job, it takes a lot to break through the noise.

That’s one reason customer service-oriented businesses like contact centers are increasingly turning to text messaging. Not only are cell phones all but ubiquitous, but many people have begun to prefer text-message-based interactions to calls, emails, or in-person visits.

In this article, we’ll cover one of the biggest text-messaging channels: Google Business Messages. We’ll discuss what it is, what features it offers, and various ways of leveraging it to the fullest.

Let’s get going!

Learn More About the End of Google Business Messages

 

What is Google Business Messages?

Given that more than nine out of ten online searches go through Google, we will go out on a limb and assume you’ve heard of the Mountain View behemoth. But you may not be aware that Google has a Business Message service that is very popular among companies, like contact centers, that understand the advantages of texting their customers.

Business Messages allows you to create a “messaging surface” on Android or Apple devices. In practice, this essentially means that you can create a little “chat” button that your customers can use to reach out to you.

Behind the scenes, you will have to register for Business Messages, creating an “agent” that your customers will interact with. You have many configuration options for your Business Messages workflows; it’s possible to dynamically route a given message to contact center agents at a specific location, have an AI assistant powered by large language models generate a reply (more on this later), etc.

Regardless of how the reply is generated, it is then routed through the API to your agent, which is what actually interacts with the customer. A conversation is considered over when both the customer and your agent cease replying, but you can resume a conversation up to 30 days later.

What’s the Difference Between Google RCS and Google Business Messages?

It’s easy to confuse Google’s Rich Communication Services (RCS) and Google Business Messages. Although the two are similar, it’s nevertheless worth remembering their differences.

Long ago, text messages had to be short, sweet, and contain nothing but words. But as we all began to lean more on text messaging to communicate, it became necessary to upgrade the basic underlying protocol. This way, we could also use video, images, GIFs, etc., in our conversations.

“Rich” communication is this upgrade, but it’s not relegated to emojis and such. RCS is also quickly becoming a staple for businesses that want to invest in livelier exchanges with their customers. RCS allows for custom logos and consistent branding, for example; it also makes it easier to collect analytics, insert QR codes, link out to calendars or Maps, etc.

As discussed above, Business Messages is a mobile messaging channel that integrates with Google Maps, Search, and brand websites, offering rich, asynchronous communication experiences. This platform not only makes customers happy but also contributes to your business’s bottom line through reduced call volumes, improved CSAT, and better conversion rates.

Importantly, Business Messages are sometimes also prominently featured in Google search results, such as answer cards, place cards, and site links.

In short, there is a great deal of overlap between Google Business Messages and Google RCS. But two major distinctions are that RCS is not available on all Android devices (where Business Messages is), and Business Messages doesn’t require you to have a messaging app installed (where RCS does).

The Advantages of Google Business Messaging

Google Business Messaging has many distinct advantages to offer the contact center entrepreneur. In the next few sections, we’ll discuss some of the biggest.

It Supports Robust Encryption

A key feature of Business Messages is that its commitment to security and privacy is embodied through powerful, end-to-end encryption.

What exactly does end-to-end encryption entail? In short, it ensures that a message remains secure and unreadable from the moment the sender types it to whenever the recipient opens it, even if it’s intercepted in transit. This level of security is baked in, requiring no additional setup or adjustments to security settings by the user.

The significance of this feature cannot be overstated. Today, it’s not at all uncommon to read about yet another multi-million-dollar ransomware attack or a data breach of staggering proportions. This has engendered a growing awareness of (and concern for) data security, meaning that present and future customers will value those platforms that make it a central priority of their offering.

By our estimates, this will only become more important with the rise of generative AI, which has made it increasingly difficult to trust text, images, and even movies seen online—none of which was particularly trustworthy even before it became possible to mass-produce them.

If you successfully position yourself as a pillar your customers can lean on, that will go a long way toward making you stand out in a crowded market.

It Makes Connecting With Customers Easier

Another advantage of Google Business Messages is that it makes it much easier to meet customers where they are. And where we are is “on our phones.”

Now, this may seem too obvious to need pointing out. After all, if your customers are texting all day and you’re launching a text-messaging channel of communication, then of course you’ll be more accessible.

But there’s more to this story. Google Business Messaging allows you to seamlessly integrate with other Google services, like Google Maps. If a customer is trying to find the number for your contact center, therefore, they could instead get in touch simply by clicking the “CHAT” button.

This, too, may seem rather uninspiring because it’s not as though it’s difficult to grab the number and call. But even leaving aside the rising generations’ aversion to making phone calls, there’s a concept known as “trivial inconvenience” that’s worth discussing in this context.

Here’s an example: if you want to stop yourself from snacking on cookies throughout the day, you don’t have to put them on the moon (though that would help). Usually, it’s enough to put them in the next room or downstairs.

Though this only slightly increases the difficulty of accessing your cookie supply, in most cases, it introduces just enough friction to substantially reduce the number of cookies you eat (depending on the severity of your Oreo addiction, of course).

Well, the exact same dynamic works in reverse. Though grabbing your contact center’s phone number from Google and calling you requires only one or two additional steps, that added work will be sufficient to deter some fraction of customers from reaching out. If you want to make yourself easy to contact, there’s no substitute for a clean integration directly into the applications your customers are using, and that’s something Google Business Messages can do extremely well.

It’s Scalable and Supports Integrations

According to legend, the name “Google” originally came from a play on the word “Googol,” which is a “1” followed by a 100 “0”s. Google, in other words, has always been about scale, and that is reflected in the way its software operates today. For our purposes, the most important manifestation of this is the scalability of their API. Though you may currently be operating at a few hundred or a few thousand messages per day, if you plan on growing, you’ll want to invest early in communication channels that can grow along with you.

But this is hardly the end of what integrations can do for you. If you’re in the contact center business there’s a strong possibility that you’ll eventually end up using a large language model like ChatGPT in order to answer questions more quickly, offboard more routine tasks, etc. Unless you plan on dropping millions of dollars to build one in-house, you’ll want to partner with an AI-powered conversational platform. As you go about finding a good vendor, make sure to assess the features they support. The best platforms have many options for increasing the efficiency of your agents, such as reusable snippets, auto-generated suggestions that clean up language and tone, and dashboarding tools that help you track your operation in detail.

Best Practices for Using Google Business Messages

Here, in the penultimate section, we’ll cover a few optimal ways of utilizing Google Business Messages.

Reply in a Timely Fashion

First, it’s important that you get back to customers as quickly as you’re able to. As we noted in the introduction, today’s consumers are perpetually drinking from a firehose of digital information. If it takes you a while to respond to their query, there’s a good chance they’ll either forget they reached out (if you’re lucky) or perceive it as an unpardonable affront and leave you a bad review (if you’re not).

An obvious way to answer immediately is with an automated message that says something like, “Thanks for your question. We’ll respond to you soon!” But you can’t just leave things there, especially if the question requires a human agent to intervene.

Whatever automated system you implement, you need to monitor how well your filters identify and escalate the most urgent queries. Remember that an agent might need a few hours to answer a tricky question, so factor that into your procedures.

This isn’t just something Google suggests; it’s codified in its policies. If you leave a Business Messages chat unanswered for 24 hours, Google might actually deactivate your company’s ability to use chat features.

Don’t Ask for Personal Information

As hackers have gotten more sophisticated, everyday consumers have responded by raising their guard.

On the whole, this is a good thing and will lead to a safer and more secure world. But it also means that you need to be extremely careful not to ask for anything like a social security number or a confirmation code via a service like Business Messages. What’s more, many companies are opting to include a disclaimer to this effect near the beginning of any interactions with customers.

Earlier, we pointed out that Business Messages supports end-to-end encryption, and having a clear, consistent policy about not collecting sensitive information fits into this broader picture. People will trust you more if they know you take their privacy seriously.

Make Business Messages Part of Your Overall Vision

Google Business Messages is a great service, but you’ll get the most out of it if you consider how it is part of a more far-reaching strategy.

At a minimum, this should include investing in other good communication channels, like Apple Messages and WhatsApp. People have had bitter, decades-long battles with each other over which code editor or word processor is best, so we know that they have strong opinions about the technology that they use. If you have many options for customers wanting to contact you, that’ll boost their satisfaction and their overall impression of your contact center.

The prior discussion of trivial inconveniences is also relevant here. It’s not hard to open a different messaging app under most circumstances, but if you don’t force a person to do that, they’re more likely to interact with you.

Schedule a Demo with Quiq

Google has been so monumentally successful its name is now synonymous with “online search.” Even leaving aside rich messaging, encryption, and everything else we covered in this article, you can’t afford to ignore Business Messages for this reason alone.

But setting up an account is only the first step in the process, and it’s much easier when you have ready-made tools that you can integrate on day one. The Quiq conversational AI platform is one such tool, and it has a bevy of features that’ll allow you to reduce the workloads on your agents while making your customers even happier. Check us out or schedule a demo to see what we can do for you!

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Leveraging Agent Insights to Boost Efficiency and Performance

Posted on The Quiq Team

In the ever-evolving customer service landscape, the role of contact center agents cannot be overstated. As the frontline representatives of a company, their performance directly impacts the quality of customer experience, influencing customer loyalty and brand reputation.

However, the traditional approach to managing agent performance – relying on periodic reviews and supervisor observations – has given way to a more sophisticated, data-driven strategy. For this reason, managing agent performance with a method that leverages the rich data generated by agent interactions to enhance service delivery, agent satisfaction, and operational efficiency is becoming more important all the time.

This article delves into this approach. We’ll begin by examining its benefits from three critical perspectives – the customer, the agent, and the contact center manager – before turning to a more granular breakdown of how you can leverage it in your contact center.

Why is it Important to Manage Agent Performance with Insights?

First, let’s start by justifying this project. While it’s true that very few people today would doubt the need to track some data related to what agents are doing all day, it’s still worth saying a few words about why it really is a crucial part of running a contact center.

To do this, we’ll focus on how three groups are impacted when agent performance is managed through insights: customers, the agents themselves, and contact center managers.

It’s Good for the Customers

The primary beneficiary of improved agent performance is the customer. Contact centers can tailor their service strategies by analyzing agent metrics to better meet customer needs and preferences. This data-driven approach allows for identifying common issues, customer pain points, and trends in customer behavior, enabling more personalized and effective interactions.

As agents become more adept at addressing customer needs swiftly and accurately, customer satisfaction levels rise. This enhances the individual customer’s experience and boosts the overall perception of the brand, fostering loyalty and encouraging positive word-of-mouth.

It’s Good for the Agents

Agents stand to gain immensely from a management strategy focused on data-driven insights. Firstly, performance feedback based on concrete metrics rather than subjective assessments leads to a fairer, more transparent work environment.

Agents receive specific, actionable feedback that helps them understand their strengths and which areas need improvement. This can be incredibly motivating and can drive them to begin proactively bolstering their skills.

Furthermore, insights from performance data can inform targeted training and development programs. For instance, if data indicates that an agent excels in handling certain inquiries but struggles with others, their manager can provide personalized training to bridge this gap. This helps agents grow professionally and increases their job satisfaction as they become more competent and confident in their roles.

It’s Good for Contact Center Managers

For those in charge of overseeing contact centers, managing agents through insights into their performance offers a powerful tool for cultivating operational excellence. It enables a more strategic approach to workforce management, where decisions are informed by data rather than gut feeling.

Managers can identify high performers and understand the behaviors that lead to success, allowing them to replicate these practices across the team. Intriguingly, this same mechanism is also at play in the efficiency boost seen by contact centers that adopt generative AI. When such centers train a model on the interactions of their best agents, the knowledge in those agents’ heads can be incorporated into the algorithm and utilized by much more junior agents.

The insights-driven approach also aids in resource allocation. By understanding the strengths and weaknesses of their team, managers can assign agents to the tasks they are most suited for, optimizing the center’s overall performance.

Additionally, insights into agent performance can highlight systemic issues or training gaps, providing managers with the opportunity to make structural changes that enhance efficiency and effectiveness.

Moreover, using agent insights for performance management supports a culture of continuous improvement. It encourages a feedback loop where agents are continually assessed, supported, and developed, driving the entire team towards higher performance standards. This improves the customer experience and contributes to a positive working environment where agents feel valued and empowered.

In summary, managing performance by tracking agent metrics is a holistic strategy that enhances the customer experience, supports agent development, and empowers managers to make informed decisions.

It fosters a culture of transparency, accountability, and continuous improvement, leading to operational excellence and elevated service standards in the contact center.

How to Use Agent Insights to Manage Performance

Now that we know what all the fuss is about, let’s turn to addressing our main question: how to use agent insights to correct, fine-tune, and optimize agent performance. This discussion will center specifically around Quiq’s Agent Insights tool, which is a best-in-class analytics offering that makes it easy to figure out what your agents are doing, where they could improve, and how that ultimately impacts the customers you serve.

Managing Agent Availability

To begin with, you need a way of understanding when your agents are free to handle an issue and when they’re preoccupied with other work. The three basic statuses an agent can have are “available,” “current conversations” (i.e. only working on the current batch of conversations), and “unavailable.” All three of these can be seen through Agent Insights, which allows you to select from over 50 different metrics, customizing and saving different views as you see fit.

The underlying metrics that go into understanding this dimension of agent performance are, of course, time-based. In essence, you want to evaluate the ratios between four quantities: the time the agent is available, the time the agent is online, the time the agent spends in a conversation, and the time an agent is unavailable.

As you’re no doubt aware, you don’t necessarily want to maximize the amount of time an agent spends in conversations, as this can quickly lead to burnout. Rather, you want to use these insights into agent performance to strike the best, most productive balance possible.

Managing Agent Workload

A related phenomenon you want to understand is the kind of workload your agents are operating under. The five metrics that underpin this are:

  1. Availability
  2. Number of completions per hour your agents are managing
  3. Overall utilization (i.e. the percentage of an agent’s available conversation limit they have filled in a given period).
  4. Average workload
  5. The amount of time agents spend waiting for a customer response.

All of this can be seen in Agent Insights. This view allows you to do many things to hone in on specific parts of your operation. You can sort by the amount of time your agents spend waiting for a reply from a customer, for example, or segment agents by e.g. role. If you’re seeing high waiting and low utilization, that means you are overstaffed and should probably have fewer agents.

If you’re seeing high waiting and high utilization, by contrast, you should make sure your agents know inactive conversations should be marked appropriately.

As with the previous section, you’re not necessarily looking to minimize availability or maximize completions per hour. You want to make sure that agents are working at a comfortable pace, and that they have time between issues to reflect on how they’re doing and think about whether they want to change anything in their approach.

But with proper data-driven insights, you can do much more to ensure your agents have the space they need to function optimally.

Managing Agent Efficiency

Speaking of functioning optimally, the last thing we want to examine is agent efficiency. By using Agent Insights, we can answer questions such as how well new agents are adjusting to their roles, how well your teams are working together, and how you can boost each agent’s output (without working them too hard).

The field of contact center analytics is large, but in the context of agent efficiency, you’ll want to examine metrics like completion rate, completions per hour, reopen rate, missed response rate, missed invitation rate, and any feedback customers have left after interacting with your agents.

This will give you an unprecedented peek into the moment-by-moment actions agents are taking, and furnish you with the hard facts you need to help them streamline their procedures. Imagine, for example, you’re seeing a lot of keyboard usage. This means the agent is probably not operating as efficiently as they could be, and you might be able to boost their numbers by training them to utilize Quiq’s Snippets tool.

Or, perhaps you’re seeing a remarkably high rate of clipboard usage. In that case, you’d want to look over the clipboard messages your agents are using and consider turning them into snippets, where they’d be available to everyone.

The Modern Approach to Managing Agents

Embracing agent insights for performance management marks a transformative step towards achieving operational excellence in contact centers. This data-driven approach not only elevates the customer service experience but also fosters a culture of continuous improvement and empowerment among agents.

By leveraging tools like Quiq’s Agent Insights, managers can unlock a comprehensive understanding of agent availability, workload, and efficiency, enabling informed decisions that benefit both the customer and the service team.

If you’re intrigued by the possibilities, contact us to schedule a demo today!

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6 Questions to Ask Generative AI Vendors You’re Evaluating

Posted on The Quiq Team

With all the power exhibited by today’s large language models, many businesses are scrambling to leverage them in their offerings. Enterprises in a wide variety of domains – from contact centers to teams focused on writing custom software – are adding AI-backed functionality to make their users more productive and the customer experience better.

But, in the rush to avoid being the only organization not using the hot new technology, it’s easy to overlook certain basic sanity checks you must perform when choosing a vendor. Today, we’re going to fix that. This piece will focus on several of the broad categories of questions you should be asking potential generative AI providers as you evaluate all your options.

This knowledge will give you the best chance of finding a vendor that meets your requirements, will help you with integration, and will ultimately allow you to better serve your customers.

These are the Questions you Should ask Your Generative AI Vendor

Training large language models is difficult. Besides the fact that it requires an incredible amount of computing power, there are also hundreds of tiny little engineering optimizations that need to be made along the way. This is part of the reason why all the different language model vendors are different from one another.

Some have a longer context window, others write better code but struggle with subtle language-based tasks, etc. All of this needs to be factored into your final decision because it will impact how well your vendor performs for your particular use case.

In the sections that follow, we’ll walk you through some of the questions you should raise with each vendor. Most of these questions are designed to help you get a handle on how easy a given offering will be to use in your situation, and what integrating it will look like.

1. What Sort of Customer Service Do You Offer?

We’re contact center and customer support people, so we understand better than anyone how important it is to make sure users know what our product is, what it can do, and how we can help them if they run into issues.

As you speak with different generative AI vendors, you’ll want to probe them about their own customer support, and what steps they’ll take to help you utilize their platform effectively.

For this, just start with the basics by figuring out the availability of their support teams – what hours they operate in, whether they can accommodate teams working in multiple time zones, and whether there is an option for 24/7 support if a critical problem arises.

Then, you can begin drilling into specifics. One thing you’ll want to know about is the channels their support team operates through. They might set up a private Slack channel with you so you can access their engineers directly, for example, or they might prefer to work through email, a ticketing system, or a chat interface. When you’re discussing this topic, try to find out whether you’ll have a dedicated account manager to work with.

You’ll also want some context on the issue resolution process. If you have a lingering problem that’s not being resolved, how do you go about escalating it, and what’s the team’s response time for issues in general?

Finally, it’s important that the vendors have some kind of feedback mechanism. Just as you no doubt have a way for clients to let you know if they’re dissatisfied with an agent or a process, the vendor you choose should offer a way for you to let them know how they’re doing so they can improve. This not only tells you they care about getting better, it also indicates that they have a way of figuring out how to do so.

2. Does Your Team Offer Help with Setting up the Platform?

A related subject is the extent to which a given generative AI vendor will help you set up their platform in your environment. A good way to begin is by asking what kinds of training materials and resources they offer.

Many vendors are promoting their platforms by putting out a ton of educational content, all of which your internal engineers can use to get up to speed on what those platforms can do and how they function.

This is the kind of thing that is easy to overlook, but you should pay careful attention to it. Choosing a generative AI vendor that has excellent documentation, plenty of worked-out examples, etc. could end up saving you a tremendous amount of time, energy, and money down the line.

Then, you can get clarity on whether the vendor has a dedicated team devoted to helping customers like you get set up. These roles are usually found under titles like “solutions architect”, so be sure to ask whether you’ll be assigned such a person and the extent to which you can expect their help. Some platforms will go to the moon and back to make sure you have everything you need, while others will simply advise you if you get stuck somewhere.

Which path makes the most sense depends on your circumstances. If you have a lot of engineers you may not need more than a little advice here and there, but if you don’t, you’ll likely need more handholding (but will probably also have to pay extra for that). Keep all this in mind as you’re deciding.

3. What Kinds of Integrations Do You Support?

Now, it’s time to get into more technical details about the integrations they support. When you buy a subscription to a generative AI vendor, you are effectively buying a set of capabilities. But those capabilities are much more valuable if you know they’ll plug in seamlessly with your existing software, and they’re even more valuable if you know they’ll plug into software you plan on building later on. You’ve probably been working on a roadmap, and now’s the time to get it out.

It’s worth checking to see whether the vendor can support many different kinds of language models. This involves a nuance in what the word “vendor” means, so let’s unpack it a little bit. Some generative AI vendors are offering you a model, so they’re probably not going to support another company’s model.

OpenAI and Anthropic are examples of model vendors, so if you work with them you’re buying their model and will not be able to easily incorporate someone else’s model.

Other vendors, by contrast, are offering you a service, and in many cases that service could theoretically by powered by many different models.

Quiq’s Conversational CX Platform, for example, supports OpenAI’s GPT models, and we have plans to expand the scope of our integrations to encompass even more models in the future.

Another thing you’re going to want to check on is whether the vendor makes it easy to integrate vector databases into your workflow. Vectors are data structures that are remarkably good at capturing subtle relationships in large datasets; they’re becoming an ever-more-important part of machine learning, as evinced by the fact that there are now a multitude of different vector databases on offer.

The chances are pretty good that you’ll eventually want to leverage a vector database to store or search over customer interactions, and you’ll want a vendor that makes this easy.

Finally, see if the vendor has any case studies you can look at. Quiq has published a case study on how our language services were utilized by LOOP, a car insurance company, to make a far superior customer-service chatbot. The result was that customers were able to get much more personalization in their answers and were able to resolve their problems fully half of the time, without help. This led to a corresponding 55% reduction in tickets, and a customer satisfaction rating of 75% (!) when interacting with the Quiq-powered AI assistant.

See if the vendors you’re looking at have anything similar you can examine. This is especially helpful if the case studies are focused on companies that are similar to yours.

4. How Does Prompt Engineering and Fine-Tuning Work for Your Model?

For many tasks, large language models work perfectly fine on their own, without much special effort. But there are two methods you should know about to really get the most out of them: prompt engineering and fine-tuning.

As you know, prompts are the basic method for interacting with language models. You’ll give a model a prompt like “What is generative AI?”, and it’ll generate a response. Well, it turns out that models are really sensitive to the wording and structure of prompts, and prompt engineers are those who explore the best way to craft prompts to get useful output from a model.

It’s worth asking potential vendors about this because they handle prompts differently. Quiq’s AI Studio encourages atomic prompting, where a single prompt has a clear purpose and intended completion, and we support running prompts in parallel and sequentially. You can’t assume everyone will do this, however, so be sure to check.

Then, there’s fine-tuning, which refers to training a model on a bespoke dataset such that its output is heavily geared towards the patterns found in that dataset. It’s becoming more common to fine-tune a foundational model for specific use cases, especially when those use cases involve a lot of specialized vocabulary such as is found in medicine or law.

Setting up a fine-tuning pipeline can be cumbersome or relatively straightforward depending on the vendor, so see what each vendor offers in this regard. It’s also worth asking whether they offer technical support for this aspect of working with the models.

5. Can Your Models Support Reasoning and Acting?

One of the current frontiers in generative AI is building more robust, “agentic” models that can execute strings of tasks on their way to completing a broader goal. This goes by a few different names, but one that has been popping up in the research literature is “ReAct”, which stands for “reasoning and acting”.

You can get ReAct functionality out of existing language models through chain-of-thought prompting, or by using systems like AutoGPT; to help you concretize this a bit, let’s walk through how ReAct works in Quiq.

With Quiq’s AI Studio, a conversational data model is used to classify and store both custom and standard data elements, and these data elements can be set within and across “user turns”. A single user turn is the time between when a user offers an input to the time at which the AI responds and waits for the next user input.

Our AI can set and reason about the state of the data model, applying rules to take the next best action. Common actions include things like fetching data, running another prompt, delivering a message, or offering to escalate to a human.

Though these efforts are still early, this is absolutely the direction the field is taking. If you want to be prepared for what’s coming without the need to overhaul your generative AI stack later on, ask about how different vendors support ReAct.

6. What’s your Pricing Structure Like?

Finally, you’ll need to talk to vendors about how their prices work, including any available details on licensing types, subscriptions, and costs associated with the integration, use, and maintenance of their solution.

To take one example, Quiq’s licensing is based on usage. We establish a usage pool wherein our customers pre-pay Quiq for a 12-month contract; then, as the customer uses our software money is deducted from that pool. We also have an annual AI Assistant Maintenance fee along with a one-time implementation fee.

Vendors can vary considerably in how their prices work, so if you don’t want to overpay then make sure you have a clear understanding of their approach.

Picking the Right Generative AI Vendor

Language models and related technologies are taking the world by storm, transforming many industries, including customer service and contact center management.

Making use of these systems means choosing a good vendor, and that requires you to understand each vendor’s model, how those models integrate with other tools, and what you’re ultimately going to end up paying.

If you want to see how Quiq stacks up and what we can do for you, schedule a demo with us today!

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Building Better Customer Relationships with Text Messaging

Posted on Max Fortis

Customer engagement is constantly evolving and the trend towards more customer-centric experiences hasn’t slowed. Businesses are increasingly having to provide faster, easier, and more friendly ways of initiating and responding to customer’s inquiries.

Businesses that adapt to this continually changing environment will ensure they deliver superior service along with desirable products, thus boosting engagement rates.

This is where customer engagement strategies based on text messaging enter the picture. This mode of communication has overtaken traditional methods, like phone and email, as consumers prefer the ease, convenience, and hassle-free nature of text messaging.

Texting isn’t just for friends and family anymore and consumers are choosing this channel more often as it fits their on-the-go lifestyle.

The move to text messaging is a part of this new era of building customer relationships, and both businesses and consumers can benefit.

The old customer engagement marketing strategies are fading

As recently as two decades ago, the world of business and customer service was a completely different place. Company agents and representatives used forms of customer engagement like trade shows, promotional emails, letters, and phone calls to promote their products and services.

While these methods are still used in a wide range of industries, many companies today are turning to new ways of maintaining customer loyalty.

According to the Pew Research Center, about 96% of Americans own a cell phone of some kind. Text messaging is a highly popular form of communication in people’s everyday lives. As such, it only seems natural that companies would use texting as a service, sales, and marketing tool. Their results have been astounding, and that’s what we’ll explore in the next section.

The advantages of digital customer engagement strategies

While sending text messages to customers may be a new frontier for many companies, businesses are finding the personal, casual nature of this medium is part of what makes it so effective.

Some of the benefits that come with text-based customer service include:

Hassle-free customer service access

Consumers love instant messaging because it’s easy and allows them to engage, ask questions, and get information without having to make a phone call or meet face-to-face.

One of the hallmarks of our increasingly digital world is how hard businesses work to make things easy – think of 1-click shopping on Amazon (you don’t have to click two buttons), how smartphones enable contactless payment (you don’t have to pull your card out), the way Alexa responds to voice commands (you don’t have to click anything), and the way Netflix automatically plays the next episode of a show you’re binging (you don’t even have to move).

These expectations are becoming more ingrained in the minds of consumers, especially young ones, and they are unlikely to be enthusiastic about needing to call an agent or go into the store to resolve any problems they have.

Timely responses and service

Few things turn a customer off faster than sending an email or making a phone call, then having to wait days for a response. With text message customer service, you can stay connected 24/7 and provide timely responses and solutions. Artificial intelligence is one customer engagement technology that will make this even easier in the years ahead (more on this below).

The personal touch

Customers are more likely to stick around if they believe you care about their personal needs. Texting will allow you to take a more individualized approach, communicating with customers in the same way they might communicate with friends. This stands in contrast to the stiffer, more formal sorts of interactions that tend to happen over the phone or in person.

A dynamic variety of solutions

Text messaging provides unique opportunities for marketing, sales, and customer support. For example, you might use texting to help troubleshoot a product, promote new sales, send coupons, and more.

None of these things are impossible to do with older approaches to customer service but think of how pain-free it would be for a busy single mom to ask a question, check the reply when she stops to pick up her daughter from school, ask another question, check the new reply when she gets home, etc. This is vastly easier than finding a way to carve three hours out of the day to go into the store to speak to an agent directly.

To make these ideas easier to digest, here is a table summarizing the ground we’ve just covered:

The Old Way The New Way
Method of Delivery Phone calls, pamphlets, trade shows, face-to-face conversations Text messaging
Difficulty Requires spending time on the phone, driving to a physical location, or making an appointment. Only requires a phone and the ability to text on it.
Timeliness Can take hours or days to get a reply. Replies should be almost instantaneous.
Personalization Good agents might be able to personalize the interaction, but it’s more difficult.  Personalizing messages and meeting a customer on their own terms because natural and easy.
Variety Does offer ways of solving problems or upselling customers, but only at the cost of more effort from the agent.  Sales and customer support can be embedded seamlessly in existing conversations, and those conversations fit better into a busy modern lifestyle.

​​Why this all matters

These benefits matter because 64% of Americans would rather receive a text than a phone call. It’s clear what the consumers want, and it’s the business’s job to deliver.

Because text messaging can help you engage with customers on a more personal level, it can increase customer loyalty, lead to more conversions, and in general boost engagement rates.

What’s more, text-based customer relationships will likely be transformed by the advent of generative artificial intelligence, especially large language models (LLMs). This technology will make it so easy to offer 24/7 availability that everyone will take it for granted, to say nothing of how it can personalize replies based on customer-specific data, translate between languages, answer questions in different levels of detail, etc.

Texting already provides agents with the ability to manage multiple customers at a time, but they’ll be able to accommodate far higher volumes when they’re working alongside machines, boosting efficiency and saving huge amounts of time.

Some day soon, businesses will look back on the days when human beings had to do all of this with a sense of gratitude for how technology has streamlined the process of delivering a top-shelf customer experience.

And it is exactly this customer satisfaction that’ll allow those businesses to increase profits and make room for business growth over time.

Request a demo from Quiq today

In the future, as in the past, customer service will change with the rise of new technologies and strategies. If you don’t want to be left behind, contact Quiq today for a demo.

We not only make it easy to integrate text messaging into your broader approach to building customer relationships, we also have bleeding-edge language models that will allow you to automate substantial parts of your workflow.

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What is Automated Customer Service? – Ultimate Guide

Posted on Michael Hartsog

From graph databases to automated machine learning pipelines and beyond, a lot of attention gets paid to new technologies. But the truth is, none of it matters if users aren’t able to handle the more mundane tasks of managing permissions, resolving mysterious errors, and getting the tools installed and working on their native systems.

This is where customer service comes in. Though they don’t often get the credit they deserve, customer service agents are the ones who are responsible for showing up every day to help countless others actually use the latest and greatest technology.

Like every job since the beginning of jobs, there are large components of customer service that have been automated, are currently being automated, or will be automated at some point soon.

That’s our focus for today. We want to explore customer service as a discipline, and then talk about some of how generative AI can automate substantial parts of the standard workflow.

What is Customer Service?

To begin with, we’ll try to clarify what customer service is and why it matters. This will inform our later discussion of automated customer service, and help us think through the value that can be added through automation.

Customer service is more or less what it sounds like: serving your customers – your users, or clients – as they go about the process of utilizing your product. A software company might employ customer service agents to help onboard new users and troubleshoot failures in their product, while a services’ company might use them for canceling appointments and rescheduling.

Over the prior few decades, customer service has evolved alongside many other industries. As mobile phones have become firmly ensconced in everyone’s life, for example, it has become more common for businesses to supplement the traditional avenues of phone calls and emails by adding text messaging and chatbot customer support to their customer service toolkit. This is part of what is known as an omni-channel strategy, in which more effort is made to meet customers where they’re at rather than expecting them to conform to the communication pathways a business already has in place.

Naturally, many of these kinds of interactions can be automated, especially with the rise of tools like large language models. We’ll have more to say about that shortly.

Why is Customer Service Important?

It may be tempting for those writing the code to think that customer service is a “nice to have”, but that’s not the case at all. However good a product’s documentation is, there will simply always be weird behaviors and edge cases in which a skilled customer service agent (perhaps helped along with AI) needs to step in and aid a user in getting everything running properly.

But there are other advantages as well. Besides simply getting a product to function, customer service agents contribute to a company’s overall brand, and the general emotional response users have to the company and its offerings.

High-quality customer service agents can do a lot to contribute to the impression that a company is considerate, and genuinely cares about its users.

What Are Examples of Good Customer Service?

There are many ways in which customer service agents can do this. For example, it helps a lot when customer service agents try to transmit a kind of warmth over the line.

Because so many people spend their days interacting with others through screens, it can be easy to forget what that’s like, as tone of voice and facial expression are hard to digitally convey. But when customer service agents greet a person enthusiastically and go beyond “How may I help you” by exchanging some opening pleasantries, they feel more valued and more at ease. This matters a lot when they’ve been banging their head against a software problem for half a day.

Customer service agents have also adapted to the digital age by utilizing emojis, exclamation points, and various other kinds of internet-speak. We live in a more casual age, and under most circumstances, it’s appropriate to drop the stiffness and formalities when helping someone with a product issue.

That said, you should also remember that you’re talking to customers, and you should be polite. Use words like “please” when asking for something, and don’t forget to add a “thank you.” It can be difficult to remember this when you’re dealing with a customer who is simply being rude, especially when you’ve had several such customers in a row. Nevertheless, it’s part of the job.

Finally, always remember that a customer gets in touch with you when they’re having a problem, and above all else, your job is to get them what they need. From the perspective of contact center managers, this means you need periodic testing or retraining to make sure your agents know the product thoroughly.

It’s reasonable to expect that agents will sometimes need to look up the answer to a question, but if they’re doing that constantly it will not only increase the time it takes to resolve an issue, it will also contribute to customer frustration and a general sense that you don’t have things well in hand.

Automation in Customer Service

Now that we’ve covered what customer service is, why it matters, and how to do it well, we have the context we need to turn to the topic of automated customer service.

For all intents and purposes, “automation” simply refers to outsourcing all or some of a task to a machine. In industries like manufacturing and agriculture, automation has been steadily increasing for hundreds of years.

Until fairly recently, however, the technology didn’t yet exist to automate substantial portions of customer service worth. With the rise of machine learning, and especially large language models like ChatGPT, that’s begun to change dramatically.

Let’s dive into this in more detail.

Examples of Automated Customer Service

There are many ways in which customer service is being automated. Here are a few examples:

  • Automated questions answering – Many questions are fairly prosaic (“How do I reset my password”), and can effectively be outsourced to a properly finetuned large language model. When such a model is trained on a company’s documentation, it’s often powerful enough to handle these kinds of low-level requests.
  • Summarization – There have long been models that could do an adequate job of summarization, but large language models have kicked this functionality into high gear. With an endless stream of new emails, Slack messages, etc. constantly being generated, having an agent that can summarize their contents and keep agents in the loop will do a lot to boost their productivity.
  • Classifying incoming messages – Classification is another thing that models have been able to do for a while, and it’s also something that helps a lot. Having an agent manually sort through different messages to figure out how to prioritize them and where they should go is no longer a good use of time, as algorithms are now good enough to do a major chunk of this kind of work.
  • Translation – One of the first useful things anyone attempted to do with machine learning was translating between different natural languages (i.e. from Russian into English). Once squarely in the purview of human beings, this is now a task that machines can do almost as well, at least for customer service work.

Should We Automate Customer Service?

All this having been said, you may still have questions about the wisdom of automating customer service work. Sure, no one wants to spend hours every day looking up words in Mandarin to answer a question or prioritizing tickets by hand, but aren’t we in danger of losing something important as customer service agents? Might we not automate ourselves out of a job?

No one can predict the future, of course, but the early evidence is quite to the contrary. Economists have conducted studies of how contact centers have changed with the introduction of generative AI, and their findings are very encouraging.

Because these models are (usually) finetuned on conversations from more experienced agents, they’re able to capture a lot of how those agents handle issues. Typical response patterns, politeness, etc. become “baked into” the models. Junior agents using these models are able to climb the learning curve more quickly and, feeling less strained in their new roles, are less likely to quit. This, in turn, puts less of a burden on managers and makes the organization overall more stable. Everyone ends up happier and more productive.

So far, it’s looking like AI-based automation in contact centers will be like automation almost everywhere else: machines will gradually remove the need for human attention in tedious or otherwise low-value tasks, freeing them up to focus on places where they have more of an advantage.

If agents don’t have to sort tickets anymore or resolve routine issues, they can spend more time working on the really thorny problems, and do so with more care.

Moving Quiq-ly into the Future!

Where the rubber of technology meets the road of real-world use cases, customer service agents are extremely important. They not only make sure customers can use a company’s tools, but they also contribute to the company brand in their tone, mannerisms, and helpfulness.

Like most other professions, customer service agents are being impacted by automation. So far, this impact has been overwhelmingly positive and is likely to prove a competitive advantage in the decades ahead.

If you’re intrigued by this possibility, Quiq has created a suite of industry-leading conversational AI tools, both for customer-facing applications and agent-facing applications. Check them out or schedule a demo with us to see what all the fuss is about.

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Top 5 Benefits of AI for Hospitality

Posted on The Quiq Team

As an industry, hospitality is aimed squarely at meeting customer needs. Whether it’s a businesswoman staying in 5-star resorts or a mother of three getting a quiet weekend to herself, the job of the hospitality professionals they interact with is to anticipate what they want and make sure they get it.

As technologies like artificial intelligence become more powerful and pervasive, customer expectations will change. When that businesswoman books a hotel room, she’ll expect there to be a capable virtual assistant talking to her about a vacation spot; when that mother navigates the process of buying a ticket, she’ll expect to be interacting with a very high-quality chatbot, perhaps one that’s indistinguishable from an actual human being.

All of this means that the hospitality industry needs to be thinking about how it will be impacted by AI. It needs to consider what the benefits of AI for hospitality are, what limitations are faced by AI, and how it can be utilized effectively. That’s what we’re here to do today, so let’s get started.

Why is AI Important for Hospitality?

AI is important in hospitality for the same reason it’s important everywhere else: it’s poised to become a transformative technology, and just about every industry – especially those that involve a lot of time interacting through text – could be up-ended by it.

The businesses that emerge the strongest from this ongoing revolution will be those that successfully anticipate how large language models and similar tools change workflows, company setups, cost and pricing structures, etc.

With that in mind, let’s work through some of the ways in which AI is (or will) be used in hospitality.

How is AI Used in Hospitality?

There are many ways in which AI is used in hospitality, and in the sections that follow we’ll walk through a number of the most important ones.

Chatbots and Customer Service

Perhaps the most obvious place to begin is with chatbots and customer service more broadly. Customer-facing chatbots were an early application of natural language processing, and have gotten much better in the decades since. With ChatGPT and similar LLMs, they’re currently in the process of taking another major leap forward.

Now that we have models that can be fine-tuned to answer questions, summarize texts, and carry out open-ended interactions with human users, we expect to see them becoming more and more common in hospitality. Someday soon, it may be the case that most of the steps involved in booking a room or changing a flight happens entirely without human assistance of any kind.

This is especially compelling because we’ve gotten so good at making chatbots that are very deferential and polite (though as we make clear in the final section on “limitations”, this is not always the case.)

Virtual Assistants

AI virtual assistants are a generalization of the idea behind chatbots. Whereas chatbots can be trained to offload many parts of hospitality work, powerful virtual assistants will take this dynamic to the next level. Once we have better agents – systems able to take strings of actions in pursuit of a goal – many more parts of hospitality work will be outsourced to the machines.

What might this look like?

Well, we’ve already seen some tools that can do relatively simple tasks like “book a flight to Indonesia”, but they’re still not all that flexible. Imagine an AI virtual assistant able to handle all the subtleties and details involved in a task like “book a flight for ten executives to Indonesia, and book lodging near the conference center and near the water, too, then make reservations for a meal each night of the week, taking into account the following dietary restrictions.”

Work into building generative agents like this is still in its infancy, but it is nevertheless an active area of research. It’s hard to predict when we’ll have agents who can be trusted to do advanced work with minimal oversight, but once we do, it’ll really begin to change how the hospitality industry runs.

Sentiment Analysis

Sentiment analysis refers to an automated, algorithmic approach to classifying the overall vibe of a piece of text. “The food was great” is obviously positive sentiment, “the food was awful” is obviously negative sentiment, and then there are many subtler cases involving e.g. sarcasm.

The hospitality industry desperately needs tools able to perform sentiment analysis at scale. It helps them understand what clients like and dislike about particular services or locations, and can even help in predicting future demand. If, for example, there’s a bunch of positive sentiment around a concert being given in Indonesia, that indicates that there will probably be a spike in bookings there.

Boosting Revenues for Hospitality

People have long been interested in using AI to make money, whether that be from trading strategies generated by ChatGPT or from using AI to create ultra-targeted marketing campaigns.

All of this presents an enormous opportunity for the hospitality industry. Through a combination of predictive modeling, customer segmentation, sentiment analysis, and related techniques, it’ll become easier to forecast changes in demand, create much more responsive pricing models, and intelligently track inventory.

What this will ultimately mean is better revenues for hotels, event centers, and similar venues. You’ll be able to cross-sell or upsell based on a given client’s unique purchase history and interests, you’ll have fewer rooms go unoccupied, and you’ll be less likely to have clients who are dissatisfied by the fact tha you ran out of something.

Sustainability and Waste Management

An underappreciated way in which AI will benefit hospitality is by making sustainability easier. There are a few ways this could manifest.

One is by increasing energy efficiency. Most of you will already be familiar with currently-existing smart room technology, like thermostats that learn when you’re leaving and turn themselves up, thus lowering your power bill.

But there’s room for this to become much more far-ranging and powerful. If AI is put in charge of managing the HVAC system for an entire building, for example, it could lead to savings on the order of millions of dollars, while simultaneously making customers more comfortable during their stay.

And the same holds true for waste management. AI systems smart enough to discover when a trash can is full means that your cleaning staff won’t have to spend nearly as much time patrolling. They’ll be able to wait until they get a notification to handle the problem, gaining back many hours in their day that can be put towards higher-value work.

What are the Limitations of AI in Hospitality?

None of this is to suggest that there won’t also be drawbacks to using AI in hospitality. To prepare you for these challenges, we’ll spend the next few sections discussing how AI can fail, allowing you to be proactive in mitigating these downsides.

Impersonality in Customer Service

By properly fine-tuning a large language model, it’s possible to get text output that is remarkably polite and conversational. Still, throughout repeated or sustained interactions, the model can come to feel vaguely sterile.

Though it might in principle be hard to tell when you’re interacting with AI v.s. a human, the fact remains that models don’t actually have any empathy. They may say “I’m sorry that you had to deal with that…”, but they won’t truly know what frustration is like, and over time, a human is likely to begin picking up on that.

We can’t say for certain when models will be capable of expressing sympathy in a fully convincing way, but for the time being, you should probably incorporate systems that can flag conversations that are going off the rails so that a human customer service professional can intervene.

Toxic Output, Bias, and Abuse

As in the previous section, a lot of work has gone into finetuning models so that they don’t produce toxic, biased, or abusive language. Still, not all the kinks have been ironed out, and if a question is phrased in just the right way, it’s often possible to get past these safeguards. That means your models might unpredictably become insulting or snarky, which is a problem for a hospitality company.

As we’ve argued elsewhere, careful monitoring is one of the prices that have to be paid when managing an AI assistant. Since this technology is so new, we have at best a very vague idea of what kinds of prompts lead to what kinds of responses. So, you’ll simply have to diligently keep your eyes peeled for examples of model responses that are inappropriate, having a human take over if and when things are going poorly.

(Or, you can work with Quiq – our guardrails ensure none of this is a problem for enterprise hospitality businesses).

AI in Hospitality

New technologies have always changed the way industries operate, and that’s true for hospitality as well. From virtual assistants to chatbots to ultra-efficient waste management, AI offers many benefits (and many challenges) for hospitality.

If you want to explore using these tools in your hospitality enterprise but don’t know the first thing about hiring AI engineers, check out the Quiq conversational CX platform. We’ve built a proprietary large language model offering that makes it easy to incorporate chatbots and other technologies, without having to worry about what’s going on under the hood.

Schedule a demo with us today to find out how you can catch the AI wave!

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4 Benefits of Using AI Assistants in the Retail Industry

Posted on Mark Kowal

Artificial intelligence (AI) has been making remarkable strides in recent months. Owing to the release of ChatGPT in November of 2022, a huge amount of attention has been on large language models, but the truth is, there have been similar breakthroughs in computer vision, reinforcement learning, robotics, and many other fields.

In this piece, we’re going to focus on how these advances might contribute specifically to the retail sector.

We’ll start with a broader overview of AI, then turn to how AI-based tools are making it easier to make targeted advertisements, personalized offers, hiring decisions, and other parts of retail substantially easier.

What are AI assistants in Retail?

Artificial intelligence is famously difficult to define precisely, but for our purposes, you can think of it as any attempt to get intelligent behavior from a machine. This could involve something relatively straightforward, like building a linear regression model to predict future demand for a product line, or something far more complex, like creating neural networks able to quickly spit out multiple ideas for a logo design based on a verbal description.

AI assistants are a little different and specifically require building agents capable of carrying out sequences of actions in the service of a goal. The field of AI is some 70 years old now and has been making remarkable strides over the past decade, but building robust agents remains a major challenge.

It’s anyone’s guess as to when we’ll have the kinds of agents that could successfully execute an order like “run this e-commerce store for me”, but there’s nevertheless been enough work for us to make a few comments about the state of the art.

What are the Ways of Building AI Assistants?

On its own, a model like ChatGPT can (sometimes) generate working code and (often) generate correct API calls. But as things stand, a human being still needs to utilize this code for it to do anything useful.

Efforts are underway to remedy this situation by making models able to use external tools. Auto-GPT, for example, combines an LLM and a separate bot that repeatedly queries it. Together, they can take high-level tasks and break them down into smaller, achievable steps, checking off each as it works toward achieving the overall objective.

AssistGPT and SuperAGI are similar endeavors, but they’re better able to handle “multimodal” tasks, i.e those that also involve manipulating images or sounds rather than just text.

The above is a fairly cursory examination of building AI agents, but it’s not difficult to see how the retail establishments of the future might use agents. You can imagine agents that track inventory and re-order crucial items when they get low, or that keep an eye on sales figures and create reports based on their findings (perhaps even using voice synthesis to actually deliver those reports), or creating customized marketing campaigns, generating their own text, images, and A/B tests to find the highest-performing strategies.

What are the Advantages of Using AI in Retail Business?

Now that we’ve talked a little bit about how AI and AI assistants can be used in retail, let’s spend some time talking about why you might want to do this in the first place. What, in other words, are the big advantages of using AI in retail?

1. Personalized Marketing with AI

People can’t buy your products if they don’t know what you’re selling, which is why marketing is such a big part of retail. For its part, marketing has long been a future-oriented business, interested in leveraging the latest research from psychology or economics on how people make buying decisions.

A kind of holy grail for marketing is making ultra-precise, bespoke marketing efforts that target specific individuals. The kind of messaging that would speak to a childless lawyer in a big city won’t resonate the same way with a suburban mother of five, and vice versa.

The problem, of course, is that there’s just no good way at present to do this at scale. Even if you had everything you needed to craft the ideal copy for both the lawyer and the mother, it’s exceedingly difficult to have human beings do this work and make sure it ends up in front of the appropriate audience.

AI could, in theory, remedy this situation. With the rise of social media, it has become possible to gather stupendous amounts of information about people, grouping them into precise and fine-grained market segments–and, with platforms like Facebook Ads, you can make really target advertisements for each of these segments.

AI can help with the initial analysis of this data, i.e. looking at how people in different occupations or parts of the country differ in their buying patterns. But with advanced prompt engineering and better LLMs, it could also help in actually writing the copy that induces people to buy your products or services.

And it doesn’t require much imagination to see how AI assistants could take over quite a lot of this process. Much of the required information is already available, meaning that an agent would “just” need to be able to build simple models of different customer segments, and then put together a prompt that generates text that speaks to each segment.

2. Personalized Offerings with AI

A related but distinct possibility is using AI assistants to create bespoke offerings. As with messaging, people will respond to different package deals; if you know how to put one together for each potential customer, there could be billions in profits waiting for you. Companies like Starbucks have been moving towards personalized offerings for a while, but AI will make it much easier for other retailers to jump on this trend.

We’ll illustrate how this might work with a fictional example. Let’s say you’re running a toy company, and you’re looking at data for Angela and Bob. Angela is an occasional customer, mostly making purchases around the holidays. When she created her account she indicated that she doesn’t have children, so you figure she’s probably buying toys for a niece or nephew. She’s not a great target for a personalized offer, unless perhaps it’s a generic 35% discount around Christmas time.

Bob, on the other hand, buys fresh trainsets from you on an almost weekly basis. He more than likely has a son or daughter who’s fascinated by toy machines, and you have customer-recommendation algorithms trained on many purchases indicating that parents who buy the trains also tend to buy certain Lego sets. So, next time Bob visits your site, your AI assistant can offer him a personalized discount on Lego sets.

Maybe he bites this time, maybe he doesn’t, but you can see how being able to dynamically create offerings like this would help you move inventory and boost individual customer satisfaction a great deal. AI can’t yet totally replace humans in this kind of process, but it can go a long way toward reducing the friction involved.

3. Smarter Pricing

The scenario we just walked through is part of a broader phenomenon of smart pricing. In economics, there’s a concept known as “price discrimination”, which involves charging a person roughly what they’re willing to pay for an item. There may be people who are interested in buying your book for $20, for example, but others who are only willing to pay $15 for it. If you had a way of changing the price to match what a potential buyer was willing to pay for it, you could make a lot more money (assuming that you’re always charging a price that at least covers printing and shipping costs).

The issue, of course, is that it’s very difficult to know what people will pay for something–but with more data and smarter AI tools, we can get closer. This will have the effect of simultaneously increasing your market (by bringing in people who weren’t quite willing to make a purchase at a higher price) and increasing your earnings (by facilitating many sales that otherwise wouldn’t have taken place).

More or less the same abilities will also help with inventory more generally. If you sell clothing you probably have a clearance rack for items that are out of season, but how much should you discount these items? Some people might be fine paying almost full price, while others might need to see a “60%” off sticker before moving forward. With AI, it’ll soon be possible to adjust such discounts in real-time to make sure you’re always doing brisk business.

4. AI and Smart Hiring

One place where AI has been making real inroads is in hiring. It seems like we can’t listen to any major podcast today without hearing about some hiring company that makes extensive use of natural language processing and similar tools to find the best employees for a given position.

Our prediction is that this trend will only continue. As AI becomes increasingly capable, eventually it will be better than any but the best hiring managers at picking out talent; retail establishments, therefore, will rely on it more and more to put together their sales force, design and engineering teams, etc.

Is it Worth Using AI in Retail?

Throughout this piece, we’ve sung the praises of AI in retail. But the truth is, there are still questions about how much sense it makes to leverage retail at the moment, given its expense and risks.

In this section, we’ll briefly go over some of the challenges of using AI in retail so you can have a fuller picture of how its advantages compare to its disadvantages, and thereby make a better decision for your situation.

The one that’s on everyone’s minds these days is the tendency of even powerful systems like ChatGPT to hallucinate incorrect information or to generate output that is biased or harmful. Finetuning and techniques like retrieval augmented generation can mitigate this somewhat, but you’ll still have to spend a lot of time monitoring and tinkering with the models to make sure that you don’t end up with a PR disaster on your hands.

Another major factor is the expense involved. Training a model on your own can cost millions of dollars, but even just hiring a team to manage an open-source model will likely set you back a fair bit (engineers aren’t cheap).

By far the safest and easiest way of testing out AI for retail is by using a white glove solution like the Quiq conversational CX platform. You can test out our customer-facing and agent-facing AI tools while leaving the technical details to us, and at far less expense than would be involved in hiring engineering talent.

Set up a demo with us to see what we can do for you.

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AI is Changing Retail

From computer-generated imagery to futuristic AI-based marketing plans, retail won’t be the same with the advent of AI. This will be especially true once we have robust AI assistants able to answer customer questions, help them find clothes that fit, and offer precision discounts and offerings tailored to each individual shopper.

If you don’t want to get left behind, you’ll need to begin exploring AI as soon as possible, and we can help you do that. Check out our product or find a time to talk with us, today!

AI in Retail: 5 Ways Retailers Are Using AI Assistants

Posted on Mark Kowal

Businesses have always turned to the latest and greatest technology to better serve their customers, and retail is no different. From early credit card payment systems to the latest in online advertising, retailers know that they need to take advantage of new tools to boost their profits and keep shoppers happy.

These days, the thing that’s on everyone’s mind is artificial intelligence (AI). AI has had many, many definitions over the years, but in this article, we’ll mainly focus on the machine-learning and deep-learning systems that have captured the popular imagination. These include large language models, recommendation engines, basic AI assistants, etc.

In the world of AI in retail, you can broadly think of these systems as falling into one of two categories: “the ones that customers see”, and “the ones that customers don’t see.” In the former category, you’ll find innovations such as customer-facing chatbots and algorithms that offer hyper-personalized options based on shopping history. In the latter, you’ll find precision fraud detection systems and finely-tuned inventory management platforms, among other things.

We’ll cover each of these categories, in order. By the end of this piece, you’ll have a much better understanding of the ways retailers are using AI assistants and will be better able to think about how you want to use this technology in your retail establishment.

Let’s get going!

Using AI Assistants for Better Customer Experience

First, let’s start with AI that interacts directly with customers. The major ways in which AI is transforming the customer experience are through extreme levels of personalization, more “humanized” algorithms, and shopping assistants.

Personalization in Shopping and Recommendations

One of the most obvious ways of improving the customer experience is by tailoring that experience to each individual shopper. There’s just one problem: this is really difficult to do.

On the one hand, most of your customers will be new to you, people about whom you have very little information and whose preferences you have no good way of discovering. On the other, there are the basic limitations of your inventory. If you’re a brick-and-mortar establishment you have a set number of items you can display, and it’s going to be pretty difficult for you to choose them in a way that speaks to each new customer on a personal level.

For a number of reasons, AI has been changing this state of affairs for a while now, and holds the potential to change it much more in the years ahead.

A key part of this trend is recommendation engines, which have gotten very good over the past decade or so. If you’ve ever been surprised by YouTube’s ability to auto-generate a playlist that you really enjoyed, you’ve seen this in action.

Recommendation engines can only work well when there is a great deal of customer data for them to draw on. As more and more of our interactions, shopping, and general existence have begun to take place online, there has arisen a vast treasure trove of data to be analyzed. In some situations, recommendation engines can utilize decades of shopping experience, public comments, reviews, etc. in making their recommendations, which means a far more personalized shopping experience and an overall better customer experience.

What’s more, advances in AR and VR are making it possible to personalize even more of these experiences. There are platforms now that allow you to upload images of your home to see how different pieces of furniture will look, or to see how clothes fit you without the need to try them on first.

We expect that this will continue, especially when combined with smarter printing technology. Imagine getting a 3D-printed sofa made especially to fit in that tricky corner of your living room, or flipping through a physical magazine with advertisements that are tailored to each individual reader.

Humanizing the Machines

Next, we’ll talk about various techniques for making the algorithms and AI assistants we interact with more convincingly human. Admittedly, this isn’t terribly important at the present moment. But as more of our shopping and online activity comes to be mediated by AI, it’ll be important for them to sound empathic, supportive, and attuned to our emotions.

The two big ways this is being pursued at the moment are chatbots and voice AI.

Chatbots, of course, will probably be familiar to you already. ChatGPT is inarguably the most famous example, but you’ve no doubt interacted with many (much simpler) chatbots via online retailers or contact centers.

In the ancient past, chatbots were largely “rule-based”, meaning they were far less flexible and far less capable of passing as human. With the ascendancy of the deep learning paradigm, however, we now have chatbots that are able to tutor you in chemistry, translate between dozens of languages, help you write code, answer questions about company policies, and even file simple tickets for contact center agents.

Naturally, this same flexibility also means that retail managers must tread lightly. Chatbots are known to confidently hallucinate incorrect information, to become abusive, or to “help” people with malicious projects, like building weapons or computer viruses.

Even leaving aside the technical challenges of implementing a chatbot, you have to carefully monitor your chatbots to make sure they’re performing as expected.

Then, there’s voice-based AI. Computers have been synthesizing speech for many years, but it hasn’t been until recently that they’ve become really good at it. Though you can usually tell that a computer is speaking if you listen very carefully, it’s getting harder and harder all the time. We predict that, in the not-too-distant future, you’ll simply have no idea whether it’s a human or a machine on the other end of the line when you call to return an item or get store hours.

But computers have also gotten much better at the other side of voice-based AI, speech recognition. Software like otter.ai, for example, is astonishingly accurate when generating transcriptions of podcast episodes or conversations, even when unusual words are used.

Taken together, advances in both speech synthesis and speech recognition paint a very compelling picture of how the future of retail might unfold. You can imagine walking into a Barnes & Noble in the year 2035 and having a direct conversation with a smart speaker or AI assistant. You’ll tell it what books you’ve enjoyed in the past, it’ll query its recommendation system to find other books you might like, and it’ll speak to you in a voice that sounds just like a human’s.

You’ll be able to ask detailed questions about the different books’ content, and it’ll be able to provide summaries, discuss details with you, and engage in an unscripted, open-ended conversation. It’ll also learn more about you over time, so that eventually it’ll be as though you have a friend that you go shopping with whenever you buy new books, clothing, etc.

Shopping Assistants and AI Agents

So far, we’ve confined our conversation specifically to technologies like large language models and conversational AI. But one thing we haven’t spent much time on yet is the possibility of creating agents in the future.

An agent is a goal-directed entity, one able to take an instruction like “Make me a reservation at an Italian restaurant” and decompose the goal into discrete steps, performing each one until the task is completed.

With clever enough prompt engineering, you can sort of get agent-y behavior out of ChatGPT, but the truth is, the work of building advanced AI agents has only just begun. Tools like AutoGPT and LangChain have made a lot of progress, but we’re still a ways away from having agents able to reliably do complex tasks.

It’s not hard to see how different retail will be when that day arrives, however. Eventually, you may be outsourcing a lot of your shopping to AI assistants, who will make sure the office has all the pens it needs, you’ve got new science fiction to read, and you’re wearing the latest fashion. Your assistant might generate new patterns for t-shirts and have them custom-printed; if LLMs get good enough, they’ll be able to generate whole books and movies tuned to your specific tastes.

Using AI Assistants to Run A Safer, Leaner Operation

Now that we’ve covered the ways AI assistants will impact the things customers can see, let’s talk about how they’ll change the things customers don’t see.

There are lots of moving parts in running a retail establishment. If you’ve got ~1,000 items on display in the front, there are probably several thousand more items in a warehouse somewhere, and all of that has to be tracked. What’s more, there’s a constant process of replenishing your supply, staying on top of new trends, etc.

All of this will also be transformed by AI, and in the following sections, we’ll talk about a few ways in which this could happen.

Fraud Detection and Prevention

Fraud, unfortunately, is a huge part of modern life. There’s an entire industry of people buying and selling personal information for nefarious purposes, and it’s the responsibility of anyone trafficking in that information to put safeguards in place.

That includes a large number of retail establishments, which might keep data related to a customer’s purchases, their preferences, and (of course) their actual account and credit card numbers.

This isn’t the place to get into a protracted discussion of cybersecurity, but much of fraud detection relies on AI, so it’s fair game. Fraud detection techniques range from the fairly basic (flagging transactions that are much larger than usual or happen in an unusual geographic area) to the incredibly complex (training powerful reinforcement learning agents that constantly monitor network traffic).

As AI becomes more advanced, so will fraud detection. It’ll become progressively more difficult for criminals to steal data, and the world will be safer as a result. Of course, some of these techniques are also ones that can be used by the bad guys to defraud people, but that’s why so much effort is going into putting guardrails on new AI models.

Streamlining Inventory

Inventory management is an obvious place for optimization. Correctly forecasting what you’ll need and thereby reducing waste can have a huge impact on your bottom line, which is why there are complex branches of mathematics aimed at modeling these domains.

And – as you may have guessed – AI can help. With machine learning, extremely accurate forecasts can be made of future inventory requirements, and once better AI agents have been built, they may even be able to automate the process of ordering replacement materials.

Forward-looking retail managers will need to keep an eye on this space to fully utilize its potential.

AI Assistants and the Future of Retail

AI is changing a great many things. It’s already making contact center agents more effective and is being utilized by a wide variety of professionals, ranging from copywriters to computer programmers.

But the space is daunting, and there’s so much to learn about implementing, monitoring, and finetuning AI assistants that it’s hard to know where to start. One way to easily dip your toe in these deep waters is with the Quiq Conversational CX platform.

Our technology makes it easy to create customer-facing AI bots and similar tooling, which will allow you to see how AI can figure into your retail enterprise without hiring engineers and worrying about the technical details.

Schedule a demo with us today to get started!

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