Category: AI

5 Tips for Coaching Your Contact Center Agents to Work with AI

Posted on The Quiq Team

Generative AI has enormous potential to change the work done at places like contact centers. For this reason, we’ve spent a lot of energy covering it, from deep dives into the nuts and bolts of large language models to detailed advice for managers considering adopting it.

Here, we will provide tips on using AI tools to coach, manage, and improve your agents.

How Will AI Make My Agents More Productive?

Contact centers can be stressful places to work, but much of that stems from a paucity of good training and feedback. If an agent doesn’t feel confident in assuming their responsibilities or doesn’t know how to handle a tricky situation, that will cause stress.

Tip #1: Make Collaboration Easier

With the right AI tools for coaching agents, you can get state-of-the-art collaboration tools that allow agents to invite their managers or colleagues to silently appear in the background of a challenging issue. The customer never knows there’s a team operating on their behalf, but the agent won’t feel as overwhelmed. These same tools also let managers dynamically monitor all their agents’ ongoing conversations, intervening directly if a situation gets out of hand.

Agents can learn from these experiences to become more performant over time.

Tip #2: Use Data-Driven Management

Speaking of improvement, a good AI platform will have resources that help managers get the most out of their agents in a rigorous, data-driven way. Of course, you’re probably already monitoring contact center metrics, such as CSAT and FCR scores, but this barely scratches the surface.

What you really need is a granular look into agent interactions and their long-term trends. This will let you answer questions like “Am I overstaffed?” and “Who are my top performers?” This is the only way to run a tight ship and keep all the pieces moving effectively.

Tip #3: Use AI To Supercharge Your Agents

As its name implies, generative AI excels at generating text, and there are several ways this can improve your contact center’s performance.

To start, these systems can sometimes answer simple questions directly, which reduces the demands on your team. Even when that’s not the case, however, they can help agents draft replies, or clean up already-drafted replies to correct errors in spelling and grammar. This, too, reduces their stress, but it also contributes to customers having a smooth, consistent, high-quality experience.

Tip #4: Use AI to Power Your Workflows

A related (but distinct) point concerns how AI can be used to structure the broader work your agents are engaged in.

Let’s illustrate using sentiment analysis, which makes it possible to assess the emotional state of a person doing something like filing a complaint. This can form part of a pipeline that sorts and routes tickets based on their priority, and it can also detect when an issue needs to be escalated to a skilled human professional.

Tip #5: Train Your Agents to Use AI Effectively

It’s easy to get excited about what AI can do to increase your efficiency, but you mustn’t lose sight of the fact that it’s a complex tool your team needs to be trained to use. Otherwise, it’s just going to be one more source of stress.

You need to have policies around the situations in which it’s appropriate to use AI and the situations in which it’s not. These policies should address how agents should deal with phenomena like “hallucination,” in which a language model will fabricate information.

They should also contain procedures for monitoring the performance of the model over time. Because these models are stochastic, they can generate surprising output, and their behavior can change.

You need to know what your model is doing to intervene appropriately.

Wrapping Up

Hopefully, you’re more optimistic about what AI can do for your contact center, and this has helped you understand how to make the most out of it.

If there’s anything else you’d like to go over, you’re always welcome to request a demo of the Quiq platform. Since we focus on contact centers we take customer service pretty seriously ourselves, and we’d love to give you the context you need to make the best possible decision!

4 Reasons Why Every Hotel Needs an AI Assistant

Posted on The Quiq Team

Artificial intelligence (AI) has been all the rage for the past year, owing to its remarkable abilities to generate convincing text (and video!), automate major parts of different jobs, and boost the productivity of everyone using it.

Naturally, this has sparked the interest of professionals in the hospitality sector, which will be our focus today. We’ll talk about how AI assistants can be used in hotels, the size of the relevant market, and some potential issues you should look out for.

It’s an exciting topic, so let’s dive right in!

What is an AI Assistant for a Hotel?

Leaving aside a bit of nuance, the phrase “AI assistant” broadly covers using algorithmic technologies such as large language models to “assist” in various aspects of your work. A very basic example is the bundle of spell checkers, suggested edits, and autocomplete that is all but ubiquitous in text editors, email clients, and blogging platforms; a more involved example would be carefully crafting a prompt to generate convincing copy to sell a product or service.

If you’re interested in digging in further, check out some of our earlier posts for more details.

What is the Importance of Artificial Intelligence in the Hotel Industry?

In the next section, we cover the nuts and bolts of what AI assistants can do to streamline your operations, reduce the burden on your (human) staff, and improve the experience of guests staying at your hotel.

But in this one, we’re just going to talk dollars and cents. And to be clear, there are a lot of dollars and cents on the table. Experts who’ve studied the potential market for AI assistants in hospitality believe that it was worth something like $90 million in 2022, and this figure is expected to climb to an eye-watering $8 billion over the next decade.

“Hang on,” you’re thinking to yourself. “That’s great for the investors who fund these companies and the early employees that work in them, but the fact that a market is worth a lot of money doesn’t mean it’s actually going to have much impact on day-to-day hospitality.”

We admire your skeptical mind, and this is indeed a worthwhile concern. AI, after all, is renowned for its ups and downs; there’ll be years of frenzied excitement and near-delirious predictions that entire segments of the economy are poised for complete automation, followed by “AI winters” so deep even Ned Stark can’t get warm behind the walls of Winterfell.

Making the case that AI in hospitality will, in fact, be a trend worth thinking about is our next task.

The 4 Reasons Every Hotel Should be Using an AI Assistant

As promised, we’ll now cover all the reasons why you should seriously investigate the potential of AI assistants in your hotel. To paraphrase a famous saying, “Fortune favors the innovative,” and you can’t afford to ignore such a transformative technology.

#1 AI Assistants Can Help Drive Bookings and Sales

There are many ways in which AI will change the hotel booking process because it can act as a dynamic tool for enhancing guest interactions and driving sales directly through your hotel’s website. To start, AI assistants can significantly reduce the likelihood of potential guests abandoning their bookings midway by providing real-time answers to their questions, alleviating doubts about the details of a stay, and offering instant booking confirmations. Not only do such seamless experiences simplify the booking experience, they also contribute to an increase in direct bookings – a crucial advantage for hotels, as it eliminates the need for commission payouts and boosts profitability.

But that’s not all. These assistants are increasingly being integrated into social media and instant messaging platforms, enabling guests to start the booking process through their preferred channel or, failing that, redirecting them to the main hotel booking system. Throughout, they can proactively gather information about the guests’ preferences and budget, making tailored recommendations that increase the likelihood of conversion.

As you’re no doubt aware, a hotel doesn’t just make its money from bookings – there are also many opportunities for upselling and cross-selling hotel services. This, too, is a place where AI assistants can help. While interacting with a potential customer, they can suggest additional breakfast options, spa appointments, room upgrades, etc., based on the customer’s current selection and previous interactions with you.

Moreover, an AI assistant can modernize hotel marketing strategies, which have traditionally relied on relatively static methods like email campaigns. Properly tuned language models are capable of engaging in personalized, two-way conversations via social media or on your website, allowing them to deliver more effective promotional messages and alerts about special events or loyalty programs. All of this makes your messaging more likely to resonate with guests, ultimately boosting the all-important bottom line.

#2 AI Assistants Can Help Reduce Burnout and Turnover

About a year ago, we covered a landmark study from economists Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond that examined how generative AI was changing contact centers. Though there were (and are) many concerns about automation taking jobs, the study concluded that this new technology was helping newer agents onboard more quickly, was making mid-tier agents perform better, and was overall reducing burnout and turnover by lessening each agent’s burden.

Most of these factors also apply to your hospitality staff. Let’s see how.

Algorithms offer the distinct advantage of providing continuous service, and operating around the clock without needing breaks or sleep. This ensures that guests receive immediate assistance whenever needed, which will go a long way to cementing their perception of your commitment to exceptional service.

Furthermore, these assistants contribute to the efficiency of face-to-face customer interactions, particularly during routine processes like check-ins and check-outs. This dynamic becomes even more powerful when you integrate conversational AI into mobile apps, guests can complete these procedures directly from their smartphones, bypassing the front desk and avoiding any wait.

Hospitality teams often face high workloads, managing in-person guest interactions, responding to digital communications across multiple platforms, and analyzing feedback from customer surveys. A good AI assistant can substantially reduce this burden by handling routine inquiries and requests. Your human staff can then be left to focus on more complex issues, thereby preventing burnout and improving their capacity to deliver quality service via the fabled “human touch.”

#3 AI Assistants Can Help Improve the Guest Experience

Let’s drill a little bit more into how AI assistants can improve your guest’s stay at your hotel.

We’ve already mentioned some of this. If a customer’s booking goes smoothly, changes are handled promptly, their 2-a.m. questions have been answered, and their stay is replete with little personalized touches, they’re probably going to reflect on it fondly.
But this is hardly everything that can be said about how AI assistants will improve the hotel experience. Consider the fact that today’s language models are almost unbelievably good at translating between languages – especially when those are “high resource” languages, such as Mandarin, Russian, and Spanish.

If you’re a monolingual native English speaker, it can be easy to forget how much cognitive effort is involved in speaking a language in which you’re not fluent. But imagine for a moment that you’re a foreign traveler whose flight was delayed and whose kids never once stopped crying. Wouldn’t you appreciate being greeted with a friendly “欢迎” or “Добро пожаловать”, rather than needing to immediately fumble around in English?

Another subject that is slightly off-topic but is nevertheless worth discussing in this context is trust. People have long known that the internet is hardly a shining example of forthrightness and rectitude, but with the rise of generative AI, it has become even harder to believe what you read online.

We’ve discussed how much AI assistants can do for your hotel, but it’s important to use them judiciously, with appropriate guardrails in place, to reap the most benefit. If one of your language models offers up bad information or harasses a guest, that will reflect negatively on you. This is too big a topic for us to cover in this article, but you can check out earlier posts for more information.

A related issue is the collection of data. Upselling customers or personalizing their room can only be done by gathering data about their preferences. This, too, is something people are gradually becoming more aware of (and worried about), so it’s worth proactively crafting a data collection policy that’s available if anyone asks for it.

#4 AI Assistants Can Help Keep Your Operations Running Smoothly

Finally, we’ll finish by considering how AI can be used to streamline your hotel’s basic operations – making sure everything is in stock, that items make it to the right room, etc.

One significant benefit (which is becoming a more important distinguishing feature) is improving energy efficiency. You’re probably already familiar with smart room technologies, such as thermostats that reduce energy consumption by automatically adjusting themselves based on occupancy. But consider how implementing AI to manage HVAC systems for an entire building could not only optimize energy use and save significant costs, but also make guests more comfortable throughout their stay.

Similarly, AI can revolutionize waste management by employing systems that detect when trash receptacles need servicing. This would reduce the time staff spend checking and clearing bins, allowing them to focus on more valuable tasks.

Beyond these sustainability-focused applications, AI’s role in automating routine hospitality operations is vast. A fun example comes from Silicon Valley, where the Crowne Plaza hotel employs a robotic system named “Dash” to deliver snacks and towels directly to guests.

Even if you’re not particularly interested in having robots wandering your halls, it should hopefully be clear that many parts of running a hotel can be outsourced to machines, freeing you and your staff up to focus on more pressing matters.

Riding the AI Wave with Quiq

After decades of false starts and false promises, it looks like AI is finally having a measurable impact on the hospitality sector.

If you want to leverage this remarkable technology to the fullest but aren’t sure where to start, set up a time to talk with us. Quiq is an industry-leading conversational AI platform that makes deploying and monitoring AI systems for hotels much easier. Let’s explore opportunities to work together!

The Ultimate Guide to RCS Business Messaging

Posted on The Quiq Team

From chiseling words into stone to typing them directly on our screens, changes in technology can bring profound changes to the way we communicate. Rich Communication Services (RCS) Business Messaging is one such technological change, and it offers the forward-looking contact center a sophisticated upgrade over traditional SMS.

In this piece, we’ll discuss RCS Business Messaging, illustrating its significance, its inner workings, and how it can be leveraged as part of a broader customer service strategy. This context will equip you to understand RCS and determine whether and how to invest in it.

Let’s get going!

What is RCS Business Messaging?

Smartphones have become enormously popular for surfing the internet, shopping, connecting with friends, and conducting many other aspects of our daily lives. One consequence of this development is that it’s much more common for contact centers to interact with customers through text messaging.

Once text messaging began to replace phone calls, emails, and in-person visits as the go-to communication channel, it was clear that it required an upgrade. The old Short Messaging Service (SMS) was replaced with Rich Communication Services (RCS), which supports audio messages, video, high-quality photos, group chats, encryption, and everything else we’ve come to expect from our messaging experience.

And, on the whole, the data indicate that this is a favorable trend:

  • More than 70% of people report feeling inclined to make an online purchase when they have the ability to get timely answers to questions;
  • Almost three-quarters indicated that they were more likely to interact with a brand when they have the option of doing so through RCS;
  • Messages sent through RCS are a staggering 35 times more likely to be read than an equivalent email.

For all these reasons, your contact center needs to be thinking about how RCS fits into your overall customer service strategy–it’s simply not a channel you can afford to ignore any longer.

How is RCS Business Messaging Different from Google Business Messages?

Distinguishing between Google’s Rich Communication Services (RCS) and Google Business Messages can be tricky because they’re similar in many ways. That said, keeping their differences in mind is crucial.

You may not remember this if you’re young enough, but text messaging was once much more limited. Texts could not be very long, and were unable to accommodate modern staples like GIFs, videos, and emojis. However, as reliance on text messaging grew, there was a clear need to enhance the basic protocol to include these and other multimedia elements.

Since this enhancement enriched the basic functionality of text messaging, it is known as “rich” communication. Beyond adding emojis and the like, RCS is becoming essential for businesses looking to engage in more dynamic interactions with customers. It supports features such as custom logos, collecting data for analytics, adding QR codes, and links to calendars or maps, and enhancing the messaging experience all around.

Google Business Messages, on the other hand, is a mobile messaging channel that seamlessly integrates with Google Maps and Search to deliver high-quality, asynchronous communication between your customers and your contact center agents.

This service is not only a boon to your satisfaction ratings, it can also support other business objectives by reducing the volume of calls and enhancing conversion rates.

While Google Business Messages and RCS have a lot in common, there are two key differences worth highlighting: RCS is not universally available across all Android devices (whereas Business Messages is), and Business Messages does not require a user to install a messaging app (whereas RCS does).

How Does RCS Business Messaging Work?

Okay, now that we’ve convinced you that RCS Business Messaging is worth the effort to cultivate, let’s examine how it works.

Once you set up your account and complete the registration process, you’ll need to create an “agent,” which is the basic interface connecting your contact center to your customers. Agents are quite flexible and able to handle very simple workflows (such as sending a notification) as well as much more complicated sequences of tasks (such as those required to help book a reservation).

From the customer’s side, communicating with an agent is more or less indistinguishable from having a standard conversation. Each participant will speak in turn, waiting for the other to respond.

Agents can be configured to initiate a conversation under a wide variety of external circumstances. They could reach out when a user’s order has been shipped, for example, or when a new sushi restaurant has opened and is offering discounts. Since we’re focused on contact centers, our agent configurations will likely revolve around events like “the customer reached out for support,” “there’s been an update on an outstanding ticket,” or “the issue has been resolved.”

However you’ve chosen to set up your agent, when it is supposed to initiate a conversation, it will use the RCS Business Messaging API to send a message. These messages are always sent as standard HTTP requests with a corresponding JSON payload (if you’re curious about the technical underpinnings), but the most important thing to know is that the message ultimately ends up in front of the user, where they can respond.

Unless, that is, their device doesn’t support RCS. RCS has become popular and prominent enough that we’d be surprised if you ran into this situation very often. Just in case, you should have your messaging set up such that you can default to something like SMS.

Any subsequent messages between the agent and the customer are also sent as JSON. Herein lies the enormous potential for customization, because you can utilize powerful technologies like natural language understanding to have your agent dynamically generate different responses in different contexts. This not only makes it feel more lifelike, it also means that it can solve a much broader range of problems.

If you don’t want to roll up your sleeves and do this yourself, you always have the option of partnering with a good conversational AI platform. Ideally, you’d want to use one that makes integrating generative AI painless, and which has a robust set of features that make it easy to monitor the quality of agent interactions, collect data, and make decisions quickly.

Best Practices for Using RCS Business Messaging

By now, you should hopefully understand RCS Business Messaging, why it’s exciting, and the many ways in which you can use it to take your contact center to new heights. In this penultimate section, we’ll discuss some of Google’s best practices for RCS.

RCS is not a General-Purpose User Interface

Tools are incredibly powerful ways of extending basic human abilities, but only if you understand when and how to use them. Hammers are great for carpentry, but they’re worse than useless when making pancakes (trust us on this–we’ve tried, and it went poorly).

The same goes for Google’s RCS Business Messaging, which is a conversational interface. Your RCS agents are great at resolving queries, directing customers to information, executing tasks, and (failing that) escalating to a human being. But in order to do all of this, you should try to make sure they speak in a way that is natural, restricted to the question at hand, and easy for the customer to follow.

For this same reason, your agents shouldn’t be seen as a simple replacement for a phone tree, requiring the user to tediously input numbers to navigate a menu of delimited options. Part of the reason agents are a step forward in contact center management is precisely because they eliminate the need to lean on such an approach.

Check Device Compatibility Beforehand

Above, we pointed out that some devices don’t support RCS, and you should therefore have a failsafe in place if you send a message to one. This is sage advice, but it’s also possible to send a “capability request” ahead of a message telling you what kind of device the user has and what messaging it supports.

This will allow you to configure your agent in advance so that it stays within the limits of a given device.

Begin at the Beginning

As you’ve undoubtedly heard from marketing experts, first impressions matter a lot. The way your agent initiates a conversation will determine the user’s experience, and thereby figure prominently in how successful you are in making them happy.

In general, it’s a good idea to have the initial message be friendly, warm, and human, to contain some of the information the user is likely to want, and to list out a few of the things the agent is capable of. This way, the person who reached out to you with a problem immediately feels more at ease, knowing they’ll be able to reach a speedy resolution.

Be Mindful of Technical Constraints

There are a few low-level facts about RCS that could bear on the end user’s experience, and you should know about them as you integrate RCS into your text messaging strategy.

To take one example, messages containing media may process more slowly than text-only messages. This means that you could end up with messages getting out of order if you send several of them in a row.

For this reason, you should wait for the RBM platform to return a 200 OK response for each message before proceeding to send the next. This response indicates the platform has received the message, ensuring users receive them as intended.

Additionally, it’s important to be on the lookout for duplicate incoming messages. When receiving messages from users, always check the `messageId` to confirm that the message hasn’t been processed before. By keeping track of `messageId` strings, duplicate messages can be easily identified and disregarded, ensuring efficient and accurate communication.

Integrate with Quiq

RCS is the next step in text messaging, opening up many more ways of interacting with the people reaching out to you for help.

There are many ways to leverage RCS, one of which is turbo-charging your agents with the power of large language models. The easiest way to do this is to team up with a conversation AI platform to do the technical heavy lifting for you.

Quiq is one such platform. Reach out to schedule a demo with us today!

AI Gold Rush: How Quiq Won the Land Grab for AI Contact Centers (& How You Can Benefit)

Posted on Michael Hartsog

There have been many transformational moments throughout the history of the United States, going back all the way to its unique founding.

Take for instance the year 1849.

For all of you SFO 49ers fans (sorry, maybe next year), you are very well aware of the land grab that was the birth of the state of California. That year, tens of thousands of people from the Eastern United States flocked to the California Territory hoping to strike it rich in a placer gold strike.

A lesser-known fact of that moment in history is that the gold strike in California was actually in 1848. And while all of those easterners were lining up for the rush, a small number of people from Latin America and Hawaii were already in production, stuffing their pockets full of nuggets.

176 years later, AI is the new gold rush.

Fast forward to 2024, a new crowd is forming, working toward the land grab once again. Only this time, it’s not physical.

It’s AI in the contact center.

Companies are building infrastructure, hiring engineers, inventing tools, and trying to figure out how to build a wagon that won’t disintegrate on the trail (AKA hallucinate).

While many of those companies are going to make it to the gold fields, one has been there since 2023, and that is Quiq.

Yes, we’ve been mining LLM gold in the contact center since July of 2023 when we released our first customer-facing Generative AI assistant for Loop Insurance. Since then, we have released over a dozen more and have dozens more under construction. More about the quality of that gold in a bit.

This new gold rush in the AI space is becoming more crowded every day.

Everyone is saying they do Generative AI in one way, shape, or form. Most are offering some form of Agent Assist using LLM technologies, keeping that human in the loop and relying on small increments of improvement in AHT (Average Handle Time) and FCR (First Contact Resolution).

However, there is a difference when it comes to how platforms are approaching customer-facing AI Assistants.

Actually, there are a lot of differences. That’s a big reason we invented AI Studio.

AI Studio: Get your shovels and pick axes.

Since we’ve been on the bleeding edge of Generative AI CX deployments, we created called AI Studio. We saw that there was a gap for CX teams, with the myriad of tools they would have had to stitch together and stay focused on business outcomes.

AI Studio is a complete toolkit to empower companies to explore nuances in their AI use within a conversational development environment that’s tailored for customer-facing CX.

That last part is important: Customer-facing AI assistants, which teams can create together using AI Studio. Going back to our gold rush comparison, AI Studio is akin to the pick axes and shovels you need.

Only success is guaranteed and the proverbial gold at the end of the journey is much, much more enticing—precisely because customer-facing AI applications tend to move the needle dramatically further than simpler Agent Assist LLM builds.

That brings me to the results.

So how good is our gold?

Early results are showing that our LLM implementations are increasing resolution rates 50% to 100% above what was achieved using legacy NLU intent-based models, with resolution rates north of 60% in some FAQ-heavy assistants.

Loop Insurance saw a 55% reduction in email tickets in their contact center.

Secondly, intent matching has more than doubled, meaning the percentage of correctly identified intents (especially when there are multiple intents) are being correctly recognized and responded to, which directly correlates to correct answers, fewer agent contacts, and satisfied customers.

That’s just the start though. Molekule hit a 60% resolution rate with a Quiq-built LLM-powered AI assistant. You can read all about that in our case study here.

And then there’s Accor, whose AI assistant across four Rixos properties has doubled (yes, 2X’ed) click-outs on booking links. Check out that case study here.

What’s next?

Like the miners in 1848, digging as much gold out of the ground as possible before the land rush, Quiq sits alone, out in front of a crowd lining up for a land grab.

With a dozen customer-facing LLM-powered AI assistants already living in the market producing incredible results, we have pioneered a space that will be remembered in history as a new day in Customer Experience.

Interested in harnessing Quiq’s power for your CX or contact center? Send us a demo request or get in touch another way and let’s talk.

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

Posted on The Quiq Team

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!

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!

6 Amazing Examples of how AI is Changing Hospitality

Posted on The Quiq Team

Recent advances in AI are poised to bring many changes. Though we’re still in the early days of seeing how all this plays out, there’s already clear evidence that generative AI is having a measurable impact in places like contact centers. Looking into the future a bit, multiple reports indicate that AI could add trillions of dollars to the economy before the close of the 2020s, and lead to as much as a doubling in rates of yearly economic growth over the next decade.

The hospitality industry has always been forward-looking, eager to adopt new best practices and technologies. If you’re working in hospitality now, therefore, you might be wondering what AI will mean for you, and what the benefits of AI will be.

That’s exactly what we’re setting out to answer in this article! Below, we’ve collected several of our favorite use cases of AI assistants in both hospitality and travel. Throughout, we’ve tried to anchor the discussion to real-world examples. We hope that, by the end, you’ll feel much better equipped to evaluate whether and how to use AI assistants in your own operations.

Let’s get going!

What is AI in Hospitality and Travel?

The term “artificial intelligence” covers a huge number of topics, approaches, and subdomains, most of which we won’t be able to cover here. But broadly, you can think of AI as being any attempt to train a machine to do useful work.

Two of the more popular methods for accomplishing this task are machine learning and generative AI, the latter of which has become famous due to the recent spectacular successes of large language models.

These are also the methods we’ll be focused on because they’re the ones most commonly used in hospitality. Machine learning, for example, will pop up in examples of dynamic pricing and demand forecasting, while generative AI is a key engine driving advances in automated concierge services.

6 Ways AI Assistants are Transforming Hospitality and Travel

Below, we’ve collected some of the most compelling use cases of AI assistants in the hospitality and travel industry. We’ll begin with their use in educating the rising generation of hospitality professionals, then move on to HR, operations, revenue, and all the other things that go into keeping guests happy!

Use Case #1 – Educating Future Hospitality Professionals

From personalized lesson plans to software-based tutors, applying artificial intelligence to education has long been a dream. This is no different for hospitality, where rising students are using the latest and greatest tools to accelerate their learning.

Students have to figure out how to comport themselves in a variety of challenging circumstances, from interactions at the front desk to ensuring the room service makes it to the right guest. When augmented with artificial intelligence, simulations can help students gain exposure to many of the issues they’ll face in their day-to-day work.

Generative AI, for example, can be used to practice and internalize strategies for dealing with guests who are distraught or downright rude. It can also be used as a general learning tool, helping to break down complex concepts, structure study routines, and more.

Use Case #2 – Hiring and Staffing

Like all businesses, hotels, resorts, and other hospitality staples have to deal with hiring. Talent acquisition is a major unsolved challenge; it can take a long time to find a good hire for a position, and mistakes can cost a lot in terms of time, energy, and money.

This, too, is a place where machine learning can help. A prominent example is Hilton, which has begun using bespoke algorithms to fill its positions. These algorithms can ingest a huge amount of information on the skills and experiences of a set of potential candidates, build profiles for them, and then measure this against the profiles of employees who have been successful in the past. This allows Hilton to better gauge how well these candidates will ultimately be able to live up to the rigors of different roles.

With this approach, Hilton has been able to fill empty positions in as little as a week, all while cutting its turnover in half. Not only does this save a great deal of time for hiring managers and recruiters, it also reduces delays and helps to build a more robust company culture.

This last point warrants a little elaboration. When employees stay with a company for a long time, they gain a very intuitive grasp of its internal workings. When they leave, they take this knowledge with them, and it can take a long time to rebuild. If AI is able to more efficiently find and place candidates, it means that an organization will function better in a thousand little ways, leading to an improved guest experience and more success in the long term.

Use Case #3 – Hotel Operations Management

Hotels have many moving parts. Keeping all the proverbial plates spinning is known as “operations,” and can involve anything from changing a reservation to fielding questions to making sure all the thermostats are functional.

Though much of this still requires the human touch, artificial intelligence can do a lot to lighten the load by automating routine parts of the job. Take booking, for example. It can be complicated, but in many cases, today’s AI assistants are more than capable of helping.

What might that look like? Consider an example of a potential guest who has questions about your amenities. They might want to know whether you have any special programs for kids, whether you have pool-side food service, etc. These are all things that a question-answering AI assistant could help with.

If we assume the guest has decided to book with you, they may later want to change their reservation by a few days. Or, after their stay, they may run into billing issues that need to be reconciled. These are both tasks that are often within the capacity of today’s systems.

This is appealing because it’ll save you time, yes, but there are more opportunities here than may be apparent at first. The Maison Mere hotel in Paris, for example, made the decision to use a contactless check-in service that allowed them to collect little details about their guests before they arrived. Afterward, they used that information to create custom touches in those guest’s rooms, such as personalized greetings and flowers. What’s more, it gave Maison Mere a chance to take advantage of targeted upselling opportunities; guests traveling with pets were offered pet kits, and promotions through the platform led to a boost in reservations at the hotel’s attached restaurant, to name but a few.

Returning to amenities, if you’ve worked in hospitality before, you’ve probably dealt with snack requests, towel deliveries, etc. In Silicon Valley, Crowne Plaza has begun rolling out a robotic system called “Dash” to outsource exactly these kinds of low-level tasks. Dash uses Wi-Fi to move around the hotel, locate guests, and deliver the requested items. It’s even able to check its own battery supply and recharge when it starts running low.

Use Case #4 – Hotel Revenue Management

Like all businesses, hotels exist to make money, and they therefore tend to keep a pretty close eye on their revenue. This might be one of the responsibilities you assume as a hospitality specialist, so it’s worth understanding how AI assistants will impact hotel revenue management.

Some of these developments have been in motion for a while. One tried-and-true technique for maximizing revenue is to better forecast future demand. Unfortunately, most hotels are not booked solid year round, there’ll be periods of extremely high activity and periods of relatively low activity. But these fluctuations aren’t random, and with the right machine learning algorithms, past historical data can be mined to arrive at a pretty accurate picture of when you’re going to be full. This allows you to better plan your inventory, for example, and have all the staff required to ensure everyone enjoys their stay.

For the same reason, many hotels choose to vary their prices based on demand. Premium suites might go for $500 a night in the busy season while commanding a much more affordable $200 a night when no one is visiting.

There exist many AI tools to help with this work, and they’re getting good results. In Thailand, the Narai Hospitality Group utilized a pricing and forecasting platform to grow their average daily rate by more than a quarter, even tripling the rates charged on some rooms during peak traffic months. Grand America Hotels & Resorts was similarly able to keep their revenue management lean and effective as they navigated the post-COVID travel boom using automation-powered software.

Use Case #5 – Marketing and Sales

Another thing the hospitality industry has in common with other industries is that it has to market its services—after all, no one can stay in a hotel they haven’t heard of. Using AI assistants for marketing purposes is hardly new, but there are some exciting developments where hospitality is concerned.

By using an AI-powered marketing intelligence service that dynamically personalizes offerings with real-time data, the U.K.’s Cheval Collection achieved an 82% revenue growth in 2023, compared to just three years prior.

Use Case #6 – Hotel Guest Experience in the AI Age

Above, we’ve discussed operations, revenue, hiring, and all the myriad aspects of running a successful hospitality enterprise. But perhaps the most important part of this process is the one we’ve saved for last: how much people enjoy actually staying with you.

This is generally known as “guest experience,” and it, too, is likely to be disrupted by the widespread use of AI assistants. Consider the example of “Rose,” an AI concierge used by Las Vegas’s Cosmopolitan hotel. When a guest checks in to the Cosmopolitan, they are given a number where they can contact Rose. They can text her if they have requests or call and talk to her if they prefer a voice interface.

Of course, it’s not hard to forecast some of the other ways AI could power an enhanced guest experience. Continuing with the concierge example, imagine smart AI assistants in each guest’s room, offering up recommendations for local restaurants or fun excursions. Since AI has made great strides in personalization, these assistants would be far from generic; they’d be able to utilize information about a guest’s preferences, prior experiences, online profiles or reviews, etc., to offer nuanced, highly-tailored advice.

If you have such a system operational in your hotel, it’s unlikely to be a thing your guests will forget.

Exploring AI in Hospitality: Industry Examples Unveiled

From large language models to machine learning to agentic systems, we’re living in something of a turning point for artificial intelligence. Today’s systems are far from perfect, but they’re clearly capable of doing economically useful work, in the hospitality industry and elsewhere.

But there remain many challenges, not least of which is working with an AI assistant platform you can trust. Quiq is a leader in the conversational AI space, and can help you integrate this cutting-edge technology into your business. Get in touch today to schedule a demo and see how we can help!

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Why Your Business Should Use Rich Messaging

Posted on The Quiq Team

A Brief Overview of Rich Messaging

Along with Eminem and Auto-Tune, text messaging was just becoming really popular back in the halcyon days of the early 2000s. It was a simpler time, and all our texts were sent via “short message service” (SMS), which was mostly confined to words. This was cutting-edge technology back then, and since we weren’t yet expressing ourselves with walls of hieroglyphic emojis or GIFs from Schitt’s Creek, it was all we needed.

Today, this is no longer the case. We’re spending much more time communicating with each other through text messaging and sending much more complicated information, to boot. In response, rich messaging was developed.

Technically known as “rich communication services” (RCS), rich messaging is the next step in the evolution of text messaging. It allows for better use of interactive media, such as high-resolution photos, messaging cards, audio messages, emojis, and GIFs. Even more important for those of us in the contact center industry, it also facilitates an enhanced customer experience, with things like sensory-rich service interactions.

Capabilities of Rich Messaging

Having covered rich messaging, let’s explore some of its vistas. While this is not a comprehensive list, it reflects what we believe to be some of RCS’s most important properties (especially from the perspective of those looking to leverage text messaging for contact centers).

Integrating with Other Services

Today, the rise of generative AI is changing how contact centers work, which also presents an opportunity for integration.

If your business is looking to integrate AI assistants to automate substantial parts of its customer service workflow, you’re almost certainly going to have to do that through rich messaging.

Secure Messaging and Transactions Processing

Big data and AI have both raised serious concerns over privacy. A decade ago, people wouldn’t have thought twice about sharing their location or putting pictures of their kids online. These days, however, more of us are privacy- and security-conscious, so the fact that rich messaging supports end-to-end encryption is important.

People are much more likely to talk to your customer service agents directly if they can rest easy knowing their data isn’t going to be exposed to malicious actors.

Better Analytics

Speaking of big data, rich messaging makes it possible to gather and conduct fairly sophisticated customer service data analysis. You can gather statistics about obvious metrics like reply rates or feed conversations into a sentiment analysis system to determine how people feel about you, your company, and your service.

This allows you to identify patterns in customer behavior, optimize your use of AI, and generally start tinkering with your procedures to better serve customer needs.

It also goes the other way, inasmuch as you can send real-time alerts confirming an issue was received, updating a customer on the status of a ticket, etc. Sure, this isn’t technically “analysis,” but it’ll help people feel more at ease when interacting with your customer service agents, so it’s worth bearing in mind.

Rich Channels of Communication

Where can you use rich messaging? In the sections that follow, we’ll answer this exact question!

WhatsApp

WhatsApp is a platform overseen by Meta (formerly Facebook) that supports rich text messaging, voice messaging, and video calling. With more than two billion users, it’s incredibly popular. A key reason for this is that all this data is sent over the internet, obviating the SMS fees that used to keep us all up at night. And it has a business API that will allow you to scale up with increased demand.

Apple Rich Messaging

Apple’s rich messaging service is called Apple Messages for Business. It offers potential and existing customers a way to communicate with your agents directly via their Apple devices.

This is a market you can’t afford to ignore; with nearly two billion Apple devices, the reach of iOS is simply gigantic, and it’s a communication channel you should be cultivating.

Google Rich Messaging

More than nine out of ten searches happen on Google, meaning that it has become the powerhouse when it comes to finding information online. And if that’s not enough to convince you, consider that the phrase “Google it” is now just what people say when they’re talking about looking something up.

However, you may not be aware that Google offers a Business Messages service that should be part of your overall customer strategy.

Building Trust through Rich Communication Service Messages

Being successful in business requires many things, but one of the more important ones is trust. This has always been true, of course, but it’s only become more so with the rise of artificial intelligence.

We’ve been singing the praises of generative AI for a while, and firmly believe that it will have a huge positive impact on the contact center industry. But there’s a downside to the fact that it’s now trivial to crank industrial quantities of text, video, and images.

There’s always been plenty of nonsense online, but once upon a time, the ability to create such content was limited by the fact that someone, somewhere, had to actually sit down and make it. That’s no longer the case, which means that users are more eager than ever for signs that they’re dealing with customer service they can rely on.

Rich messaging has a part to play in that, and in the next few sections, we’ll explain why.

High-Quality Interactions Will Have Customers Coming Back

Rich messaging has many tools that make it easier to ensure that customers have a first-class experience interacting with your contact center. The rich messaging services described above have APIs, for example, that allow you to better organize conversations. This means agents can stay on top of their workloads, leading to less of the kind of frustration and distress that might negatively color their replies.

These services can also be integrated with high-quality conversational AI platforms. When agents can outsource simple, standard queries to algorithms or reuse snippets, they have more time to focus on solving trickier problems.

The net result is that agents feel less burned out, and customers get better help, faster.

Consistency in Experiences

Another way to build trust is to ensure your style is consistent across channels. Just as you wouldn’t use a different logo on Facebook and Instagram, you shouldn’t use a dramatically different tone of voice on one platform than you use on another.

When people know what to expect from you, they’re more likely to trust you. Because rich messaging supports many different kinds of media, you can ensure that customer experiences remain consistent.

This is also a place where generative AI comes in handy. The best conversational AI platforms train models on the conversations of senior agents and make this available to everyone in the contact center. This means that each agent can format replies with the same empathy, patience, and understanding as their very best peers.

Verified Business Profiles

Finally, using a verified account is a basic step you can take to increase trust. If you thought getting junk mail was bad, pause for a moment and consider the absolute barrage of text messages, bogus phone calls, and DMs most of us get every single day. There is a never-ending sea of bot accounts on Twitter and other platforms trying to dupe everyone into one crypto scam or another, and this has substantially eroded people’s trust in online interactions.

The rich messaging services offered by Google, WhatsApp, and Apple all have a fairly lengthy process for verifying the authenticity of your profile. By itself, this isn’t going to ensure that customers trust you, but it helps. People want to know that they’re talking to a real business, not an imposter; the “proof of work” (speaking of crypto) required to verify a rich messaging account is a crucial part of establishing that rapport.

Rich Messaging is the Future of Text

The world today looks very different from the world of the early 2000s. Our technologies, including our text messaging, have evolved along with it, and businesses have to keep up if they want to remain relevant.

Rich messaging is a great way to build trust and loyalty, and it opens up many new opportunities. But to get the most out of rich messaging, it really helps to work with a platform that offers robust tooling, language models, analytics, and so on.

Quiq is one such platform. Reach out to us to schedule a demo, and see how we can ensure your text-messaging outreach is profitable, productive, and easy!

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How to Improve Contact Center Performance (With Data)

Posted on The Quiq Team

Contact centers are a crucial part of offering quality products. Long after the software has been built and the marketing campaigns have been run, there will still be agents helping customers reset their passwords and debug tricky issues.

This means we must do everything we can to ensure that our contact centers are operating at peak efficiency. Data analytics is an important piece of the puzzle, offering the kinds of hard numbers we need to make good decisions, do right by our customers, and support the teams we manage.

That will be our focus today. We’ll cover the basics of implementing a data analysis process, as well as how to use it to assess and improve various contact center performance metrics.

Let’s get going!

How to Use Data Analytics to Increase Contact Center Performance

A great place to start is with a broader overview of the role played by data analytics in making decisions in modern contact centers. Here, we’ll cover the rudiments of how data analytics works, the tools that can be used to facilitate it, and how it can be used in making critical decisions.

Understanding the Basics of Data Analytics in Contact Centers

Let’s define data analytics in the context of contact center performance management. Like the term “data scientist”—which could cover anything from running basic SQL queries to building advanced reinforcement learning agents—“data analytics” is a nebulous term that can be used in many different conversations and contexts.
Nevertheless, its basic essence could be summed up as “using numbers to make decisions.”

If you’re reading this, the chances are good that you have a lot of experience in contact center performance management already, but you may or may not have spent much time engaging in data analytics. If so, be aware that data analysis is an enormously powerful tool, especially for contact centers.

Imagine, for example, a new product is released, and you see a sudden increase in average handle time. This could mean there is something about it that’s especially tricky or poorly explained. You could improve your contact center performance metrics simply by revisiting that particular product’s documentation to see if anything strikes you as problematic.

Of course, this is just a hypothetical scenario, but it shows you how much insight you can gain from even rudimentary numbers related to your contact center.

Implementing Analytics Tools and Techniques

Now, let’s talk about what it takes to leverage the power of contact center performance metrics. You can slice up the idea of “analytics tools and techniques” in a few different ways, but by our count, there are (at least) four major components.

Gathering the Data

First, like machine learning, analytics is “hungry,” meaning that it tends to be more powerful the more data you have. For this reason, you have to have a way of capturing the data needed to make decisions.

In the context of contact center performance, this probably means setting up a mechanism for tracking any conversations between agents and customers, as well as whatever survey data is generated by customers reflecting on their experience with your company.

Storing the Data

This data has to live somewhere, and if you’re dealing with text, there are various options. “Structured” textual data follows a consistent format and can be stored in a relational database like MySQL. “Unstructured” textual data may or may not be consistent and is best stored in a non-relational database like MongoDB, which is better suited for it.

It’s not uncommon to have both relational and non-relational databases for storing specific types of data. Survey responses are well-structured so they might go in MySQL, for instance, while free-form conversations with agents might go in MongoDB. There are also more exotic options like graph databases and vector databases, but they’re beyond the scope of this article.

Analyzing the Data

Once you’ve captured your data and stored it somewhere, you have to analyze it—the field isn’t called data analytics for nothing! A common way to begin analyzing data is to look for simple, impactful, long-term trends—is your AHT going up or down, for instance? You can also look for cyclical patterns. Your AHT might generally be moving in a positive direction, but with noticeable spikes every so often that need to be explained and addressed.

You could also do more advanced analytics. After you’ve gathered a reasonably comprehensive set of survey results, for example, you could run them through a sentiment analysis algorithm to find out the general emotional tone of the interactions between your agents and your customers.

Serving Up Your Insights

Finally, once you’ve identified a set of insights you can use to make decisions about improving contact center performance, you need to make them available. By far the most common way is by putting some charts and graphs in a PowerPoint presentation and delivering it to the people making actual decisions. That said, some folks opt instead to make fancy dashboards, or even to create monitoring tools that update in real time.

Effectively Leveraging Data

As you can see, creating a top-to-bottom contact center performance solution takes a lot of effort. The best way to save time is to find a tool that abstracts away as much of the underlying technical work as possible.

Ideally, you’d be looking for quick insights generated seamlessly across all the many messaging channels contact centers utilize these days. It’s even better if those insights can easily be published in reports that inform your decision-making.

What’s the payoff? You’ll be able to scrutinize (and optimize) each step taken during a customer journey, and discover how and why your customers are reaching out. You’ll have much more granular information about how your agents are functioning, giving you the tools needed to improve KPIs and streamline your internal operations.

We’ll treat each of these topics in the remaining sections, below.

How to Improve KPIs in Contact Center

After gathering and analyzing a lot of data, you’ll no doubt notice key performance indicators (KPIs) that aren’t where you want them to be. Here, we’ll discuss strategies for getting those numbers up!

Identifying Key Performance Indicators (KPIs)

First, let’s briefly cover some of the KPIs you’d be looking for.

  • First Contact Resolution (FCR) – The first contact resolution is the fraction of issues a contact center is able to resolve on the first try, i.e. the first time the customer reaches out.
  • Average Handle Time (AHT) – The average handle time is one of the more important metrics contact centers track, and it refers to the mean length of time an agent spends on a task (this includes both talking to customers directly and whatever follow-up work comes after).
  • Customer Satisfaction (CSAT) – The customer satisfaction score attempts to gauge how customers feel about your product and service.
  • Call Abandon Rate (CAR) – The call abandon rate is the fraction of customers who end a call with an agent before their question has been answered.
  • Net Promoter Score (NPS) – The net promoter score is a number (usually from 1-10) that quantifies how likely a given customer would be to recommend you to someone they know.

Of course, this is just a sampling of the many contact center performance metrics you can track. Ultimately, you want to choose a set of metrics that gives you a reasonably comprehensive view of how well your contact center is doing, and whether it’s getting better or worse over time.

Strategies for Improving Key KPIs

There are many things you can do to improve your KPIs, including up-training your personnel or making your agents more productive with tools like generative AI.

This is too big a topic to cover comprehensively, but since generative AI is such a hot topic let’s walk through a case study where using it led to dramatic improvements in efficiency.

LOOP is a Texas-based car insurance provider that partnered with Quiq to deploy a generative AI assistant. Naturally, they already had a chatbot in place, but they found it could only offer formulaic answers. This frustrated customers, prevented them from solving their own problems, and negatively impacted KPIs overall.

However, by integrating a cutting-edge AI assistant powered by large language models, they achieved a remarkable threefold increase in self-service rates. By the end, more than half of all customer issues were resolved without the need for agents to get involved, and fully three-quarters of customers indicated that they were satisfied with the service provided by the AI.

Now, we’re not suggesting that you can solve every problem with fancy new technology. No, our point here is that you should evaluate every option in an attempt to find workable contact center performance solutions, and we think this is a useful example of what’s possible with the right approach.

Tips to Boost Contact Center Operational Efficiency

We’ve covered a lot of ground related to data analysis and how it can help you make decisions about improving contact center performance. In this final section, we’ll finish by talking about using data analytics and other tools to make sure you’re as operationally efficient as you can be.

Streamlining Operations with Technology

The obvious place to look is technology. We’ve already discussed AI assistants, but there’s plenty more low-hanging fruit to be picked.

Consider CRM integrations, for example. We’re in the contact center business, so we know all about the vicissitudes of trying to track and manage a billion customer relationships. Even worse, the relevant data is often spread out across many different locations, making it hard to get an accurate picture of who your customers are and what they need.

But if you invest in solutions that allow you to hook your CRM up to your other tools, you can do a better job of keeping those data in sync and serving them up where they’ll be the most use. As a bonus, these data can be fed to a retrieval augmented generation system to help your AI assistant create more accurate replies. They can also form a valuable part of your all-important data analytics process.

What’s more, these same analytics can be used to identify sticking points in your workflows. With this information, you’ll be better equipped to rectify any problems and keep the wheels turning smoothly.

Empowering Agents to Enhance Performance

We’ve spent a lot of time in this post discussing data analytics, AI, and automation, but it’s crucial not to forget that these things are supplements to human agents, not replacements for them. Ultimately, we want agents to feel empowered to utilize the right tools to do their jobs better.

First, to the extent that it’s possible (and appropriate), agents should be given access to the data analytics you perform in the future. If you think you’re making better decisions based on data, it stands to reason that they would do the same.

Then, there are various ways of leveraging generative AI to make your agents more effective. Some of these are obvious, as when you utilize a tool like Quiq Snippets to formulate high-quality replies more rapidly (this alone will surely drop your AHT). But others are more out-of-the-box, such as when new agents can use a language model to get up to speed on your product offering in a few days instead of a few weeks.

Continuously Evaluating and Refining Processes

To close out, we’ll reiterate the importance of consistently monitoring your contact center performance metrics. These kinds of numbers change in all sorts of ways, and the story they tell changes along with them.

It’s not enough to measure a few KPIs and then call it a day, you need to have a process in place to check them consistently, revising your decisions along the way.

Next Steps for Improving Your Contact Center Metrics

They say that data is the new oil, as it’s a near-inexhaustible source of insights. With the right data analysis, you can figure out which parts of your contact center are thriving and which need more support, and you can craft strategies that set you and your teams up to succeed.

Quiq is well-known as a conversational AI platform, but we also have a robust suite of tools for making the most out of the data generated by your contact center. Set up a demo to figure out how we can give you the facts you need to thrive!

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WhatsApp Business: A Guide for Contact Center Managers

Posted on The Quiq Team

In today’s digital era, businesses continually seek innovative ways to connect with their customers, striving to enhance communication and foster deeper relationships. Enter WhatsApp Business – a game-changer in the realm of digital communication. This powerful tool is not just a messaging app; it’s a bridge between businesses and their customers, offering a plethora of features designed to streamline communication, improve customer service, and boost engagement. Whether you’re a small business owner or part of a global enterprise, understanding the potential of WhatsApp Business could redefine your approach to customer communication.

What is Whatsapp Business?

WhatsApp is an application that supports text messaging, voice messaging, and video calling for over two billion global users. Because it leverages a simple internet connection to send and receive data, WhatsApp users can avoid the fees that once made communication so expensive.

Since WhatsApp already has such a large base of enthusiastic users, many international brands have begun leveraging it to communicate with their own audiences. It also has a number of built-in features that make it an attractive option for businesses wanting to establish a more personal connection with their customers, and we’ll cover those in the next section.

What Features Does WhatsApp Business Have?

In addition to its reach and the fact that it reduces the budget needed for communication, WhatsApp Business has additional functionality that makes it ideal for any business trying to interact with its customers.

When integrated with a tool like the Quiq conversational AI platform, WhatsApp Business can automatically transcribe voice-based messages. Even better, WhatsApp Business allows you to export these conversations later if you want to analyze them with a tool like natural language processing.

If your contact center agents and the customers they’re communicating with have both set a “preferred language,” WhatsApp can dynamically translate between these languages to make communication easier. So, if a user sends a voice message in Russian and the agent wants to communicate in English, they’ll have no trouble understanding one another.

What are the Differences Between WhatsApp and WhatsApp Business?

Before we move on, it’s worth pointing out that WhatsApp and WhatsApp Business are two different services. On its own, WhatsApp is the most widely used messaging application in the world. Businesses can use WhatsApp to talk to their customers, but with a WhatsApp Business account, they get a few extra perks.

Mostly, these perks revolve around building brand awareness. Unlike a basic WhatsApp account, a WhatsApp Business account allows you to include a lot of additional information about your company and its services. It also provides a labeling system so that you can organize the conversations you have with customers, and a variety of other tools so you can respond quickly and efficiently to any issues that come up.

The Advantages of WhatsApp Messaging for Businesses

Now, let’s spend some time going over the myriad advantages offered by a WhatsApp outreach strategy. Why, in other words, would you choose to use WhatsApp over its many competitors?

Global Reach and Popularity

First, we’ve already mentioned the fact that WhatsApp has achieved worldwide popularity, and in this section, we’ll drill down into more specifics.

When WhatsApp was acquired by Meta in 2014, it already boasted 450 million active users per month. Today, this figure has climbed to a remarkable 2.7 billion, but it’s believed it will reach a dizzying 3.14 billion as early as 2025.

With over 535 million users, India is the country where WhatsApp has gained the most traction by far. Brazil is second with 148 million users, and Indonesia is third with 112 million users.

The gender divide among WhatsApp users is pretty even – men account for just shy of 54% of WhatsApp users, so they have only a slight majority.

The app itself has over 5 billion downloads from the Google Play store alone, and it’s used to send 140 billion messages each day.

These data indicate that WhatsApp could be a very valuable channel to cultivate, regardless of the market you’re looking to serve or where your customers are located.

Personalized Customer Interactions

Firstly, platforms like WhatsApp enable businesses to customize communication with a level of scale and sophistication previously unavailable.

This customization is powered by machine learning, a technology that has consistently led the charge in the realm of automated content personalization. For example, Spotify’s ability to analyze your listening patterns and suggest music or podcasts that match your interests is powered by machine learning. Now, thanks to advancements in generative AI, similar technology is being applied to text messaging.

Past language models often fell short in providing personalized customer interactions. They tended to be more “rule-based” and, therefore, came off as “mechanical” and “unnatural.” However, contemporary models greatly improve agents’ capacity to adapt their messages to a particular situation.

While none of this suggests generative AI is going to entirely take the place of the distinctive human mode of expression, for a contact center manager aiming to improve customer experience, this marks a considerable step forward.

Below, we have a section talking a little bit more about integrating AI into WhatsApp Business.

End-to-End Encryption

One thing that has always been a selling point for WhatsApp is that it takes security and privacy seriously. This is manifested most obviously in the fact that it encrypts all messages end-to-end.

What does this mean? From the moment you start typing a message to another user all the way through when they read it, the message is protected. Even if another party were to somehow intercept your message, they’d still have to crack the encryption to read it. What’s more, all of this is enabled by default – you don’t have to spend any time messing around with security settings.

This might be more important than you realize. We live in a world increasingly beset by data breaches and ransomware attacks, and more people are waking up to the importance of data security and privacy. This means that a company that takes these aspects of its platform very seriously could have a leg up where building trust is concerned. Your users want to know that their information is safe with you, and using a messaging service like WhatsApp will help to set you apart.

Scalability

Finally, WhatsApp’s Business API is a sophisticated programmatic interface designed to scale your business’s outreach capabilities. By leveraging this tool, companies can connect with a broader audience, extending their reach to prospects and customers across various locations. This expansion is not just about increasing numbers; it’s about strategically enhancing your business’s presence in the digital world, ensuring that you’re accessible whenever your customers need to reach out to you.

By understanding the value WhatsApp’s Business API brings in reaching and engaging with more people effectively, you can make an informed decision about whether it represents the right technological solution for your business’s expansion and customer engagement strategies.

Enhancing Contact Center Performance with WhatsApp Messaging

Now, let’s turn our attention to some of the concrete ways in which WhatsApp can improve your company’s chances of success!

Improving Response and Resolution Metrics Times

Integrating technologies like WhatsApp Business into your agent workflow can drastically improve efficiency, simultaneously reducing response times and boosting customer satisfaction. Agents often have to manage several conversations at once, and it can be challenging to keep all those plates spinning.

However, a quality messaging platform like WhatsApp means they’re better equipped to handle these conversations, especially when utilizing tools like Quiq Compose.

Additionally, less friction in resolving routine tasks means agents can dedicate their focus to issues that necessitate their expertise. This not only leads to more effective problem-solving, it means that fewer customer inquiries are overlooked or terminated prematurely.

Integrating Artificial Intelligence

According to WhatsApp’s own documentation, there’s an ongoing effort to expand the API to allow for the integration of chatbots, AI assistants, and generative AI more broadly.

Today, these technologies possess a surprisingly sophisticated ability to conduct basic interactions, answer straightforward questions, and address a wide range of issues, all of which play a significant role in boosting customer satisfaction and making agents more productive.

We can’t say for certain when WhatsApp will roll out the red carpet for AI vendors like Quiq, but if our research over the past year is any indication, it will make it dramatically easier to keep customers happy!

Gathering Customer Feedback

Lastly, an additional advantage to WhatsApp messaging is the degree to which it facilitates collecting customer feedback. To adapt quickly and improve your services, you have to know what your customers are thinking. And more specifically, you have to know the details about what they like and dislike about your product or service.

In the Olde Days (i.e. 20 years ago year, or so), the only real way to do this was by conducting focus groups, sending out surveys – sometimes through the actual mail, if you can believe it – or doing something similarly labor-intensive.

Today, however, your customers are almost certainly walking around with a smartphone that supports text messaging. And, since it’s pretty easy for them to answer a few questions or dash off a few quick lines describing their experience with your service, odds are that you can gather a great deal of feedback from them.

Now, we hasten to add that you must exercise a certain degree of caution in interpreting this kind of feedback, as getting an accurate gauge of customer sentiment is far from trivial. To name just one example, the feedback might be exaggerated in both the positive and negative direction because the people most likely to send feedback via text messaging are the ones who really liked or really didn’t like you.

That said, so long as you’re taking care to contextualize the information coming from customers, supplementing it with additional data wherever appropriate, it’s valuable to have.

Wrapping Up

From its global reach and popularity to the personalized customer interactions it facilitates, WhatsApp Business stands out as a powerful solution for businesses aiming to enhance their digital presence and customer engagement strategies. By leveraging the advanced features of WhatsApp Business, companies can avail themselves of end-to-end encryption, enjoy scalability, and improve contact center performance, thereby positioning themselves at the forefront of the contact center game.

And speaking of being at the forefront, the Quiq conversational CX platform offers a staggering variety of different tools, from AI assistants powered by language models to advanced analytics on agent performance. Check us out or schedule a demo to see what we can do for your contact center!

Your CX Strategy Should Include Apple Messages for Business. Here’s Why.

Posted on The Quiq Team

Your CX Strategy Should Include Apple Messages for Business. Here’s Why.

A common piece of marketing advice says you should “Meet your customers where they’re at.” These days, there are something like 23 billion text messages sent daily across the world, so your customers are probably on their phones.

Twenty years ago, you could be forgiven for thinking that text messaging was a method of communication reserved for teenagers sending each other inscrutable strings of hieroglyphic emojis, but more and more business is being done this way. It’s now relatively common for contact centers to offer customer support over chat, which means text messaging has emerged as a vital customer service channel.

In this piece, we will focus specifically on one text messaging service, Apple Messages, and how it can be leveraged to create personalized and efficient customer interactions. Along the way, we’ll talk about some of the exciting work being done to leverage AI assistants through text messaging so you can stay one step ahead of the competition.

The Advantages of Apple Messages in Customer Service

Here, we’re going to discuss the myriad advantages conferred by using Apple Messages. But before we do that, it’s worth making sure we’re all on the same page by discussing what Apple Messages is in the first place.

You probably already know that Apple’s line of iPhones supports text messaging, like all mobile phones. But Apple Messages is a distinct product designed to allow businesses like yours to interact with customers.

It makes it easy to set up a variety of touchpoints, like QR codes, an app, or an email message, through which customers can make appointments, raise (and resolve) problems, or pay for your services.

There are many ways in which utilizing Apple Messages can help you, which we’ll discuss now.

Personalization at Scale

First, tools like Apple Messages allow businesses to personalize communication at a scale and sophistication never seen before.

This personalization is achieved with machine learning, which has consistently been at the forefront of automated content customization. For instance, Netflix is well-known for identifying trends in your viewing habits and using algorithms to recommend shows that align with your preferences. Now, thanks to generative AI, this technology is making its way into text messaging.

Yesterday’s language models often lacked the flexibility for personalized customer interactions, sounding “robotic” and “artificial.” Modern models significantly bolster agents’ ability to tailor their conversations to the specific context. Though they do not completely replace the unique human element, for a contact center manager focused on enhancing customer experience, this represents a significant advancement.

Speed and Convenience

Another place where text messaging shines strategically is its speed and convenience. Texting became popular in the first place because it streamlined the communication process. But, unlike with a phone call, this communication could be done privately, without disturbing others.

Customers needing to troubleshoot an issue while they’re on the bus or somewhere public will likely want to do so with a chat interface. This provides the opportunity to

High Engagement Rates

One aspect of a customer communication strategy you’ll have to consider is what the likely engagement with it will be. Text messaging, particularly through platforms like Apple Messages, boasts higher open and response rates than other channels.

The statistics backing this up are compelling – 98% of text messages sent to customers are opened and eventually read, with fully 90% of them being read just three minutes after being received. Even better, nearly half (48%) of text messages sent to customers get responses.

On its own, this indicates the enormous potential for text-messaging strategies to get your customers talking to you, but when you consider the fact that only around a quarter of emails are opened and read, it’s hard to escape the conclusion that you should be investing seriously in this channel.

Leveraging AI in Apple Messages

Artificial intelligence, especially large language models, are all the rage these days, and they’re being deployed in text messages as well. Since Apple Messages allows you to use your own bots and virtual agents, it’s worth spending a few minutes talking about how generative AI can help.

There are a few different ways in which an AI customer service agent can streamline your customer service operations.

The simplest is by directly resolving issues—or helping customers to directly resolve their own issues—with little need for intervention by human contact center agents. There are many problems that are too involved for this to work, of course, but if all a customer needs to do is reset a password it could well be sufficient.

(Note, however, that Apple Messages requires you to include an option allowing a customer to escalate to a human agent. As things stand today, that part is non-negotiable).

Even when a human agent needs to get involved, however, generative AI can help. The Quiq conversational CX platform has a tool called “Quiq Compose”, for example, which can help format replies. An agent can input a potential reply with grammatical mistakes, misspellings, and a lack of warmth, and Quiq Compose will work its magic to turn the reply into something polished and empathic.

Improving Contact Center Performance with Apple Messages

Assuming that you’ve set up Apple Messages and supercharged it with the latest and greatest AI customer service agent, what can you expect to happen? That’s the question we’ll address in these sections.

Reducing Response Times

When combined with AI assistants and related technologies, Apple Messages can significantly reduce response times and increase customer satisfaction. It’s well known that contact center agents are often juggling multiple conversations at a time, and it can be hard to keep it all straight. But when they’re backed up by chatbots, Quiq Compose, etc., they can handle this volume in less time than ever before.

Generative AI is now good enough to carry on relatively lightweight interactions, answer basic questions, and help solve myriad issues; this, by itself, will almost certainly reduce response times. But it also means that agents can pivot to focusing on the thorniest, highest-priority tasks, which will further drive response times down.

Increasing Resolution Rates

For all the reasons just mentioned, AI assistants can increase resolution rates. Part of this will stem from the fact that fewer customers will fall through the cracks or end their calls early. But it will also come from agents being less rushed and more able to work on those tickets that really require their attention.

This is easy to see with an example. Imagine two people, each with daunting lists of chores they’re not sure they can finish. One of them is all on their own, while the other can outsource the most banal 30% of their tasks to robots.

Who would you bet on to have the highest chore resolution rate?

Implementing Apple Messages in Your Contact Center

The basic steps for getting started with Apple Messages are easy to follow.

First, you have to register your account. We’ve been using the name “Apple Messages” throughout this piece, but its full name is “Apple Messages for Business,” and your account must be tied to an actual business to be eligible.

Then, you have to create an account where your branding assets will live and where you’ll select the Messaging Service Provider (MSP) that you’d like to use. Apple will then review your submission, and, after a few days, will tell you whether you’ve been approved. As you’re planning your text messaging efforts, make sure that you’re factoring in the approval process.

With that done, you’ll have to start thinking in detail about your customer’s journey by filling out a Use Case template. You need to outline what you hope to achieve with text messaging, then decide on the entry points you want to offer your customers.

Next up, you’ll work out the user experience. This involves creating the automated messages you want to use, configuring Apple Pay if relevant, and designing customer satisfaction surveys.

Afterward, you need to set up metrics to figure out how your text messages are landing and whether there are things you can do to improve. If you’ve read our past articles on leveraging customer insights, you know how important data is to your ultimate success.

Last of all, Apple will spend a week or two reviewing everything you’ve accomplished in these steps and deciding whether anything else needs to be tweaked. Assuming you pass, you’re ready to go with Apple Messages!

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Final Thoughts on Why Your Business Should Use Apple Messages

Contact centers are increasingly coming to resemble technology companies, and the rise of Apple Messages is a great illustration of that. Apple Messages makes it easy to deploy AI assistants to interact with your customers, thereby reaping the enormous benefits of automation.

And speaking of the benefits of automation, check out the Quiq platform while you’re at it. We’ve worked hard to suss out the best ways of applying artificial intelligence to contact centers, and have built a product around our findings. We’ve helped many others, and we can help you too!

Retrieval Augmented Generation – Ultimate Guide

Posted on The Quiq Team

Introduction

From contact centers to copywriting and more, a lot has changed since the advent of large language models a little over a year ago. But, incredibly, there are already many attempts at extending the functionality of the underlying technology.

One broad category of these attempts is known as “tool use”, and consists of augmenting language models by giving them access to things like calculators. Stories of these models failing at simple arithmetic abound, and the basic idea is that we can begin to shore up their weaknesses by connecting them to specific external resources.

Because these models are famously prone to “hallucinating” incorrect information, the technique of retrieval augmented generation (RAG) has been developed to ground model output more effectively. So far, this has shown promise as a way of reducing hallucinations and creating much more trustworthy replies to queries.

In this piece, we’re going to discuss what retrieval augmented generation is, how it works, and how it can make your models even more robust.

Understanding Retrieval Augmented Generation

To begin, let’s get clear on exactly what we’re talking about. The next few sections will overview retrieval augmented generation, break down how it works, and briefly cover its myriad benefits.

What is Retrieval Augmented Generation?

Retrieval augmented generation refers to a large and growing cluster of techniques meant to help large language models ground their output in facts obtained from an external source.

By now, you’re probably aware that language models can do a remarkably good job of generating everything from code to poetry. But, owing to the way they’re trained and the way they operate, they’re also prone to simply fabricating confident-sounding nonsense. If you ask for a bunch of papers about the connection between a supplement and mental performance, for example, you might get a mix of real papers and ones that are completely fictitious.

If you could somehow hook the model up to a database of papers, however, then perhaps that would ameliorate this tendency. That’s where RAG comes in.

We will discuss some specifics in the next section, but in the broadest possible terms, you can think of RAG as having two components: the generative model, and a retrieval system that allows it to augment its outputs with data obtained from an authoritative external source.

The difference between using a foundation model and using a foundation model with RAG has been likened to the difference between taking a closed-book and an open-booked test – the metaphor is an apt one. If you were to poll all your friends about their knowledge of photosynthesis, you’d probably get a pretty big range of replies. Some friends would remember a lot about the process from high school biology, while others would barely even know that it’s related to plants.

Now, imagine what would happen if you gave these same friends a botany textbook and asked them to cite their sources. You’d still get a range of replies, of course, but they’d be far more comprehensive, grounded, and replete with up-to-date details. [1]

How RAG Works

Now that we’ve discussed what RAG is, let’s talk about how it functions. Though there are many subtleties involved, there are only a handful of overall steps.

First, you have to create a source of external data or utilize an existing one. There are already many such external resources, including databases filled with scientific papers, genomics data, time series data on the movements of stock prices, etc., which are often accessible via an API. If there isn’t already a repository containing the information you’ll need, you’ll have to make one. It’s also common to hook generative models up to internal technical documentation, of the kind utilized by e.g. contact center agents.

Then, you’ll have to do a search for relevancy. This involves converting queries into vectors, or numerical representations that capture important semantic information, then matching that representation against the vectorized contents of the external data source. Don’t worry too much if this doesn’t make a lot of sense, the important thing to remember is that this technique is far better than basic keyword matching at turning up documents related to a query.

With that done, you’ll have to augment the original user query with whatever data came up during the relevancy search. In the systems we’ve seen this all occurs silently, behind the scenes, with the user being unaware that any such changes have been made. But, with the additional context, the output generated by the model will likely be much more grounded and sensible. Modern RAG systems are also sometimes built to include citations to the specific documents they drew from, allowing a user to fact-check the output for accuracy.

And finally, you’ll need to think continuously about whether the external data source you’ve tied your model to needs to be updated. It doesn’t do much good to ground a model’s reply if the information it’s using is stale and inaccurate, so this step is important.

The Benefits of RAG

Language models equipped with retrieval augmented generation have many advantages over their more fanciful, non-RAG counterparts. As we’ve alluded to throughout, such RAG models tend to be vastly more accurate. RAG, of course, doesn’t guarantee that a model’s output will be correct. They can still hallucinate, just as one of your friends reading a botany book might misunderstand or misquote a passage. Still, it makes hallucinations far less prevalent and, if the model adds citations, gives you what you need to rectify any errors.

For this same reason, it’s easier to trust a RAG-powered language model, and they’re (usually) easier to use. As we said above a lot of the tricky technical detail is hidden from the end user, so all they see is a better-grounded output complete with a list of documents they can use to check that the output they’ve gotten is right.

Applications of Retrieval Augmented Generation

We’ve said a lot about how awesome RAG is, but what are some of its primary use cases? That will be our focus here, over the next few sections.

Enhancing Question Answering Systems

Perhaps the most obvious way RAG could be used is to supercharge the function of question-answering systems. This is already a very strong use case of generative AI, as attested to by the fact that many people are turning to tools like ChatGPT instead of Google when they want to take a first stab at understanding a new subject.

With RAG, they can get more precise and contextually relevant answers, enabling them to overcome hurdles and progress more quickly.

Of course, this dynamic will also play out in contact centers, which are more often leaning on question-answering systems to either make their agents more effective, or to give customers the resources they need to solve their own problems.

Chatbots and Conversational Agents

Chatbots are another technology that could be substantially upgraded through RAG. Because this is so closely related to the previous section we’ll keep our comments brief; suffice it to say, a chatbot able to ground its replies in internal documentation or a good external database will be much better than one that can’t.

Revolutionizing Content Creation

Because generative models are so, well, generative, they’ve already become staples in the workflows of many creative sorts, such as writers, marketers, etc. A writer might use a generative model to outline a piece, paraphrase their own earlier work, or take the other side of a contentious issue.

This, too, is a place where RAG shines. Whether you’re tinkering with the structure of a new article or trying to build a full-fledged research assistant to master an arcane part of computer science, it can only help to have more factual, grounded output.

Recommendation Systems

Finally, recommendation systems could see a boost from RAG. As you can probably tell from their name, recommendation systems are machine-learning tools that find patterns in a set of preferences and use them to make new recommendations that fit that pattern.

With grounding through RAG, this could become even better. Imagine not only having recommendations, but also specific details about why a particular recommendation was made, to say nothing of recommendations that are tied to a vast set of external resources.

Conclusion

For all the change we’ve already seen from generative AI, RAG has yet more more potential to transform our interaction with AI. With retrieval augmented generation, we could see substantial upgrades in the way we access information and use it to create new things.

If you’re intrigued by the promise of generative AI and the ways in which it could supercharge your contact center, set up a demo of the Quiq platform today!

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Footnotes

[1] This assumes that the book you’re giving them is itself up-to-date, and the same is true with RAG. A generative model is only as good as its data.

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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Your Guide to Trust and Transparency in the Age of AI

Posted on The Quiq Team

Over the last few years, AI has really come into its own. ChatGPT and similar large language models have made serious inroads in a wide variety of natural language tasks, generative approaches have been tested in domains like music and protein discovery, researchers have leveraged techniques like chain-of-thought prompting to extend the capabilities of the underlying models, and much else besides.

People working in domains like customer service, content marketing, and software engineering are mostly convinced that this technology will significantly impact their day-to-day lives, but many questions remain.

Given the fact that these models are enormous artifacts whose inner workings are poorly understood, one of the main questions centers around trust and transparency. In this article, we’re going to address these questions head-on. We’ll discuss why transparency is important when advanced algorithms are making ever more impactful decisions, and turn our attention to how you can build a more transparent business.

Why is Transparency Important?

First, let’s take a look at why transparency is important in the first place. The next few sections will focus on the trust issues that stem from AI becoming a ubiquitous technology that few understand at a deep level.

AI is Becoming More Integrated

AI has been advancing steadily for decades, and this has led to a concomitant increase in its use. It’s now commonplace for us to pick entertainment based on algorithmic recommendations, for our loan and job applications to pass through AI filters, and for more and more professionals to turn to ChatGPT before Google when trying to answer a question.

We personally know of multiple software engineers who claim to feel as though they’re at a significant disadvantage if their preferred LLM is offline for even a few hours.

Even if you knew nothing about AI except the fact that it seems to be everywhere now, that should be sufficient incentive to want more context on how it makes decisions and how those decisions are impacting the world.

AI is Poorly Understood

But, it turns out there is another compelling reason to care about transparency in AI: almost no one knows how LLMs and neural networks more broadly can do what they do.

To be sure, very simple techniques like decision trees and linear regression models pose little analytical challenge, and we’ve written a great deal over the past year about how language models are trained. But if you were to ask for a detailed account of how ChatGPT manages to create a poem with a consistent rhyme scheme, we couldn’t tell you.

And – as far as we know – neither could anyone else.

This is troubling; as we noted above, AI has become steadily more integrated into our private and public lives, and that trend will surely only accelerate now that we’ve seen what generative AI can do. But if we don’t have a granular understanding of the inner workings of advanced machine-learning systems, how can we hope to train bias out of them, double-check their decisions, or fine-tune them to behave productively and safely?

These precise concerns are what have given rise to the field of explainable AI. Mathematical techniques like LIME and SHAP can offer some intuition for why a given algorithm generated the output it did, but they accomplish this by crudely approximating the algorithm instead of actually explaining it. Mechanistic interpretability is the only approach we know of that confronts the task directly, but it has only just gotten started.

This leaves us in the discomfiting situation of relying on technologies that almost no one groks deeply, including the people creating them.

People Have Many Questions About AI

Finally, people have a lot of questions about AI, where it’s heading, and what its ultimate consequences will be. These questions can be laid out on a spectrum, with one end corresponding to relatively prosaic concerns about technological unemployment and deepfakes influencing elections, and the other corresponding to more exotic fears around superintelligent agents actively fighting with human beings for control of the planet’s future.

Obviously, we’re not going to sort all this out today. But as a contact center manager who cares about building trust and transparency, it would behoove you to understand something about these questions and have at least cursory answers prepared for them.

How do I Increase Transparency and Trust when Using AI Systems?

Now that you know why you should take trust and transparency around AI seriously, let’s talk about ways you can foster these traits in your contact center. The following sections will offer advice on crafting policies around AI use, communicating the role AI will play in your contact center, and more.

Get Clear on How You’ll Use AI

The journey to transparency begins with having a clear idea of what you’ll be using AI to accomplish. This will look different for different kinds of organizations – a contact center, for example, will probably want to use generative AI to answer questions and boost the efficiency of its agents, while a hotel might instead attempt to automate the check-in process with an AI assistant.

Each use case has different requirements and different approaches that are better suited for addressing it; crafting an AI strategy in advance will go a long to helping you figure out how you should allocate resources and prioritize different tasks.

Once you do that, you should then create documentation and a communication policy to support this effort. The documentation will make sure that current and future agents know how to use the AI platform you decide to work with, and it should address the strengths and weaknesses of AI, as well as information about when its answers should be fact-checked. It should also be kept up-to-date, reflecting any changes you make along the way.

The communication policy will help you know what to say if someone (like a customer) asks you what role AI plays in your organization.

Know Your Data

Another important thing you should keep in mind is what kind of data your model has been trained on, and how it was gathered. Remember that LLMs consume huge amounts of textual data and then learn patterns in that data they can use to create their responses. If that data contains biased information – if it tends to describe women as “nurses” and men as “doctors”, for example – that will likely end up being reflected in its final output. Reinforcement learning from human feedback and other approaches to fine-tuning can go part of the way to addressing this problem, but the best thing to do is ensure that the training data has been curated to reflect reality, not stereotypes.

For similar reasons, it’s worth knowing where the data came from. Many LLMs are trained somewhat indiscriminately, and might have even been shown corpora of pirated books or other material protected by copyright. This has only recently come to the forefront of the discussion, and OpenAI is currently being sued by several different groups for copyright infringement.

If AI ends up being an important part of the way your organization functions, the chances are good that, eventually, someone will want answers about data provenance.

Monitor Your AI Systems Continuously

Even if you take all the precautions described above, however, there is simply no substitute for creating a robust monitoring platform for your AI systems. LLMs are stochastic systems, meaning that it’s usually difficult to know for sure how they’ll respond to a given prompt. And since these models are prone to fabricating information, you’ll need to have humans at various points making sure the output is accurate and helpful.

What’s more, many machine learning algorithms are known to be affected by a phenomenon known as “model degradation”, in which their performance steadily declines over time. The only way you can check to see if this is occurring is to have a process in place to benchmark the quality of your AI’s responses.

Be Familiar with Standards and Regulations

Finally, it’s always helpful to know a little bit about the various rules and regulations that could impact the way you use AI. These tend to focus on what kind of data you can gather about clients, how you can use it, and in what form you have to disclose these facts.

The following list is not comprehensive, but it does cover some of the more important laws:

  • The General Data Protection Regulation (GDPR) is a comprehensive regulatory framework established by the European Union to dictate data handling practices. It is applicable not only to businesses based in Europe but also to any entity that processes data from EU citizens.
  • The California Consumer Protection Act (CCPA) was introduced by California to enhance individual control over personal data. It mandates clearer data collection practices by companies, requires privacy disclosures, and allows California residents to opt-out of data collection.
  • Soc II, developed by the American Institute of Certified Public Accounts, focuses on the principles of confidentiality, privacy, and security in the handling and processing of consumer data.
  • In the United Kingdom, contact centers must be aware of the Financial Conduct Authority’s new “Consumer Duty” regulations. These regulations emphasize that firms should act with integrity toward customers, avoid causing them foreseeable harm, and support customers in achieving their financial objectives. As the integration of generative AI into this regulatory landscape is still being explored, it’s an area that stakeholders need to keep an eye on.

Fostering Trust in a Changing World of AI

An important part of utilizing AI effectively is making sure you do so in a way that enhances the customer experience and works to build your brand. There’s no point in rolling out a state-of-the-art generative AI system if it undermines the trust your users have in your company, so be sure to track your data, acquaint yourself with the appropriate laws, and communicate clearly.

Another important step you can take is to work with an AI vendor who enjoys a sterling reputation for excellence and propriety. Quiq is just such a vendor, and our Conversational AI platform can bring AI to your contact center in a way that won’t come back to bite you later. Schedule a demo to see what we can do for you, today!

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Getting the Most Out of Your Customer Insights with AI

Posted on The Quiq Team

The phrase “Knowledge is power” is usually believed to have originated with 16th- and 17th-century English philosopher Francis Bacon, in his Meditationes Sacræ. Because many people recognize something profoundly right about this sentiment, it has become received wisdom in the centuries since.

Now, data isn’t exactly the same thing as knowledge, but it is tremendously powerful. Armed with enough of the right kind of data, contact center managers can make better decisions about how to deploy resources, resolve customer issues, and run their business.

As is usually the case, the data contact center managers are looking for will be unique to their field. This article will discuss these data, why they matter, and how AI can transform how you gather, analyze, and act on data.

Let’s get going!

What are Customer Insights in Contact Centers?

As a contact center, your primary focus is on helping people work through issues related to a software product or something similar. But you might find yourself wondering who these people are, what parts of the customer experience they’re stumbling over, which issues are being escalated to human agents and which are resolved by bots, etc.

If you knew these things, you would be able to notice patterns and start proactively fixing problems before they even arise. This is what customer insights is all about, and it can allow you to finetune your procedures, write clearer technical documentation, figure out the best place to use generative AI in your contact center, and much more.

What are the Major Types of Customer Insights?

Before we turn to a discussion of the specifics of customer insights, we’ll deal with the major kinds of customer insights there are. This will provide you with an overarching framework for thinking about this topic and where different approaches might fit in.

Speech and Text Data

Customer service and customer experience both tend to be language-heavy fields. When an agent works with a customer over the phone or via chat, a lot of natural language is generated, and that language can be analyzed. You might use a technique like sentiment analysis, for example, to gauge how frustrated customers are when they contact an agent. This will allow you to form a fuller picture of the people you’re helping, and discover ways of doing so more effectively.

Data on Customer Satisfaction

Contact centers exist to make customers happy as they try to use a product, and for this reason, it’s common practice to send out surveys when a customer interaction is done. When done correctly, the information contained in these surveys is incredibly valuable, and can let you know whether or not you’re improving over time, whether a specific approach to training or a new large language model is helping or hurting customer satisfaction, and more.

Predictive Analytics

Predictive analytics is a huge field, but it mostly boils down to using machine learning or something similar to predict the future based on what’s happened in the past. You might try to forecast average handle time (AHT) based on the time of the year, on the premise that when an issue arises has something to do with how long it will take to get it resolved.

To do this effectively you would need a fair amount of AHT data, along with the corresponding data about when the complaints were raised, and then you could fit a linear regression model on these two data streams. If you find that AHT reliably climbs during certain periods, you can have more agents on hand when required.

Data on Agent Performance

Like employees in any other kind of business, agents perform at different levels. Junior agents will likely take much longer to work through a thorny customer issue than more senior ones, of course, and the same could be said for agents with an extensive technical background versus those without the knowledge this background confers. Or, the same agent might excel at certain kinds of tasks but perform much worse on others.

Regardless, by gathering these data on how agents are performing you, as the manager, can figure out where weaknesses lie across all your teams. With this information, you’ll be able to strategize about how to address those weaknesses with coaching, additional education, a refresh of the standard operating procedures, or what have you.

Channel Analytics

These days, there are usually multiple ways for a customer to get in touch with your contact center, and they all have different dynamics. Sending a long email isn’t the same thing as talking on the phone, and both are distinct from reaching out on social media or talking to a bot. If you have analytics on specific channels, how customers use them, and what their experience was like, you can make decisions about what channels to prioritize.

What’s more, customers will often have interacted with your brand in the past through one or more of these channels. If you’ve been tracking those interactions, you can incorporate this context to personalize responses when they reach out to resolve an issue in the future, which can help boost customer satisfaction.

What Specific Metrics are Tracked for Customer Insights?

Now that we have a handle on what kind of customer insights there are, let’s talk about specific metrics that come up in contact centers!

First Contact Resolution (FCR)

The first contact resolution is the fraction of issues a contact center is able to resolve on the first try, i.e. the first time the customer reaches out. It’s sometimes also known as Right First Time (RFT), for this reason. Note that first contact resolution can apply to any channel, whereas first call resolution applies only when the customer contacts you over the phone. They have the same acronym but refer to two different metrics.

Average Handle Time (AHT)

The average handle time is one of the more important metrics contact centers track, and it refers to the mean length of time an agent spends on a task. This is not the same thing as how long the agent spends talking to a customer, and instead encompasses any work that goes on afterward as well.

Customer Satisfaction (CSAT)

The customer satisfaction score attempts to gauge how customers feel about your product and service. It’s common practice, to collect this information from many customers, then averaging them to get a broader picture of how your customers feel. The CSAT can give you a sense of whether customers are getting happier over time, whether certain products, issues, or agents make them happier than others, etc.

Call Abandon Rate (CAR)

The call abandon rate is the fraction of customers who end a call with an agent before their question has been answered. It can be affected by many things, including how long the customers have to wait on hold, whether they like the “hold” music you play, and similar sorts of factors. You should be aware that CAR doesn’t account for missed calls, lost calls, or dropped calls.

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Data-driven contact centers track a lot of metrics, and these are just a sample. Nevertheless, they should convey a sense of what kinds of numbers a manager might want to examine.

How Can AI Help with Customer Insights?

And now, we come to the “main” event, a discussion of how artificial intelligence can help contact center managers gather and better utilize customer insights.

Natural Language Processing and Sentiment Analysis

An obvious place to begin is with natural language processing (NLP), which refers to a subfield in machine learning that uses various algorithms to parse (or generate) language.

There are many ways in which NLP can aid in finding customer insights. We’ve already mentioned sentiment analysis, which detects the overall emotional tenor of a piece of language. If you track sentiment over time, you’ll be able to see if you’re delivering more or less customer satisfaction.

You could even get slightly more sophisticated and pair sentiment analysis with something like named entity recognition, which extracts information about entities from language. This would allow you to e.g. know that a given customer is upset, and also that the name of a particular product kept coming up.

Classifying Different Kinds of Communication

For various reasons, contact centers keep transcripts and recordings of all the interactions they have with a customer. This means that they have access to a vast amount of textual information, but since it’s unstructured and messy it’s hard to know what to do with it.

Using any of several different ML-based classification techniques, a contact center manager could begin to tame this complexity. Suppose, for example, she wanted to have a high-level overview of why people are reaching out for support. With a good classification pipeline, she could start automating the processing of sorting communications into different categories, like “help logging in” or “canceling a subscription”.

With enough of this kind of information, she could start to spot trends and make decisions on that basis.

Statistical Analysis and A/B Testing

Finally, we’ll turn to statistical analysis. Above, we talked a lot about natural language processing and similar endeavors, but more than likely when people say “customer insights” they mean something like “statistical analysis”.

This is a huge field, so we’re going to illustrate its importance with an example focusing on churn. If you have a subscription-based business, you’ll have some customers who eventually leave, and this is known as “churn”. Churn analysis has sprung up to apply data science to understanding these customer decisions, in the hopes that you can resolve any underlying issues and positively impact the bottom line.

What kinds of questions would be addressed by churn analysis? Things like what kinds of customers are canceling (i.e. are they young or old, do they belong to a particular demographic, etc.), figuring out their reasons for doing so, using that to predict which similar questions might be in danger of churning soon, and thinking analytically about how to reduce churn.

And how does AI help? There now exist any number of AI tools that substantially automate the process of gathering and cleaning the relevant data, applying standard tests, making simple charts, and making your job of extracting customer insights much easier.

What AI Tools Can Be Used for Customer Insights?

By now you’re probably eager to try using AI for customer insights, but before you do that, let’s spend some talking about what you’d look for in a customer insights tool.

Performant and Reliable

Ideally, you want something that you can depend upon and that won’t drive you crazy with performance issues. A good customer insights tool will have many optimizations under the hood that make crunching numbers easy, and shouldn’t require you to have a computer science degree to set up.

Straightforward Integration Process

Modern contact centers work across a wide variety of channels, including emails, chat, social media, phone calls, and even more. Whatever AI-powered customer insights platform you go with should be able to seamlessly integrate with all of them.

Simple to Use

Finally, your preferred solution should be relatively easy to use. Quiq Insights, for example, makes it a breeze to create customizable funnels, do advanced filtering, see the surrounding context for different conversations, and much more.

Getting the Most Out of AI-Powered Customer Insights

Data is extremely important to the success or failure of modern businesses, and it’s getting more important all the time. Contact centers have long been forward-looking and eager to adopt new technologies, and the same must be true in our brave new data-powered world.

If you’d like a demo of Quiq Insights, reach out to see how we can help you streamline your operation while boosting customer satisfaction!

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Security and Compliance in Next-Gen Contact Centers

Posted on The Quiq Team

Along with almost everyone else, we’ve been singing the praises of large language models like ChatGPT for a while now. We’ve noted how they can be used in retail, how they’re already supercharging contact center agents, and have even put out some content on how researchers are pushing the frontiers of what this technology is capable of.

But none of this is to say that generative AI doesn’t come with serious concerns for security and compliance. In this article, we’ll do a deep dive into these issues. We’ll first provide some context on how advanced AI is being deployed in contact centers, before turning our attention to subjects like data leaks, lack of transparency, and overreliance. Finally, we’ll close with a treatment of the best practices contact center managers can use to alleviate these problems.

What is a “Next-Gen” Contact Center?

First, what are some ways in which a next-generation contact center might actually be using AI? Understanding this will be valuable background for the rest of the discussion about security and compliance, because knowing what generative AI is doing is a crucial first step in protecting ourselves from its potential downsides.

Businesses like contact centers tend to engage in a lot of textual communication, such as when resolving customer issues or responding to inquiries. Due to their proficiency in understanding and generating natural language, LLMs are an obvious tool to reach for when trying to automate or streamline these tasks; for this reason, they have become increasingly popular in enhancing productivity within contact centers.

To give specific examples, there are several key areas where contact center managers can effectively utilize LLMs:

Responding to Customer Queries – High-quality documentation is crucial, yet there will always be customers needing assistance with specific problems. While LLMs like ChatGPT may not have all the answers, they can address many common inquiries, particularly when they’ve been fine-tuned on your company’s documentation.

Facilitating New Employee Training – Similarly, a language model can significantly streamline the onboarding process for new staff members. As they familiarize themselves with your technology and procedures, they may encounter confusion, where AI can provide quick and relevant information.

Condensing Information – While it may be possible to keep abreast of everyone’s activities on a small team, this becomes much more challenging as the team grows. Generative AI can assist by summarizing emails, articles, support tickets, or Slack threads, allowing team members to stay informed without spending every moment of the day reading.

Sorting and Prioritizing Issues – Not all customer inquiries or issues carry the same level of urgency or importance. Efficiently categorizing and prioritizing these for contact center agents is another area where a language model can be highly beneficial. This is especially so when it’s integrated into a broader machine-learning framework, such as one that’s designed to adroitly handle classification tasks.

Language Translation – If your business has a global reach, you’re eventually going to encounter non-English-speaking users. While tools like Google Translate are effective, a well-trained language model such as ChatGPT can often provide superior translation services, enhancing communication with a diverse customer base.

What are the Security and Compliance Concerns for AI?

The preceding section provided valuable context on the ways generative AI is powering the future of contact centers. With that in mind, let’s turn to a specific treatment of the security and compliance concerns this technology brings with it.

Data Leaks and PII

First, it’s no secret that language models are trained on truly enormous amounts of data. And with that, there’s a growing worry about potentially exposing “Personally Identifiable Information” (PII) to generative AI models. PII encompasses details like your actual name, residential address, and also encompasses sensitive information like health records. It’s important to note that even if these records don’t directly mention your name, they could still be used to deduce your identity.

While our understanding of the exact data seen by language models during their training remains incomplete, it’s reasonable to assume they’ve encountered some sensitive data, considering how much of that kind of data exists on the internet. What’s more, even if a specific piece of PII hasn’t been directly shown to an LLM, there are numerous ways it might still come across such data. Someone might input customer data into an LLM to generate customized content, for instance, not recognizing that the model often permanently integrates this information into its framework.

Currently, there’s no effective method to extract data from a language model, and no finetuning technique that ensures it never reveals that data again has yet been found.

Over-Reliance on Models

Are you familiar with the term “ultracrepidarianism”? It’s a fancy SAT word that refers to a person who consistently gives advice or expresses opinions on things that they simply have no expertise in.

A similar sort of situation can arise when people rely too much on language models, or use them for tasks that they’re not well-suited for. These models, for example, are well known to hallucinate (i.e. completely invent plausible-sounding information that is false). If you were to ask ChatGPT for a list of 10 scientific publications related to a particular scientific discipline, you could well end up with nine real papers and one that’s fabricated outright.
From a compliance and security perspective, this matters because you should have qualified humans fact-checking a model’s output – especially if it’s technical or scientific.

To concretize this a bit, imagine you’ve finetuned a model on your technical documentation and used it to produce a series of steps that a customer can use to debug your software. This is precisely the sort of thing that should be fact-checked by one of your agents before being sent.

Not Enough Transparency

Large language models are essentially gigantic statistical artifacts that result from feeding an algorithm huge amounts of textual data and having it learn to predict how sentences will end based on the words that came before.

The good news is that this works much better than most of us thought it would. The bad news is that the resulting structure is almost completely inscrutable. While a machine learning engineer might be able to give you a high-level explanation of how the training process works or how a language model generates an output, no one in the world really has a good handle on the details of what these models are doing on the inside. That’s why there’s so much effort being poured into various approaches to interpretability and explainability.

As AI has become more ubiquitous, numerous industries have drawn fire for their reliance on technologies they simply don’t understand. It’s not a good look if a bank loan officer can only shrug and say “The machine told me to” when asked why one loan applicant was approved and another wasn’t.

Depending on exactly how you’re using generative AI, this may not be a huge concern for you. But it’s worth knowing that if you are using language models to make recommendations or as part of a decision process, someone, somewhere may eventually ask you to explain what’s going on. And it’d be wise for you to have an answer ready beforehand.

Compliance Standards Contact Center Managers Should be Familiar With

To wrap this section up, we’ll briefly cover some of the more common compliance standards that might impact how you run your contact center. This material is only a sketch, and should not be taken to be any kind of comprehensive breakdown.

The General Data Protection Regulation (GDPR) – The famous GDPR is a set of regulations put out by the European Union that establishes guidelines around how data must be handled. This applies to any business that interacts with data from a citizen of the EU, not just to companies physically located on the European continent.

The California Consumer Protection Act (CCPA) – In a bid to give individuals more sovereignty over what happens to their personal data, California created the CCPA. It stipulates that companies have to be clearer about how they gather data, that they have to include privacy disclosures, and that Californians must be given the choice to opt out of data collection.

Soc II – Soc II is a set of standards created by the American Institute of Certified Public Accounts that stresses confidentiality, privacy, and security with respect to how consumer data is handled and processed.

Consumer Duty – Contact centers operating in the U.K. should know about The Financial Conduct Authority’s new “Consumer Duty” regulations. The regulations’ key themes are that firms must act in good faith when dealing with customers, prevent any foreseeable harm to them, and do whatever they can to further the customer’s pursuit of their own financial goals. Lawmakers are still figuring out how generative AI will fit into this framework, but it’s something affected parties need to monitor.

Best Practices for Security and Compliance when Using AI

Now that we’ve discussed the myriad security and compliance concerns facing contact centers that use generative AI, we’ll close by offering advice on how you can deploy this amazing technology without running afoul of rules and regulations.

Have Consistent Policies Around Using AI

First, you should have a clear and robust framework that addresses who can use generative AI, under what circumstances, and for what purposes. This way, your agents know the rules, and your contact center managers know what they need to monitor and look out for.

As part of crafting this framework, you must carefully study the rules and regulations that apply to you, and you have to ensure that this is reflected in your procedures.

Train Your Employees to Use AI Responsibly

Generative AI might seem like magic, but it’s not. It doesn’t function on its own, it has to be steered by a human being. But since it’s so new, you can’t treat it like something everyone will already know how to use, like a keyboard or Microsoft Word. Your employees should understand the policy that you’ve created around AI’s use, should understand which situations require human fact-checking, and should be aware of the basic failure modes, such as hallucination.

Be Sure to Encrypt Your Data

If you’re worried about PII or data leakages, a simple solution is to encrypt your data before you even roll out a generative AI tool. If you anonymize data correctly, there’s little concern that a model will accidentally disclose something it’s not supposed to down the line.

Roll Your Own Model (Or Use a Vendor You Trust)

The best way to ensure that you have total control over the model pipeline – including the data it’s trained on and how it’s finetuned – is to simply build your own. That being said, many teams will simply not be able to afford to hire the kinds of engineers who are equal to this task. In such case, you should utilize a model built by a third party with a sterling reputation and many examples of prior success, like the Quiq platform.

Engage in Regular Auditing

As we mentioned earlier, AI isn’t magic – it can sometimes perform in unexpected ways, and its performance can also simply degrade over time. You need to establish a practice of regularly auditing any models you have in production to make sure they’re still behaving appropriately. If they’re not, you may need to do another training run, examine the data they’re being fed, or try to finetune them.

Futureproofing Your Contact Center Security

The next generation of contact centers is almost certainly going to be one that makes heavy use of generative AI. There are just too many advantages, from lower average handling time to reduced burnout and turnover, to forego it.

But doing this correctly is a major task, and if you want to skip the engineering hassle and headache, give the Quiq conversational AI platform a try! We have the expertise required to help you integrate a robust, powerful generative AI tool into your contact center, without the need to write a hundred thousand lines of code.

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LLM-Powered AI Assistants for Hotels – Ultimate Guide

Posted on The Quiq Team

New technologies have always been disruptive, supercharging those firms that embrace it and requiring the others to adapt or be left behind.

With the rise of new approaches to AI, such as large language models, we can see this dynamic playing out again. One place where AI assistants could have a major impact is in the hotel industry.

In this piece, we’ll explore the various ways AI assistants can be used in hotels, and what that means for the hoteliers that keep these establishments running.

Let’s get going!

What is an AI Assistant?

The term “AI assistant” refers to any use of an algorithmic or machine-learning system to automate a part of your workflow. A relatively simple example would be the autocomplete found in almost all text-editing software today, while a much more complex example might involve stringing together multiple chain-of-thought prompts into an agent capable of managing your calendar.

There are a few major types of AI assistants. Near and dear to our hearts, of course, are chatbots that function in places like contact centers. These can be agent-facing or customer-facing, and can help with answering common questions, helping draft replies to customer inquiries, and automatically translating between many different natural languages.

Chatbots (and large language models more generally) can also be augmented to produce speech, giving rise to so-called “voice assistants”. These tend to work like other kinds of chatbots but have the added ability to actually vocalize their text, creating a much more authentic customer experience.

In a famous 2018 demo, Google Duplex was able to complete a phone call to a local hair salon to make a reservation. One remarkable thing about the AI assistant was how human it sounded – its speech even included “uh”s and “mm-hmm”s that made it almost indistinguishable from an actual person, at least over the phone and for short interactions.

Then, there are 3D avatars. These digital entities are crafted to look as human as possible, and are perfect for basic presentations, websites, games, and similar applications. Graphics technology has gotten astonishingly good over the past few decades and, in conjunction with the emergence of technologies like virtual reality and the metaverse, means that 3D avatars could play a major role in the contact centers of the future.

One thing to think about if you’re considering using AI assistants in a hotel or hospitality service is how specialized you want them to be. Although there is a significant effort underway to build general-purpose assistants that are able to do most of what a human assistant does, it remains true that your agents will do better if they’re fine-tuned on a particular domain. For the time being, you may want to focus on building an AI assistant that is targeted at providing excellent email replies, for example, or answering detailed questions about your product or service.

That being said, we recommend you check the Quiq blog often for updates on AI assistants; when there’s a breakthrough, we’ll deliver actionable news as soon as possible.

How Will AI Assistants Change Hotels?

Though the audience we speak to is largely comprised of people working in or managing contact centers, the truth is that there are many overlaps with those in the hospitality space. Since these are both customer-service and customer-oriented domains, insights around AI assistants almost always transfer over.

With that in mind, let’s dive in now to talk about how AI is poised to transform the way hotels function!

AI for Hotel Operations

Like most jobs, operating a hotel involves many tasks that require innovative thinking and improvisation, and many others that are repetitive, rote, and quotidian. Booking a guest, checking them in, making small changes to their itinerary, and so forth are in the latter category, and are precisely the sorts of things that AI assistants can help with.

In an earlier example, we saw that chatbots were already able to handle appointment booking five years ago, so it requires no great leap in imagination to see how slightly more powerful systems would be able to do this on a grander scale. If it soon becomes possible to offload much of the day-to-day of getting guests into their rooms to the machines, that will free up a great deal of human time and attention to go towards more valuable work.

It’s possible, of course, that this will lead to a dramatic reduction in the workforce needed to keep hotels running, but so far, the evidence points the other way; when large language models have been used in contact centers, the result has been more productivity (especially among junior agents), less burnout, and reduced turnover. We can’t say definitively that this will apply in hotel operations, but we also don’t see any reason to think that it wouldn’t.

AI for Managing Hotel Revenues

Another place that AI assistants can change hotels is in forecasting and boosting revenues. We think this will function mainly by making it possible to do far more fine-grained analyses of consumption patterns, inventory needs, etc.

Everyone knows that there are particular times of the year when vacation bookings surge, and others in which there are a relatively small number of bookings. But with the power of big data and sophisticated AI assistants, analysts will be able to do a much better job of predicting surges and declines. This means prices for rooms or other accommodations will be more fluid and dynamic, changing in near real-time in response to changes in demand and the personal preferences of guests. The ultimate effect will be an increase in revenue for hotels.

AI in Marketing and Customer Service

A similar line of argument holds for using AI assistants in marketing and customer service. Just as both hotels and guests are better served when we can build models that allow for predicting future bookings, everyone is better served when it becomes possible to create more bespoke, targeted marketing.

By utilizing data sources like past vacations, Google searches, and browser history, AI assistants will be able to meet potential clients where they’re at, offering them packages tailored to exactly what they want and need. This will not only mean increased revenue for the hotel, but far more satisfaction for the customers (who, after all, might have gotten an offer that they themselves didn’t realize they were looking for.)

If we were trying to find a common theme between this section and the last one, we might settle on “flexibility”. AI assistants will make it possible to flexibly adjust prices (raising them during peak demand and lowering them when bookings level off), flexibly tailor advertising to serve different kinds of customers, and flexibly respond to complaints, changes, etc.

Smart Buildings in Hotels

One particularly exciting area of research in AI centers around so-called “smart buildings”. By now, most of us have seen relatively “smart” thermostats that are able to learn your daily patterns and do things like turn the temperature up when you leave to save on the cooling bill while turning it down to your preferred setting as you’re heading home from work.

These are certainly worthwhile, but they barely even scratch the surface of what will be possible in the future. Imagine a room where every device is part of an internet-of-things, all wired up over a network to communicate with each other and gather data about how to serve your needs.

Your refrigerator would know when you’re running low on a few essentials and automatically place an order, a smart stove might be able to take verbal commands (“cook this chicken to 180 degrees, then power down and wait”) to make sure dinner is ready on time, a smoothie machine might be able to take in data about your blood glucose levels and make you a pre-workout drink specifically tailored to your requirements on that day, and so on.

Pretty much all of this would carry over to the hotel industry as well. As is usually the case there are real privacy concerns here, but assuming those challenges can be met, hotel guests may one day enjoy a level of service that is simply not possible with a staff comprised only of human beings.

Virtual Tours and Guest Experience

Earlier, we mentioned virtual reality in the context of 3D avatars that will enhance customer experience, but it can also be used to provide virtual tours. We’re already seeing applications of this technology in places like real estate, but there’s no reason at all that it couldn’t also be used to entice potential guests to visit different vacation spots.

When combined with flexible and intelligent AI assistants, this too could boost hotel revenues and better meet customer needs.

Using AI Assistants in Hotels

As part of the service industry, hoteliers work constantly to best meet their customers’ needs and, for this reason, they would do well to keep an eye on emerging technologies. Though many advances will have little to do with their core mission, others, like those related to AI assistants, will absolutely help them forecast future demands, provide personalized service, and automate routine parts of their daily jobs.

If all of this sounds fascinating to you, consider checking out the Quiq conversational CX platform. Our sophisticated offering utilizes large language models to help with tasks like question answering, following up with customers, and perfecting your marketing.

Schedule a demo with us to see how we can bring your hotel into the future!

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Explainability vs. Interpretability in Machine Learning Models

Posted on The Quiq Team

In recent months, we’ve produced a tremendous amount of content about generative AI – from high-level primers on what large language models are and how they work, to discussions of how they’re transforming contact centers, to deep dives on the cutting edge of generative technologies.

This amounts to thousands of words, much of it describing how models like ChatGPT were trained by having them e.g. iteratively predict what the final sentence of a paragraph will be given the previous sentences.

But for all that, there’s still a tremendous amount of uncertainty about the inner workings of advanced machine-learning systems. Even the people who build them generally don’t understand how particular functions emerge or what a particular circuit is doing.

It would be more accurate to describe these systems as having been grown, like an inconceivably complex garden. And just as you might have questions if your tomatoes started spitting out math proofs, it’s natural to wonder why generative models are behaving in the way that they are.

These questions are only going to become more important as these technologies are further integrated into contact centers, schools, law firms, medical clinics, and the economy in general.

If we use machine learning to decide who gets a loan, who is likely to have committed a crime, or to have open-ended conversations with our customers, it really matters that we know how all this works.

The two big approaches to this task are explainability and interpretability.

Comparing Explainability and Interpretability

Under normal conditions, this section would come at the very end of the article, after we’d gone through definitions of both these terms and illustrated how they work with copious examples.

We’re electing to include it at the beginning for a reason; while the machine-learning community does broadly agree on what these two terms mean, there’s a lot of confusion about which bucket different techniques go into.

Below, for example, we’ll discuss Shapley Additive Explanations (SHAP). Some sources file this as an approach to explainability, while others consider it a way of making a model more interpretable.

A major contributing factor to this overlap is the simple fact that the two concepts are very closely related. Once you can explain a fact you can probably interpret it, and a big part of interpretation is explanation.

Below, we’ve tried our best to make sense of these important research areas, and have tried to lay everything out in a way that will help you understand what’s going on.

With that caveat out of the way, let’s define explainability and interpretability.

Broadly, explainability means analyzing the behavior of a model to understand why a given course of action was taken. If you want to know why data point “a” was sorted into one category while data point “b” was sorted into another, you’d probably turn to one of the explainability techniques described below.

Interpretability means making features of a model, such as its weights or coefficients, comprehensible to humans. Linear regression models, for example, calculate sums of weighted input features, and interpretability would help you understand what exactly that means.

Here’s an analogy that might help: you probably know at least a little about how a train works. Understanding that it needs fuel to move, has to have tracks constructed a certain way to avoid crashing, and needs brakes in order to stop would all contribute to the interpretability of the train system.

But knowing which kind of fuel it requires and for what reason, why the tracks must be made out of a certain kind of material, and how exactly pulling a brake switch actually gets the train to stop are all facets of the explainability of the train system.

What is Explainability in Machine Learning?

In machine learning, explainability refers to any set of techniques that allow you to reason about the nuts and bolts of the underlying model. If you can at least vaguely follow how data are processed and how they impact the final model output, the system is explainable to that degree.

Before we turn to the techniques utilized in machine learning explainability, let’s talk at a philosophical level about the different types of explanations you might be looking for.

Different Types of Explanations

There are many approaches you might take to explain an opaque machine-learning model. Here are a few:

  • Explanations by text: One of the simplest ways of explaining a model is by reasoning about it with natural language. The better sorts of natural-language explanations will, of course, draw on some of the explainability techniques described below. You can also try to talk about a system logically, by i.e. describing it as calculating logical AND, OR, and NOT operations.
  • Explanations by visualization: For many kinds of models, visualization will help tremendously in increasing explainability. Support vector machines, for example, use a decision boundary to sort data points and this boundary can sometimes be visualized. For extremely complex datasets this may not be appropriate, but it’s usually worth at least trying.
  • Local explanations: There are whole classes of explanation techniques, like LIME, that operate by illustrating how a black-box model works in some particular region. In other words, rather than trying to parse the whole structure of a neural network, we zoom in on one part of it and say “This is what it’s doing right here.”

Approaches to Explainability in Machine Learning

Now that we’ve discussed the varieties of explanation, let’s get into the nitty-gritty of how explainability in machine learning works. There are a number of different explainability techniques, but we’re going to focus on two of the biggest: SHAP and LIME.

Shapley Additive Explanations (SHAP) are derived from game theory and are a commonly-used way of making models more explainable. The basic idea is that you’re trying to parcel out “credit” for the model’s outputs among its input features. In game theory, potential players can choose to enter a game, or not, and this is the first idea that is ported over to SHAP.

SHAP “values” are generally calculated by looking at how a model’s output changes based on different combinations of features. If a model has, say, 10 input features, you could look at the output of four of them, then see how that changes when you add a fifth.

By running this procedure for many different feature sets, you can understand how any given feature contributes to the model’s overall predictions.

Local Interpretable Model-Agnostic Explanation (LIME) is based on the idea that our best bet in understanding a complex model is to first narrow our focus to one part of it, then study a simpler model that captures its local behavior.

Let’s work through an example. Imagine that you’ve taken an enormous amount of housing data and fit a complex random forest model that’s able to predict the price of a house based on features like how old it is, how close it is to neighbors, etc.

LIME lets you figure out what the random forest is doing in a particular region, so you’d start by selecting one row of the data frame, which would contain both the input features for a house and its price. Then, you would “perturb” this sample, which means that for each of its features and its price, you’d sample from a distribution around that data point to create a new, perturbed dataset.

You would feed this perturbed dataset into your random forest model and get a new set of perturbed predictions. On this complete dataset, you’d then train a simple model, like a linear regression.

Linear regression is almost never as flexible and powerful as a random forest, but it does have one advantage: it comes with a bunch of coefficients that are fairly easy to interpret.

This LIME approach won’t tell you what the model is doing everywhere, but it will give you an idea of how the model is behaving in one particular place. If you do a few LIME runs, you can form a picture of how the model is functioning overall.

What is Interpretability in Machine Learning?

In machine learning, interpretability refers to a set of approaches that shed light on a model’s internal workings.

SHAP, LIME, and other explainability techniques can also be used for interpretability work. Rather than go over territory we’ve already covered, we’re going to spend this section focusing on an exciting new field of interpretability, called “mechanistic” interpretability.

Mechanistic Interpretability: A New Frontier

Mechanistic interpretability is defined as “the study of reverse-engineering neural networks”. Rather than examining subsets of input features to see how they impact a model’s output (as we do with SHAP) or training a more interpretable local model (as we do with LIME), mechanistic interpretability involves going directly for the goal of understanding what a trained neural network is really, truly doing.

It’s a very young field that so far has only tackled networks like GPT-2 – no one has yet figured out how GPT-4 functions – but already its results are remarkable. It will allow us to discover the actual algorithms being learned by large language models, which will give us a way to check them for bias and deceit, understand what they’re really capable of, and how to make them even better.

Why are Interpretability and Explainability Important?

Interpretability and explainability are both very important areas of ongoing research. Not so long ago (less than twenty years), neural networks were interesting systems that weren’t able to do a whole lot.

Today, they are feeding us recommendations for news, entertainment, driving cars, trading stocks, generating reams of content, and making decisions that affect people’s lives, forever.

This technology is having a huge and growing impact, and it’s no longer enough for us to have a fuzzy, high-level idea of what they’re doing.

We now know that they work, and with techniques like SHAP, LIME, mechanistic interpretability, etc., we can start to figure out why they work.

Final Thoughts on Interpretability vs. Explainability

In contact centers and elsewhere, large language models are changing the game. But though their power is evident, they remain a predominately empirical triumph.

The inner workings of large language models remain a mystery, one that has only recently begun to be unraveled through techniques like the ones we’ve discussed in this article.

Though it’s probably asking too much to expect contact center managers to become experts in machine learning interpretability or explainability, hopefully, this information will help you make good decisions about how you want to utilize generative AI.

And speaking of good decisions, if you do decide to move forward with deploying a large language model in your contact center, consider doing it through one of the most trusted names in conversational AI. In recent weeks, the Quiq platform has added several tools aimed at making your agents more efficient and your customers happier.

Set up a demo today to see how we can help you!

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