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Month: February 2026

4 Conversational AI Software Tools for CX and eCommerce in 2026—and How Gen AI is Making Them Better

Posted on Max Fortis

Key Takeaways

  • Real ROI: The biggest gains come from automating repetitive work across multiple channels so your human agents can handle the nuance that requires empathy.
  • Terminology matters: Conversational AI, Gen AI, and Agentic AI are distinct. Knowing the difference protects you from buying hype instead of results.
  • The four tools: The most effective applications right now are eCommerce agents, voice assistants, multilingual chat, and AI assistants for human agents.
  • Resolution over chat: Older chatbots just talked. Modern conversational AI Agents resolve issues, adapt to context, and execute tasks across complex workflows for seamless self-service.
  • Safety first: Enterprise-grade deployments need verified safety, data privacy, and visible logic, especially in regulated industries.

There is a lot of noise in the market right now. If you lead CX for a major brand, you’re likely inundated with pitches promising “revolutionary” results from AI.

But you don’t need a revolution. You need resolution.

This guide looks at the four most practical conversational AI tools shaping customer experience and eCommerce in 2026. We’ll cut through the buzzwords to help you understand what these tools actually do, where they fit in your stack—and why the shift from “conversational” to “agentic” is the only shift that really matters.

What is a conversational AI platform?

Technically, a conversational AI platform is a tech stack that allows machines to understand and respond to human language. It uses Natural Language Processing (NLP) and Natural Language Understanding (NLU) to interpret what a customer wants, and Natural Language Generation (NLG) to reply.

But here is where most vendors get it wrong: they focus on the conversation alone.

At Quiq, we believe the conversation is just the vehicle. The destination is resolution.

Clearing up the confusion: Conversational vs. Agentic vs. Gen AI

You’ll hear these terms used interchangeably. They aren’t the same.

  • Conversational AI often refers to the previous generation of bots. They follow a script. They are polite, but they hit walls easily. Think of a chatbot that says, “I didn’t quite get that” three times in a row. They can’t facilitate context-aware interactions.
  • Generative AI (Gen AI) creates content based on patterns. It’s great at sounding human in user interactions, but on its own, it’s passive. It speaks when spoken to and lacks the ability to take independent action.
  • Agentic AI is the workhorse. It doesn’t just talk; it does. It can make decisions, access backend systems to check order status, and persist until a task is complete.

A conversational AI platform enables natural language interactions between users and systems. In contrast, AI that’s agentic autonomously makes decisions and executes tasks. In fact, agentic artificial intelligence can independently decide what actions to take, persist in completing tasks, and adapt its approach based on outcomes—similar to how a human agent would work through a problem.

Generative AI, on the other hand, creates new content based on existing data patterns.

Think about it like this: A conversational AI platform is like a back-and-forth conversation between two friends, like you are used to experiencing with chatbots. You say something like “Hi, how are you?” I reply, “Fine, thanks, how are you?” and we go on and on until the conversation stops.

But with Gen AI, it’s like a “speak when spoken to” situation: It’s up to you to ask me questions you want responses to. Generative experiences are typically not programmed to ask clarifying questions or continuously improve on their own. And agentic AI, well, it’s a workhorse!

Nowadays, the term “conversational AI” tends to describe previous-generation technologies, while “agentic AI”, which Quiq is the leader in, is currently next-generation. We are using “conversational AI” here for a couple reasons:

  1. Many people use “conversational AI” to describe AI software, even if they technically refer to agentic or gen AI.
  2. The tools described here are not agentic, or autonomous decision-making, by default. But we’ve highlighted how generative or agentic elements make them more effective.

Okay, with that out of the way, let’s dive in.

Comparison table: Conversational AI agents vs. traditional chatbots

Even though a conversational AI platform is not the latest and greatest AI out there, it’s still miles ahead of the basic chatbots of yesteryear, and the technology can still do a lot. Let’s look at a side-by-side comparison of where conversational agents elevated the previous tech.

FeatureConversational AITraditional Chatbots
Learning CapabilityContinuous learning from interactionsStatic, rule-based responses
Language ProcessingAdvanced natural language understandingBasic keyword matching
Contextual UnderstandingMaintains context across conversationsLimited or no context retention
PersonalizationAdaptive and personalized responsesGeneric, pre-programmed responses
Complexity of TasksCan handle tasks involving complex queriesLimited to simple, predefined tasks

Benefits of a conversational AI platform

By combining natural language processing with machine learning capabilities, a conversational AI platform can provide intelligent, automated AI solutions that enhance customer satisfaction, the overall customer experience, and eCommerce business operations. Here’s a detailed look at the key benefits across different areas:

Benefits for enterprise-grade customer experience

The customer experience landscape has been dramatically enhanced through conversational AI platform implementation. Here are the biggest benefits for CX:

  • Personalized experiences: Uses historical data and context to provide tailored recommendations and solutions.
  • Quick issue resolution: Virtual agents handle common queries immediately, providing instant answers and reducing resolution time and customer frustration.
  • Scalable support: Manages multiple customer interactions simultaneously, without compromising service quality.
  • Language support: Communicates in several languages, making services accessible to a global audience.
  • 24/7 availability: Provides instant support to customers around the clock, eliminating wait times and improving user satisfaction.
  • Consistent interactions: Delivers uniform responses and maintains brand voice across all customer touch points.

Benefits for eCommerce

In the eCommerce sector, conversational AI platform tools have become a crucial game changer for driving business growth and efficiency. Here are the primary benefits of conversational AI for eCommerce:

  • Increased conversion rates: Guides customers through the purchase journey, addressing concerns in real-time to boost sales.
  • Reduced cart abandonment: Proactively engages with customers, shows exit intent, and resolves checkout issues. This proactive approach extends to reducing cart abandonment, as the AI can engage with customers showing exit intent and swiftly resolve any checkout issues that might arise.
  • Product discovery: Helps customers find relevant products through intelligent recommendations and natural conversation.
  • Upselling opportunities: Suggests complementary products and premium options based on customer preferences, directly impacting revenue growth.
  • Cost efficiency: Reduces operational costs by automating routine customer interactions across various channels.
  • Data collection: AI gathers valuable customer insights and shopping behavior patterns for business optimization, delivering deep insights through real-time analytics.
  • Inventory management: Inventory management becomes more streamlined, with the AI providing real-time stock information and automated customer notifications about product availability.
  • Streamlined returns: Simplifies the returns process at the contact center level by guiding customers through procedures and policies.

These automated solutions continue to evolve, offering increasingly sophisticated capabilities that benefit both businesses and their customers. By implementing conversational AI platform tools, organizations can significantly improve their customer service operations, while driving sales and efficiency in their eCommerce platforms.

The 4 best conversational AI tools

Now that we’ve properly defined conversational AI and outlined the main benefits for CX and eCommerce, here are the four best conversational AI services and tools across both sectors.

Tool #1: Conversational eCommerce assistants

A conversational eCommerce assistant is a virtual assistant designed to streamline customer interactions and enhance the customer shopping experience by providing real-time support directly on your website through web chat or other business messaging channels. These assistants can help facilitate sales and improve customer engagement by offering a range of valuable features. Even last-generation conversational agents are equipped with capabilities such as:

  • Personalized product recommendations: Tailored suggestions based on a customer’s browsing history, preferences, or past purchases, helping them find exactly what they need—even on mobile devices.
  • Intelligent cart abandonment prevention: Proactively engaging with customers in personalized conversations to remind them about items left in their cart and encourage them to complete their purchase.
  • Real-time inventory updates: Ensuring customers have accurate information about product availability, reducing the chance of disappointment or frustration, thereby boosting customer satisfaction.
  • Seamless payment processing integration: Simplifying the checkout process with smooth and secure payment options, minimizing barriers to purchase.

However, while traditional conversational AI is effective, Gen AI-powered tools take these capabilities to the next level. Gen AI excels at contextualizing conversations, understanding customer needs in greater detail, and delivering offerings that feel more natural and personalized.

This enhanced ability to adapt and respond in a natural way to individual shoppers makes Gen AI an even more powerful tool for driving sales and creating positive customer experiences. Leading brands use a multi-LLM architecture to fine tune responses and enable rapid iteration as customer needs evolve. (By the way: Check out how we’re harnessing both agentic and Gen AI via next-generation AI agents).

Tool #2: Voice AI Agents

If you’re leading eCommerce, you’ve likely explored Voice Commerce, with voice assistants like Amazon Alexa and Google Assistant leading the charge. Lots of people love the shopping experiences these robust bots offer, enhanced with artificial intelligence.

Key features include:

  • Hands-free shopping experience across mobile devices
  • Natural language order processing with advanced speech recognition
  • Voice-based product search and comparison to answer questions in real time
  • Integration with smart home devices

On the customer experience side, applying multimodal virtual assistants to your phone calls harnesses the latest tech in speech recognition and intent recognition.

Using LLM-powered AI, it can create incredible, modern voice experiences with major cost reduction benefits for businesses. Not conversational AI, but impressive and worth checking out for your contact center:

Tool #3: Multilingual AI chat solutions for the contact center

For global businesses handling a high volume of support inquiries across multiple markets in the contact center, AI-powered translation has become an invaluable tool. These AI solutions allow companies to break down language barriers while maintaining efficiency and quality in customer interactions.

With the help of conversational AI platform tools, businesses can enjoy features such as:

  • Real-time translation in over 100 languages, enabling seamless communication with customers worldwide.
  • Context preservation, ensuring that the nuances and intent of conversations remain accurate across languages.
  • Automatic language detection, eliminating the need for customers to select their preferred language manually.
  • Consistent brand voice across languages, aligning your messaging and tone, no matter where your customers are located.

Thanks to advances in generative AI, these contact center tools have evolved into far more powerful solutions, offering faster, smarter, and more accurate translations at lower operating costs. A user friendly interface and dynamic automation platform capabilities make these tools especially powerful for contact center teams when choosing a vendor with a unified platform.

For businesses aiming to expand globally, multilingual AI chat solutions are critical for delivering exceptional customer experiences while reducing operational challenges.

Tool #4: AI-powered assistants for human agents

AI-powered training assistants transform the way employees learn and grow within organizations. While rule-based tools may work in certain applications, such as HR benefit matching, virtual agents built on understanding-based tools take training to the next level by leveraging advanced AI capabilities.

These virtual agents revolutionize employee training by offering:

  • Personalized learning paths: Tailored to each employee’s strengths, weaknesses, and learning pace, ensuring more effective skill development.
  • Real-time feedback and assessment: Providing instant insights to help employees answer questions about their progress and areas for improvement.
  • Interactive scenario-based training: Simulating real world scenarios to equip employees with practical skills and better decision-making abilities—accessible from mobile devices and optimized for a user friendly experience.
  • Progress tracking and reporting: Monitoring individual and team performance over time, allowing managers to identify trends and adjust strategies as needed. Built-in scheduling tools help managers track sessions and plan follow-ups with ease.

By combining AI technology with interactive and personalized learning, these tools enhance employee engagement and make training more impactful across various industries. When deployed as part of a broader contact center or conversational AI platform strategy, they improve data security outcomes by ensuring agents are well-trained on compliance protocols.

Interested in learning more? Check out how Quiq’s employee-facing AI assistants work—and discover how our technology helped one National Furniture Retailer Reduce Escalations to Human Agents by 33%.

Final thoughts on conversational AI software

Conversational AI might be a last-gen term, but conversational AI platform tools can still be valuable for businesses aiming to deliver exceptional customer experiences while maintaining operational efficiency.

To stay competitive and future-proof your operations, consider strategically implementing these AI Agents or any of their next-gen successors, starting with areas where they can provide the most immediate impact—whether in the contact center, eCommerce, or employee training. Remember, the key to success lies in selecting the right tool, proper implementation, and continuous optimization.

Frequently Asked Questions (FAQs)

How do conversational AI solutions help reduce operating costs?

By automating repetitive tasks—such as answering common customer queries, processing returns, and routing phone calls—a conversational AI platform significantly reduces the volume of interactions that require human agents. This lowers staffing costs in the contact center, shortens handle times, and allows virtual agents to take on routine work, so human staff can focus on higher-value, complex interactions where empathy and judgment matter most.

Is conversational AI suitable for regulated industries?

Yes, provided the platform is built with enterprise grade security, data security controls, and compliance with relevant industry standards. Modern enterprise-grade conversational AI platform solutions—especially those with seamless integration into enterprise systems like AWS services and Google Cloud—are specifically designed to meet the requirements of industries such as finance, healthcare, and insurance.

Can conversational AI really support multiple languages effectively?

Absolutely. Today’s multilingual support capabilities—powered by Gen AI and advanced large language models—enable real-time translation across 100+ languages with strong context preservation. Automatic language detection means customers don’t need to manually select their preferred language, and businesses can maintain a consistent brand voice across all markets.

How do I know which conversational AI tool is right for my business?

It depends on your primary business needs and where your biggest gaps are in the customer journey. If your focus is eCommerce conversion, a conversational AI platform with eCommerce assistant features is a natural starting point. If you’re handling high call volumes in your contact center, voice assistants and speech recognition tools may deliver the fastest ROI.

Global businesses with multilingual customer bases should prioritize multilingual chat solutions, while companies scaling their teams rapidly will benefit most from AI agents built for training. When in doubt, start with a unified platform for your contact center that offers pre-built integrations and can grow with you.

2026 Sierra AI Reviews: Pros, Cons, Pricing and More

Posted on Lauren Winder

Do you want to provide exceptional customer support that goes beyond chatbots?

In the past couple of years, there have been many enterprise-grade companies offering AI agents. Unlike chatbots, AI agents can update accounts, book something, retrieve order information, and more. In this arena, Sierra AI quickly built a name for itself as a platform that offers more than just simple chatbots.

While Sierra gets some favorable reviews, choosing this platform carries risk, too. It can get pretty expensive (the pricing is not very transparent, either), and the onboarding requires time and resources you may not have, especially the considerable development work for the initial setup.

So, here’s an honest take on Sierra AI in 2026 based on real customer reviews. We’ll show you what works, what doesn’t, and why users look at Sierra AI competitors in 2026.

Looking for a better alternative to Sierra? Get a demo of Quiq today.

What is Sierra AI, and who is it for?

Sierra AI is an enterprise software platform that builds and runs conversational agents for customer support, sales, and service operations. In simple terms, it has voice and chat assistants that can handle customer interactions without a human agent or work alongside human teams.

In practice, Sierra AI focuses on automating customer conversations across channels. Companies use it to handle support requests, inbound sales questions, account updates, and routine tasks at scale.

  • It connects to a company’s data, systems, and knowledge base.
  • It uses large language models to understand customer questions.
  • It responds in natural language through chat or voice.
  • It can take actions such as updating an account, booking something, or retrieving order info.
  • It passes complex cases to human agents when needed.

Sierra AI is mainly used by large companies that deal with high volumes of customer interactions, such as:

  • Telecom providers
  • Banks and fintech firms
  • Retail and e-commerce brands
  • Travel and hospitality companies

In short, it’s built for companies that don’t want just a chatbot. Sierra can hold full conversations, understand intent, and complete tasks from start to finish. It is positioned as a high-end, enterprise-level solution, rather than a simple website chatbot.

Sierra AI is a relatively new entrant in the AI agent market, but it got a lot of attention because of its foundations. It was built by Clay Bavor, who ran Google Labs for 18 years before launching Sierra into the world, along with Bret Taylor, former co-CEO of Salesforce.

What real user reviews are saying

Sierra generally gets some favorable reviews, but if you take a deeper look at what customers are saying, you can notice some common themes. Here’s what real users are saying.

Sierra’s agents can struggle with performance

In more complex environments, Sierra’s AI engine can underperform. Users have complained about this in longer conversations when the AI agent is left on their own.

“Sierra AI may struggle to maintain context in longer conversations, leading to repetitive or irrelevant responses.

At times, the AI’s responses can feel generic and lack the depth or nuance of a human conversation.” – G2 review

There are two ways to go around this: escalate to a human agent sooner or refine the AI agents for more complex workflows.

Sierra is very secretive when it comes to pricing plans and initial setup

We’ve discussed Sierra AI pricing before and like any other enterprise AI tool, pricing is not publicly available. In general, Sierra will set you back about $150k per year, depending on the complexity of your setup.

However, there are hidden costs such as implementation fees to account for. It may not be the right tool for businesses that want a clear ROI, as this review states:

“What I dislike about Sierra is the limited transparency on technical details and pricing, which makes it harder to fully assess long-term costs and integration, and the fact that scalability and consistency at enterprise scale are still largely unproven.” – G2 review

Performance can suffer

One of the main reasons large enterprises choose tools like Sierra is the performance under heavy load. Unfortunately, user reviews state that Sierra isn’t the fastest when it comes to performance.

And in situations when seconds lost can lead to churn, speed really matters. As one user put it:

“The platform can be slow at times, and there are occasional bugs that need fixing.” – G2 review

How Sierra AI agents work

Sierra AI agents handle customer conversations by connecting to your existing tools, pulling the right information, and completing tasks during the interaction. Here is what that looks like, step by step, in a real setup.

1. Connect to your data sources

Start by linking Sierra to the systems your support team already uses. This usually includes your knowledge base, CRM, help desk, order system, and internal docs. Prepare the data before importing to avoid a broken system later.

The agent needs access to accurate, current information so it can answer questions and take action without guessing.

The problem with Sierra is that you’re probably going to have to manually get all this data ready by yourself.

2. Learn from real company content

Upload help articles, FAQs, policy docs, product details, and past support conversations. This gives the agent a clear understanding of how your company communicates and what answers are acceptable.

The better the source material, the more accurate the responses.

This puts the weight of the work on your team, and you have to manually prep the data before launching your agents. You have to monitor your foundational content and make sure that it’s always consistent and up to date, which can be a chore if you need to process a large volume of queries.

3. Understand what the customer wants

When a customer sends a message or calls in, the agent reads or listens to the request, then identifies the intent. For example, it can tell the difference between someone asking for a refund, tracking an order, or changing account details.

The problem here is accuracy. Intent can be difficult to read, especially with edge cases when requests are complex, vague, or unusual. For example, someone might ask about a refund while also complaining about a billing error and a delivery delay in the same message. The agent may lock onto the wrong part.

4. Pull the right information in real time

Once it knows the intent, the agent looks up the relevant data. It might check an order status, find account details, or search your knowledge base for the correct explanation. This happens during the conversation, not before it.

Here, you need to heavily rely on integrations with the rest of your tech stack. Sierra is notorious for its slow ramp-up time and the integrations can only make this worse. Your best bet is to set aside a good chunk of time for the initial setup.

5. Respond and complete the task

The agent replies in natural language and can carry out simple actions. That can include updating account info, checking balances, booking appointments, or sending instructions. The goal is to resolve the request in one interaction when possible.

6. Hand off when needed

If the question is complex, sensitive, or outside its scope, the agent passes the conversation to a human. The agent shares the context, including what the customer asked and what has already been done, so the person can pick up where it left off.

Sierra has a bad rap for being slow and not understanding context well, and this translates very poorly to hand-off. By the time the AI agent realizes it’s time to escalate, the customer may already be frustrated and ready to start looking at competitors.

There is also the added issue of integration complexity. To make the hand-off work as intended, you’ll need to integrate Sierra into your live agent platform. Until this starts working, you’ll have a number of dropped conversations, as well as lost context and revenue.

7. Improve over time

Teams can review conversations, fix weak answers, and add new content. This helps the agent get better at handling edge cases and new types of requests as your product and support needs change.

Your team can become better at handoff or ticket deflection by studying the analytics and audit trails in Sierra.

There’s just one issue: to keep answers accurate and relevant, teams need to regularly review conversations, identify weak spots, update content, and refine rules. This becomes a recurring responsibility, not a one-time setup. If the team stops maintaining it, performance can slowly decline.

And to make matters worse, you have to review conversations in two separate systems: Sierra and your contact center. Double the work, which is the exact opposite of what you want an AI CX platform to do.

Key features of the Sierra conversational AI platform

Sierra comes packed with a rich feature set to help you improve customer satisfaction while leaning into your existing systems and customer data. While this can be a benefit for some companies, large enterprise businesses (and those in the mid-market sector) may struggle with the scope of the available features.

Here’s what Sierra offers in 2026 in terms of conversational commerce.

AI-powered customer support automation

This feature handles repetitive support requests without waiting for a human agent. It can answer common questions, guide users through basic tasks, and take care of simple account related actions.

For support teams, this means fewer tickets piling up and more time for cases that need real attention. It works best when paired with clear support content and well-defined processes.

This makes Sierra more than just a simple AI tool, but the true value of this feature shines only after proper setup. Unfortunately, there is a bit of a steep learning curve involved, but once everything is in place, you can automate routine tasks that would otherwise require human intervention.

The issue here is that for all of this automation to work, your company needs to cover the groundwork. Teams need to define processes, connect systems, prepare content, and test different scenarios before it can reliably take over day-to-day requests. That setup phase can be long and technical, especially in larger organizations with complex workflows.

Knowledge-based response generation

Instead of relying on fixed scripts, the system pulls answers from your existing help articles, product docs, and internal support material. When a customer asks a question, it searches for the closest match and forms a reply based on that information.

The quality of answers depends heavily on how clear and up-to-date your knowledge base is, so teams often review and refine content over time.

Workflow automation across support tools

This feature connects conversations with the tools your team already uses. A simple request can trigger actions in the background, such as creating a ticket, updating an account, or tagging a conversation for follow-up.

It helps reduce the back-and-forth between systems and keeps tasks moving without someone manually pushing every step. If you want to combine AI automation and a human touch, this is a nice way to bridge the gap.

The main issue is that this kind of automation only works as well as the systems behind it, and connecting everything can be more complex than it sounds.

Setting up workflows across multiple tools usually requires deep integrations. Each action, like creating a ticket, updating an account, or tagging a case, has to be mapped carefully to the right system and process. In large environments, this can take time and technical support to get right.

There is also a risk of over-automation. If workflows are triggered too aggressively, the system might create unnecessary tickets, apply the wrong tags, or start processes that a human would have handled differently. Fixing those mistakes later can add extra work, instead of reducing it.

Human in the loop controls

Support teams stay in control at all times. If a conversation becomes sensitive, confusing, or too specific, the system can pass it to a human agent. Staff can also review responses, step in mid-conversation, or set limits on what the agent is allowed to handle on its own. This keeps quality high and reduces the risk of wrong answers.

This can improve the customer experience and allow your agents to get involved only in those cases where AI cannot resolve the issue.

From our own research, we’ve seen that this process is less than ideal. Sierra has to be integrated with your contact center software, and conversations will be split between the two platforms.

It’s easy to lose track of key data, and it increases maintenance. Most importantly, it creates a worse customer experience.

This is an aspect where Sierra is similar to its strongest competitors, such as Decagon.

Multi-channel customer interactions

Customers can reach out through different channels, and the system keeps the experience consistent across all of them. Whether someone sends a message from a website, mobile app, or another platform, the conversation can continue without losing context. This makes it easier to support people where they already are, rather than forcing them into one channel.

Sierra also rolled out Voice AI, their tool for AI phone calls. Instead of an IVR, customers can call you on the phone and talk to a proper AI agent that sounds like a real human being. It can connect to your existing VoIP or contact center setup and hand off to human agents when it’s time to escalate.

One important detail is that Sierra doesn’t support channel switching, e.g., switching from voice to WhatsApp in one conversation. Multimodal communication is not supported either, so you won’t be able to text during voice calls either.

Integration with existing helpdesk platforms

Sierra fits into the support stack most companies already have. Conversations, tickets, and updates can flow into your helpdesk, so agents do not need to switch tools to see what is happening. In theory, this should keep records in one place for reporting, training, and quality checks.

In practice, Sierra disperses your data all over the place. Sierra has the granular bot data and it sends some context to your contact center. However, Sierra doesn’t have the human agent conversation, and the contact center doesn’t have the bot context, which leads to customers looking both places to get scraps of information.

There is also a concern for additional (hidden) costs. Sierra AI can be expensive as is, and when paired with a tool such as Zendesk, this can lead to thousands spent on support tools per month.

Analytics and performance insights

Teams can track how conversations are handled and where the system performs well or struggles.

You can see how many requests are resolved, how often humans step in, and which topics come up most. These insights help support managers spot gaps in content, adjust processes, and improve coverage over time.

Sierra isn’t the best when it comes to reports and analytics because it leaves very little room for customization. You’re left with templated reports that need additional work to be valuable.

Custom rules and response logic

Support teams can define what the system should and should not do. For example, you can set rules for when it is allowed to answer on its own, when it must ask for more details, and when to hand things off. These guardrails help match the system to your policies, tone, and risk level.

Security and data handling controls

Since support conversations often include personal and account data, there are controls around how information is accessed and stored. Permissions can be limited based on role, and sensitive actions can require extra checks. This helps companies stay aligned with internal security standards and industry requirements.

Pricing plans

Sierra AI does not publish pricing on its website. There are no public tiers or calculators, and companies need to go through a sales process to get a quote. Based on reports and buyer feedback, deals often start around $150,000 per year, with additional setup costs that can begin near $50,000 depending on the scope of the project.

In practice, Sierra charges based on outcomes instead of seats or usage. An outcome can mean things like a resolved support conversation, a saved cancellation, or a completed upsell. Instead of paying per conversation, you pay for the value the system is seen to create.

This outcome-based pricing makes planning harder because it is not always clear what counts as a successful result, and costs can shift over time as usage changes.

Every contract is custom. The final quote depends on several factors, including:

  • Number of channels, markets, and languages supported
  • Complexity of tasks the agents need to handle
  • Level of customization required
  • Size of the support team and how every single agent fits into workflows
  • Monthly and yearly contact volume

Sierra needs this information to estimate how much value the system will create, which is why pricing is negotiated case by case.

On top of the yearly contract, there are onboarding and implementation fees. These are reported to start around $50,000, and they can increase if the deployment requires deep integrations, custom workflows, or long rollout timelines.

Try the better Sierra AI alternative for managing customer interactions

Sierra is a decent choice for companies that want to automate conversations and handle large volumes of support. It focuses on building agents that can manage requests, respond with empathy, and integrate with core systems.

This can work well for many enterprise teams, especially when the goal is to introduce automation into existing support channels.

However, some organizations look for a platform that keeps the full customer journey connected from first contact to final resolution. This is where Quiq comes in.

Quiq brings together agents, human support teams, and workflow execution in one environment. Context is carried across channels, so interactions feel continuous instead of fragmented.

At Quiq, we combine automation with human support, instead of treating them as separate layers. Conversations can move between automated agents and staff without losing history or forcing customers to repeat themselves. Quiq is also built to execute actions, not just respond, so it can update accounts, trigger processes, and complete tasks during the interaction.

There is a strong focus on transparency and control. Teams can see how decisions are made, apply guardrails, and shape how the system follows their workflows and brand voice. That makes it a practical option for companies that want automation, but still need oversight and consistency across channels.

Try the best Sierra alternative for AI support. Get a demo of Quiq today.

Posted in AI

12 Call Center Best Practices to Deliver Exceptional Customer Service

Posted on Michael Hartsog

Key Takeaways

  • Strong fundamentals matter. Empathy, clear communication, and problem-solving remain the foundation of great customer service and drive improved customer satisfaction across every interaction.
  • Infrastructure drives efficiency. Knowledge bases, smart routing, and modern call center software reduce wait times and unnecessary transfers — directly boosting call center efficiency.
  • Consistency builds trust. Omnichannel support and shared customer data create more predictable, reliable customer experiences that reinforce customer loyalty.
  • Training reduces churn. Well-trained customer service agents perform better, feel more confident, and are more likely to stay — a key advantage for call center leaders focused on customer retention.
  • Agentic AI amplifies teams. AI can resolve routine customer inquiries, support agents in real time, and help contact center operations scale without sacrificing customer experience.

Phone calls remain one of the most direct and trusted ways to serve customers — especially when issues are complex, emotional, or urgent. While digital channels have seen tremendous growth, phone calls still account for approximately 68% of contact center interactions. Call center agents are often the first (and most critical) touchpoint in the customer journey. When managed well, they don’t just resolve issues — they build lasting relationships and drive business growth.

Research shows that 79% of customers expect effective self-service options, but 77% say poor self-service is worse than not providing it at all. This creates a critical challenge: how do you deliver exceptional service at scale while managing rising costs and agent burnout?

We’ve put together 12 call center best practices to help your call center agents deliver exceptional customer service, improve call center performance, and support successful call center management — including how modern solutions like agentic AI and Voice AI are transforming the industry.

12 Call Center Best Practices

1. Listen to Customer Feedback — Before and After Calls

Listening is the cornerstone of effective call center management. Before diving into a script, take time to fully understand the customer’s concern. This reduces back-and-forth, accelerates resolution, and makes the customer feel genuinely heard.

After interactions, actively collect feedback through customer surveys, post-call ratings, and follow-up emails. This data surfaces recurring problems, highlights training gaps, and provides valuable insights into how your call center’s service is perceived.

How Quiq helps: Quiq’s Conversation Analysts analyze every interaction — both AI and human agent conversations — providing custom, actionable insights at scale. These AI-powered analysts automatically identify patterns in customer sentiment, resolution effectiveness, and knowledge gaps, enabling leaders to make data-driven improvements without manual analysis.

2. Your Call Center Agents Should Demonstrate Empathy on Every Call

There’s often a significant gap between customer expectations and the reality of their experience. Bridging that gap starts with empathy.

Use phrases like “I understand how frustrating that must be” or briefly repeat back what the customer said to confirm you were listening. These techniques are especially important over the phone, where you can’t rely on visual cues like eye contact or body language.

Empathy is not just a soft skill — it’s a measurable driver of customer satisfaction. Contact center operations that invest in empathy training consistently see stronger customer satisfaction scores and lower escalation rates.

How Quiq helps: Quiq’s AI Assistants provide real-time guidance to human agents during conversations, suggesting empathetic responses, and helping agents maintain brand voice even during high-stress interactions. These assistants analyze conversation context and recommend next-best actions that balance efficiency with genuine care.

3. Let Your Call Center Team Go Off Script — Thoughtfully

Call center scripts are essential tools. They help call center reps stay on track, cover compliance requirements, and ensure important information isn’t missed. But customers can tell when an agent reads verbatim — and it creates distance.

Effective call center management means giving agents a framework, not a cage. Encourage your center team to inject personality, adjust tone based on the customer’s mood, and treat scripts as guides rather than gospel.

How Quiq helps: Quiq’s agentic AI uses Process Guides instead of rigid scripts. These guides provide instructions, best practices, and available tools while allowing AI Agents to reason through conversations naturally, just like your human agents. When human agents receive escalations, they inherit full conversational context — including the Process Guide path the AI Agent followed — enabling them to continue seamlessly without forcing customers to repeat themselves.

4. Reduce Wait Times to Meet Customer Expectations

Customers who choose to call expect faster resolutions than other channels. Long hold times signal dysfunction, erode trust, and increase call abandonment — one of the most damaging metrics in contact center management.

Reducing wait times isn’t just a frontline challenge. It’s a systemic one. Key improvements include:

  • A centralized, searchable knowledge base so agents can find answers fast
  • A modern AI for CX software platform with intelligent call routing and real-time dashboards
  • Accurate call volume forecasting to ensure proper staffing
  • Interactive voice response (IVR) systems to handle simple queries automatically

How Quiq helps: Quiq’s Voice AI Agents handle routine inquiries autonomously, resolving common questions like order status updates, appointment scheduling, and account management without human intervention. According to customer data, brands using Quiq achieve 40%+ automated resolution rates, dramatically reducing wait times for customers who need human assistance. Voice AI uses natural language processing to understand customer intent across varied phrasing, routing only complex issues to human agents with full context preserved.

5. Prepare Your Agents to Tackle Complex Issues

As digital self-service improves, customers increasingly turn to the phone only when other options have failed. That means call center reps today are handling a higher concentration of difficult, emotional, or multi-layered customer issues than ever before.

Effective call center management requires preparing agents for this reality. In addition to deep product knowledge, consider:

  • Conflict resolution training for de-escalating upset customers
  • Role-playing exercises to practice handling unusual or high-stakes scenarios
  • Clear escalation paths, so agents know when and how to involve a call center manager

When dealing with an angry customer, coach your team to:

  1. Stay calm — Don’t take it personally or respond defensively
  2. Show empathy — Acknowledge frustration before offering solutions
  3. Avoid arguing — Let emotions settle before presenting facts
  4. Take responsibility — Apologize when appropriate, even if the issue wasn’t the agent’s fault
  5. Offer a solution — Resolve within company guidelines or escalate if needed

How Quiq helps: Quiq’s AI Assistants act as real-time coaches for human agents, providing suggested responses, surfacing relevant knowledge base articles, and flagging conversations that show signs of escalation based on sentiment analysis. When Voice AI Agents detect customer frustration during phone interactions, they can seamlessly transition to human agents with detailed conversation summaries — including sentiment indicators — so agents arrive prepared.

6. Personalize Customer Interactions Using Customer Data

Customers expect to be treated like individuals, not ticket numbers. Personalization means using available customer data — past interactions, purchase history, account details — to tailor conversations and eliminate the need for customers to repeat themselves.

Modern call center platforms automatically surface context, giving agents a 360-degree view of the customer before the conversation even begins. This transforms the dynamic: instead of starting from scratch, agents can open with relevance and empathy.

How Quiq helps: Quiq integrates directly with CRM systems like Salesforce, Oracle, SAP, and Zendesk, providing AI Agents and human agents immediate access to customer history, preferences, and account details. Voice AI Agents can proactively greet returning customers by name and reference previous interactions: “I see you called about your order last week — is this about the same shipment?” This level of personalization reduces average handle time and strengthens customer retention.

7. Offer Omnichannel Support Across All Customer Service Channels

Today’s customers don’t stay in one channel. They might start a conversation via web chat, follow up by email, and then call in to resolve the issue. If those channels don’t share context, customers have to start over — one of the most common sources of frustration in the customer journey.

Offering true omnichannel support means integrating all customer service channels — voice, email, SMS, social, and chat — on a unified call center platform. Shared customer data and interaction history ensure continuity regardless of how a customer reaches out.

How Quiq helps: Quiq maintains continuous conversational context across voice, SMS, web chat, WhatsApp, Instagram, Facebook Messenger, Apple Messages for Business, and more. A customer can start a conversation on web chat, receive follow-up details via SMS, and seamlessly transition to a phone call with Voice AI or a human agent — without repeating information. This unified experience is powered by Quiq’s Digital Engagement Center, which provides agents with a single workspace to manage all channels.

8. Invest in Ongoing Call Center Training for Your Customer Service Team

Even the best software can’t compensate for underprepared agents. Ongoing training is one of the highest-leverage investments in call center operations — it directly impacts every interaction, every metric, and every customer outcome.

Training should cover:

  • Product and policy knowledge (regularly updated as things change)
  • Communication skills, including active listening and empathetic language
  • Proper use of software and internal tools
  • Handling complex or emotionally charged calls
  • Quality assurance standards and how interactions are evaluated

How Quiq helps: Quiq’s AI Assistants act as continuous learning tools for human agents. They suggest responses, provide coaching in real-time, and help agents adhere to best practices without requiring them to memorize every process. And, Quiq’s Conversation Analysts identify specific coaching opportunities by analyzing agent performance patterns and flagging knowledge gaps — enabling call center managers to provide targeted training where it’s needed most.

9. Set Clear Goals and Key Performance Indicators

Without measurable goals, it’s impossible to know what great looks like — or where to improve. Defining key performance indicators (KPIs) gives your call center team clear targets to work toward, and gives leaders the visibility they need to coach effectively.

Core call center metrics to track include:

  • Customer Satisfaction (CSAT) — Direct measure of the customer experience
  • First Contact Resolution (FCR) — Indicates how effectively agents resolve issues on the first attempt
  • Average Handle Time (AHT) — Reflects efficiency without sacrificing quality
  • Service Level — Measures how quickly customer calls are answered
  • Abandonment Rate — Signals when wait times are driving customers away

How Quiq helps: Quiq Insights provides unified reporting across AI Agent and human agent conversations, eliminating data silos. Call center managers can track performance metrics in real-time, drill down into individual conversations for quality assurance, and create custom analytics dashboards. Quiq’s Conversation Analysts generate custom metrics tailored to your business — like “appropriate escalation rate” or “policy adherence score” — ensuring KPIs align with your specific objectives.

10. Implement Agentic AI to Boost Agent Performance

Agentic AI represents a significant leap beyond basic chatbots and scripted automation. Rather than replacing human agents, it empowers them — handling routine customer queries autonomously while giving human reps the context, recommendations, and in-the-moment guidance they need to handle complex issues.

In practice, agentic AI in call center operations can:

  • Resolve common inquiries (order status, account updates, FAQs) without agent involvement
  • Surface relevant customer data and suggested responses during live calls
  • Automatically log interaction summaries, reducing after-call work time
  • Detect customer sentiment in real time and flag calls that may need escalation

For call center teams managing high call volume, this kind of intelligent automation is transformative. It reduces average handle time, improves first contact resolution, and ensures agents can deliver exceptional service even during peak periods — without burning out.

How Quiq helps: Quiq’s agentic AI platform includes AI Agents that autonomously resolve customer inquiries across voice and digital channels, AI Assistants that support to human agents, and Conversation Analysts that continuously monitor quality and identify improvement opportunities. Brands like Spirit Airlines have achieved 40%+ automated resolution rates using Quiq’s Voice AI, while Brinks Home reduced cost per contact by 67% and improved NPS scores by more than 90 points.

11. Adopt Voice AI for Your Phone Calls

Voice AI is redefining what’s possible in call center operations. Powered by natural language processing (NLP), modern Voice AI systems can understand customer intent, route calls more accurately, and even handle entire interactions end-to-end for common use cases — all without a human agent or clunky IVR.

For customers, this means shorter wait times, faster resolutions, and less frustration. For call center operations, it means reduced call volume pressure on human agents, lower operational costs, and more consistent service delivery across every call.

Voice AI also excels at capturing structured data from unstructured conversations. A customer might describe a billing issue in dozen different ways. NLP allows the system to understand intent across all of them, classify the issue accurately, and instantly route to the right resource.

Beyond call handling, Voice AI can help human call center representatives. As a customer explains their issue, AI tools can pull up relevant knowledge base articles, surface data, or suggest next-best actions — enabling agents to serve customers faster and more confidently.

How Quiq helps: Quiq’s Voice AI seamlessly connects with existing phone systems, enabling brands to add intelligent automation without replacing their infrastructure. Voice AI Agents follow the same Process Guides as digital AI Agents, ensuring consistent service quality across channels. When complex issues require human expertise, Voice AI transitions customers to agents with complete conversation context — including sentiment analysis and any troubleshooting steps already attempted. One manufacturing brand using Quiq’s Voice AI reduced routing errors by 31%, while a Fortune 500 office supply retailer achieved a 51% inbound sales call conversion rate.

12. Analyze Customer Feedback to Drive Continuous Improvement

Collecting customer feedback is essential — but it only delivers value when you act on it. A robust feedback analysis process transforms raw data from customer surveys, post-call ratings, and interaction transcripts into actionable intelligence for improving customer service across the board.

Effective contact center management means building a structured feedback loop:

  1. Collect — Use post-call surveys, CSAT ratings, and quality assurance reviews to gather consistent data
  2. Analyze — Look for patterns: recurring complaints, common praise, and emerging issues that signal process breakdowns
  3. Act — Update training materials, revise scripts, adjust workflows, or address systemic issues in software or routing
  4. Measure — Track whether changes improve customer satisfaction scores, FCR, and other metrics over time

How Quiq helps: Quiq’s Conversation Analysts automatically analyze every interaction at scale, identifying patterns that would take weeks to uncover manually. These AI-powered analysts flag knowledge gaps, detect shifts in sentiment, spot topics that frequently lead to escalation, and measure resolution effectiveness. Leaders receive actionable insights — like “customers asking about return policies are escalating 40% of the time due to missing shipping information” — enabling them to make targeted improvements based on real data, not assumptions.

Contact Center Management Metrics to Track

Tracking the right metrics is fundamental to effective call center management. Without clear benchmarks and data-driven insights, even the most talented teams can fall short of their goals. These five core customer service metrics offer a well-rounded view of call center performance and where to optimize.

Average Handle Time (AHT)

Formula: AHT = (Talk Time + Hold Time + After-Call Work Time) / Total Number of Calls

What it measures: Average Handle Time is the total time agents spend on customer calls from greeting to final documentation. While a lower AHT often signals greater operational efficiency, it’s critical to strike the right balance. An AHT too low may indicate rushed interactions that compromise service quality and hurt customer satisfaction.

How to optimize AHT:

  • Build a robust internal knowledge base, so agents can find answers quickly during live calls
  • Equip your call center agents with real-time guidance tools to reduce time spent searching for solutions
  • Use software to automate repetitive tasks, like call summaries or ticket categorization

How Quiq helps: Quiq’s AI Assistants automatically surface relevant knowledge base articles and suggest responses to agents in real-time, reducing search time. Voice AI handles routine inquiries end-to-end, freeing human agents to focus on complex cases. Spirit Airlines reduced average conversation times by 16% after implementing Quiq’s agentic AI platform.

First Call Resolution (FCR)

Formula: FCR = (Issues Resolved on First Contact / Total Issues) × 100

What it measures: First Call Resolution measures your team’s ability to fully resolve issues during the first interaction — no callbacks or follow-ups needed. High FCR is closely correlated with improved customer satisfaction, lower operating costs, and stronger customer loyalty.

How to improve FCR:

  • Train agents to handle complex issues confidently and independently
  • Analyze repeat customer calls to identify and address recurring problems
  • Equip agents with complete data before interactions begin
  • Ensure call center scripts and escalation paths are clear and current

How Quiq helps: Quiq’s omnichannel platform ensures agents receive full conversation history regardless of channel, eliminating the need for customers to repeat information. Voice AI Agents resolve routine inquiries autonomously — achieving 40%+ automated resolution rates at brands like Spirit Airlines — while seamlessly escalating complex issues to human agents with complete context. This approach improves FCR for both AI and human-handled interactions.

Customer Satisfaction (CSAT)

Formula: CSAT = (Satisfied Customers / Total Survey Responses) × 100

What it measures: Customer satisfaction scores are the most direct reflection of how your team is perceived. Typically gathered through post-interaction customer surveys, CSAT captures the emotional reality of the customer experience — did they feel heard, respected, and helped?

How to increase CSAT:

  • Send surveys immediately after interactions while the experience is still fresh
  • Follow up on low scores to identify coaching opportunities and process improvements
  • Focus training on empathy and communication skills to build stronger rapport

How Quiq helps: Quiq’s Conversation Analysts automatically estimate CSAT scores for every interaction based on conversation patterns, sentiment, and resolution effectiveness — providing insights even when customers don’t complete surveys. Brands using Quiq have seen significant CSAT improvements: Molekule increased CSAT by 42%, while Panasonic achieved a 75+ NPS score on WhatsApp.

Service Level

Formula: Service Level = (Calls Answered Within Threshold / Total Calls) × 100

What it measures: Service level tracks how quickly your team answers incoming customer calls. Most call center operations target answering 80% of calls within 20 seconds. Meeting this benchmark reduces call abandonment, manages customer expectations, and signals that your operation is staffed and ready.

How to maintain strong service levels:

  • Improve call volume forecasting to anticipate demand spikes
  • Adjust staffing in real time to avoid under-coverage
  • Use self-service tools to deflect simpler customer queries

How Quiq helps: Quiq’s Voice AI dramatically reduces the burden on human agents by autonomously handling routine inquiries 24/7. This means fewer customers waiting in queue for simple questions like order status or appointment scheduling. One Fortune 500 office supply retailer handles peak call volume without adding staff by leveraging Quiq’s Voice AI for initial triage and routing.

Abandonment Rate

Formula: Abandonment Rate = (Abandoned Calls / Total Incoming Calls) × 100

What it measures: Abandonment rate reveals how many customers hang up before reaching an agent. A high rate is a clear signal that wait times are too long or that routing isn’t efficient enough — both fixable with the right center software and staffing strategy.

How to reduce abandonment:

  • Offer virtual hold or scheduled callback options to eliminate frustrating waits
  • Display estimated wait times so customers can make informed decisions
  • Use intelligent routing and self-service tools to resolve simple issues faster

How Quiq helps: Quiq’s Voice AI provides immediate service to inbound callers, eliminating hold times for routine inquiries. For more complex issues requiring human expertise, Voice AI can offer callback scheduling or gather preliminary information while customers wait — reducing perceived wait times and abandonment rates. The platform’s intelligent routing ensures customers reach the right resource faster, whether that’s a specialized AI Agent or a human expert.

Key Next Steps for Effective Call Center Management

Implementing these call center best practices and tracking the right customer service metrics creates the conditions for stronger agent performance and happier customers. But metrics alone aren’t enough. The real impact of successful call center management comes from acting on insights — adjusting workflows, coaching agents, refining contact center scripts, and evolving your strategy as customer expectations change.

Set a regular cadence for performance reviews. Benchmark your metrics against industry standards and internal goals. Whether you’re focused on improving inbound call center efficiency or expanding across new communication channels, aligning your key performance indicators with business outcomes will improve your call center experience.

Modern technology is transforming what’s possible. Brands implementing agentic AI and Voice AI are seeing measurable improvements:

  • 67% reduction in cost per contact (Brinks Home)
  • 40%+ automated resolution rates (Spirit Airlines)
  • 31% reduction in call routing errors (leading manufacturer)
  • 51% inbound sales call conversion rate (Fortune 500 retailer)

These results aren’t theoretical — they’re happening right now at brands that have partnered with platforms like Quiq to augment their contact center with intelligent automation.

Call Center Representatives Are the Frontline of Your Brand

Contact centers play a pivotal role in shaping how customers perceive your company. Every incoming call is a moment of truth — an opportunity to turn a frustrated customer into a loyal advocate, or a satisfied one into a lost one.

The most important thing to remember for your call center representatives is this: they are your company’s voice. Follow company policy, but don’t stop there. Use these best practices, leverage your call center platform, and bring genuine care to every interaction. That’s what defines exceptional customer experiences — and it’s what separates great contact centers from the rest.

For call center managers and CX leaders, the path forward is clear: invest in your people, optimize your processes, and embrace technology that amplifies — not replaces — human expertise. Platforms like Quiq provide the infrastructure to deliver consistent, personalized experiences at scale while giving your team the tools they need to succeed.

Frequently Asked Questions (FAQs)

What are call center agent best practices?

Call center agent best practices include active listening, demonstrating empathy, providing personalized customer service, efficient problem-solving, and using the right call center software to resolve customer issues quickly and consistently. Modern best practices also include leveraging AI tools like real-time coaching assistants and knowledge base automation to support agents during complex interactions.

What KPIs should call centers track to improve performance?

Key call center metrics include customer satisfaction (CSAT), first contact resolution (FCR), average handle time (AHT), service level, and abandonment rate. These customer service metrics help teams identify gaps, measure progress, and ensure excellent customer service delivery. Leading contact centers also track AI-specific metrics like automated resolution rate and escalation appropriateness when using agentic AI platforms.

How does training impact call center performance?

Ongoing training equips call center representatives to handle complex customer issues, use center software effectively, and deliver consistent service — which improves outcomes, strengthens loyalty, and reduces agent turnover. Training combined with AI tools like Quiq’s AI Assistants provides continuous learning opportunities, with agents receiving real-time coaching suggestions during customer interactions.

How can agentic AI support call center agents without replacing them?

Agentic AI can resolve routine customer inquiries, provide real-time guidance, surface relevant data, and handle high call volume scenarios — freeing human agents to focus on complex, high-value customer interactions that require empathy and judgment. Platforms like Quiq use Process Guides to give AI Agents the flexibility to reason through conversations naturally while maintaining brand voice and following company policies. When escalation is needed, human agents receive complete context, enabling them to deliver exceptional service.

What is the role of natural language processing in call center operations?

NLP enables Voice AI systems to understand customer intent across varied phrasing, improving call routing accuracy and enabling more intelligent self-service. It also powers sentiment analysis tools that help call center leaders monitor customer satisfaction in real time and empower agents with proactive coaching. Quiq’s Voice AI uses advanced NLP to handle complex, multi-turn conversations — going beyond simple keyword matching to understand context and nuance.

How does a call center manager improve customer retention?

A call center manager improves customer retention by setting clear goals, investing in agent training, ensuring quality assurance standards are met, analyzing customer feedback, and fostering a culture of continuous improvement across the center team. Modern managers also leverage technology like AI-powered analytics to identify at-risk customers, spot patterns in escalations, and measure the effectiveness of different service approaches — enabling data-driven decisions that strengthen customer loyalty.

What is Voice AI and how does it work in call centers?

Voice AI connects intelligent conversation systems to inbound calls, enabling customers to get help through natural spoken conversation. The system uses speech-to-text technology to convert customer questions into text, processes them using large language models and agentic AI, then converts responses back to speech. Unlike traditional IVR systems with rigid menu options, Voice AI can understand complex, multi-part questions and take autonomous actions like updating reservations or processing refunds — all while maintaining natural conversation flow.

How do I know if Voice AI is right for my call center?

Voice AI is particularly effective for call centers handling high volumes of routine inquiries — like order tracking, appointment scheduling, account updates, or FAQs — that don’t require complex judgment but consume significant agent time. It’s also valuable for organizations struggling with long wait times, high abandonment rates, or seasonal volume spikes. Brands in retail, travel and hospitality, and consumer services have seen the strongest results. Quiq’s Voice AI integrates with existing phone systems, making it possible to add intelligent automation without replacing your current infrastructure.

Multimodal LLM: What They Are and How They Work

Posted on Bill O’Neill

Key Takeaways

  • Multimodal LLMs extend beyond text to “see” and “hear.” These models natively process inputs like speech, images, and video in addition to language, enabling a richer, more context-aware understanding compared to traditional text-only LLMs.
  • They unify multiple modalities via a shared architecture. Text, vision, and audio inputs are encoded and then fused in a joint embedding or transformer-based architecture so the model can reason across modalities.
  • The combination of modalities enhances customer experience (CX). In customer service scenarios, a user might speak, send a picture, or upload a video.  A multimodal agent can interpret all simultaneously and respond cohesively, just like a human agent would. 
  • Real-world deployment introduces alignment and infrastructure challenges. Multimodal systems must be able to convert non-text inputs, synchronize and unify text across multiple channels, and handle modality alignment errors.

Artificial Intelligence has entered a new era where language alone is no longer enough. Multimodal large language models are setting the pace for this shift, enabling AI systems to understand and respond to a wide range of human inputs, including text, speech, images, and even video. According to the Gartner, 2024 Hype Cycle for Generative AI, Multimodal GenAI, and open-source LLMs are two transformative technologies with the potential to deliver substantial competitive advantage.

These multimodal LLM models are already transforming customer experience (CX), enabling AI agents to process voice input, trigger an SMS, analyze visual data like uploaded images, and resolve issues—seamlessly.

In this article, we’ll explain multimodal large language models (MLLMs), how they work, what distinguishes them from traditional LLMs, and how Quiq helps businesses orchestrate them into powerful, real-world customer experiences.

Multimodal CX vs. Multimodal LLMs

While the terms ‘multimodal’ and ‘multichannel’ are often used interchangeably, it’s important to understand their distinctions, especially in CX.

A multimodal LLM can natively process different modalities or input types (text, images, speech, video). A multimodal CX experience, however, may involve multiple communication channels—such as voice and digital messaging—being used at once.

Quiq brings these together, letting users speak on the phone while texting images, with AI understanding and unifying all the inputs in real time, just like a human agent would.

What is a Multimodal LLM?

A multimodal large language model (MLLM) is an AI model that processes diverse data types, not just written text but also images, speech, and videos. Unlike traditional LLMs that rely solely on language, these models ‘see’ and ‘hear’ to better understand complex contexts. A large language model becomes a tool that can process textual data alongside image or video content to grasp meaning more holistically.

Industry analysts recognize the impact of this shift. Forrester Research identifies multimodal AI as a key trend reshaping automation, particularly in customer experience, where enterprises must meet users across diverse channels and content formats.

Consider an AI-powered customer support agent. A user might describe an issue verbally, send a photo of the product, or even provide a short video. A multimodal LLM integrates all this input to generate intelligent, human-like responses.

This distinction is crucial in CX. A customer might speak to an agent on the phone while simultaneously texting images. The AI interprets both, delivering a coherent experience as a live agent would.

Models like GPT-4V and Gemini are redefining automation capabilities—from identifying garage door models via photos to streamlining flight add-ons over messaging channels.

How Do Multimodal Large Language Models (MLLMs) Work?

To understand how multimodal LLMs work, let’s look at the types of input they handle and how these are processed together:

  • Text: Messages, emails, or voice-to-text transcription
  • Images: Product photos, documents, screenshots
  • Speech: Real-time audio or pre-recorded messages
  • Video: Short clips for product issues or training

Multimodal models rely on a shared architecture—often based on transformers—to encode and unify inputs into a common ‘language’ for reasoning.

Here’s how it typically works:

  • Text Processing interprets language using classic NLP.
  • Vision Modules analyze visual content like product images using computer vision techniques.
  • Speech Recognition turns audio data into structured language.
  • Fusion Layers synthesize inputs to generate relevant, personalized outputs.
Diagram of an LLM AI Agent connected to five functions: Memory, Tools, Action, Multimodal Inputs, and Reflect, illustrating AI orchestration in customer experience.

Picture this: A customer calls to report a stuck recliner. The AI understands the spoken issue and initiates a text message requesting an image of the problem. The customer responds with a photo, and the AI determines whether it’s the correct product, or flags it with something like, ‘Maybe you uploaded the wrong image?’

It’s not just about recognizing the image—it’s about understanding the full context of the conversation and how the image relates to it.

Quiq’s AI Studio orchestrates this complex symphony. It enables enterprises to manage conversations across voice and digital channels, track multimodal data in a unified view, and deliver responsive, seamless customer experiences.

From Data Chaos to Clarity: Managing Visual Inputs and Embeddings

A significant challenge in CX is processing image-heavy documentation, like manuals or product guides. Multimodal LLMs enable AI agents to interpret these visual elements by converting them into numerical representations known as embeddings. These allow the AI to understand and retrieve matching visuals or information when customers describe issues via text or upload multiple images.

For example, when a customer texts, ‘The blue light is blinking on my garage opener,’ the AI can correlate it to the correct image in a visual troubleshooting guide and offer accurate support instantly.

This multimodal approach requires substantial computational resources to integrate data from multiple data sources and generate contextually relevant descriptions.

Top Multimodal Large Language Models

Multimodal LLMs are evolving quickly, and with them, the ways businesses can deliver intelligent automation across customer touchpoints. Each model brings unique strengths: some are designed for high-resolution image analysis, and others excel at parsing spoken conversations or synthesizing insights from video.

At Quiq, we believe understanding these capabilities is essential, but what matters even more is having the flexibility to choose the right model for the job. The models below represent some of the most powerful multimodal LLMs in use today.

1. GPT-4V (OpenAI)

  • Combines text data and visual understanding
  • Powers complex tasks like document parsing or image captioning
  • Enables real-time visual Q&A in AI agents

Use Case: A customer calls about a recliner stuck open. The voice AI processes the inquiry, sends a text requesting an image, and then analyzes the uploaded image to determine if a technician is needed. It sends a follow-up message offering three appointment times to choose from. The result is automation that’s efficient, conversational, and friction-free.

2. Gemini (Google DeepMind)

  • Supports seamless input from text, images, and video
  • Strong at contextual reasoning across formats
  • Deep integration with Google tools

Use Case: A customer calls to add a golf bag to a flight. Instead of sharing credit card info over voice, the AI sends a secure payment link via text, avoiding input errors and improving security.

3. Flamingo (DeepMind)

  • Optimized for image-to-text workflows
  • Learns new tasks with minimal data

Use Case: A customer sends a blurry product image. The model identifies it as the NeuroLift 5500 series and retrieves the right troubleshooting steps—no training data required.

4. LLaVA (Large Language and Vision Assistant)

  • Open-source and ideal for experimentation
  • Supports image + text prompts
  • Great for accessibility and research

Use Case: Customers upload multiple images of issues, such as blinking lights, error messages, and faulty parts. The model interprets and acts on these visuals with precision through image understanding tasks.

5. Kosmos-1 (Microsoft AI)

  • Excels at vision language and audio-text integration
  • Ideal for enterprise voice assistants

Use Case: A voice assistant offers to send a rescheduling link via SMS instead of finishing the task by voice. The shift from synchronous to asynchronous reduces user frustration.

LLM-Agnostic by Design: Why Flexibility Matters

While each model offers its own unique characteristics—some models excel at image captioning, others at voice-to-text integration—the most powerful CX platforms aren’t defined by the model they use; they’re defined by how well they adapt to what’s needed.

That’s why Quiq is LLM agnostic. Our AI platform isn’t tied to a single provider or model. Instead, we integrate with whichever multimodal LLM best fits the use case, whether that’s GPT-4V for document parsing, Gemini for seamless video-to-text processing, or open-source models like LLaVA for rapid iteration and customization.

This model-agnostic flexibility means:

  • You can select the model that best aligns with your specific CX needs, whether that’s visual accuracy, conversational flow, or speed of response.
  • Your AI architecture stays adaptable as new LLMs and capabilities enter the market.
  • You’re empowered to build around your existing tech ecosystem and regulatory requirements, without compromise.

This LLM-agnostic architecture is at the heart of how we help enterprise teams future-proof their automation strategies while delivering better outcomes for customers and agents alike.

Learn more about our approach to LLM-agnostic AI.

How Quiq Measures AI Agent Success

Quiq employs a variety of performance metrics to assess AI agents:

  • Estimated CSAT: Compares customer satisfaction among human-only, AI-only, and hybrid interactions.
  • Automated Resolution Rate: Evaluates whether the customer’s issue was genuinely resolved, going beyond mere containment.
  • Sentiment Shift: Monitors the emotional tone from the beginning to the end of a conversation.
  • Goal Completion: Measures how often an AI successfully reschedules an appointment, adds a bag, or completes other business-specific outcomes.

These insights help Quiq’s clients continuously refine and improve their AI deployments.

Quiq AI Studio: The Orchestration Layer That Makes It All Work

Multimodal AI agents require orchestration, especially when inputs come from multiple data types and/or different channels simultaneously. Quiq AI Studio acts as the conductor, tracking and aligning input streams (voice, text, images) in real time, ensuring every message is attributed to the right user session.

The platform can efficiently process raw input data, handle sequential data streams, and manage handling sequential data from multiple forms of communication. It supports debugging, prompt testing, and conversation state management. More than a toolkit, it’s a full orchestration layer purpose-built for CX automation.

Learn more about how Quiq’s AI agents enhance customer engagement and streamline support operations.

Future-Proofing with Confidence

Large language models (LLMs) are evolving rapidly—getting faster, cheaper, and smarter. Quiq AI Studio allows businesses to seamlessly upgrade to the latest models using built-in tests, replays, and evaluation tooling. This protects performance while introducing new capabilities like improved visual reasoning, better audio comprehension, or expanded context windows. With Quiq, enterprises can stay ahead of the curve without compromising quality or stability.

What’s Next for Multimodal AI

Looking ahead, multimodal AI will only become more pervasive. Large language models will become true reasoning engines with faster processing, lower cost, and expanded input comprehension.

These vision language models will be capable of performing tasks that require comprehensive analysis of visual and textual data, generating images, providing natural language descriptions, and even generating descriptive text for image description purposes.

According to Forrester, the rise of predictive and generative AI will transform customer service operations by automating interactions, capturing customer intent, and routing inquiries to appropriately skilled agents. This evolution will allow human agents to focus on complex tasks requiring empathy and personalization, enhancing overall customer satisfaction.

For CX leaders, this opens doors to:

  • Proactive support through predictive multimodal inputs
  • Enhanced personalization from visual cues
  • Smoother handoffs between voice and digital journeys

Yet, as powerful as LLMs are, they are still just tools. The real value lies in how they are orchestrated into real-world customer journeys—removing friction, saving time, and creating brand loyalty through seamless experiences. That’s the promise of multimodal LLMs, brought to life by Quiq.

Explore More

  • Learn how Quiq’s AI Studio makes multimodal AI practical for enterprises.
  • See Customer Success Stories where multimodal models improve automation and satisfaction.
  • Get started with AI Agent Design built for your CX ecosystem.

Frequently Asked Questions (FAQs)

What is a multimodal LLM?

A multimodal LLM is an AI system that can process multiple types of input –  not just text, but also images, audio, and even video. By combining these modalities, it can interpret complex, real-world scenarios in a manner more akin to a human’s. These systems can perform tasks involving multimodal understanding across different data types.

How is a multimodal LLM different from a traditional LLM?

Traditional LLMs only work with text input and output, while multimodal models can analyze and generate insights across multiple data types. This allows them to respond to visual cues, interpret tone of voice, or describe an image. These are capabilities that text-based models lack. Vision language models can process visual data and textual information simultaneously, enabling cross modal reasoning that traditional models cannot achieve.

Why are multimodal LLMs important for customer experience (CX)?

In CX, customers often communicate in multiple ways – sending image data, speaking, or typing messages. A multimodal LLM enables seamless understanding across all these inputs, helping brands respond more accurately and naturally while reducing friction for customers. These models can integrate data from multiple sources, process sensory data, and provide a unified multimodal representation of customer interactions.

What are the main challenges in building multimodal systems?

The biggest challenges include aligning different modalities into a shared representation, synchronizing context across channels, and maintaining accuracy when one modality is unclear. The training process requires large scale datasets, substantial computational resources, and sophisticated architectures that may include convolutional neural networks, linear projection layers, and image encoders to process effectively. Ensuring smooth integration across voice, chat, and visual channels also requires robust infrastructure to handle spatial data, structured data, and spatial patterns across data formats.

How can businesses start using multimodal LLMs?

Companies can integrate multimodal capabilities through APIs or enterprise platforms that support multimodal processing. The key is to start small. For example, by enabling image understanding or voice understanding in customer service, and expanding as infrastructure and data maturity improve. These platforms can assist with multimodal tasks such as object detection, image classification, video understanding, and image generation, serving as valuable educational tools for both customers and agents.

What’s next for multimodal AI?

Future advancements will focus on improving real-time reasoning, context retention across modalities, and personalization through multimodal learning. As models evolve, we’ll see them enabling more natural, human computer interaction across industries. Advanced systems will better handle textual descriptions, process sensor data, manage audio data streams, and create contextually relevant descriptions from visual elements. The integration of textual and visual information will become more seamless, allowing these systems to perform tasks with unprecedented accuracy and efficiency.

What Is Conversational Commerce?

Posted on Max Fortis

Key Takeaways

  • Conversational commerce bridges chat and shopping — allowing customers to browse, ask questions, and complete purchases directly through messaging apps, AI agents, or voice assistants.
  • It meets customers where they already are — creating a faster, more convenient buying experience.
  • Personalization drives engagement — real-time, two-way conversations help brands tailor recommendations and build stronger customer relationships.
  • Automation improves efficiency — Agentic AI and integrations reduce manual workloads while ensuring round-the-clock support.
  • Getting started is simple — brands can launch conversational commerce by choosing familiar messaging channels and connecting them to existing systems like CRM or inventory tools for seamless experiences.

You’ve probably heard the term conversational commerce floating around — it’s catchy, sure, but what does it actually mean, and why is it getting so much attention? 

Here are a few conversational commerce examples in real life:

  • Consumers are shopping and completing transactions within a messaging conversation.
  • Brands help consumers shop by finding a store location or online promotions and deals.
  • Customer experience agents interact with customers to reschedule an appointment or a delivery.

These are just a few ways messaging has greased the conversational commerce wheel, making it easier and faster for businesses to get things done. In this article, we’ll take a close look at conversational commerce and what companies need to do to fully leverage its benefits.

Conversational Commerce Definition

Conversational commerce is the use of messaging apps, AI agents, or voice assistants to facilitate online shopping experiences – allowing customers to browse, ask questions, receive personalized recommendations, and make purchases through natural, real-time conversations. Conversational commerce bridges the gap between e-commerce and direct customer interaction to create a more seamless and personalized buying journey.

How Does Conversational Commerce Work?

Consider the smartphone — the device that rarely leaves anyone’s hands — and think about how much searching, browsing, buying, and texting happens on it every single day. Even before lockdowns accelerated digital habits, consumers were already drawn to the ease, convenience, and speed of shopping right from their phones.

Recently, we’ve seen a surge in use as more consumers have looked to online shopping, delivery, and curbside pickup to get through their days.

According to comScore, more than half of consumers (85%) spend their smartphone time using only 5 apps, which tend to be either social media or messenger, apps. It makes sense that businesses would marry the power of messaging with consumers’ preferences.

Conversational commerce works by integrating chat, messaging, or voice technology into the shopping experience, allowing customers to interact with brands in real time. Through AI-powered agents, messaging apps, or voice assistants, users can ask questions, get personalized recommendations, and complete purchases — all within a single, natural conversation rather than navigating multiple web pages or forms.

What Are the Different Types of Conversational Commerce?

AI Agents and Agentic AI

AI agents powered by agentic AI are transforming conversational commerce by going beyond simple, rule-based chatbots. These intelligent systems can understand context with natural language processing, make decisions, and take autonomous actions — from recommending products and managing orders to integrating with CRM or inventory systems. Unlike static chatbots, agentic AI incorporates machine learning and gets better from each interaction.

Voice Assistants

Voice assistants like Alexa, Siri, and Google Assistant have brought conversational commerce into people’s homes and daily routines. By using simple voice commands, customers can reorder products, check delivery statuses, or discover new items without ever touching a screen.

For brands, this channel offers an opportunity to build more natural, hands-free shopping experiences — especially for routine purchases or multitasking consumers who value convenience.

Messaging Apps

Messaging platforms like WhatsApp, Facebook Messenger, and SMS are the heart of conversational commerce. These channels allow brands to connect directly with customers through personalized messages, automated chat flows, and instant checkout options — all within the same conversation.

For brands looking to scale conversational SMS specifically, tools like TxtCart help turn text messaging into a revenue-driving channel by enabling real-time, two-way conversations, automated cart recovery, and personalized follow-ups. This approach not only improves response rates but also creates a more natural shopping experience within the same SMS thread customers already use daily.

Because customers already use these apps daily, conversational experiences here feel natural and convenient, helping businesses increase engagement, reduce friction, and boost conversion rates.

Why Invest In a Conversational Commerce Strategy?

Consumer expectations of speed and convenience have birthed new innovations that are opening up seamless communication between brands and customers. Gone are the days when customers were satisfied with dialing an 800 number or having to write an email to get help.

Consumers will choose brands that go that extra mile to make their experience personalized and efficient. Needless to say, businesses are investing to make that happen.

Consider these statistics by Gartner:

  • At least 60% of companies will use artificial intelligence to support digital conversational commerce.
  • 30% of digital commerce revenue growth will be attributable to artificial intelligence technologies, such as those that power conversational commerce.
  • 5% of all digital commerce transactions will come from a bot, such as those that power conversational commerce.

All of the major trends in conversational commerce over the past couple of decades have been in moving to where customers are. Rather than forcing customers to come to you, you go to where they are. The next generation of that is conversational commerce.

The Benefits of Conversational Commerce for Your Customer Journey

Investing in conversational commerce also unlocks tangible business benefits that can elevate your customer experience and bottom line:

Improved Customer Experience

With AI and data-driven insights, conversational commerce allows brands to deliver more personalized interactions. These tools can understand customer preferences and intent, allowing for contextually relevant responses that feel tailored to the individual.

Whether it’s personalized product recommendations via a chat widget, a voice assistant helping narrow down options, or live customer support through messaging apps, these conversational commerce examples help deepen engagement and drive loyalty. The interactions feel more natural and human, making the customer feel seen, heard, and supported throughout the buying journey.

Customer Satisfaction, Loyalty and Retention

Conversational commerce strengthens CSAT and loyalty by delivering personalized, real-time interactions that make customers feel valued and understood. Through AI-powered tools, brands can offer tailored recommendations, instant support, and seamless shopping experiences, creating a sense of trust and reliability. When customers receive quick answers and solutions, they are more likely to return, fostering long-term loyalty and higher retention rates.

24/7 Availability

Conversational commerce strengthens customer loyalty by creating more personal, responsive interactions throughout the buying journey. When customers can get immediate answers, personalized recommendations, and real-time support through chat or voice, they feel valued and understood — increasing trust and satisfaction. Over time, these seamless experiences encourage repeat purchases, boost lifetime value, and turn casual buyers into loyal brand advocates who keep coming back.

AI-powered systems and AI agents enable brands to offer round-the-clock support, meeting rising expectations for always-on service. This is especially valuable for global brands, allowing them to deliver consistent, timely assistance across time zones—without the overhead of a 24-hour support team.

Cost Reduction

Conversational commerce solutions are designed not only to improve customer satisfaction but also to reduce operational costs. By automating routine tasks and frequently asked questions, businesses can scale without heavily increasing headcount. Human agents are freed up to handle higher-impact conversations, improving overall efficiency and productivity.

Set Your Business Apart

Conversational commerce gives your brand a competitive edge by delivering personalized, real-time interactions that traditional e-commerce can’t match. By meeting customers where they already are — in chat apps, messaging platforms, or through voice assistants — your business creates a more seamless, human buying experience. This not only enhances convenience but also helps your brand stand out as innovative, approachable, and customer-first in a crowded digital marketplace.

Conversational Commerce Use Cases

Conversational commerce is transforming the way businesses interact with customers by integrating real-time, personalized communication into the shopping journey. Here are some practical use cases that showcase its versatility and impact:

Streamlined Customer Support

Brands can use conversational commerce to provide immediate answers to common questions, such as product availability, shipping updates, or return policies. AI-powered agents and messaging apps ensure customers receive timely support, reducing frustration and improving satisfaction.

Personalized Shopping Assistance

Through AI agents and messaging platforms, businesses can offer tailored product recommendations based on returning customer preferences, browsing history, or past purchase history. This level of personalization not only enhances the shopping experience but also increases the likelihood of increasing sales.

Appointment Scheduling and Reminders

Conversational commerce simplifies appointment management by letting customers to schedule, reschedule, or cancel appointments directly through messaging apps. Automated reminders ensure customers stay informed, reducing no-shows and improving operational efficiency.

Order Management and Tracking

Customers find and easily track their orders, make changes, or request updates through conversational channels. This eliminates the need to navigate multiple web pages and try to use a search bar on mobile devices, creating a more seamless and convenient purchase journey.

Loyalty Program Integration

Messaging apps and AI agents can integrate with loyalty programs to provide customers with real-time updates on points, rewards, and exclusive offers. This keeps customers engaged and incentivized to continue shopping with the brand.

Voice-Activated Commerce

Personal shopping assistants on voice, like Alexa and Google Assistant, enable hands-free shopping experiences, allowing customers to reorder products, check delivery statuses, or discover new items with simple voice commands. This is particularly useful for multitasking consumers who value convenience.

From these use cases alone, it’s clear how conversational commerce can meet customer expectations, enhance engagement, streamline operations, and drive customer satisfaction levels up across various touchpoints.

Pitfalls of Conversational Commerce and Strategies to Avoid Them

Conversational commerce has the power to transform customer engagement by making shopping more seamless, personalized, and efficient. However, if implemented carelessly, it can lead to frustration, lost sales, and diminished trust. Below are some common pitfalls and practical strategies to avoid them.

1. Not enough empathy in customer support

Customers expect understanding, not canned responses. When you implement conversational commerce with AI technology, make sure your virtual assistants can engage in personalized conversations. Always have an escalation path in your existing tech stack to a human, too.

Strategy: Blend automation with a human touch and Agentic AI. Use AI to manage simple requests, but make it easy for users to connect with a live representative when situations become complex or emotional. This balance maintains efficiency while preserving empathy.

2. Disconnected systems and inconsistent experiences

An AI agent that isn’t connected to tools like CRM, order tracking, or inventory systems can’t deliver accurate, helpful, or truly personalized responses. Integrating these systems with a modern B2B commerce platform ensures that conversational agents can handle complex business logic and automated workflows seamlessly.

Strategy: Integrate your conversational commerce tools with backend systems. This ensures every interaction reflects real-time data from order status to personalized product discovery, for a consistent and streamlined shopping experience.

3. Data privacy and security gaps

Because conversational platforms handle sensitive information, even small lapses in data security can damage brand credibility. Implementing guardrails and reviewing real-world AI data security examples can help teams proactively identify where sensitive PII might be at risk during AI interactions.

Strategy: Clearly communicate data usage policies, use encryption, and stay compliant with regulations like GDPR and CCPA. Transparency builds trust.

4. Lack of continuous optimization

Launching an AI agent for an interactive shopping experience is just the beginning. Without regular updates, responses can become outdated or inaccurate.

Strategy: Continuously analyze conversations, review customer feedback, and retrain AI models to keep the experience of your online stores relevant and effective.

When executed thoughtfully, conversational commerce becomes more than a convenience — it’s a long-term strategy that deepens trust, strengthens relationships, and drives sustainable growth.

How Does Agentic AI Fit Into Conversational Commerce?

Agentic AI marks the next evolution of conversational commerce for online retailers — shifting from simple, rule-based, conversational AI chatbots to intelligent systems that can reason, act, and learn autonomously.

Instead of relying on preprogrammed responses, agentic AI understands context, intent, and customer goals, enabling it to manage more complex interactions from start to finish.

From reactive to proactive interactions

In practice, this means agentic AI can do much more than just generate automated answers. It can anticipate customer needs, recommend relevant products, manage returns, or adjust an order before it ships.

For example, with an agentic AI agent, you can engage shoppers based on their browsing behavior, guiding customers and suggesting relevant products based on what you already know about them.

These systems continuously learn from each interaction, refining their understanding to deliver more natural, human-like conversations over time.

Deep integration across business systems

Agentic AI also connects seamlessly with backend platforms like CRM, inventory, and marketing automation tools. This allows it to pull real-time data into every conversation, letting customers know when an item will restock, applying loyalty rewards automatically, or sending follow-up care tips after a purchase.

Smarter, more personal commerce

By combining natural conversation with autonomous decision-making, agentic AI transforms everyday interactions into personalized, value-driven experiences. It helps brands scale customer engagement without losing the human touch — improving satisfaction, efficiency, and ultimately revenue.

The bridge between automation and empathy

In short, agentic AI is what makes conversational commerce truly intelligent. It closes the gap between automation and emotional connection, helping businesses move from reactive service to proactive engagement. For brands competing on experience, it’s not just a technological upgrade — it’s the foundation of the next generation of smarter, more human commerce.

How to Measure Success With Conversational Commerce

Measuring the success of conversational commerce means understanding how well your interactions drive both customer satisfaction and business results. The right mix of quantitative and qualitative metrics will give you a complete picture and valuable insights for optimizing your purchasing process.

Key performance indicators (KPIs) to track include:

  • Conversion rate: How often conversations lead to a sale or completed action, especially across multiple channels.
  • Average order value (AOV): Whether personalized recommendations are leading to incremental revenue gains.
  • Customer satisfaction (CSAT): Customer satisfaction scores show how happy customers are with their conversational experience.

Customer engagement metrics that matter:

  • Response time: How quickly customers receive helpful answers.
  • Session length and repeat interactions: Indicators of meaningful engagement and ongoing value.
  • Retention and re-engagement rates: Show how conversations help build long-term loyalty.

Don’t forget qualitative insights:

  • Gather feedback and analyze sentiment to identify patterns in tone, helpfulness, and personalization.

Create A More Natural Brand-Consumer Relationship With Conversational Commerce

Conversational commerce, much like regular conversations, is meant to build relationships. Conversational commerce is an opportunity to move beyond email blasts, promotional posts on social media platforms, and other communication methods that provide just one-way communication. SMS and other messaging apps enable you to keep your customers informed with updates, and allow them to respond on the same message thread.

A real-time exchange of information? Now that’s a conversation.

Creating engaging experiences on these channels is better and easier if your agents have a single view of your customers. Quiq makes it easy for companies to manage conversations with an intuitive agent desktop and native integrations with Salesforce.com, Zendesk, Shopify, and Oracle Service Cloud.

Quiq is architected with an “API First” strategy, which means we seek to work in harmony with your existing systems. With Quiq’s integration frameworks, you can customize our UI to include data from your internal operations systems or synchronize conversation data into your reporting, customer, and other backend systems.

Get Started with a Conversational Commerce Platform

Getting started with conversational commerce isn’t complicated. Start with one new channel that customers visit every day, such as WhatsApp or Facebook Messenger, and work with a vendor like Quiq to optimize your social commerce and post-purchase support.

If you’re ready to put a conversational commerce platform to work so that your business can thrive, get a demo today to see our Agentic AI solution in action.n action.

Frequently Asked Questions (FAQs)

What is conversational commerce?

Conversational commerce is the integration of chat, messaging, or voice technology into the shopping online experience, allowing customers to interact with brands, ask questions, and make purchases through natural, real-time conversations.

What are the 4-types of eCommerce?

The four main types of eCommerce are B2C (Business-to-Consumer), B2B (Business-to-Business), C2C (Consumer-to-Consumer), and C2B (Consumer-to-Business) — each describing the relationship between buyers and sellers in online transactions.

What is chat commerce?

Chat commerce refers to buying and selling products or services directly through chat-based platforms like WhatsApp, Facebook Messenger, or live website chat — where customers can browse, ask for help, and complete transactions without leaving the conversation.

Are AI agents an example of conversational commerce?

Yes, AI agents or chatbots are key examples of conversational commerce because they enable automated, real-time communication that helps customers discover products with personalized shopping experiences, get support, and make purchases more efficiently.

What are the main benefits of conversational commerce?

It improves customer experience by offering convenience, faster responses, and personalized engagement. For ecommerce businesses especially, it can increase conversions, boost retention, and reduce support costs.

Sierra AI Pricing: How Much Does it Cost in 2026?

Posted on Lauren Winder

How much does Sierra AI cost? We don’t know, since the pricing is not publicly available on their website, and they don’t even have a pricing page. From what we hear, It’s completely custom and depends on how many AI Agents you need and what you need them for. A number of sources online show that pricing starts at around $150,000 per year, which is one of the most common reasons why users look at Sierra AI competitors.

If you’re considering Sierra AI to improve your customer experiences with agentic AI, pricing is a major concern. Today, we’ll show you everything we could find on the Sierra AI pricing model and how it works.

Looking for a Sierra AI alternative with clearer pricing and no hidden costs? Book a free demo with Quiq today.

PS. We also have a full Sierra AI review that goes into more detail on the ease of use, AI agent capabilities, agent hand-off and more.

Sierra AI has outcome-based pricing

Outcome-based pricing means that Sierra only charges you for outcomes, such as “a resolved support conversation, a saved cancellation, an upsell, a cross-sell, or any number of valuable outcomes”.

sierra ai outcome based pricing

This is opposed to usage-based pricing models, where you are charged a smaller amount for every conversation.

This outcome-based pricing model means that you pay for measurable business outcomes, rather than paying per seat or usage, unlike consumption-based pricing. 

This may seem like a good thing, but it is very complicated to predict upfront. 

What is a good outcome? 

If the customer doesn’t escalate from the AI Agent to a human agent, is that a good outcome? Or maybe they had a bad experience and are done with your brand? Determining ‘good outcomes’ is likely very complicated and can create hard conversations with your vendor.

And, more importantly, it is very difficult to predict what your pricing will be, even after you’ve bought the product. You know how many conversations you’ve had from your current tools, but how many outcomes have you had? 

While it may be annoying that you don’t get the exact Sierra AI pricing from us (or Sierra themselves), there’s a good reason for this.

Why Sierra AI Agent pricing is not publicly available

Sierra is going after an enterprise market, where pricing is typically not disclosed publicly, and it’s for a very good reason.

To calculate pricing, Sierra AI needs many data points:

  • How many channels, markets, languages and use cases you need
  • How complex the tasks that agents handle are
  • How much customization is needed for those AI Agents to achieve tangible business impacts for your business
  • How many human agents you have, and how AI Agents fit into your workflow
  • Contact volume and how many (potential) customers you talk to on a monthly/annual basis

This outcome-based pricing varies based on these factors, but there are also implementation fees for each account, starting at a reported sum of $50,000.

So, you really have to get in touch with Sierra to find out your fixed and ongoing costs.

How Sierra AI compares against major competitors

If you’re looking to improve your customer experience with AI Agents, Sierra is far from the only choice out there. Below, we list the (un)known costs of some of their major competitors with their pricing models.

Pricing modelReported starting costSetup feesPredictabilityNotes
QuiqUsage-based tiers by conversations, channels, and automation level. Addons available (AI agents/assistants, channels, translations, etc.)Mid five figures per year is common for enterprise programsYes, usually lower than peersHighBest balance of enterprise features and forecastable spend. Faster deployment reduces total contract cost.
Kore.aiSession-based billing plus an enterprise licenseReported enterprise deals often start around $300,000 per yearYes, often significantLow to mediumBilling sessions and platform licensing add complexity. Costs can spike with long or repeated conversations.
DecagonCapacity or per-conversation pricingRoughly $95,000 to $590,000 per year based on usageYesMediumLess opaque than outcome pricing, but still a large enterprise commitment. Easier to budget than Sierra-style models.
Poly.aiPer-minute voice usageAround $0.90 to $1.00 per call minuteYesMediumVoice only focus. Costs scale quickly with call volume, forecasting depends heavily on traffic stability.
CognigyCustom enterprise contracts. A mix of  a number of messages and time passedCommonly six-figure annual dealsYesLowPowerful platform, but pricing details stay opaque until late in sales discussions.
ReplicantUsage-based voice automationSix-figure annual contracts are typicalYesMedium to lowOutcome and usage-based billing tied to voice traffic. Can become costly at scale.

Quiq

Quiq does not publicly list exact prices, but pricing is structured around usage tiers that scale with conversation volume, channels supported, and depth of AI automation.

Compared with the others below, Quiq typically lands at lower total enterprise costs while offering transparent, scalable pricing that teams can forecast and justify to finance leaders. With a robust platform, its AI agents can support multi-channel coverage (SMS, WhatsApp, web chat, social, voice, and email) without requiring extensive engineering to launch. 

This reduces project costs and shortens time to value as reported by their enterprise customer,s such as Spirit Airlines, Roku, Panasonic, and Urban Outfitters, who’ve all launched AI Agents to handle complex scenarios and are seeing efficiency gains such as a 40% automatic resolution rate. 

“We’ve able to launch a cohesive self-service experience that spans voice, chat, and all of our messaging touchpoints. The results have been remarkable. Over 40% of our requests resolve automatically without needing a live agent and we’ve seen a 16% reduction in our conversation time.”

This mix of predictable usage-based billing and faster deployment makes Quiq a strong option for teams that want enterprise-grade AI without opaque outcome pricing or massive upfront commitments.

The price is barely the tip of the iceberg. Quiq stands out with an excellent return on investment that goes beyond the value in money. You can’t put a price on improved customer experience and increased customer service KPIs across the board.

Kore.ai

Kore.ai does not publish standard pricing and primarily sells custom enterprise deals. Based on third-party research, lower-tier reported plans for small teams have appeared in the $50 to $180 per month range on annual billing, but these are unofficial and inconsistent.

For true enterprise deployments, most deals start around $300,000 per year and require a heavy implementation effort before realizing value. Billing is complicated because it uses “billing sessions” based on 15-minute blocks of conversation, which can make costs unpredictable month to month.

Decagon

Decagon’s pricing is also custom and enterprise-focused. It typically bills based on usage, either on a per-conversation basis (a fixed fee for each conversation) or on a per-resolution model (a higher fee for fully resolved interactions).

Public estimates suggest that annual contracts with Decagon can land anywhere from roughly $95,000 up to $590,900 or more, depending on volume, complexity, and integration needs, though these figures are based on external reviews rather than vendor price lists.

This makes Decagon cheaper than opaque outcome models like Sierra at the high end, but still an enterprise-level commitment that can outstrip platforms with simpler subscription tiers.

Make sure to read our full comparison of Sierra AI vs Decagon, too.

Poly.ai

Poly.ai sells voice agents with pricing based on usage, typically on a per-minute basis for calls, rather than fixed subscription tiers. Enterprise buyers have reported pricing around $0.95 per minute of voice interaction, which means costs can scale quickly with volume.

There is no flat rate or published tier sheet, and custom quotes are required. Compared with Quiq’s usage tiers or predictable billing, Poly.ai’s per-minute model can make forecasting harder for companies with high inbound voice volumes.

NICE Cognigy

Cognigy’s platform also does not list public pricing tiers. Like the big enterprise competitors, buyers must reach out for a custom quote that reflects channel volume, integrations, and deployment scope.

Organizations that choose Cognigy generally commit to enterprise contracts, though exact numbers are not published. It sits in the same general category as Kore.ai and Decagon in terms of pricing unpredictability, but exact figures are hard to find without vendor engagement.

Replicant

Replicant similarly requires custom quotes for pricing. It focuses on intelligent voice automation with outcome-driven billing, and enterprise deals can run into six figures, particularly when teams choose fully managed voice solutions with high call volumes. Like Poly.ai, Replicant’s costs are tied to usage metrics rather than simple seat or subscription models, and there are no published entry-level prices for smaller teams.

The true cost of Sierra AI

Like any AI Agent pricing model, Sierra’s pricing isn’t expensive or secretive. It merely depends on a variety of different factors, and it’s difficult to give a ballpark number on a pricing page. The outcome-based model that focuses on providing revenue gains from customer interactions is fair, but it’s difficult to predict and that’s not all the cost involved.

Long onboarding cycles, deep customization, and ongoing vendor involvement often add time and internal effort that are not obvious at the start. Over time, these factors can push the total cost well beyond the original estimate and make changes or exits expensive.

That is why many teams look beyond headline pricing and focus on predictability and speed to value, which is where Quiq shines.

Quiq uses clearer usage-based pricing, launches faster, and avoids outcome-driven ambiguity, which usually leads to a lower total cost of ownership and fewer surprises as teams scale.

Book a free demo to find out more about Quiq’s pricing model and how we can help.

8 Best Sierra AI Competitors for Conversational AI

Posted on Lauren Winder

TL;DR:

  • Sierra AI offers strong enterprise AI agents, but users report issues with speed, support quality, and maintaining context
  • Many Sierra alternatives focus on a single strength, such as voice automation, call deflection, or global coverage, which can lead to fragmented customer experiences
  • PolyAI, Replicant, and NICE Cognigy are best for phone-heavy contact centers, not unified customer journeys
  • Decagon and Kore.ai offer broader or more autonomous AI, but often require heavier setup and ongoing technical effort that increases cost substantially
  • Intercom works well for SaaS support teams handling basic questions, but struggles with complex, multi-step resolutions
  • Quiq stands out with a robust multi-channel platform for all stages of the customer journey and delivers complete transparency into what the AI is doing, safety controls, and enterprise-level customization. For enterprise teams that care about control, continuity, and customer experience quality, Quiq is the strongest alternative to Sierra AI

Are you looking for AI support across voice and digital channels? Something that represents your brand and improves your customer experience? Sierra AI is an enterprise-grade platform that helps support teams automate customer interactions with AI voice agents and contextually appropriate chat. However, like any AI platform, it has a few downsides.

Today, we take a look at some of the most common reasons why users look for Sierra AI alternatives and suggest some of the best replacements for customer service AI.

For more detailed insights on Sierra, check out our review, too.

Why look for Sierra AI competitors in the first place?

Like most other AI-powered enterprise systems, Sierra doesn’t have publicly available pricing. This is one of the most common complaints about Sierra, but it can hardly be taken as a downside. There are, however, a series of other issues that users have with Sierra.

Performance issues

There are multiple accounts of users complaining that Sierra AI is slow at times, which can be an issue if you’re handling a large volume of customer conversations at once.

“The platform can be slow at times, and there are occasional bugs that need fixing.” – G2 review

If you’re looking for a truly enterprise-grade platform that doesn’t fail under heavy load, this can be a major concern.

The customer support isn’t the best

Non-technical teams may need additional hand-holding to set up Sierra AI. When you reach out to their customer support, the quality of responses can vary quite a bit from agent to agent, and depending on your specific problem.

“Cost and customer support, although the company provides support, the quality and responsiveness of customer service may vary.” – G2 review

The experience for customers is not always human-like

A good agentic AI platform should produce natural-sounding conversations so that customers feel like they’re talking to a real person, despite knowing that it’s a chatbot.

Customer reviews show that Sierra AI can struggle with keeping the context going when the conversations get longer. This leads to AI agents sending the same responses, which ultimately results in irritated customers.

“Sierra AI may struggle to maintain context in longer conversations, leading to repetitive or irrelevant responses. At times, the AI’s responses can feel generic and lack the depth or nuance of a human conversation.” – G2 review

Best Sierra AI competitors for conversational AI

If you’re looking for the next best conversational AI platform to replace Sierra, the good news is that the market is booming, with new support platforms launched every day. We singled out some of the best Sierra alternatives to help you provide better customer support with AI agents.

1. Quiq

Quiq is an enterprise customer journey platform built for brands that deal with high volumes of customer conversations and complex service scenarios.

It is used by large consumer-facing companies in retail, travel, hospitality, financial services, and home services that need to deliver a unique branded experience with the safety controls and transparency to trust the AI Agent will stay on task.

Instead of focusing on a single AI use case, Quiq supports the full journey from the first customer message to final resolution. This spans across digital and voice channels, with every interaction connected.

Key features:

  • Customer-facing AI agents that do more than answer questions. They complete real tasks like changing bookings, processing returns, or updating account details across messaging and voice
  • AI assistants for human agents that suggest replies, summarize conversations, take action just like the AI Agents, and help agents move faster without breaking their flow
  • Continuous conversation context that carries across AI, humans, and channels. This way, customers never have to repeat themselves when switching from chat to voice or escalating to an agent
  • Transparent AI decision logic that shows exactly how the AI reached an answer or took an action, making it easier to trust, tune, and troubleshoot
  • Verified safety and governance with built in guardrails, testing, and claim checks that prevent the AI from going off script in sensitive situations
  • Brand and workflow customization using plain language process guides that reflect how your teams actually work, rather than forcing rigid templates
  • Conversation analytics and quality scoring that analyze every interaction, AI and human alike, to surface trends, risks, and coaching opportunities

How Quiq is better than Sierra AI

Quiq is designed to resolve customer issues from the first customer message to final resolution.

Sierra AI places a strong emphasis on empathetic conversations and brand-aligned tone. On the other hand, Quiq goes further and connects AI directly to backend systems, allowing it to complete real actions when appropriate, then hand off smoothly to humans when nuance is required.

Another major difference is transparency.

Quiq shows how decisions are made, and lets teams test and govern AI behavior before it reaches customers. Sierra AI is often described as powerful but technically heavy to customize, while Quiq gives CX teams more visibility and control without needing deep engineering involvement for every change.

Quiq also has a long history with asynchronous messaging like SMS and WhatsApp. This allows human agents to handle multiple conversations at once and reduces reliance on expensive phone support. That messaging first foundation, combined with seamless voice support when needed, makes it easier to scale service without fragmenting the experience.

Lastly, Quiq uses AI to help you across the full customer journey. Your work compounds across AI agents, human agents, the level of analysis you can do, the AI services you can introduce, and much more.

Pricing

Quiq has a usage-based pricing model tied to conversation volume and enabled capabilities. Customers typically commit to annual plans based on their expected scale and use cases.

Pricing is customized to align with your operational needs and rollout plan.

Book a demo to learn more about Quiq today.

2. Kore.ai

Kore.ai is an enterprise conversational AI platform built for large organizations that want to deploy AI agents across customer service, internal operations, and business processes.

Global 2000 companies typically use it in industries like banking, healthcare, telecom, and insurance, where security, compliance, and flexibility are non-negotiable.

Instead of focusing only on customer support, Kore.ai positions itself as a broad AI platform that can power both external service experiences and internal employee workflows.

Key features

  • Enterprise AI agent platform that supports customer service, employee support, and process automation within a single ecosystem
  • No code and low code tools through the XO Platform, which lets teams design complex conversational flows with a drag-and-drop interface
  • Strong security and compliance controls built for regulated industries, including governance, access management, and data handling standards
  • Omnichannel deployment across chat, voice, messaging apps, and internal enterprise tools
  • Extensive integration capabilities with CRMs, contact center platforms, backend systems, and enterprise data sources
  • Reusable AI components that let organizations standardize agents across teams and use cases

How Kore.ai is better than Sierra AI

Kore is broader in scope than Sierra AI.

Sierra focuses primarily on customer-facing AI agents with strong brand alignment and safeguards. Meanwhile, Kore.ai provides a broader platform that can also support internal use cases like IT help desks, HR assistants, and process automation.

For large enterprises that want a single AI framework spanning multiple departments, Kore.ai can feel more flexible and future-proof than Sierra AI’s more CX centric approach.

That said, this larger scope comes with some trade-offs. Kore often requires more upfront design, configuration, and ongoing management, especially for customer service teams that want a fast time to value.

Pricing

Kore.ai pricing is custom and varies based on deployment size, channels, and use cases. Costs typically scale with conversation volume, integrations, and the level of customization required.

Pricing is not publicly listed and generally targets large enterprise budgets. Prospective customers need to work directly with sales to receive a tailored quote.

3. NICE Cognigy

NICE Cognigy is an enterprise conversational AI platform built primarily for large contact centers that already rely on NICE for voice, routing, and workforce management. It is popular with global enterprises in regulated and high-volume environments where voice automation and strict governance are central requirements.

Cognigy is often chosen by organizations looking to modernize IVR and voice-driven support while keeping everything closely tied to their existing contact center stack.

Key features

  • Voice first conversational AI built to handle complex call flows and replace traditional IVR systems for inbound and outbound calls
  • Advanced dialog orchestration that supports long, multi-step conversations with branching logic and context handling
  • Deep contact center integration with NICE CXone and other enterprise telephony systems
  • Strong governance and compliance controls designed for regulated industries and large-scale deployments
  • Omnichannel support across voice, chat, and messaging channels with shared logic across experiences
  • Developer-friendly tooling that allows technical teams to fine-tune flows, logic, and integrations

How NICE is better than Sierra AI

Cognigy is better for organizations that are heavily focused in voice support and traditional contact center infrastructure. Compared to Sierra AI, it has a more mature tooling for call automation, IVR replacement, and large-scale telephony use cases.

Sierra AI tends to shine in brand-aligned, empathetic digital experiences, while Cognigy focuses on operational depth and voice reliability. For enterprises where call handling is still the dominant channel, Cognigy can feel like a safer and more familiar choice.

However, Cognigy’s strength in voice assistants also shows its limitations. Customization often implies significant technical involvement, and digital messaging experiences can feel secondary rather than central.

For CX teams that want conversations to feel connected rather than routed through systems, Cognigy can be more rigid compared to newer agentic platforms.

Pricing

NICE Cognigy pricing is not publicly available and is typically bundled as part of a broader NICE enterprise agreement. Costs depend on call volume, channels, integrations, and deployment complexity.

4. PolyAI

PolyAI is a voice-focused conversational AI platform built for large enterprises that handle high volumes of phone-based customer support. It is popular with companies in hospitality, utilities, financial services, and transportation that want to replace rigid IVR systems with more natural-sounding voice agents.

PolyAI is best known for its deep specialization in voice and its managed service model. In this approach, most of the design, tuning, and optimization is handled by PolyAI’s own team.

Key features

  • Voice-first AI agents designed to handle natural, free-flowing phone conversations instead of menu-based IVR flows
  • Proprietary voice models tuned specifically for spoken dialogue, interruptions, and long-form requests
  • High-quality speech recognition and synthesis that prioritizes natural pacing, tone, and clarity
  • Managed service delivery where PolyAI’s team builds, maintains, and optimizes agents on behalf of the customer
  • Enterprise voice integrations with telephony and contact center infrastructure
  • Support for complex call handling, such as authentication, routing, and transactional requests

How PolyAI is better than Sierra AI

PolyAI is a stronger option than Sierra AI for organizations where the phone channel still dominates customer support.

Sierra AI focuses on digital experiences and empathetic brand alignment. Meanwhile, PolyAI puts most of its energy into making voice interactions sound natural and reliable at scale.

For enterprises that want to modernize IVR without rebuilding their contact center from scratch, PolyAI can feel more focused and mature than Sierra AI’s broader agent platform. Instead of trying to improve your AI setup by combining multiple systems, you can use one fully focused on voice.

That said, PolyAI’s voice specialization is also its main limitation.

Pricing

PolyAI does not publish standard pricing. Costs are typically based on call volume, deployment complexity, and the level of managed service required.

5. Replicant

Replicant is a voice automation platform built for large contact centers that want to offload repetitive phone calls from human agents. It is commonly used in retail, travel, utilities, and financial services, where call volume is high, and many inquiries follow predictable patterns.

The platform is focused on automating voice conversations end-to-end, especially for routine support requests that would otherwise tie up live agents.

Key features

  • Voice-based AI agents that handle common inbound calls, such as order status, appointment scheduling, cancellations, and basic account questions
  • Call containment and deflection aimed at resolving issues without transferring customers to a human agent
  • Natural language understanding for voice, designed to handle open-ended spoken requests rather than strict menu trees
  • Telephony and contact center integrations that connect Replicant to existing voice infrastructure
  • Prebuilt voice use cases that speed up deployment for common support scenarios
  • Analytics for call outcomes that track resolution rates, transfers, and automation performance

How Replicant is better than Sierra AI

Replicant is better than Sierra AI for organizations where the biggest challenge is reducing inbound phone volume.

While Sierra AI focuses on brand-aligned, empathetic AI agents across channels, Replicant is more narrowly focused on voice containment and call automation.

For teams looking to replace or augment IVR quickly and deflect large numbers of routine calls, Replicant can feel more direct and operationally focused than Sierra AI’s broader agent platform.

That said, Replicant’s narrow focus is also its main limitation. It is primarily a voice solution and does not treat messaging as a core channel. Context between voice, digital conversations, AI, and human agents is limited, which can create fragmented experiences.

Pricing

Replicant does not publish standard pricing. Costs are typically based on call volume, use case complexity, and the scope of voice automation deployed.

Pricing is enterprise-focused and requires a direct sales engagement to receive a custom quote.

6. Yellow.ai

Yellow.ai is an enterprise conversational AI platform built for companies that want to automate customer support and basic sales interactions across chat and voice.

It is usually used by large global brands in e-commerce, telecom, banking, and consumer services that need multilingual coverage and wide channel support.

The platform positions itself as a broad automation layer, attempting to cover many regions, languages, and use cases from a single system.

Key features

  • Omnichannel AI agents that support chat, voice, messaging apps, and social channels from one platform
  • Multilingual and regional support designed for global teams operating across multiple markets
  • Low-code platform and bot builder that allows teams to design conversational flows and reuse components
  • Prebuilt industry use cases for common support and sales scenarios such as order tracking, payments, and FAQs
  • Voice automation capabilities for handling inbound calls and basic call routing
  • Analytics and reporting tools to track containment, intent performance, and conversation outcomes

How Yellow.ai is better than Sierra AI

Yellow.ai has broader geographic and language coverage than Sierra AI. For global organizations that need to roll out multilingual AI agents, Yellow.ai can feel more scalable and easier to standardize.

It also provides more flexibility around channels and use cases, whereas Sierra AI tends to focus more narrowly on high-quality, brand-aligned customer interactions. For companies prioritizing reach and speed of deployment, Yellow.ai may feel like a more practical option.

All of this comes with some trade-offs. Yellow.ai often relies on structured flows and predefined logic, which can limit its ability to solve complex use cases. Context between AI and human agents can also feel less continuous, especially when conversations move across channels.

Pricing

Yellow.ai does not publish standard prices, but it’s known for its enterprise-scale pricing. Costs vary based on conversation volume, channels, languages, and deployment scope.

7. Decagon

Decagon is an enterprise agentic AI platform built for fast-growing, digital-first companies that want AI agents to handle complex customer issues end-to-end. It is typically used by modern consumer brands and SaaS companies that are comfortable giving AI a high degree of autonomy in customer interactions.

The platform positions itself around a concierge-style experience, where AI agents are expected to resolve complex customer interactions and nuanced issues rather than just answer simple questions.

Key features

  • Autonomous AI agents designed to resolve complex customer requests without human involvement
  • Agent operating procedures that define AI behavior using natural language logic instead of rigid flowcharts
  • Unified knowledge graph that combines customer data, conversation history, and business rules into a shared context
  • Iterative testing and simulation tools that allow teams to validate AI behavior before rolling changes live
  • Deep system integrations that give AI agents access to backend tools and data
  • High resolution focus aimed at maximizing containment for advanced support scenarios

How Decagon is better than Sierra AI

For a more in-depth comparison, make sure to read our review of Sierra AI vs Decagon.

Decagon is more aggressive than Sierra AI when it comes to autonomy.

Sierra AI puts a lot of focus trust, empathy, and guardrails. On the other hand, Decagon is built for companies that want AI agents to take ownership of entire workflows and resolve issues with minimal human involvement.

For teams that prioritize maximum automation and are comfortable with AI handling sensitive or complex tasks, Decagon can feel more capable than Sierra AI’s more controlled approach.

That same strength can also be a drawback. Decagon is often viewed as expensive and heavy to implement, and changes to agent behavior may require deeper technical involvement. The platform can also feel like overkill for teams that want AI and humans to collaborate closely rather than fully hand work off to machines.

Pricing

Decagon pricing is not public. Costs typically scale based on conversation volume, automation depth, and integration complexity.

8. Intercom

Intercom is a customer support and engagement platform best known for its help desk, live chat, and in-app messaging tools. It is commonly used by SaaS companies and digital-first businesses that want a single system for customer support, onboarding, and product communication.

In recent years, Intercom has added AI capabilities through Fin, positioning itself as a support platform with built-in AI assistance rather than a standalone agentic AI system.

Key features

  • Shared inbox and help desk for managing customer conversations across chat, email, and in app messaging
  • Fin AI agent that answers customer questions using help center content and existing support data
  • In app messaging and chat which are tightly integrated into product experiences
  • Ticketing and workflow tools for routing, prioritizing, and managing support requests
  • Help center and knowledge base used as the primary source for automated answers
  • Ecosystem integrations with CRMs, issue tracking tools, and other SaaS platforms

How Intercom is better than Sierra AI

Intercom is easier to adopt than Sierra AI for teams that already rely on a traditional support desk. Fin can be activated quickly, requires less upfront customization, and fits neatly into existing Intercom workflows.

For SaaS companies that want AI to deflect common questions without rethinking their entire support architecture, Intercom is more accessible than Sierra AI’s more complex agent platform.

However, Intercom’s AI capabilities are closely tied to its help desk and knowledge base model.

Fin is optimized for answering questions, not for executing complex actions or managing multi-step resolutions. Context between AI and human agents is limited by ticket based workflows, and conversations can feel fragmented as customers move across channels.

Pricing

Intercom pricing is publicly listed at $19 per user per month with $0.99 per resolution by Fin AI. This seems affordable at first, but if you handle a lot of customer inquiries and support tickets, Intercom costs can easily go into thousands of dollars per month. And at that point, you may be better off getting enterprise customer service platforms with more powerful AI agents.

Improve your customer support with the best AI agents available

Finding an alternative to Sierra AI doesn’t necessarily mean finding a platform that is easier to set up, costs less and doesn’t break as often. With AI systems, the entire purpose is to find tools that understand context and make it easy to employ AI agents that sound and feel human and this is where Quiq can help.

Quiq treats conversations as ongoing threads instead of isolated tickets, keeps context intact across AI and human agents, and uses automation to take action without losing oversight. The balance between intelligence, transparency, and human collaboration is exactly what many businesses are missing in Sierra.

Book a demo today to find out how Quiq can help you deliver amazing customer support with agentic AI.

AI Data Preparation: The Hidden Step Between Your Data and Your AI Success

Posted on Greg Dreyfus

Key Takeaways

  • Data Preparation is the Foundation: The success of AI in customer experience depends more on the quality of the “fuel” (your data) than the specific AI model you choose. Poor data quality and inadequate data preparation are leading reasons why AI projects fail, often resulting in unreliable or biased outcomes.
  • Digital Transformation Doesn’t Equal AI Readiness: Simply having data in the cloud isn’t enough. AI requires context and retrieval, meaning documents must be “chunked,” de-duplicated, and stripped of outdated “noise.”
  • Avoid the “Data Dump”: Quality beats quantity. Feeding an AI outdated or conflicting documents leads to hallucinations and poor customer trust.
  • Always Be Optimizing: Data prep is not a “one-and-done” task. It requires a human-in-the-loop workflow to flag errors and update the knowledge base as policies evolve.

Every customer experience leader wants to deploy AI that feels natural, helpful, and brand-aligned. We all want the “magic” outcome: an AI agent that knows your return policy by heart, checks order status in seconds, and speaks with the same empathy as your best human agent.

But often, when leaders plug their existing data into a new AI model, the magic doesn’t happen. Instead, the AI hallucinates, gives outdated answers, or gets stuck in a loop.

The problem usually isn’t the AI model itself. It’s the fuel you’re putting in the tank.

Most enterprise data — scattered across PDFs, legacy CRMs, and dusty knowledge bases — isn’t ready for generative or agentic AI. It needs to be refined first. 

In the CX world, data preparation isn’t just a technical hurdle; it’s the bridge between a generic chatbot and a brand-aligned AI agent. It involves the intentional collection, cleaning, and structuring of your enterprise knowledge to ensure the model produces reliable outcomes rather than creative fiction. This process is called AI data preparation, and it is the single most critical factor in whether your AI project succeeds or stalls. Inadequate data preparation and poor data quality are primary reasons why AI projects fail, leading to unreliable models and flawed predictions.

Here is what it actually takes to get your data ready for prime time, and why it matters more than the model you choose.

What is AI Data Preparation?

In simple terms, AI data preparation is the process of collecting, cleaning, labeling, and structuring your raw data (these are key data preparation steps) so an AI model can actually understand and use it. Data cleaning and maintaining data consistency are crucial during this process to ensure the data is accurate, reliable, and ready for AI modeling.

Think of your current data like a library, where all the books have been thrown onto the floor in a pile. The information is there, but if you ask someone to “find the answer to X,” they will spend hours searching. Effective AI data preparation requires gathering data from multiple sources, converting data into standardized formats, and combining it into a unified dataset with a consistent data structure.

AI data preparation is the act of picking up those books, dusting them off, organizing them by topic, and indexing them. It transforms raw information into a structured resource that an AI can retrieve instantly.

For a customer experience leader, this usually involves three specific types of data work:

  1. Refining Knowledge: Taking human-readable documents (FAQs, PDFs, manuals) and turning them into machine-readable chunks.
  2. Structuring Customer History: Ensuring your CRM data (past orders, loyalty status) is clean and accessible via API.
  3. Sanitizing Logs: Cleaning up past conversation transcripts to remove Personally Identifiable Information (PII) before using them for training.

Why “General” Data Transformation Isn’t Enough

You might be thinking, “We already did a digital transformation project three years ago. Our data is in the cloud. We’re good.”

Unfortunately, digital readiness is not the same as AI readiness.

General data transformation usually focuses on storage and analytics — moving data from on-premise servers to the cloud so humans can look at dashboards. AI readiness focuses on context and retrieval.

For example, a PDF of your 2023 Holiday Return Policy might be stored safely in the cloud. But if that PDF also contains the 2022 and 2021 policies in the appendix, a generative AI model might get confused and quote the wrong year. During AI data preparation, it is crucial to standardize data formats and address inconsistent data through careful data processing. This ensures that information is consistent, accurate, and ready for effective AI analysis.

Preparing data for AI means stripping away the noise, ensuring accuracy, and formatting it so the AI knows exactly what piece of information applies to which customer question.

Get 3 Simple Steps to Prepare Your Data for Agentic AI here.

Data Collection: Laying the Groundwork for AI

Every successful AI project starts with one essential step: data collection. This is where the data preparation process truly begins, as you gather raw data from a variety of sources—databases, APIs, third-party providers, and even legacy systems. 

The quality and diversity of this initial data set the stage for everything that follows, directly influencing how well your AI systems will perform.

Effective data collection means more than just amassing large volumes of information. It means ensuring that the data is relevant, accurate, and representative of the real-world scenarios your AI model will encounter. 

Data engineers play a pivotal role here, carefully navigating challenges like missing values, inconsistent formats, and the need for data cleansing. They transform raw data into a usable format, addressing gaps and errors before the data ever reaches your AI model.

The 3 Pillars of Data Preparation for AI 

To move from “we have data” to “we have AI-ready data,” you must focus on these three foundational pillars. Think of these not just as technical tasks, but as the guardrails for your brand’s reputation.

1. Curating Your Knowledge: Defining the “Source of Truth”

Your AI agent is only as smart as the documents you feed it. In the CX world, the greatest risk isn’t a lack of information; it’s conflicting information. If your public-facing website says “Free Returns within 30 days,” but an internal PDF manual from 2022 still says “14 days,” your AI will eventually hallucinate a contradiction. When an AI is forced to choose between two “truths,” it fails, and so does your customer’s trust.

The Cleanup Process:

  • Audit: Identify where your “truth” lives. Is it on the website? In a Google Drive folder? In a legacy knowledge base?
  • Consolidate: Bring these sources together. Using a data warehouse can help you efficiently consolidate and manage large volumes of structured data from multiple sources.
  • De-duplicate: If you have three versions of a “How to reset password” guide, delete the two old ones.
  • Chunking: This is a technical step where long documents are broken down into smaller, bite-sized pieces (chunks) that an LLM can digest easily.

Why it matters: When a customer asks, “Can I bring my dog to the hotel?”, the AI shouldn’t have to read a 50-page employee handbook to find the answer. It needs a clean, single paragraph stating the pet policy.

2. Connecting the Pipes: Closing the “Actionability Gap”

There is a massive difference between a Chatbot and an AI Agent. A chatbot can tell you your policy; an agent can actually execute it.

Without API connectivity, you are left with the “Actionability Gap”, the frustrating moment where an AI can identify a customer’s problem but has to hand them off to a human to actually fix it. To close this gap, your data must be “transactional.”

The Connection Process:

  • Identify Systems: Which tools hold the data the customer cares about? Usually, this is your CRM (Salesforce, Zendesk), your Order Management System (OMS), and your Booking Engine.
  • API Health Check: Do these systems have open APIs? Can they “talk” to external tools safely?
  • Field Mapping: For example, ensuring that “Customer_ID” in your chat system matches “Cust_Ref_No” in your shipping system. This mapping is crucial for transferring structured data accurately between systems, as APIs typically handle organized, well-defined data formats.

Why it matters: Without this step, your AI is just a conversational FAQ bot. With this step, it becomes an agent capable of resolving complex issues.

3. Sanitizing Raw Data for Safety: Preventing “LLM Leakage”

This is the step that keeps CX leaders awake at night. You cannot feed raw customer data into a public LLM without creating major privacy risks.

The Safety Process:

  • PII Redaction: Automatically detecting and scrubbing names, credit card numbers, and addresses from training data, with special attention to identifying and protecting sensitive data to ensure compliance with privacy laws and safeguard user information.
  • Bias Detection: Reviewing historical data to ensure the AI doesn’t learn bad habits from past human interactions.
  • Access Control: ensuring the AI only accesses the data it is authorized to see.

From Raw Documents to AI-ready Assets: The Power of AI Resources

Once you’ve collected your data, you must give it meaning. In traditional data science, this involves manual data labeling and numerical encoding. In the modern CX stack, Quiq’s AI Studio streamlines this through an integrated ETL (Extract, Transform, Load) engine within AI Resources.

Instead of manual annotation, you can import knowledge bases, product catalogs, or manuals and run them through a series of specialized LLM prompts to make them “AI-ready”. This engine automates the heavy lifting, for example:

  • Extracting Helpful Links: Identifying and surfacing relevant URLs within the content.
  • Clarification: Removing unnecessary HTML formatting or outdated instructions like “contact us” to reduce noise.
  • Contextual Questioning: Automatically generating a set of potential customer questions that each specific article is designed to answer.

These types of transformations ensure your data isn’t just understandable to humans, but highly searchable and “digestible” for advanced AI agents.

Common Pitfalls in the Data Preparation Process (And How to Avoid Them)

We see many brands try to rush the data preparation process. Inaccurate data can undermine model performance, making it essential to address data quality issues early. Here are the traps to watch out for.

The “Dump Everything” Approach

  • The Mistake: Uploading every document the company has ever produced into the AI model, hoping it will figure it out. This often results in a mix of unstructured data, making AI data preparation more complex and error-prone because AI Agents can’t tell you which answer is right without a clear distinction if you have two conflicting sources of information.
  • The Consequence: The AI gets confused by outdated info (like that Return Policy from 2019) and hallucinates answers.
  • The Fix: Be selective. It is better to have a small, 100% accurate knowledge base than large and diverse datasets that are messy.

The “Perfect Data” Paralysis

  • The Mistake: Waiting until every single data point in the company is perfect before launching an AI pilot.
  • The Consequence: You never launch. Competitors pass you by.
  • The Fix: Use a “Crawl, Walk, Run” approach. Start with proper data preparation for one specific use case — like “Order Status” or “Password Reset” — to ensure data quality and improve model accuracy, then expand from there.

Ignoring the Human Loop

  • The Mistake: Assuming the data preparation is a one-time setup.
  • The Consequence: Your products change, your policies update, but your AI stays stuck in the past.
  • The Fix: Build a workflow where human agents can flag incorrect AI answers, which then triggers an update to the source data. Incorporate strong data governance practices to ensure ongoing updates are tracked, data remains accurate, and compliance with regulations like GDPR and HIPAA is maintained.

Your Next Step: The Data Audit

You probably don’t need to hire a team of data scientists to get started. You just need to look at your current CX operations with an AI lens.

Start with a simple audit of your top 10 call drivers. For each topic (e.g., “Where is my order?”), ask three questions:

  1. Is the answer written down clearly somewhere? (Knowledge)
  2. Is it accurate and up to date? (Quality)
  3. Does the answer require checking a system? (Connectivity)

If you can answer “Yes” to these, you are closer to AI readiness than you think. Ensuring your data is high-quality and relevant directly leads to more accurate and reliable outcomes.

Quiq’s Approach to AI Data Preparation

Getting your data AI-ready isn’t a fast project—most organizations underestimate just how time-consuming and complex it can be. That’s why we built AI-powered data restructuring directly into our process at Quiq. 

We handle the heavy lifting behind the scenes, so you can move from raw data to real results without sacrificing speed or confidence. Compared to other vendors, Quiq can host multiple datasets (e.g., 10 separate FAQs for 10 different markets, with 10 different product catalogs). This means you don’t have conflicting data co-mingled, and your AI Agent can select the right sources based on the conversation context.

But don’t just take our word for it: Read our case study with A Closer Look to learn how we unified their huge data volume of existing records for more efficient AI outcomes and success.

Frequently Asked Questions (FAQs)

What is AI data preparation in customer experience?

AI data preparation is the critical process of collecting, cleaning, and structuring raw enterprise data—such as FAQs, PDFs, and CRM logs—so generative AI models can understand and use it accurately. It transforms scattered information into a machine-readable format to prevent hallucinations and ensure brand-aligned responses.

Why does my AI agent give incorrect or outdated answers?

High quality data matters. Usually, the problem is not the AI model, but the data source. If your AI has access to poor quality data or conflicting documents (e.g., a 2022 return policy and a 2024 update), it may retrieve the wrong information. Proper AI data preparation involves de-duplicating files and ensuring only the “current truth” is accessible.

What is the difference between digital transformation and AI readiness?

Digital transformation focuses on storage and analytics (data movement to the cloud for humans to see). AI readiness focuses on context and retrieval (organizing data so a machine can find a specific answer in seconds). AI-ready data is “chunked” into small pieces that LLMs can digest easily.

How do I make my AI agent perform actions like processing refunds?

To move beyond a simple chatbot, you need to connect your AI to internal systems via APIs. This allows the AI to “talk” to your CRM (like Salesforce or Zendesk) and Order Management Systems, enabling it to perform transactional tasks like checking order status or updating a booking based on your business data.

Is it safe to use customer data with generative AI?

Safety requires a strict sanitization process. Before using customer logs for training, you must redact PII (names, addresses, and credit cards) and implement access controls. This ensures the AI only accesses authorized data and protects your organization from privacy risks.

Generative AI in Travel: Benefits, Considerations & Use Cases

Posted on Lauren Winder

Key Takeaways

  • Generative AI is reshaping travel customer experience. Travel brands are using generative AI to deliver faster, more personalized support across booking, in-trip assistance, and post-travel engagement.
  • Not all AI solutions are created equal. Rule-based chatbots can’t handle the complexity of modern travel experiences, while generative and agentic AI can reason, adapt, and resolve issues in real time.
  • Agentic AI unlocks real operational impact. By assigning specialized AI agents to tasks like disruption management, bookings, and loyalty support, travel brands can automate outcomes, not just conversations.
  • Trust, accuracy, and integration are critical. Successful generative AI deployments depend on secure data handling, reliable system integrations, and transparent handoffs between AI and human agents.
  • Early adopters will set the new customer experience standard. Travel brands that invest in agentic AI today are better positioned to scale support, improve customer satisfaction, and stay competitive as traveler expectations rise.

For decades, the travel industry has operated on a simple premise: complexity is the customer’s problem. If a flight is canceled, the traveler waits on hold. If a booking needs changing, the traveler navigates a maze of policies. If a bag goes missing, the traveler chases answers.

But the tolerance for that friction is gone. Travelers today expect immediate, personal, and accurate resolutions.

Generative AI has emerged as the technology capable of meeting this demand, shifting the industry from reactive support to proactive care. According to recent research from Amadeus, traveler usage of generative AI for planning has jumped 64% in just one year. The appetite is there. Customer experience leaders no longer question whether to adopt this technology, but how to deploy it safely and effectively to drive real business value.

What is Generative AI in the Travel Industry?

To cut through the noise: Generative AI in travel isn’t just a better chatbot. It is a fundamental shift in how systems process and generate information.

While traditional rule-based chatbots rely on decision trees — if customer says X, reply with Y — generative AI uses large language models (LLMs) to understand intent, reason through complex queries, and generate unique, conversational responses in real time.

It differs from traditional automation in its ability to handle ambiguity. In travel, where no two trips (and no two disruptions) are exactly alike, this flexibility is critical. Unlike predictive AI, which analyzes historical data to forecast trends (like pricing surges), generative AI creates new content and interactions.

It’s uniquely suited for the travel sector because it thrives in high-volume, information-dense environments. When a traveler asks, “Can I change my flight to the evening one, and will I lose my upgrade?”, a rule-based bot fails. Generative AI reviews the policy, checks availability, understands the nuance of the upgrade status, and formulates a coherent answer.

How Generative AI is Transforming Travel

The shift improves the quality of the journey itself in three big ways:

1. From Static Support to Dynamic Experiences

The days of forcing high-value customers through rigid IVR menus or static FAQ pages are numbered. Generative AI allows for fluid, context-aware conversations.

Consider the difference in experience: 

A static system forces a user to select “Baggage” from a menu. A generative system understands, “My golf clubs didn’t arrive in Denver,” and immediately triggers a specific tracing workflow while empathizing with the frustration. It understands the traveler’s intent — whether it’s an urgent disruption or a loyalty inquiry — without requiring them to speak a “keyword” or match a specific phrase.

2. Global Customer Engagement

Travel is inherently global, but support teams often aren’t. Generative AI bridges this gap by offering 24/7 multilingual support that goes beyond basic translation.

For example, when Quiq partnered with Accor, they deployed an AI agent capable of fluent engagement in English, French, German, Arabic, Spanish (Euro & Latam), Portuguese (Euro & Brazilian), Dutch, and Italian. This didn’t just mean translating words. It meant understanding cultural nuances and context across languages. The result was a support system that felt native to the guest, regardless of where they came from.

3. Proactive vs. Reactive Service

The most significant transformation is the move toward proactive service. Instead of waiting for a customer to call about a delayed connection, generative AI systems can anticipate issues and integrate with flight operations data to message the traveler before they even land: “Your connecting flight was delayed 45 minutes. We’ve rebooked you on the 6:15 flight at no charge, and you’ll receive a complimentary beverage.”

This reduces inbound contact volume by resolving problems before they become complaints.

Read our 2026 State of AI Agents in Travel & Hospitality. Get guide >

Key Benefits of Generative AI in Travel

For customer experience leaders, the value of generative AI is measurable in operational metrics and customer sentiment.

Faster Resolutions at Scale

Speed is the currency of customer service. Generative AI reduces Average Handle Times (AHT) for both common and complex requests.

In the airline industry—where operational complexity is constant—Quiq helped Spirit Airlines implement an agentic AI agent. The result was an automated resolution rate of over 40%, with conversation times that are 16% faster. And by automating routine inquiries, the system freed up human agents to focus on more complex issues.

Personalized Travel Journeys

Personalization has historically been difficult to scale. Amadeus research indicates that 37% of travelers cite personalized recommendations as a key benefit of AI.

Generative AI can ingest a traveler’s history, loyalty status, and current context to tailor every interaction. It doesn’t just suggest “hotels in Paris”. It suggests “boutique hotels in Le Marais near your last stay, available for your dates next week.”

Operational Efficiency & Cost Savings

The operational impact is stark. By deflecting repetitive inquiries — like “what is my baggage allowance?” or “when is breakfast served?” — brands can significantly lower their cost per contact.

The Accor partnership demonstrates this efficiency in action. Their generative AI agent handled a massive volume of guest inquiries, allowing the brand to scale support without linearly scaling headcount.

Improved Customer Satisfaction & Loyalty

There is a misconception that automation kills satisfaction. The data suggests the opposite: good automation builds loyalty.

In the Accor case study, the deployment of a competent generative AI agent didn’t just deflect tickets. It raised Customer Satisfaction (CSAT) scores from 67% to 89%. When guests get fast, accurate answers — even from a machine — they are happier. 

Use Cases for Generative AI in Travel

Booking & Pre-Trip Support

The booking phase is often riddled with anxiety. Is this the right hotel? What if I need to cancel?

Generative AI acts as a concierge. Booking.com’s AI Trip Planner and Expedia’s ChatGPT integration are prime examples of this. They allow travelers to ask open-ended questions like, “I need a family-friendly resort in Bali with a kids’ club,” and receive curated options with deep links to booking.

For Accor, their AI agent drove a tangible business outcome: intent-to-book click-outs doubled. By answering pre-stay questions accurately (“Is the pool heated?”, “Do you have vegan options?”), the AI removed hesitation and drove revenue.

In-Trip Assistance & Disruption Management

This is the high-stakes environment where AI shines. When a flight is cancelled or a hotel room isn’t ready, emotions run high.

Generative AI can handle rebooking during mass disruptions — a scenario that typically crashes call centers. It provides real-time updates and “next-best actions” to thousands of travelers simultaneously, something human teams simply cannot do at scale.

Loyalty, Rewards, & Account Management

Loyalty programs are notoriously complex. Generative AI simplifies them. Instead of a traveler reading a PDF to understand blackout dates, they can simply ask, “Can I use my points for a flight to Tokyo next month?” The AI reviews the specific tier benefits and provides a clear answer, potentially identifying an upsell opportunity in the process.

Post-Trip Support & Retention

The journey doesn’t end at checkout. Generative AI handles the tedious administrative tail of travel — refunds, invoice requests, and lost-and-found reports. By automating these tasks, brands ensure the final touchpoint is efficient, leaving a positive lasting impression that encourages retention.

Challenges of Generative AI in Travel

While the benefits are clear, the risks are real. Customer experience leaders must approach deployment with eyes wide open.

Accuracy & Hallucination Risks

Trust is hard to gain and easy to lose. A major concern with LLMs is “hallucination” — confidently stating false information. In the Amadeus study referenced above, 25% of travelers reported experiencing outdated or inaccurate information from AI.

In travel, a hallucination isn’t just a funny quirk. It’s a stranded passenger. If an AI incorrectly states that a visa isn’t required, the brand is liable. Success requires grounding the AI in trusted, verified knowledge bases and implementing strict guardrails.

Discover how Quiq prevents hallucinations here:

Data Privacy & Security

Travel companies hold a treasure trove of sensitive data: passports, credit cards, and PII. Using public tools like ChatGPT carries risk, so companies in the hospitality industry should be aware of the dangers of feeding guest data into public tools.

Enterprise-grade deployments must ensure that data remains secure and compliant with global regulations like GDPR. The AI solution must act as a vault, not a sieve.

Integration with Existing Systems

An AI that can chat but can’t act is just a toy. For generative AI to provide value, it must integrate deeply with reservation systems (PMS/GDS), CRMs, and loyalty platforms. 

It needs to know who the customer is and what their booking looks like. Without this, the AI creates “islands” of conversation that don’t connect to operations.

Trust & Adoption

There is a delicate balance between human and machine. Travelers need to know when they are speaking to an AI. Transparency is non-negotiable. 

Furthermore, the goal is to enhance human agents, not replace them. The most successful models use AI to handle the routine, handing off to humans with full context when empathy or complex judgment is required.

How Leading Brands Are Using Generative AI in Travel

The most forward-thinking brands are moving beyond simple Q&A bots. They are adopting a new framework: Agentic AI.

Agentic AI vs. Generative AI

To understand the future of travel customer experience, you must distinguish between two concepts:

  • Generative AI focuses on understanding and responding. It uses large language models to interpret intent and generate human-like responses, recommendations, and summaries. It excels at making conversations feel natural.
  • Agentic AI goes a step further by taking action. It combines generative AI with decision-making, tools, and workflows. Specialized AI agents can reason, execute tasks, collaborate with other agents or humans, and resolve issues end-to-end — not just talk about them.

Discover details on the differences in our guide: LLM vs Generative AI vs Agentic AI: What’s the Difference?

Where Agentic AI Fits In

Agentic AI represents the shift from “conversation” to “resolution.” In a travel context, a generative AI agent might tell you how to change a flight. An agentic AI agent will change the flight for you, issue the new ticket, and charge the difference to your card.

It involves orchestration between AI agents and live support teams. An AI agent might handle the rebooking, while a human agent steps in to handle the customer’s anxiety about missing a wedding.

How Quiq Enables Agentic AI in Travel

Quiq’s platform is built on a multi-agent architecture purpose-built for customer experience in travel and hospitality. Instead of “black box” A, we offer:

  • Secure Deployment: We verify before launch. Every agent is tested and governed.
  • Seamless Handoff: When a situation escalates, the human agent steps in with the full conversation history. The context is continuous.
  • Transparency & Real-Time Insights: We show our work. You see exactly how decisions are made, giving you the confidence to scale.

Learn more about what sets Quiq’s agentic AI for travel and hospitality apart from other vendors.

A New Era in Travel

Generative AI is redefining how travel brands engage, support, and retain travelers. We are moving away from the era of static menus and long hold times into an age of instant, personalized resolution.

However, the biggest gains won’t come from standalone chatbots that simply chat nicely. They will come from agentic AI — systems that can take action and resolve problems. Success depends on trust, tight integration with your data, and intelligent orchestration between humans and machines.

Early adopters like Accor and Spirit Airlines are already setting the new standard. Those who invest in agentic AI agents today will be the ones who define the traveler experience of tomorrow.

Frequently Asked Questions (FAQs)

What is generative AI in the travel industry?

Generative AI in travel uses large language models to create human-like responses, recommendations, and actions for travelers, enabling personalized booking support, real-time assistance, and automated customer service across channels.

How is generative AI different from travel chatbots?

Traditional travel chatbots follow predefined rules and scripts, while generative AI understands intent and adapts responses in real time. Going further, agentic AI can handle complex, multi-step travel requests like rebooking flights or resolving disruptions.

How does generative AI improve the travel customer experience?

Generative AI improves travel customer experience by delivering faster responses, personalized recommendations, 24/7 support, and consistent service across channels, especially during high-stress moments like delays or cancellations.

What is agentic AI, and why does it matter for travel?

Agentic AI refers to AI systems made up of specialized agents that can reason, take action, and collaborate. In travel, agentic AI enables end-to-end issue resolution, such as rebooking, notifications, and follow-ups, without relying solely on human agents.

What are the most common use cases for generative AI in travel?

Common use cases include booking assistance, itinerary changes, disruption management, loyalty and rewards support, multilingual customer service, and proactive travel notifications before issues escalate.

8 Customer Retention Strategies That Work

Posted on Lauren Winder

Key Takeaways

  • Retention drives growth: Keeping existing customers is far more cost effective than acquiring new ones, and loyal customers spend nearly 70% more.
  • A loyal customer base builds advocacy: Repeat customers are more likely to recommend your brand and provide valuable feedback through surveys like CSAT and NPS.
  • Track the right metrics: Monitoring Customer Retention Rate (CRR) alongside Cost per Acquisition (CPA) gives you a clearer picture of profitability and long-term success.
  • Effective strategies matter: From building relationships on shared values to empowering customer service teams, offering omnichannel support, personalizing communications, and rewarding loyalty can significantly improve retention.
  • Feedback fuels improvement: Surveys, complaints, and direct customer input are opportunities to make meaningful adjustments that keep customers engaged.
  • Sustainable success: Strong customer retention doesn’t just stabilize revenue, it creates lasting relationships that separate enduring businesses from short-lived ones.

Recruiting new customers costs seven to nine times as much as it does to keep existing customers happy. Besides the obvious foregone revenue, dissatisfied customers are not going to recommend you to the people they know, and they might even go out of their way to tell their friends and family about their negative experiences. This kind of fallout can have long-term consequences for customer retention.

For all these reasons, it’s imperative to retain customers – and one of the best ways of doing that is to implement an effective customer retention strategy.

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

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

What is customer retention?

Customer retention refers to any effort to keep a customer satisfied enough with you to keep them using your products or services.

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

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

Why is it important to retain customers?

What makes customer retention important is that acquiring more customers costs more than keeping old ones. It’s also worth pointing out that existing customer accounts spend an average of almost 70% more than new customers.

Even better, customer value goes up with loyalty. Think about it: Loyal customers are far more likely to share their experiences with their social media circles, build a strong customer community, and drive repeat business. The more loyal the customer, the higher the customer lifetime value (CLV).

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

Calculating your customer retention rate

To measure customer retention, start with determining your current customer retention rate (CRR).

The CRR measures how many customers are retained over a particular period (usually one year) and allows you to gauge the long-term profitability of your marketing and sales efforts. It’s also a key metric for evaluating the success of your overall customer retention strategies. The math is pretty straightforward: we just need to divide your number of customers who made repeat purchases by the total number of active customers over the same time period.

So, if we have 50 return customers and 200 active customers for the year 2026, our CRR would be 25%.

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

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

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

8 Effective customer retention strategies

Use these robust customer retention strategies to meet customer expectations and keep more current customers.

1. Strong values strengthen customer relationships

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

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

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

2. Empower your customer experience team

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

To achieve this, focus on comprehensive training programs that emphasize empathy, active listening, and effective problem-solving. For instance, role-playing scenarios can prepare agents to handle customer feedback with confidence and care. Implementing regular workshops and continuous learning can help your team stay up to date on the latest trends and best practices.

Many organizations are now leveraging AI-powered roleplay platforms such as Kendo AI to simulate realistic customer conversations, helping teams sharpen their communication skills and deliver more consistent, high-quality experiences that directly impact customer retention.

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

3. Make yourself easy to work with

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

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

If your agents are sympathetic and your information is easy to navigate, a refund needn’t be the end of a professional relationship. More broadly, it pays to invest the time required to make your content easy to follow and your agents easy to contact. Frustration at this level can erode trust and severely impact your customer retention programs.

One often overlooked factor in customer retention is experience consistency across touchpoints. When interfaces, flows, or messaging feel disjointed, even strong products can become frustrating to use. Superside’s research into customer experience design shows that consistent UI patterns, predictable interactions, and clear visual hierarchy reduce friction and build trust over time, especially as products scale and teams grow.

4. Offer omnichannel customer support

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

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

  • It enables you to respond very quickly to incoming queries, which can be a huge advantage for reasons already discussed above.
  • By integrating with technology like large language models, you can personalize your replies at scale and even offer services like real-time translation.
  • Faster resolution means you’ll increase customer retention and enhance customer loyalty.

5. Reply ASAP in customer interactions

Few things will result in customers lost than taking too long to respond to an issue. As a general rule, people have never enjoyed waiting around. It tends to dampen customer experience.

For this reason, you want to reply to issues as quickly as possible so they remain loyal.

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

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

6. Personalize your communications

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

With the power of agentic AI, platforms like Quiq are elevating personalized experiences to unprecedented levels. Agentic AI enables AI agents to customize customer interactions and improve their customer journey based on past preferences and interactions.

Once upon a time, only human agents could analyze a customer’s profile and tailor their responses with relevant information. Now, a well-optimized generative language model can achieve this almost instantaneously – and on a much larger scale – translating into greater customer success. You can even re-engage customers on a 1:1 scale with smart AI use.

7. Implement customer feedback into your products or services

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

  • They’re a great way to understand customer behavior and your customer lifecycle.
  • Encouraging customers to give honest and open feedback makes your brand appear open to customer concerns and are willing to do whatever it takes to make them happy.
  • These customers will be more likely to give both positive and negative feedback in the future if they see changes implemented based on their pain points.
  • Survey feedback can result in positive adjustments to your processes, products or services.
  • It can help ensure you’re pursuing the right targeting strategy
  • They can help you identify dissatisfied customers before they leave, and create campaigns or offers to win them back.
  • By the same token, surveys help you better understand why satisfied customers are happy.

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

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

8. Reward customer engagement and loyalty

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

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

Loyalty programs can also be a super effective tactic in your customer retention plan. Just look at how Starbucks has built brand loyalty and reduced customer churn with its star program. There’s a cost savings aspect to it, gamification of a customer education program, and a whole customer experience within the app. Consider how you might drive similar business growth while boosting customer retention metrics with a loyalty program of your own.

Building customer relationships

Customers are the foundation of any business. But it’s not enough to just get customers; you must also ensure that you invest in improving customer retention if you want to improve your overall customer lifetime value. Today, customer retention is what separates sustainable growth from short-lived success. You can do this by using the strategies presented in this post to build world-class relationships with your customers.

Want more effective customer retention strategies? Check out our free guide to uncover the 4 major silos hurting your customers, agents, and business. Get actionable tips on how to shatter them and boost your customer retention rates with agentic AI.tionable tips on how to shatter them and boost your customer retention rates with agentic AI.

Frequently Asked Questions (FAQs)

How do I know if my customer retention strategy is working?

Start by tracking your Customer Retention Rate (CRR), Customer Satisfaction Score (CSAT), and Net Promoter Score (NPS). If these metrics are trending upward and repeat purchase or renewal rates are increasing, your strategy is on the right track for business growth. You can also measure reductions in churn and improved lifetime value (CLV).

Which customer retention strategy delivers the fastest results?

While results vary by industry, prioritizing quick response times and omnichannel support often yields immediate impact. Customers notice when you’re easy to reach and proactive in resolving issues – on the channels they prefer using to contact you. Acknowledging their pain points promptly can quickly build trust and prevent customer churn.

How can AI improve customer retention?

AI helps build personalizes experiences at scale. With agentic AI, CX leaders can deliver hyper-relevant communications, anticipate customer needs, and provide faster resolutions through automation across messaging channels (including social media). This frees up human agents to focus on high-value, relationship-building conversations.

Should small businesses focus on retention as much as large enterprises?

Yes. In fact, small businesses often rely more heavily on repeat business and word-of-mouth referrals. Retention strategies across the customer journey, such as personalized communication, loyalty rewards, and attentive customer service, keeps customers engaged.