Conversational AI for eCommerce: How to Unlock Its Real Value

You might think that conversational commerce is yet another fad. But the concept has been around for a decade now: in fact, Chris Medina wrote about it in his Medium blog in 2015.

At the time, however, conversational AI was still in its early stages of development. Chatbots relied on predefined menus and flows. Results often contained irrelevant items. Search was limited to basic keyword matching.

Today’s conversational AI for eCommerce largely overcame these issues. AI models are far better at understanding and processing natural language, both written and spoken. Generative AI makes queries like “Recommend something to wear to a wedding” not just possible but effective, too. Product catalogs, customer data, and support systems help personalize results.

The result? Shoppers find products that hit just right — and instantly get answers to their questions. Your customer service team, in turn, can focus on interactions that genuinely require a human touch.

But, of course, not all eCommerce conversational AI is created equal — and how you implement it can make or break its added value.

What Is Conversational AI in eCommerce?

Conversational AI is a technology that enables users to interact with chatbots and virtual assistants in natural language — and get human-like responses. With it, users don’t have to overthink the phrasing or look for the right keywords. They can just type in or speak their question as if they’re talking to another person.

In eCommerce, conversational AI is the technology that lets shoppers search and compare products or ask questions about store policies in natural language. It can also guide them through checkout and handle simple requests like delivery status updates.

Conversational eCommerce bots have come a long way over the past decade:

01.

Rule-based chatbots. These janky systems could work only with precise queries or carefully designed menus and conversation branches.

02.

NLP-powered systems. They gave way to systems powered by natural language processing (NLP), an AI technology focused on bridging the gap between human language and machine-understandable commands. They were better at understanding context, sentiment, and semantic meaning — but still far from perfect.

03.

Large language models (LLMs). Today’s LLMs power assistants that can “remember” conversations, adapt to user preferences, manage the back-and-forth, and decide on the next best action.

How Conversational AI Works in eCommerce

Wondering what happens under the hood when a user asks a conversational eCommerce chatbot a question? Here’s a step-by-step breakdown:

• The user sends their request. They open the chatbot on the website, in the app, or on social media and type in their prompt. For example: “I’m looking for an easy-to-transport dog bed for my border collie.”
• The system processes the request. A modern conversational AI chatbot solution for eCommerce uses an LLM to understand the user’s request. It goes well beyond semantic meaning: the model also considers the underlying intent and context. It then determines which data it needs to generate a relevant response.
• It gets relevant data from knowledge sources. A RAG layer enables an AI model to get up-to-date information stored in databases or other systems (PIM, ERP, CRM, store policies, etc.). For our dog bed example, the AI model can get dog bed dimensions from the PIM and SKU availability from the inventory management system.
• The AI model returns a response or triggers an action. Based on that data, the system ranks products, identifies the most relevant products, and generates a response. For example, it can generate a comparison table for the three most suitable dog beds for our customer. In some cases, you can also set up permissions for the system to trigger certain actions (e.g., add to cart or submit a refund request).

Key Use Cases of Conversational AI in eCommerce

Conversational AI can do a lot more than replace the traditional search bar. It can handle routine customer support inquiries, proactively upsell and cross-sell, and even recover abandoned carts. Here’s how.

Product Discovery and Recommendations

Some shoppers arrive with a clear idea of what they want to buy. Others are still in the exploration phase. Conversational AI chatbots can help the latter discover products and compare items, all while taking their unique needs into account.

Let’s say a customer is looking for headphones for their commute, and their budget is $150. A traditional discovery process would look something like this:

• Look up what makes headphones good for commutes (e.g., buds vs. over-the-ear).
• Head to the online store.
• Type “headphones buds bluetooth” into the search bar.
• Use filters to narrow it down (e.g., price range, noise-canceling features).
• Manually compare products based on reviews, other specs, etc.

Conversational AI for eCommerce product discovery is times easier:

• Open the chatbot and type in, “What are the best headphones for long commutes under $150?”
• Get a response that describes what specs matter the most, along with a comparison table for specific SKUs with specs, photos, and price.

Solutions like OpenAI’s ChatGPT can go a step further and recommend specific products that match the items in the photos users send together with a prompt.

This kind of AI personalization can make shopping less overwhelming, foster trust among shoppers, and make them feel understood. The result? Higher engagement, conversions, and average order value (AOV).

Customer Support Automation

“My order was supposed to arrive two days ago, but it’s still not here. Where is it?” “Can I reschedule delivery for next week?” “The product I received is smaller than I expected. I want to return it.” “The order arrived damaged. Can I get a refund?”

These are the routine questions your customer service team spends their time handling. But they don’t require out-of-the-box thinking or human judgment. If the order hasn’t been delivered yet, a customer support rep needs to check live order tracking data. An eCommerce AI chatbot can access the same data via shipping carrier API.

That’s not all. Conversational AI for customer support can also:

• Answer questions about shipping costs or payments
• Guide consumers through free delivery, return, or refund policies
• Submit basic service requests (e.g., initiating refunds)
• Collect feedback from customers
• Route callers to live agents via interactive voice response (IVR) systems
• Provide real-time suggestions during calls based on live transcriptions

For example, Motel Rocks, a vintage fashion brand with an online store, used Zendesk AI to power self-service customer support options. This eCommerce automation AI reduced incoming tickets by 50%, all while boosting the store’s CSAT by 9.44%.

Order Tracking and Post-Purchase Support

In a similar vein, conversational AI can initiate contact with customers after they’ve placed an order. For example, an AI chatbot for eCommerce can:

• Inform customers about any changes to the order or its planned delivery time
• Pull the order status from the order management system to provide timely updates before it’s shipped
• Use live data from shipping carriers to send personalized messages whenever the delivery status changes

As for post-order support, a well-designed AI system is more than an LLM-powered chatbot. It can handle the whole range of requests beyond answering policy questions, such as:

• Pulling order information from history and verifying details
• Analyzing photos to assess damaged items
• Checking eligibility for refunds and returns
• Initiating refund and return requests
• Collecting customer feedback
• Answering questions about product use post-purchase

Cart Recovery and Purchase Assistance

Shoppers abandon carts for many reasons. Some don’t see shipping costs or delivery times right away. Some doubt that the product is right for their needs. Some are hazy on the return or refund policy.

Traditional cart recovery relies on simple triggers. If a cursor moves to the corner, the page displays a pop-up with a discount code. If the user closes the tab, they receive an email.

A generative AI eCommerce solution can identify what’s making the shopper hesitate before they leave. Then, it can jump in and remove that blocker:

• Before AI: The user receives a generic “Don’t forget your cart!” email.
• With AI: They see a chatbot message as they’re browsing; it reads, “I noticed you were interested in Fluffy Cat Bed. Would you like to know more about it?”

Keep in mind: missing product information is the leading cause behind drop-offs, according to Rep AI.

The same report also highlights that availability is the second most common reason why shoppers drop off. Conversational AI can automatically notify customers if the stock is low or suggest pre-ordering or alternative products if the item is out of stock.

For example, Defender, a marine retailer, turned to Loomi AI to add a shopping assistant to its online store. It engages customers throughout their journey, including at that final stage before conversion. The result? A nearly 3% increase in add-to-cart rate.

Cross-Selling and Upselling

What if your AI shopping assistant were also your most effective salesperson? This isn’t yet another sales pitch; it’s an actual capability of conversational AI in eCommerce.

An AI system can analyze customer behavior in real time and understand how it fits within historical context (previous purchases, browsing history, demographic data). As it identifies patterns in that data, it can pinpoint upselling and cross-selling opportunities on the fly.

And the best part? An AI chatbot can instantly act on these signals and nudge the customer toward other products — without coming across as pushy. For example, a conversational AI solution can:

• Suggest premium versions of the product already in the cart
• Recommend complementary items based on the user’s history
• Offer bundles to customers who consistently take advantage of good deals

Personalized and well-timed, these nudges can increase the average order value by as much as 20%. Victoria Beckham is one example here: its AOV increased by a fifth after the brand introduced a multi-agent AI system that serves as a personal shopping assistant. The assistant offers styling advice — and recommends whole looks instead of single items.

5 Benefits of Conversational AI for eCommerce Businesses

Consumers are already using AI for shopping research. So, embedding conversational AI is a way to respond to the changes in how people shop online. But it’s not the only benefit of conversational AI for eCommerce businesses:

• Higher conversion rates. Customers who get quick answers and more relevant suggestions are more likely to buy. Don’t take our word for it: Stord found that 20% of consumers are more likely to convert when AI recommends a product.

• Enhanced customer experience. Finding a needle in a product catalog haystack creates a massive cognitive load, even with all the filters imaginable. Conversational AI eliminates friction by reducing that cognitive load.

• Lower customer support costs. A conversational AI chatbot can take care of routine requests and questions 24/7, no human required. The result? Customer service teams can focus on the queries that can’t be automated away — and you can cut the cost per call in half.

• Around-the-clock availability. AI-powered shopping assistants are online 24/7, ready to answer any question on store policy, product availability, or order status. So, customers don’t need to wait until your contact center opens up.

• More relevant product suggestions. What the user says is important for understanding their needs, but what they don’t say can be just as important. Their browsing behavior, context, previous chatbot interactions, and long-term preferences can all point the system to truly relevant recommendations.

Ready to reap these benefits of conversational AI? You’ll need a development partner that knows their way around LLMs, RAG, integrations, and compliance. Integrio could be a good match — explore our conversational AI services to learn more.

5 Challenges of Implementing Conversational AI in eCommerce

Conversational AI isn’t a plugin you can install in a couple of clicks and call it a day. Without proper data, it might spout nonsense in a very convincing way. Without real-time integrations, it might recount stale information.

All in all, implementing conversational AI isn’t a cakewalk. You’ll have to face — and overcome — these five challenges:

• 

  Data quality and freshness. In AI, data is king. That data has to be structured, continuously vetted for quality, and fresh. Otherwise, your chatbot might provide outdated product specs or pricing — or rely on old policies.

• 

  Hallucinations and inaccurate responses. Quality, fresh data is the first step to reducing the risk of both, but it doesn’t eliminate that risk altogether. Inaccurate specs, pricing, availability, or return conditions can undermine customer experiences — or even leave you liable (just look up Air Canada’s AI chatbot case).

• 

  Integration complexity. Your AI system will need fresh data from the CRM, knowledge base, ticketing systems, PIM, order management, and inventory systems. Legacy tech and interoperability issues can make integration a nightmare — and that’s where many projects spiral out of control.

• 

  Conversational UX. Even the most intelligent AI system won’t get on the user’s good side if its responses are too long, convoluted, or confusing. The ideal conversational UX equals short, straight-to-the-point answers — and quick links to products or checkout.

• 

  Privacy and security. Customer data — on-site behavior, purchase history, payment information — is sensitive data. Supplying it to AI systems inherently carries risk, especially if you use a third-party solution. Keep in mind that you may be subject to privacy regulations like the GDPR, CCPA, and PIPEDA, too.

How to Implement Conversational AI (& Not Regret It)

By this point, we hope you’re convinced: implementing conversational AI isn’t something to take lightly. But fret not. We used our experience with conversational AI chatbot development for eCommerce to prepare this mini-guide for you.

• Understand the why before the how. What do you hope to achieve with conversational AI? Increasing AOV with better product discovery? Automating customer support or order tracking? Improving cart recovery? Define your objectives, choose use cases accordingly, and pick the KPIs to track your initiative’s success.
• Map customer journeys and conversation flows. Put yourself into the user’s shoes. How will they get from point A to point B in this or that scenario? Understand how the conversation may start and what questions users may ask. Map all the data the chatbot may need to answer them, and define when human agents should intervene, too.
• Prepare data sources. Chatbots typically need access to the product catalog, pricing, availability, inventory, delivery, return and refund policies, and FAQs. Inventory where all that data is stored and evaluate its readiness for AI. Clean, structure, and organize data where necessary.
• Design the solution architecture. How will the LLM, RAG layer, eCommerce data, backend systems, and customer-facing channels interact? That’s the million-dollar question in architecture design. Remember to outline the necessary guardrails to ensure output accuracy and reliability, too.
• Build, integrate, and test in iterations. The team builds the solution and integrates it with the eCommerce platform, CRM, and other systems. Testing is done in parallel; it validates responses, conversation flows, integrations, and edge cases.
• Validate the complete solution. This is the final battery of tests that checks end-to-end conversation flows, response accuracy, integrations, privacy, and security. Make sure your solution triggers handoff when necessary and properly handles complex or unexpected requests, too.
• Launch, monitor, and improve. Now, it’s time to keep an eye on the system to see how it handles real-world conversations, catch issues, and solve them. Remember to track the solution’s impact on conversions, support load, and customer experience, too.

Conclusion

In 2015, conversational commerce was a vision, not the reality. A decade later, conversational AI has made it possible — and awoken consumers’ appetite for it.

The eCommerce businesses that can satiate that appetite will see higher engagement, conversions, and revenue. The ones that don’t may lose their competitive edge for good.

Looking to transform your store’s shopping experience with conversational AI? We’ll make sure it’s secure, accurate, convenient, and well-prepared for real-world interactions. Learn more about what makes our conversational AI services so effective.

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