In today’s world of conversational commerce, chatbots are far more than just a nice-to-have feature. They guide customers through the purchasing process, answer questions in real-time, and can significantly boost conversion rates. Almost like having a personal sales assistant available around the clock. Yet the reality is often very different. Let’s take an honest look at the current landscape of e-commerce websites: Many online shops either don’t use AI-driven chatbots at all or, even more frustratingly, have implemented chatbots that feel like a hastily applied patch. These bots operate in their own little bubble, completely disconnected from the rest of the website.
The result? A fragmented user experience that frustrates customers rather than helping them.
What’s Going Wrong with Conventional Online Shop Chatbots?
Granted, many of these chatbots can provide competent advice. They answer questions about product features, make recommendations, and assist with general enquiries. But their fundamental weaknesses become apparent when it comes to real interactions on the website:
No Cart Integration: The bot cannot see what is already in the shopping cart, nor can it add or remove items. Customers must leave the chat and navigate manually.
No Filter Control: If a customer wants to narrow product selections by price, brand, or features, the bot cannot set category filters. Users must navigate manually. Conversely, the bot is unaware of filters already applied and cannot use this information to improve its recommendations.
Isolated Product Display: Product recommendations typically appear as links to product pages with images, rather than in the familiar, visually appealing website layout with all relevant information at a glance. Customers must click each link to compare products properly.
Rigid Conversation Flow: Often, there is no natural advisory dialogue; it feels more like following a decision tree.
These bots were never designed as an integral part of the user experience, and this is the core problem. While a sales assistant in a physical store intuitively understands what constitutes good advice, online chatbots lack this natural interactive capability.
What We Can Learn from Brick-and-Mortar Retail
A competent store assistant does three things simultaneously: they listen, act, and adapt. Imagine you are looking for a coffee machine. The salesperson asks targeted questions: “For how many people do you usually brew coffee?”, “Do you mainly drink espresso or filter coffee as well?”, “How important is ease of use in the morning?”
As you answer, they already guide you to suitable machines, explain the differences, and let you try out various models. They react to your body language, notice hesitation, and adjust recommendations accordingly.
The result: you are not overwhelmed by a product catalogue, but are guided towards the 2–3 products that genuinely suit your needs. This type of advisory leads to more satisfied customers and fewer returns.
This highlights the economic challenge for online retailers: without intuitive guidance, the chances of returns significantly increase. In Germany, for example, an EHI study reports that 65% of online fashion and accessories retailers experience a return rate of over 25%. This incurs substantial costs for logistics, handling, and often product loss.
A large portion of these returns stems from products that do not meet the customer’s actual needs – a problem that a good in-store sales assistant would have prevented through targeted questioning.
Here lies the greatest potential for intelligent advisory systems: more precise recommendations can not only reduce returns but also increase customer satisfaction and the average order value.
The Solution: Agentic AI with Full Integration
We asked ourselves: what if a chatbot could not only talk but also act? What if it were a true digital assistant that could navigate the website on behalf of the customer?
Our answer is an Agentic AI concept that goes far beyond traditional chatbots. Rather than operating in isolation, our solution has access to various tools and can actively manipulate the visible state of the website.
What Sets Our Concept Apart
Intelligent Cart Management: Our solution can view the current cart, check for consistency, and directly add or remove items. Targeted feedback helps prevent poor purchases. For example: “You have a filter machine and a tamper in your cart. This accessory doesn’t match the selected machine, as tamping isn’t required for filter coffee.”
Dynamic Website Control: The bot sets filters, navigates categories, and adjusts the product display in real time to match customer preferences, all while the customer remains in the chat.
RAG-Enhanced Advice: Using Retrieval-Augmented Generation, the model accesses the complete product database to provide precise, up-to-date information.
Natural Conversation Flow: Instead of rigid decision trees, the bot conducts a genuine advisory dialogue: “You mentioned you have little time in the mornings, so a fully automatic machine would be more practical than a traditional espresso machine.”
Smart Recommendations: Suggestions are seamlessly integrated into the advisory process: “Customers with similar requirements also bought this grinder; it pairs perfectly with your machine.” Or, “Hello, Mr. Müller, would you like to reorder the descaler?”
Seamless User Experience: Product recommendations appear both in the chat and in the familiar website layout. Customers interact via chat or by clicking on the site, while the bot always has full context to provide effective advice.
Prominent Chat Placement: A smart sales assistant shouldn’t be hidden as a small bubble in the corner. It deserves a central position with enough space for natural conversation and should be visible as soon as the page loads.
Case Study: “Coffee Companion” in a Coffee Machine Shop
To bring this vision to life, we developed a demo: our Coffee Companion bot in a simulated coffee machine shop. This demo illustrates how our concept enhances the shopping experience and manages customer interactions in real-time.
Imagine a customer asks: “I’m looking for a fully automatic machine.” Instead of just posting links, our Coffee Companion does the following:
- Automatically sets the relevant filters (Category: Coffee Machines → Type: Fully Automatic).
- Updates the website in real time to display suitable products.
- Asks which features the customer wants.
- Receives the input: “I want variety in coffee types,” which conflicts with the current filter.
- Queries whether a fully automatic machine is truly the best choice, given the usually large bean container, which makes frequent coffee type changes impractical, and suggests considering capsule or pod machines.
- The customer and bot exchange information throughout the dialogue, narrowing down the relevant product selection to a few concrete options, which can then be added to the cart via chat or a click.
- After a machine is added to the cart, the virtual sales assistant recommends compatible accessories.
The result? A shopping experience that feels natural and intuitive, as if a competent sales assistant were at your side, advising and acting simultaneously.
From Problem to Practical Solution
This solution is not just a theory; it is already a reality. Rather than endless discussion, we simply tested it. Our Coffee Companion is the result: a working proof of concept that showcases today’s technical possibilities.
Our focus is on seamlessly integrating AI into existing customer solutions. This approach applies not only to online shops but to workflow optimisation in general. It can save time, improve quality, or, as in this case, measurably enhance the customer experience. Fewer returns, higher satisfaction, increased revenue – these are the metrics that matter.
The future of e-commerce lies in intelligent AI integration, not isolated chatbot islands.
Would you like to learn how GenAI can be seamlessly integrated into your existing application? Get in touch for an informal chat.
