Anyone who shops online knows the feeling: hundreds of products, similar descriptions, and a lack of clarity. What a sales associate in a brick-and-mortar store can resolve with a few targeted questions is often left up to the customer to figure out on their own in an online store. An online product advisor bridges this gap: It helps customers choose products, reduces abandoned carts, and lowers the return rate. In this article, you’ll learn what makes a good online product advisor and why digital advice is more important today than ever before.
Here's what you can expect to find in this blog article:
Why an online product advisor is useful
Lack of guidance: The gap in the digital shopping experience
Changing search behavior: From keywords to natural queries
Online product advisor vs. search function
From Guided Selling to AI Shopping Assistant
The AI Shopping Assistant as a Modern Online Product Advisor
What Makes a Modern Online Product Advisor
What digital consulting with an AI Shopping Assistant actually looks like
When it’s worth using an AI Shopping Assistant
How an AI Shopping Assistant works
How the integration of the online product advisor works
Conclusion: Expert advice, just like in brick-and-mortar stores, is also available online
Are you wondering why digital consulting is so important today? Two factors play a key role here, and both have a significant impact on your customers’ behavior and expectations.
Online shopping often lacks what is taken for granted in brick-and-mortar stores—someone to answer questions, address concerns, and offer recommendations. Instead, customers are faced with endless product lists and similar descriptions. The result: uncertainty, abandoned carts, and returns, because without assistance, customers often don’t buy the product that’s truly right for them.
The way people search for products online has changed: Whereas users used to enter short keywords into the search bar, such as “men’s running shoes” or “15-inch laptop,” today they increasingly type in complete sentences and ask specific questions: “Which running shoes are suitable for a beginner?” or “I need a laptop for graphic design under 1,500 euros.”
No wonder: voice assistants, AI chatbots, and generative search engines have reshaped user behavior. Today, customers expect digital systems to understand their context —not just match individual terms. They don’t want to scroll through endless product lists; they want an answer and product recommendations tailored to their needs.
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These two factors make it clear: Search behavior has changed, user needs have become more diverse, and traditional search functions without natural language capabilities are increasingly reaching their limits. What customers need today is guidance that adapts flexibly to their behavior and individual needs. This is precisely where the strength of an online product advisor lies.
A valid question at this point might be: Why does my online store need an online product advisor if I already have a good search function? The answer is actually quite simple: Not all customers come to your store with the same expectations. Those who already know what they’re looking for—for example, “Nike running shoes for men, size 44”—can easily find what they need using the search function. Filters help narrow down the results, and the on-site search delivers relevant hits. This works well as long as the customer knows what they need and has prior knowledge.
The situation is different when someone has a need but doesn’t yet have a specific product in mind. If you don’t know which running shoe suits your terrain and running style, you’re left with just a list of results. This is exactly where the online product advisor comes in: it identifies your needs, asks the right questions, and guides you to the right product.
Both approaches address different stages of the digital customer journey: Search is most effective during the awareness stage and fulfills the need for guidance —ideal for users who already know what they’re looking for. An online product advisor, on the other hand, provides support during the consideration stage, when customers want to understand and compare their options and prepare to make a decision, thereby fulfilling the need for advice.
Search and consultation address different needs throughout the customer journey (Source: Author’s own illustration)
Search and advisory functions do not have to be mutually exclusive. Modern e-commerce solutionscombine both approaches at a single entry point—the system automatically determines whether a simple keyword search is sufficient or whether the query must first be interpreted by an LLM in order to provide a relevant, advisory response.
Online product advisors aren't a new phenomenon. For a long time, guided selling was the standard approach to digital shopping advice. It guided customers step by step toward the right product selection using structured questions and visual filters.
With the emergence of large language models (LLMs), the possibilities for digital consulting have expanded significantly. Instead of predefined click paths, the focus is now on a natural, conversation-based user experience —similar to a face-to-face consultation—as embodied by modern solutions such as the AI Shopping Assistant. You can learn more about this development in our blog post on Guided Selling Software 2.0.
The core of the process has remained the same: helping customers choose products, alleviating uncertainty, and guiding them toward the right purchasing decision. What has changed is the way this is done.
This shift is part of a broader trend: conversational commerce —the idea that purchasing processes are increasingly shaped by genuine conversations between customers and retailers. Learn more in our blog post on conversational commerce.
TheAI Shopping Assistant is the modern solution to this trend: it combines the strengths of traditional advisory systems with thecapabilities of cutting-edge AI technologies, taking digital consulting to a whole new level.
In the following sections, we’ll take a closer look at this form of digital counseling.
A modern online product advisor—such as an AI shopping assistant—operates through dialogue. Customers type in their questions using natural language; the assistant understands the context, asks follow-up questions if necessary, and presents relevant products. No rigid click path, no predefined sequence of questions—just a real conversation.
This offers concrete benefits:
The AI Shopping Assistant is suitable for a variety of online store environments, regardless of industry or product range. The following two examples illustrate how digital assistance can work in practice in different contexts.
A customer writes in a fashion store’s chat: “Outdoor summer wedding, I sweat easily, nothing see-through, budget €150.” The AI Shopping Assistant understands the request and all the criteria mentioned, specifically searches for light, breathable, and elegant summer dresses within the desired price range, and presents them in a clear, personalized list of results. This turns a vague idea into a perfectly tailored selection in seconds—without any lengthy filtering or scrolling.
The AI Shopping Assistant understands complex, personalized queries, provides relevant product recommendations, and asks helpful follow-up questions (Source: Own illustration)
A customer asks an online pharmacy: “What should I pack in my first-aid kit for a camping trip to Corsica?” The AI Shopping Assistant thinks along with the customer: It recognizes that Corsica means plenty of sun, mosquitoes, and potential gastrointestinal risks, and specifically asks whether any children are traveling along. This makes the recommendations even more precise: From sunscreen and mosquito repellent to a compact first-aid kit, the customer finds everything they need—without having to think of everything themselves.
The AI Shopping Assistant provides context-based product recommendations for a first-aid kit in response to a natural-language search query (Source: Own illustration)
Such scenarios can be applied to many industries, ranging from electronics and furniture to DIY, toys, and B2B.
An AI shopping assistant is generally a useful addition to any online store. It is particularly effective in the following situations:
You can track information on return rates and abandoned carts through your store platform or Google Analytics, for example.
An AI shopping assistant integrates seamlessly into the familiar shopping environment without disrupting user behavior. Communication takes place in natural language. The assistant understands complex queries, recognizes colloquial language and typos, and adapts to the user’s speaking style.
Behind the scenes, an AI engine processes queries and combines the power of large language models (LLMs) with store-specific data such as your product catalog, user interactions, and existing domain knowledge.
In addition, the AI Shopping Assistant offers various features that further simplify the consultation process, such as:
You can add the products you find directly to your cart from the results list—without having to switch screens or lose your place.
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Integrating an AI shopping assistant is easier than many people expect and can be done in two simple steps:
The foundation for the recommendations is your product catalog, typically in XML or CSV format. It’s important to note that the higher the quality and the more complete the product data, the better the quality of the recommendations. This is because the assistant can only use a product’s relevant attributes to generate targeted recommendations if those attributes are accurately maintained. When you use the AI Shopping Assistant, the product feed is automatically expanded and enriched, which significantly reduces your manual effort.
The product catalog is supplemented by data on your online store customers’ click and purchase behavior, which is collected via a tracking code in compliance with data protection regulations. This combination creates a store-specific knowledge base that serves as the foundation for truly personalized recommendations.
The intelligent knowledge base links product and user data with large language models (LLMs) and algorithms to enable digital consulting (Source: Company illustration)
In the second step, you’ll receive a div element to serve as a placeholder for the AI Shopping Assistant’s front end. Simply integrate it into your shop system at the desired location, and your customers will immediately have access to personalized, AI-powered advice, seamlessly embedded within your shop’s familiar customer journey.
An online product advisor bridges the advisory gap that has always existed in e-commerce. Digital advice in the form of an online product advisor can now be easily integrated into any online store—across all categories, around the clock, and so naturally that it feels like a real conversation to the customer. Those who offer this experience to their customers create genuine confidence in purchasing while also benefiting from fewer abandoned carts and returns.
The search function works well when customers already know what they want. An online product advisor helps customers who have a specific need but haven’t yet identified a particular product. Instead of matching keywords, it determines the customer’s needs through targeted questions or a dialogue, thereby guiding them to the right product selection.
For a long time, guided selling was the standard approach to digital product advice: customers were guided step by step through the product selection process using predefined questions and visual filters. Modern online product advisors are dialogue-based. Customers describe their needs in their own words and receive tailored recommendations—just like in a real consultation.
In general, an online product advisor is suitable for any online store, regardless of industry. It is particularly effective in sectors where products require explanation (e.g., electronics, sports, health) or where purchasing decisions are heavily influenced by personal preferences (e.g., fashion, home furnishings, beauty). Stores with a large or complex product range also benefit significantly.
An AI-powered product advisor is based on large language models (LLMs) that understand natural language and recognize context. In a store, this means that customers describe their needs in their own words, and the assistant asks specific follow-up questions and provides personalized product recommendations based on their answers and user behavior.
A good online shopping assistant understands the customer’s needs—even if the customer can’t express them in technical terms—and guides them to the right product in just a few steps. Key factors here include a high-quality database, the ability to communicate through dialogue, and seamless integration into the shop’s daily operations.
The easiest way is to integrate a ready-made solution, which is less time-consuming than many people expect: Your existing product catalog serves as the foundation; based on that, you’ll receive a div element that you can integrate into your shop system as a placeholder wherever you want. Above all, it’s important that your product data is complete and well-maintained. The better the data, the more accurate the recommendations will be.
Want to dive deeper into this topic? In our e-book on conversational commerce, you’ll learn how chat-based customer service and AI shopping assistants are transforming online retail—and what that means for your store.
Download the e-book now!
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