Shopping Assistant: solving a problem that does not exist

In e-commerce, it is often not only the product that is being sold, but also the promise of a better experience. Shopping Assistants fit precisely into this narrative: it is meant to help customers in the same way as a good salesperson in a brick-and-mortar store.

The problem is that online shopping follows a different logic than talking to a person. And that is precisely why this solution may look modern while failing to address users’ real needs.

Why Shopping Assistants May Be Solving the Wrong E-Commerce Problem

A customer entering a physical store may need help discovering what to buy. Online shoppers, however, rarely begin from zero. Before visiting an online store, they may already have:

• Searched Google for information
• Read customer reviews
• Compared different models
• Checked prices across several stores
• Identified specific features or specifications
• Formed an initial idea of what they want to purchase

By the time they arrive, many customers already have a clear purchasing hypothesis. They are not necessarily looking for a conversation–they are looking for confirmation.

In this situation, a chat can make the process slower. Instead of comparing products immediately, customers must ask a question, wait for an answer, interpret the recommendation, and possibly ask another question. What is intended to simplify the buying journey may actually add another layer between the customer and the shopping cart.

Customers usually want decisions, not dialogue

For most people, online shopping is a task rather than an experience. They want to quickly find the relevant information, compare options, and complete their purchase.

Important questions are often straightforward:

• What is the price?
• Is the product in stock?
• What are its specifications?
• When can it be delivered?
• How does it compare with similar products?

Customers frequently answer these questions while browsing several tabs or comparing multiple stores. A conversational interface is not always the most efficient way to support this behavior.

Shopping Assistants may be useful for customers who genuinely do not know what they are looking for. However, that group may represent only a small portion of a store’s visitors. For everyone else, sequential questioning can be less convenient than a well-designed product listing with visible filters and side-by-side comparisons.

An AI assistant is not necessarily an expert

The most attractive promise of Shopping Assistants is that artificial intelligence can understand a customer’s needs and recommend the right product. In practice, many assistants function more like rule-based recommendation systems presented through natural language.

They may:

• Identify product attributes
• Match customer responses to predefined criteria
• Filter products according to selected parameters
• Recommend items based on preset scenarios

That can be useful, but it is not the same as receiving advice from an experienced specialist. A human expert can understand context, explain trade-offs, recognize unusual requirements, and draw on practical knowledge. A Shopping Assistant may simply match answers to a product database.

The difference is subtle but important. Customers may expect expertise and receive a matching mechanism instead.

Recommendations are not always neutral

There is also an obvious conflict of interest. A store may describe its assistant as helping customers find the “right product,” but the business may define the right product as the one that is most profitable to sell.

That product might be:

• A more expensive model
• An item with a higher profit margin
• A product the store wants to promote
• Stock that needs to be cleared

As a result, customers should not automatically assume that recommendations are impartial. Questions about budget and preferences may appear helpful, but they also allow the store to narrow the selection toward products that serve its commercial goals.

Shopping Assistants are not equally useful for every customer

Online shoppers can generally be divided into three broad groups.

The first group consists of customers who are unsure what they need and genuinely require guidance. A Shopping Assistant may be helpful for them.

The second group knows what it wants, either by product name, model, category, or specifications. These customers want to reach the relevant products quickly.

The third group consists of comparison shoppers. They want to review several options side by side, assess the differences, and choose the best fit.

The second and third groups are often the most valuable to an online store because they arrive with strong purchase intent. For these customers, a chat may not be a form of support. It may simply become another obstacle that slows down comparison and completion.

Positive performance metrics need context

Shopping Assistant providers often highlight metrics such as:

• Higher conversion rates
• Increased average order value
• Fewer product returns

These outcomes sound compelling, but they are difficult to evaluate without detailed data. A higher average order value, for example, may indicate that the assistant is helping customers–or that it is steering them toward more expensive products.

Similarly, a higher conversion rate may come from a very narrow segment of users rather than from the store as a whole. Without proper A/B testing, it is impossible to know whether the assistant genuinely improves the customer experience or simply changes the behavior of a limited group.

Businesses should also examine usage and abandonment rates. How many customers open the assistant? How many complete the interaction? How many leave after being asked several questions? These figures are essential for understanding whether the tool creates value or friction.

The real problem may be basic usability

In many online stores, lost sales are not caused by the absence of a virtual advisor. They are caused by fundamental usability problems, such as:

• An inaccurate or ineffective search engine
• Confusing product categories
• Unclear navigation
• Missing or poorly designed filters
• Incomplete product descriptions
• Weak product comparison tools
• Inconsistent product data

These issues directly affect the customer’s ability to find and evaluate products. A Shopping Assistant does not necessarily solve them. Instead, it may hide them behind a conversational interface.

If customers cannot find the right products because the search function is poor, adding a chat does not repair the underlying search experience. It simply gives customers another way to work around it.

Better foundations should come first

Shopping Assistants have a legitimate role in e-commerce. They can help customers who need advice, especially in complex categories where products are difficult to understand or compare. In those cases, guided recommendations can be valuable.

However, most online shopping journeys require something simpler: fast, clear, and precise access to information.

Before investing in a conversational assistant, stores should make sure they have:

• A reliable onsite search engine
• Logical categories
• Useful filters
• Clear product pages
• Accurate specifications
• Relevant product recommendations
• Effective comparison tools

When these foundations work well, a chat may be unnecessary for most customers. When they do not, the assistant is unlikely to solve the underlying problem. It may only mask poor usability with the appearance of personalized help.

The central question is therefore not whether a store can add an AI Shopping Assistant. It is whether customers actually need a conversation, or whether they simply need a faster, clearer way to make a decision.