Digital Commerce: What Happens When AI Agents Shop for Customers?

A customer wants to purchase a new laptop. They don’t do the research themselves; they ask their AI assistant what they want. The AI assistant performs the research and makes the purchase. The customer does not have to visit any product webpage or talk to a retailer.

The shopping process is no more restricted to customers doing all the research and buying themselves.AI agents are doing it on their behalf, changing how consumers discover products and complete transactions.   

This article explains the changing relationship of digital commerce.

AI Shopping agents know the customer’s budget, preferences, prior selections, purchasing behavior, and even the rationale for their decisions. They know their preferences better than the brands selling to them.  

This creates a new dynamic for how customer relationships are formed since the customers are interacting with AI Shopping Agents for product research and comparison. Retailers have no direct interaction with decision-making, and the agent acts as the intermediary to know what the customer wants and the products worth considering.      

This also raises a challenge for brands. The retailer may have customer data, but it doesn’t mean that it will affect the recommendation of the AI agent. The product details, its price, availability, and how well it satisfies the requirements hold equal significance.  

1. Ratings as a Signal for Shortlisting Products 

AI Shopping Agents consider the scores of the product along with its other features and prices, and accordingly they select the products. It is not necessary that the ratings are the sole factors because good ratings increase the likelihood of being selected.    

The consumer approaches an AI agent to select a product within $150. In case of similarity between in the features, the one having better ratings is selected.    

2. Negative Feedback Influences Product Recommendation

Feedback regarding delayed delivery, defective products, poor customer service, and issues with returning the goods affects an agent’s decision to recommend a certain retailer. 

An online retailer sells products at a cheaper rate than that of other sellers. Nevertheless, when customers face issues regarding late delivery and refund, an AI agent recommends a different retailer.  

3. Consider the Importance of Recency for Reviews 

Past reviews cannot determine how good a product is currently, or how well the services are being provided. Thus, Recency is also considered in evaluating if a certain brand continues to satisfy the expectations of its consumers.     

Skin care product that had been praised two years ago is having complaints of packaging or quality by recent buyers. An AI agent recommending today’s choices will take these into account.

4. Trust is Built on Verifiable Reviews 

Purchases that have been verified, along with detailed experiences of using products, build trust in the reputation of a brand. 

When a product has a lot of five-star reviews but no verifiable reviews, the AI agent treats that product differently from other products. 

5. The Reputation of a Brand Goes Beyond Its Product

Apart from the product, the purchasing experience is significant too. The return policy, the warranty, and the reliability of delivery decide if the retailer will be considered by the AI shopping agent.

Two retailers sell similar products at almost the same price point. While one has a policy of free returns and reliable deliveries, the other has a track record of complaints that have not been settled. An AI shopping agent would prefer the former retailer over the latter.  

1. Loss of the Sponsored Listings’ Edge

The sponsored products appear in a higher position since they pay for their visibility. The AI Shopping Agent selects products based on price, features, rating, and availability. Being a sponsored listing does not guarantee that the product will be recommended.

A customer asks an AI agent to find a specific product. A retailer's sponsored product appears in traditional search results, but the agent could recommend a different product if it offers better specifications at a lower price.     

2. Paid Visibility Moves into AI Recommendations

Sponsored recommendations and placement in AI-based shopping results are among the emerging spaces of competition. But the brands will have to ensure that the consumer knows about the sponsored recommendations.  

A consumer approaches an AI shopping agent for product comparison. The agent recommends three products, among which one is a sponsored product. The brand is getting visibility; however, the consumer should be able to identify the sponsored recommendation. 

3. Brands Have to Understand the Criteria on Which Agents Evaluate Products

Brands have to be aware of what factors make their products chosen, selected or excluded from AI recommendations. Factors like product information, reviews, prices and availability could affects these criteria.

The brand spends a lot on sponsored placements but does not show up in any AI product comparisons. By examining their product descriptions, it is evident that the information regarding ingredients and skin types provided is insufficient.

4. Retailers Need to Rethink Their Marketing Strategies

When AI agents do product discovery, retailers need to develop advertisements that suit the shopping experience. 

A retailer tries out sponsored product recommendations from its AI shopping assistant, with brands paying to promote their products that fulfill the customer's needs.  

To prepare the MarTech stack for this shift is not about having to replace all existing platforms and buy all new tools. It’s about making sure that the whole stack operates together in harmony. Brands need to be aware of how these agents make their decisions in a buyer's journey where there is no actual buyer browsing anymore.