carts, e-commerce brands have mastered the art of measuring clicks, conversions, rankings, reviews, left carts, and revenue. However, the customer journey is getting a new twist as AI-powered shopping enters the picture, a phase that’s not often accounted for by traditional analytics.
Customers are increasingly turning to AI agents to find products, make comparisons, gain understanding of specs, and suggest appropriate products. An AI system can even sway the consideration set of shoppers before they even step onto an e-commerce site.
That could be a new metric that is useful in e-commerce. A product might sell out like crazy but not be understood by AI systems because of the reviews it has. A product can sell like it’s made of paper and have amazing reviews, and yet still not be understood by AI systems. If an intelligent shopping assistant is not able to clearly explain or recommend a product, this can create opportunities that are lost before traditional analytics has been able to determine the issue.
The Hidden Product-Understanding Problem Inside AI Shopping
The traditional e-commerce metrics typically begin once a customer has engaged with an e-commerce website or marketplace. The shopping process starts before people make an inquiry, with AI systems choosing which product to consider as a result of the questions being asked.
Product Visibility Now Goes Beyond Search Rankings.
In order to be searchable, a document needs to be understandable to AI. For intelligent agents to be able to decide on what products to offer, who they should offer them to, how they are different from other options, and if they meet a customer’s requirement, they must have reliable information to do so.
For instance, if a shopper inquires: Do you have headphones that are suitable for traveling? Do you have a laptop that is capable of running the software you want? In the event an essential piece of information is missing, the AI agent might not be able to give a definite answer or even suggest the product.
Five Signals Can Reveal AI Product Confusion
- AI confidence shows the consistency of systems’ understanding of the key product information.
- Today, there are unanswered questions about key product attributes that are uncovered by unknown responses.
- Agents can be confident that they are recommending products within results—this is expressed in the recommendation frequency.
- Data consistency is the process that recognizes different specifications in the crucial commerce channels online.
- Question coverage is a way to rate the extent to which the product information answers customers’ questions.
Marketing language cannot replace helpful product facts.
Marketing jargon like “premium quality,” “advanced performance,” and “next-generation design” will grab human shoppers‘ attention but doesn’t necessarily ensure they’ll provide detailed purchase inquiries.
Factual information regarding dimensions, materials, compatibility, features, warranties, operating requirements, limitations, and intended use is needed for AI systems.
Repeated “I Don’t Know” Responses Reveal Content Gaps
When a customer isn’t sure about the answer, that can be a sign of uncertainty in the AI, which can help emphasize the questions customers are truly interested in. Let’s say a customer is constantly asking if a product is compatible with another to a certain type of device, and you don’t have a definite answer; that’s a definite content opportunity.
Other gaps might include service size, installation hassles, service maintenance, delivery limitations, software compatibility, returns, or product limitations.
Building a Better AI-Ready Product Information Strategy
So measuring uncertainty is useful when e-commerce teams leverage that insight to make improvements. The goal isn’t to get rid of all the responses that could be ambiguous, and it’s okay if there’s information that’s actually hard to find.
Rather, brands should minimize unnecessary uncertainty by completing, clarifying, organizing, and standardizing key product information everywhere.
Start with your most important products.
High-value products and very common questions should be firstly audited by the brands. Information that is the same should be conveyed on product pages, FAQs, specifications, structured data, marketplace listings, manuals, and support documents.
This is particularly relevant as AI systems can access a variety of information sources. Sometimes the specifications may be contradictory and the information may be ambiguous despite the fact that each page may seem correct.
Five practical steps can help mitigate uncertainty around AI.
- Regularly review product specifications to ensure information details are not missing and are not conflicting.
- Add more questions to the FAQs section regarding questions customers may have prior to buying products.
- Use consistent vocabulary on websites, marketplaces, and catalogs, and provide consistent support of content.
- Enhance structured product data to help machines interpret it more easily, no matter the channel.
- Track AI answers to look for any repeated uncertainty and fact errors.
Test Products’ Realistic Customer Questions
AI shopping is not necessarily a straightforward process. There are questions that customers ask in a conversational manner, and they have a number of requirements embedded in the question.
You could ask someone which camera is good for novice photography, which refrigerator is good for a small kitchen, or which smartphone has a good camera and a long battery life.
AI systems need to link various attributes of products to answer these types of questions. Ecommerce teams should thus develop practical question sets and continually test their most crucial products.
Connect AI Understanding With Commercial Results
When combined with tangible business results, AI uncertainty takes on a new level of significance. Based on a variety of metrics, including answer-confidence rates, product visibility, assisted conversions, and sales, brands can compare their answer-confidence rates with product visibility, AI referrals, recommendation frequency, assisted conversions, and sales.
When products with higher information coverage are better seen in AI, businesses are able to gather valuable product-data quality that has commercial value insights.
Make AI Understanding Part Of Ecommerce Merchandising
Traditional emphasis of merchandising is on pricing, promotions, inventory, placement, photography, and product presentation. AI commerce is an additional responsibility: making sure that intelligent systems can properly comprehend the merchandise.
This could be a feature for future e-commerce dashboards that will include the AI answer coverage and the frequency of recommendations offered.
Conclusion
While e-commerce brands have become very advanced in tracking customer behavior, AI-powered shopping opens the floodgates of another dimension that is difficult to track with traditional analytics.
Even the most effective AI systems can struggle to comprehend a product if it ranks highly, sells at a high volume, and has great reviews. That is why the proportion of answers that are “I don’t know” is a new metric that should be tracked.
The aim isn’t just to look for ways to answer more questions with AI. To develop product information that is accurate, complete, consistent, and useful in the modern shopping context.
Frequently Asked Questions
1. The “I don’t know” ecommerce metric is a measure of what?
It quantifies the rate at which an AI agent consistently fails to provide accurate information to customers regarding the product. It indicates the proportion of times that an AI agent provides incorrect information to a customer about a product. High uncertainty can be a sign of lack of product information, inconsistencies, outdated information, and unclear information.
2. What does this uncertainty of AI mean for eCommerce brands?
AI agents are becoming more a part of the product discovery and shopping process. These systems are not able to accurately understand a product if they don’t, or fail to, understand it correctly; then the customer may get less or less accurate information or lower-level recommendations.
3. What strategies and actions can businesses take to diminish the uncertainty of AI?
Brands have the opportunity to enhance their specifications, FAQs, structured data, terminology, descriptions, and other supporting documentation. It is also possible to find out key areas of interest gaps by testing realistic customer queries.
4. So is there a degree of uncertainty with AI that’s the same as with SEO performance?
No. The uncertainty of AI is about how much the intelligent systems understand the product information, and the visibility and discoverability of SEO is about how much the users see. Both are linked but have different performance areas assessed.