How to Measure Visibility and Sales in Agentic Commerce: New KPIs for E-commerce

• Traditional analytics is no longer enough: More and more consumers use AI to make purchase decisions, and standard attribution models (e.g., in GA4) miss AI’s dispersed impact on sales.
• A new agentic commerce funnel: Measuring e-commerce performance has to rely on 5 new stages that reflect how AI works: Accessibility → Visibility → Recommendation → Selection → Purchase.
• Accurate product data plays a key role: Simply appearing in LLM answers isn’t enough. An AI agent has to receive error-free information on price, variants and availability (AI Product Accuracy Rate), or the conversion probably won’t happen.
• Technical readiness for agents (UCP and ACP): For tools such as Gemini or ChatGPT to recommend and eventually buy products on the user’s behalf, a store has to take care of its product feed and integrate with the Universal Commerce Protocol (Google) or the Agentic Commerce Protocol (OpenAI).
• AI traffic converts better: Users who come to e-commerce stores from AI tools can generate 53% more revenue per visit and convert 54% more often, which calls for new KPIs that measure assisted impact (AI Assisted Revenue) and conversions completed by the agents themselves (Agent Checkout Conversion Rate).

To verify whether your online store is properly prepared for the growth of agentic commerce, it’s not enough to analyze sales and traffic from AI tools alone. AI agents can influence the buying process in a dispersed way and at different stages, from product search and research, through comparing offers and generating recommendations, all the way to the agent completing the purchase.

That’s why an agentic commerce funnel should cover your store’s readiness for specific stages and verify it:

A funnel built this way, with KPIs tailored to it, lets you answer the key question for each stage:

This model lets you look at the agentic commerce process as a whole, and it also helps you spot the stages of the funnel where your store may be underperforming. You can analyze the store’s technical readiness, its visibility in AI tools and its impact on purchase decisions and final transactions separately. Below, we’ve put together specific KPIs for each of these stages.

Before an AI agent recommends your products to a user, it first needs access to them. To recommend a product, or to buy it, the agent needs unambiguous information about:

• the product’s price
• its availability/stock levels
• variants
• technical specifications
• delivery options and return policy
• promotions
• the possible ways to fulfill the order

To help stores communicate product details to agents, Google created UCP (Universal Commerce Protocol), an open standard that enables AI agents to make purchases on the user’s behalf. You can integrate with UCP from your Google Merchant Center account, where it can be switched on in the settings.

Agent Readiness KPIs for an Online Store

Below are 6 detailed KPIs that will help you verify the accuracy of your product feed and of the offer presented on your store’s website:

Proposed Metric

All of the KPIs above can be rolled into one summary metric, the Agent Accessibility Score, which shows what percentage of your offer is complete, up to date and usable by an agent.

If your products are available through the information provided to AI agents, the next stage is your brand’s visibility in answers. Accessibility alone, although it’s the foundation, doesn’t guarantee visibility in AI tools.

Another challenge is the accuracy of the information presented about your offer and products. If the data is imprecise and differs between systems, a misled agent may present false information in its answers.

What’s worth checking here?

• Does the brand appear in LLM answers?
• For which prompts and user intents is it shown?
• Which products are mentioned in recommendations?
• Does AI present the correct price, variant and use of the product?
• How does the brand’s visibility compare with its competitors?

The KPIs below will help you monitor the answers to these questions.

Visibility KPIs

The biggest challenge will be choosing which prompts to monitor. You need to approach this comprehensively, thinking in terms of potential scenarios of your target audience’s needs and intents rather than single, generic prompts. Here are a few examples of queries that reflect different needs:

• “Fragrance-free skincare for sensitive skin”
• “Laptop for graphic design under $1,500”
• “Running shoes for beginners with wide feet”
• “Product X available with delivery before Friday”

Learn more: SEO in the Era of Agentic Commerce. How to Prepare Your E-commerce for AI Customers and Agents

Accuracy KPIs

The KPIs below focus on verifying the accuracy of your offer. For the monitored set of prompts, it’s worth checking whether the LLM or a dedicated AI agent provides correct data on:

Based on the results of tests covering the KPIs above, you get an accuracy metric: the AI Product Accuracy Rate. It can be defined as the percentage of answers in which the key product information matches the store’s current offer.

Going one step further, a product mention alone doesn’t equal a purchase decision. A Gartner survey from May 2026 shows that, for now, users are much more open to using AI tools to narrow down their product choices (31%) than to fully handing the purchase decision over to an agent (11%). This means it will be especially important right now to monitor whether AI tools include the brand’s products in their narrowed-down shortlists and whether they point to them as a worthwhile option.

KPIs for AI’s Influence on Purchase Decisions

It’s worth splitting them into three levels:

When it comes to traffic and conversions from AI tools, some KPIs can already be measured in Google Analytics 4. These metrics won’t show every situation in which AI influenced a user’s purchase decision, but they’re still worth monitoring. They include:

• referral sessions from AI tools
• the number of transactions and revenue from AI traffic
• the conversion rate for this traffic
• average order value
• engagement of users from these sessions
• information about the products viewed

KPIs That Develop Alongside Agentic Checkout

As agents become more able to make purchases, the scope of data available for analysis will change too, and new KPIs will emerge. If agents complete transactions themselves, e-commerce businesses should add the metrics from the list below to their KPIs:

• the number of carts created by agents
• the number of checkouts started by AI
• Agent Checkout Completion Rate, the number of checkout processes completed at purchase
• the value of agentic transactions (average and total)
• agent checkout and authorization errors during purchase

Are these KPIs still a thing of the future? In fact, some of them already work if your e-commerce store is integrated with UCP and ACP. The UCP mentioned earlier supports purchases in AI Mode and Gemini, but only with the right integration and Google’s approval. Once you’re integrated, you can already track the KPIs listed above. OpenAI also has its own dedicated solution, ACP (Agentic Commerce Protocol), which lets stores integrate with ChatGPT agents. Both solutions connect product data and the purchase process with the actions AI agents perform on users’ behalf.

Traditional attribution models won’t be enough to measure today’s customer purchase path, which can be highly fragmented. Let’s say a customer asks ChatGPT to recommend a coffee machine. In its answer, AI compares the available models and recommends three products. The user remembers them but doesn’t visit the website that day. A few days later, they type the name of the chosen model into Google, go to the store and place an order. In this scenario, Google Analytics will attribute the conversion to organic traffic and, in a standard model, completely ignore AI’s influence on the purchase decision. Matt Calik, CEO of Delante, discussed this in an expert article for XYZ.pl.

That’s why it’s worth splitting sales KPIs into three views:

The last metric won’t be a precise value taken from GA4, so it’s hard to talk about hard data here. Its value should be estimated from several sources:

• GA4 referrals and parameters
• server logs
• post-purchase surveys
• user research
• CRM data
• checkout identifiers (for stores integrated with UCP or ACP)

While it’s worth monitoring more than a handful of KPIs day to day, a dashboard summarizing agentic commerce results should include one key metric for each area, so the report stays readable.

The proposed KPIs are:

Transactions completed directly by AI agents aren’t a widespread buying model yet, but their share is steadily growing, just as ChatGPT and other LLMs grew in popularity. It’s worth setting up your analytics now, so that from the very start you can draw valuable conclusions based on a Data Insights approach that will help your brand build an edge over its competitors in agentic commerce.

Źródła:

• https://www.mckinsey.com/capabilities/quantumblack/our-insights/europes-agentic-commerce-moment-decision-influence-is-here-execution-is-coming
• https://www.gartner.com/en/newsroom/press-releases/2026-05-27-gartner-survey-finds-consumers-want-ai-shopping-help-but-not-ai-purchase-decisions
• https://nielseniq.com/global/en/news-center/2026/42-of-consumers-now-use-ai-tools-to-shop-niq-data-shows/
• https://business.adobe.com/blog/ai-traffic-surge-retail-sites-not-machine-readable