How We Find Winning Tests Faster With an AI CRO Analyst

The slow part of CRO is not the testing

Running an A/B test is the easy part. Tools handle the split, the tracking, and the math. The slow part is deciding what to test. Most teams either guess, copy a competitor, or spend days digging through data and recordings before they have a list worth running.

We built a workflow that uses AI to do the heavy reading. It does not replace judgment. It gets us from raw data to a ranked list of test ideas much faster, so we spend our time on the decisions that matter.

Step 1: Watch what people do with session recordings

We start with Microsoft Clarity. Session recordings and heatmaps show where real visitors tap, scroll, stop, and leave. The problem is volume. A store with decent traffic has thousands of recordings, and nobody has time to watch them all.

So we export the key data, rage clicks, dead clicks, scroll depth, and quick backs, along with notes from a sample of recordings, and feed it to the AI analyst. We ask it to group what it sees into patterns. For example: many mobile visitors scroll past the first screen of the product page without tapping anything, or visitors tap the product photo expecting it to zoom and nothing happens.

These are behavior problems. They tell us where people get stuck.

Step 2: Read what people say in reviews

Behavior shows where people struggle. Reviews show why they buy and what nearly stopped them. We pull product reviews, and when we have them, survey answers and support tickets. Then we ask the AI to pull out:

• The reasons people bought.
• The worries they had before buying.
• The exact words they use to describe the product and the problem it solves.

This is where many of the best test ideas come from. If reviews keep saying I was worried about the fit, but it was perfect, that worry belongs on the product page, with an answer, near the buy button.

Step 3: Check the funnel to rank by impact

The last step is the funnel. We pull Google Analytics data for each step: product views, add to cart, checkout started, and purchase, split by device and channel. This tells us where the biggest drop is and how much traffic each page gets.

We give the AI the problems from step 1, the objections from step 2, and the funnel numbers from step 3. Then we ask it to rank test ideas by likely impact. A problem on a page with lots of traffic and a big drop ranks higher than a problem on a page few people see.

What comes out

The output is a ranked list of test ideas. Each one has:

• The problem, with evidence from recordings, reviews, or the funnel.
• The proposed change.
• The page and the device it affects.
• The metric to watch, usually revenue per visitor or conversion rate.
• A rough priority based on traffic and the size of the drop.

We then match each idea against our database of past winning tests. If a pattern like a sticky Add to Cart or price anchoring has won before on similar stores, the idea moves up.

Where humans still decide

AI is good at reading a lot of data and spotting repeats. It is not good at knowing your brand, your margins, or what your customers will tolerate. So we review every idea by hand. We cut ideas that are off brand, too costly to build, or unlikely to reach significance with the traffic the store has.

We also check the evidence. If the AI says reviews mention shipping worries, we read those reviews ourselves before we build a test around it.

Why this is faster

Before this workflow, building a solid test plan for a new store took several days of watching recordings and reading reviews. Now the first draft of the list takes a few hours, and we spend the rest of the time refining it. The quality is also more consistent, because the AI reads everything instead of the sample a tired analyst manages to get through.

How you can try a version of this

You do not need a custom tool to start. Here is a simple version:

• Install Microsoft Clarity and let it collect a couple of weeks of data.
• Export your product reviews to a spreadsheet.
• Pull a basic funnel report from Google Analytics, split by mobile and desktop.
• Paste each into an AI assistant one at a time and ask for patterns, objections, and the biggest drop off.
• Combine the three into a list and rank by how much traffic each problem touches.

Then pick the top idea and test it.

A note on the video

In the short video version of this workflow, the presenter is an AI avatar. We say that openly. The workflow and the results are ours.

The short version

Finding what to test is the slow part of CRO. We speed it up by having AI read session recordings, customer reviews, and funnel data, then rank ideas by impact. People still make the final call.

If you want us to run this process on your store, book a free CRO audit.