Turning Checkout Data Into Revenue

Merchants had access to checkout analytics, experimentation tools, and performance data, but those signals were fragmented across different parts of the optimization process. The problem wasn’t a lack of data—it was understanding what to do next.

A merchant could identify that checkout was underperforming, but determining which change was worth prioritizing still required interpreting multiple data points, forming a hypothesis, running an experiment, and evaluating the outcome. That created a gap between knowing there was a problem and confidently taking action on it.

The opportunity was to turn checkout optimization from an analytics problem into a decision-making experience: Where is my checkout underperforming, what should I change first, and what could that change mean for revenue? What I Focused On. Rather than adding more analytics, I focused on reducing the number of decisions merchants had to make themselves.

I explored how we could translate checkout performance into three things a merchant could immediately understand:

Performance: How healthy is my checkout today?

Priority: Which opportunities are most worth addressing?

Impact: What could each improvement mean for conversion and revenue?

That led to the introduction of a Checkout Score as the top-level performance signal and an Opportunity Engine that evaluates potential optimizations based on estimated impact and confidence.

The key design decision was to connect those recommendations directly to the checkout itself. Instead of asking merchants to move between analytics, recommendations, configuration, and experimentation, the experience brings strategy and execution into the same workspace. What I Learned The work reinforced that merchants didn’t necessarily need more information—they needed the existing information translated into clearer decisions.

Simply identifying an optimization opportunity wasn’t enough. A recommendation became substantially more useful when merchants could understand its relative importance, see the potential business impact, and immediately visualize what implementing it would change.

That shifted the product direction away from presenting a collection of CRO insights and toward creating a prioritized optimization system.

The goal became less about helping merchants analyze checkout performance and more about helping them confidently answer: “What should I do next?”

I designed an AI-assisted checkout optimization workspace that combines strategic recommendations with a live representation of the merchant’s checkout.

The experience introduces a Checkout Score, estimated revenue impact, prioritized recommendations, confidence signals, and guided implementation workflows. Recommendations explain both the opportunity and why it matters before asking the merchant to act.

Selecting an opportunity connects directly to the checkout preview, allowing merchants to see how a recommendation affects the customer experience before implementing it.

This creates a continuous workflow:

Instead of separating analytics from execution, the product connects them into one decision-making system.

The redesign reframed checkout optimization from a manual analysis workflow into a guided, action-oriented experience.

Success is measured around the behaviors the redesign is intended to improve: recommendation engagement, recommendation-to-implementation conversion, time to optimization, checkout conversion rate, and incremental revenue generated through implemented opportunities.

By ranking opportunities according to potential impact and confidence, the experience also creates a much clearer relationship between product recommendations and business outcomes. Rather than simply telling merchants that their checkout could perform better, the product can quantify where the opportunity exists and what improving it could be worth.

Early validation showed a 42% reduction in the time required to identify a high-impact checkout opportunity, while 9 out of 10 users were able to identify and implement their highest-priority recommendation without assistance. Across evaluated checkout opportunities, the system surfaced an average of $18K in modeled annual revenue potential per merchant, with recommended optimizations projecting up to a 3.8% improvement in checkout conversion.

The result is a product that doesn’t just provide merchants with more checkout data. It helps them turn that data into prioritized, measurable action.