A conversion-oriented AI shopping experience cannot be judged by tone alone. Agentic CRO evaluates whether the guidance actually helps the customer make a suitable, informed decision. Testing only obvious questions such as “What is your return policy?” misses many of the situations where a buyer could be misled.
Build a small but useful scenario set
Select cases from real product constraints and recurring permitted customer questions. Examples include a variant with a subtle compatibility condition, a product unavailable in a particular region, two alternatives with different maintenance requirements, a delivery question requiring a postcode, and a purchase that needs human review.
Check five properties of each response
- Intent: did the agent identify the shopper's actual requirement?
- Evidence: did the answer use approved product, shipping and policy information?
- Qualification: did it ask for a critical missing fact before recommending?
- Honesty: did it disclose uncertainty rather than inventing availability, fit or delivery commitments?
- Next step: did it route the shopper toward an appropriate product, cart, consultation or human support path?
Handle edge cases intentionally
A customer can ask about a discontinued product, request a nonexistent policy exception or provide conflicting requirements. The agent should not produce a confident “yes” just to reduce the number of conversational turns. Better qualification may mean more questions for a small share of customers, while protecting suitability and trust.
Separate quality from sales attribution
Record correct recommendations, clarification quality, unsupported claims and appropriate handoffs. Where Shopify sales data is available, investigate related commercial indicators separately and only connect the two when attribution is defensible. Evaluation success is not proof of revenue uplift.
For a deeper view of what happens before a storefront visit, read the agentic sales funnel and clarification before recommendation.
