Measuring Shopping Assistant Quality
A shopping assistant that recommends the wrong product loses the sale. Learn how to measure quality and protect your e-commerce revenue.
Vanity Metrics vs. Revenue Metrics
When measuring the quality of an AI shopping assistant, most platforms provide basic metrics: session length, deflection rate, and CSAT. But in e-commerce, deflection isn't always the goal—conversion is. If an agent "deflects" a user by giving them a generic, unhelpful answer, you haven't saved a ticket; you've lost a customer.
Assay measures true conversational quality by evaluating interactions against your specific e-commerce goals. It checks if the agent successfully identified intent, recommended the right SKU, and maintained the brand voice necessary to close the sale.
The E-commerce Quality Checklist
Metrics like "Chats Handled" are vanity metrics. True e-commerce quality requires measuring these dimensions.
Product Recommendation Accuracy
Does the agent recommend products that actually match the user's constraints and your catalog availability?
Promotional Constraint Adherence
Can the agent correctly apply or deny discounts based on complex promotional rules without hallucinating exceptions?
Checkout Conversion Funnel
Does the conversation flow naturally guide the user toward checkout, or does the AI get stuck in endless conversational loops?
Implement these checks automatically.
Don't build this observability pipeline from scratch. Assay provides out-of-the-box behavioral monitoring and rubric scoring for any AI agent.
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