Before a product is chosen, it must become an option.

Start with a situation, not a keyword

A product search becomes commercially useful when it describes who is buying, what they need to accomplish and what would make an option unsuitable. ‘A laptop for a consultant travelling by train’ is a different investigation from ‘the best laptop’. Budget, software compatibility, weight and seller preference can each change the eligible set.

Use scenarios that reflect actual commercial priorities. Sales questions, support tickets and category expertise can inform them, but a research prompt set should not be presented as representative customer demand without a sampling basis.

Separate the signals

A brand name appearing in a response can mean several things. It may be an example, an option to avoid or a genuine recommendation. Classify the role before calculating a metric.

Change one condition at a time

Compare an unrestricted scenario with the same scenario constrained to a retailer, market or budget. If the shortlist changes, record the difference before proposing an explanation. Inventory, information quality, model assumptions and retrieval can all be candidates for further investigation.

A paired test does not isolate causality by itself. Keep platform, date, language and instructions comparable; repeat the test; inspect the underlying offers. The output should be a testable explanation, not a confident story attached to one answer.

Turn an omission into a useful question

When a product is absent, first check whether it should qualify. If it does, examine its identity, attributes, supporting explanations, seller coverage and current availability. Prioritize defects you can verify and correct. A reasonable product-data improvement is valuable even before a measurable recommendation effect can be established.