Help first. Ask at the right time.

For a running jacket, the first useful message might explain the brand’s verified fit and care guidance. A feedback request belongs after a reasonable opportunity to use it. The right timing depends on the product and the journey.

Ask questions that leave room for an honest answer.

• What did you buy it for?
• How did it perform in that situation?
• What should another customer know?
• What could have been better?

These are prompts, not a script for praise. Our approach welcomes negative and positive feedback, without selecting only happy customers or rewarding favourable ratings.

Separate experience from specification.

“Comfortable on my half-hour run” is one customer’s experience. It does not establish a universal performance claim. Keep the context and meaning intact; verify technical facts separately.

Use recurring questions to improve public information.

If buyers repeatedly ask whether a running vest fits over the jacket, investigate that question. The answer may improve a size guide, an FAQ and a relevant email. Obtain appropriate permission before republishing attributed feedback.

That same question can shape a future AI shopping scenario. This connects the research behind the services; it does not mean that sending a review email directly improves AI visibility.

For review-handling principles, see the CMA guidance on online reviews.