Voice of Customer

Customer Feedback

Customer feedback in AI support is the structured collection of customer satisfaction signals — survey scores, thumbs ratings, open-text comments — after support interactions, used to measure resolution quality and guide AI improvement.

In ecommerce support, customer feedback serves two functions: it measures quality at the individual interaction level, and it aggregates into patterns that reveal systemic issues. The most common collection mechanism is a post-resolution CSAT (customer satisfaction) survey — a brief rating request sent to the customer after their ticket is closed, often a 1–5 star rating or a thumbs up/down with an optional comment field. The score for each interaction is useful for catching egregious failures; the aggregate CSAT score across thousands of interactions is useful for tracking whether support quality is improving or declining. For AI-driven support, customer feedback is particularly valuable because it is the clearest external signal of resolution quality. Unlike internal metrics (classification accuracy, response time), feedback reflects what the customer actually experienced. Negative feedback triggers review of the specific interaction, which often reveals actionable problems: incorrect information in the knowledge base, a misclassified intent, a tone miscalibration.

Customer feedback closes the quality loop. Without it, a support operation is measuring its own effort — tickets closed, response time, handle time — without knowing whether any of it actually helped the customer. For ecommerce brands where every support interaction is an opportunity to retain or lose a customer, feedback is the direct connection between support quality and revenue: low CSAT correlates with increased churn, increased return rates, and decreased repeat purchase probability.