Exitloom

Solo founders often learn about churn through a cancellation count or free-text box, but lack time to run interviews, normalize reasons, connect them to account context, and decide what to test. The supplied research confirms a broader behavioral-trigger interview platform with voice and text, a high-volume product-event tier with webhooks, and usage-based voice infrastructure. It found no cancellation-only product constrained to one ranked reason and one suggested fix. Exitloom receives a confirmed cancellation event, applies contact preferences and suppression, asks one short optional question by text or an explicitly requested voice channel, preserves the customer's words, and groups responses under a versioned taxonomy. The top reason is a sample-dependent classification, not why every customer left. A suggested fix is a hypothesis tied to evidence and counterexamples, not a prescription or guaranteed churn reduction. Cancellation, response, classified reason, founder decision, experiment exposure, retention window, reactivation, refund, and outcome remain separate. Voice never calls automatically from cancellation alone; it requires appropriate consent, truthful identity, recording notice, and an easy decline. Cross-customer learning requires explicit participation, compatible definitions, minimum cohorts, and privacy protection. The original stage reports one verified API, not a complete billing, product-event, messaging, voice, experimentation, and outcome chain.

A solo founder or very small subscription-product team with recurring cancellations, product-event access, and no dedicated researcher.