Stop trying to make every data point right. Start making the pattern useful. Win/loss analysis promises to explain why organizations win and lose. But the data is imperfect: sellers may blame price, customers may not reveal their real motivations, CRM categories oversimplify decisions, and individual deal reviews can be wrong. Yet across enough deals, imperfect data can reveal powerful patterns. This session explores the “Trust Gap” in win/loss analysis and how to move beyond individual loss reasons to identify patterns that improve future performance. We’ll examine why renewal and expansion losses matter alongside new-logo losses, why battlecards should support—not replace—customer understanding, and how AI can triangulate interviews, transcripts, CRM data and other signals to surface emerging patterns faster. The goal isn’t simply to explain why you lost yesterday. It’s to change the probability of winning tomorrow. Key Takeaways • Separate reported loss reasons from root causes. • Triangulate across customers, sellers, CRM data, interviews, and transcripts. • Include renewal and expansion losses to uncover dissatisfaction and changing needs. • Find strategic patterns without over-trusting individual data points. • Turn insights into action across sales, product, customer success, and competitive strategy. • Use AI to identify patterns and signals—not manufacture certainty.