数据驱动决策意味着用证据来支撑产品选择——使用数据、实验、研究——而不是仅凭观点。更成熟的做法是数据知情:数据磨练判断力,但不会替代它。目标是降低凭猜测下注的风险。
证据来源
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✓ QUANTITATIVE → analytics, funnels, retention, A/B tests (what is happening)
✓ QUALITATIVE → user interviews, support tickets, session recordings (why)
→ combine both: numbers tell you WHAT, conversations tell you WHY
仅定性数据会告诉你漏斗在第 3 步下降,但不会告诉你为什么——而且你无法修复你不理解的原因。
实验(A/B 测试)
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✓ Form a HYPOTHESIS → "shorter signup → higher completion"
✓ Define the SUCCESS METRIC up front (and guardrails)
✓ Run on a meaningful, RANDOMIZED sample to significance
✓ Decide BEFORE looking → avoid cherry-picking results
✓ Beware false positives, tiny samples, and short-term wins that hurt long-term
