AI Product Analytics

[admin]

AI Product Analytics    

Discovery and research tell you what customers say they want; product analytics tells you what they actually do — and a mature PO practice triangulates both rather than trusting either alone. Amplitude and Mixpanel are the established players here, letting you build funnels, cohorts and retention curves to see whether a shipped feature actually moved the metric it was built to move, with AI layers increasingly able to answer plain-English questions ('why did activation drop last week?') without a data analyst writing a query by hand. PostHog is the newer, more developer-friendly entrant, bundling analytics, session replay, feature flags and A/B testing into one usage-priced platform, which appeals to teams that want one tool rather than stitching several together. The core discipline this category enforces is closing the loop: a feature shipped without an instrumented success metric is a feature nobody can honestly evaluate, and 'we shipped it' is not the same claim as 'it worked.' The practical warning worth carrying into any vendor conversation: usage-based pricing in this category can escalate quickly once you add session replay and experimentation on top of basic event tracking, so it's worth modelling your expected event volume before committing to a plan.