Create a product analytics workspace that investigates features users rarely discover, features that were adopted and later abandoned, and features that may create maintenance costs without delivering meaningful value.
Allow teams to import feature usage events, release notes, customer feedback, support tickets, documentation visits, and feature-development costs. Group features by purpose and compare adoption, repeat usage, retention associations, customer segment, and support burden where data exists.
Detect possible discoverability problems, confusing workflows, overlapping features, and discrepancies between product positioning and actual usage. Show the evidence behind each hypothesis and identify where missing instrumentation prevents reliable conclusions.
For every feature, generate options such as improving onboarding, changing its placement, simplifying the workflow, conducting customer interviews, or considering retirement.
Include a retirement impact checklist that examines dependencies, contractual commitments, accessibility, customer workflows, and migration requirements. Never equate low usage with low value without investigating context. Export a prioritized product investigation plan.
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