Framework overview (6-month window)
- Month 0: define research questions, success metrics, and baseline.
- Months 1–3: discovery + validation (rapid studies, prototypes).
- Months 3–5: pilot integration with product teams, A/B or feature flags.
- Month 6: evaluate outcomes, consolidate attribution, recommend scale/stop.
Leading vs. lagging indicators
- Leading (show early momentum): number of testable hypotheses created, stakeholder adoption rate (requests for follow-up studies), prototype usability scores, percentage of designs updated per research recommendation.
- Lagging (business/product outcomes): feature engagement (DAU/WAU for feature), task completion rate, conversion or retention lift, NPS/CSAT change, revenue per user change for the cohort.
Tracking hypothesis → implementation
- Use a Research Impact Board (shared doc + kanban): columns: Hypothesis → Study plan → Findings → Recommendation → Implementation ticket → Live experiment → Outcome.
- Each card includes: hypothesis statement, primary/secondary metrics, owner, due dates, links to artifacts, and implementation ticket IDs.
- Weekly sync with PM/Design to update status; monthly impact review to decide scale.
Attribution methods
- Quantitative: randomized A/B or feature-flag cohorts tied to hypotheses; pre/post cohort analysis with difference-in-differences when randomization isn’t possible; instrument event-level analytics with experiment tagging linking findings to shipped changes.
- Qualitative: structured customer interviews targeted to users in exposed vs. control cohorts; session recordings and thematic coding to surface causal mechanisms; stakeholder feedback logs to trace decision lineage.
- Combined: causal pathway document mapping which insight informed which design change, validated by experiment lift and corroborating qualitative quotes.
Example OKR (ties to company goal: grow conversion by 15% in 6 months)
- Objective: Increase onboarding conversion by 15% through insight-driven design changes.
- Key Results: (1) Run 4 hypothesis-driven studies and deliver 8 prioritized recommendations within 8 weeks. (2) Ship 3 design changes derived from research and run experiments with ≥95% instrumentation coverage. (3) Demonstrate +10% conversion lift in experiment cohort and qualitative improvement in new-user task success rate from 68% → 85%.
Why this works
- Combines operational tracking, measurable metrics, and mixed-method attribution so research outcomes are visible, defensible, and tied to product KPIs.
