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Update Checklist - Research

Update Cadence

As Needed - Update when experiments complete, new research questions emerge, or strategic pivots considered.

Pre-Update Checklist

Before You Start

  • [ ] Review experiment results from past period
  • [ ] Check for new technical questions from development
  • [ ] Assess which research areas still relevant
  • [ ] Review competitive technical developments

Update Process

1. Key Findings (key-findings.md)

  • [ ] New experiments completed? Document results.
  • [ ] Technical approaches validated or invalidated?
  • [ ] Market research insights documented?
  • [ ] Competitive technical analysis updated?
  • [ ] Customer discovery learnings captured?

2. Technical Explorations (technical-explorations.md)

  • [ ] New technical approaches evaluated?
  • [ ] Architecture decisions validated?
  • [ ] Performance benchmarks updated?
  • [ ] Technology risks identified?
  • [ ] Integration challenges documented?

3. Next Experiments (next-experiments.md)

  • [ ] Prioritized list of experiments current?
  • [ ] Resource requirements estimated?
  • [ ] Success criteria defined?
  • [ ] Timeline realistic?
  • [ ] Dependencies mapped?

4. Decision Log (decision-log.md)

  • [ ] Key decisions documented with rationale?
  • [ ] Revisit dates set for contingent decisions?
  • [ ] Decision reversal criteria defined?
  • [ ] Impact of decisions tracked?
  • [ ] Learnings from decision outcomes recorded?

Experiment Documentation

For each completed experiment: - [ ] Hypothesis clearly stated - [ ] Methodology documented - [ ] Results captured with data - [ ] Conclusions supported by data - [ ] Implications for product/strategy noted - [ ] Next steps identified

Research Categories

Maintain current research in: - [ ] Technical: Architecture, performance, scalability - [ ] Market: Customer needs, competitive analysis - [ ] Product: Feature validation, usability - [ ] Business Model: Pricing, revenue, unit economics - [ ] Go-to-Market: Channel effectiveness, messaging

Experiment Prioritization

Use framework to prioritize: - [ ] Impact: High/Medium/Low - [ ] Confidence: High/Medium/Low - [ ] Effort: High/Medium/Low - [ ] Learning: High/Medium/Low

Prioritize: High Impact + High Learning, regardless of effort

  • 01-Product: Research informs MVP and roadmap decisions
  • 02-Market: Market research validates competitive positioning
  • 04-Marketing: Research on channels and messaging effectiveness
  • 05-Beta: Beta testing as ongoing research
  • 07-Fundraising: Research findings support investor narrative
  • 08-Operations: Technical research impacts operations strategy

Research Best Practices

  • [ ] Experiments have clear success/failure criteria
  • [ ] Small, fast experiments preferred over large, slow ones
  • [ ] Results documented with data (not just opinions)
  • [ ] Learnings shared with relevant stakeholders
  • [ ] Decisions and rationale tracked (decision-log.md)

Time Budget

Post-experiment: 1-2 hours (document results) Monthly review: 1 hour (assess research portfolio) Quarterly planning: 2-3 hours (prioritize next experiments)

Red Flags

⚠️ If you notice: - Experiments without clear success criteria - Technical debt from quick experiments accumulating - Research findings not being incorporated into decisions - Long gaps between experiments (>4 weeks) - Decisions made without data/experimentation

Research-to-Action Pipeline

Ensure: - [ ] Clear path from research to decision - [ ] Resource allocation for experiments - [ ] Results reviewed with stakeholders - [ ] Learnings incorporated into planning - [ ]失败 learnings captured and shared

Last Updated: YYYY-MM-DD Next Review: When experiments complete