Research & Analysis Checklist for SaaS & Startups

Capture a one sentence decision and the options under consideration, then map the decision to KPIs like activation, retention, or payback period. This keeps research from drifting and ensures analysis ends in action.

Choose one North Star metric and 3 to 5 guardrails using AARRR or RARRA frameworks. Define exact SQL or tool calculations to prevent metric drift across Mixpanel, Looker, or Mode.

Document who approves, who is consulted, and who is informed using a simple RACI. Identify a single decision owner to avoid last minute surprises and rework.

Write falsifiable hypotheses and specify what result will change the roadmap, including minimum detectable effect and confidence levels. This prevents fishing expeditions and clarifies when to stop.

Time box work into a two week sprint with mid sprint check and an end of sprint decision review. Define when to pause data collection to avoid analysis paralysis.

Confirm access to GA4, Mixpanel or Amplitude, CRM, Stripe or Paddle, and the warehouse. Review PII handling, consent, and SOC 2 scope to keep the work compliant.

Create a shared workspace in Notion or Confluence, a Mode or Looker folder, and a Git repo for SQL or notebooks. Standardize naming and folder structure for easy reuse.

Use LinkedIn, Crunchbase, government statistics, and pricing to quantify market size by segment. Document assumptions and formulas to support investor or exec review.

Map competitors by ICP, use case, and deployment model, then score strength by feature depth and distribution. Pull evidence from G2, Capterra, and customer reviews.

Capture tiers, usage metrics, overage, discounting, and common price fences from websites and sales calls. Identify opportunities for land and expand, and risk of margin compression.

Use Similarweb and Ahrefs to estimate top channels, branded vs non branded share, and CPC ranges. Benchmark your blended CPA and payback against public peers.

Extract headline claims and proof points from competitor homepages and sales decks, then map to Jobs To Be Done. Identify gaps where your differentiated value is strongest.

Run BuiltWith or Wappalyzer and scan hiring posts to infer product bets and integrations. Use this to anticipate roadmap moves and partnership options.

Track product changelogs, release notes, and GitHub stars or issues for open source competitors. Look for patterns in weekly or monthly ship cadence and adoption mentions.

Build firmographic and technographic segments using enrichment from Clearbit or ZoomInfo. Define positive and negative personas to focus research and campaigns.

Recruit 8 to 12 users per segment, record with Zoom, transcribe, and code by JTBD and pain intensity. Use a consistent script to reduce bias and increase signal.

Search Gong or Chorus for common objections, competitor mentions, and feature requests. Quantify frequency and tie themes to segments and deal size.

Define key events and properties, then build funnels and retention in Mixpanel, Amplitude, or PostHog. Align activation to a clear aha moment tied to value delivery.

Use ChartMogul, Baremetrics, or warehouse SQL to chart retention curves and payback by segment and channel. Identify where LTV to CAC is weakest and why.

Export Zendesk or Intercom tickets, cluster by theme, and compute rate per 100 active accounts. Feed top issues into roadmap and help center updates.

Run micro surveys with Typeform, Hotjar, or native widgets and tag responses to user IDs. Correlate NPS or CSAT with feature usage and segments for targeted fixes.

Create a tracking plan with consistent naming, required properties, and owner per event. Validate events end to end via Segment or SDKs to avoid broken funnels.

Pipe GA4, product events, Stripe or Paddle, CRM, and support data into BigQuery or Snowflake with Fivetran or Airbyte. Set freshness SLAs and lineage visibility.

Model canonical tables for accounts, events, and revenue, then define metrics like MRR and activation in a single layer. Version control the logic to prevent metric drift.

Implement dbt tests and Great Expectations for nulls, uniqueness, and referential integrity. Send Slack alerts when tests fail and gate dashboards on data health.

Pre compute sample size and minimum effect using tools like Statsig calculators or Optimizely Stats. Define a fixed analysis window and avoid peeking to reduce false positives.

Use Prophet or ARIMA in notebooks like Hex or Jupyter to project signups, MRR, and infrastructure load. Include seasonality and promo effects where applicable.

Calculate LTV to CAC, gross margin, and payback by channel and segment. Run sensitivity tests on churn and discounting to define safe growth ranges.

Create a TLDR with the decision, 3 to 5 findings, risks, and next actions, and link every claim to a data artifact. Keep it readable for busy execs and product leaders.

Build Looker, Mode, or Hex views for activation, retention, payback, and funnel drop offs, broken out by ICP and plan. Add data health indicators on every page.

Score research driven initiatives by Reach, Impact, Confidence, and Effort and include sensitivity ranges. Use this to prioritize roadmap items in a transparent way.

Maintain a log in Notion that records decisions, supporting evidence, and assumptions that need validation. Review monthly to retire or update assumptions.

Translate chosen actions into Jira or Linear epics with clear acceptance criteria tied to metrics. Align to quarterly OKRs and define owners and due dates.

Schedule SQL or notebook jobs with Airflow, Dagster, or native schedulers and send Slack alerts on threshold breaches. Reduce manual reporting time for the team.

Collect SOC 2, ISO 27001, DPAs, and subprocessor lists and map them to your data flows. Flag gaps in encryption, access controls, and data residency.

Reconcile MRR, churn, and expansion from Stripe or Paddle to your ledger and analytics outputs. Investigate anomalies in plan upgrades, refunds, and proration.

Review GitHub repo activity, dependency risk with Snyk, and deployment frequency with CI logs. Note hotspots that may slow shipping speed or increase downtime risk.

Verify consent, retention, and deletion policies across GDPR and CCPA, and audit access logs. Confirm analytics and research use respects customer agreements.

Estimate customer density by segment and region using CRM and enrichment data, then map underserved niches. Quantify addressable whitespace for focused go to market.

For partnerships or acquisitions, draft an integration plan for APIs, webhooks, identity, and analytics. Define a 30, 60, 90 day timeline with measurable milestones.