Descriptive → Diagnostic → Predictive Analytics

Data isn’t just about the past—it’s a tool to understand the present and prepare for the future. This section introduces a simple but powerful 3-phase model to help founders evolve their analytics maturity over time.

Why it's Important

Helps you understand where you are in your analytics maturity.
Moves you from reporting to explaining to forecasting.
Prioritizes deeper thinking and better decisions.
Reduces guesswork as you grow.
Aligns data usage with evolving product/market maturity.

How to Implement

Start by identifying descriptive metrics (what happened): traffic, signups, churn.
Layer in diagnostic questions (why it happened): funnel drop-offs, feature usage, exit feedback.
Build toward predictive: models based on past user behavior, weighted pipeline forecasts.
Use simple tools like spreadsheets or Mixpanel to build trend analyses.
Create 1–2 monthly predictions based on observed trends.
Tie each level to decision-making moments (e.g., roadmap prioritization, GTM shifts).
Set milestones for moving from one stage to the next.

Available Workshops

Analytics Stage Mapping: Categorize current metrics into D/D/P.
Root Cause Analysis Drill: Diagnose why a recent KPI dropped.
Forecast Challenge: Predict next month’s results based on today’s data.
Confidence Calibration: Score your team’s trust in each analysis level.
Retro-Driven Diagnostics: Use sprint retros to practice cause-finding.
From Trends to Bets: Turn 3 descriptive metrics into one hypothesis.

Deliverables

Categorized metric table (descriptive, diagnostic, predictive).
Diagnostic analysis log (what dropped, why, what we did).
Prediction sheet with accuracy scoring over time.
Analytics maturity roadmap (where we are, where we’re going).
Monthly “analytics spotlight” deck for internal or investor use.

How to Measure

% of metrics that go beyond descriptive.
Prediction accuracy rate over time.
Time to uncover root causes of changes.
Movement along maturity roadmap.
Confidence level in predictions from team.

Real-World Examples

Segment

Built a layered analytics culture from diagnostic insights into churn before automating predictive usage scoring.

Amplitude

Taught customers to progress through this model with guided product analytics.

Slack

Used predictive behavior signals (DAU frequency) to drive enterprise expansion strategies.

Get It Right

Start simple—don’t try to predict everything at once.
Use real past data to inform future forecasts.
Get good at diagnostic questions before moving to prediction.
Encourage healthy skepticism of predictions.
Review predictions monthly to improve calibration.

Don't Make These Mistakes

Jumping to predictions before understanding “why.”
Confusing correlation with causation.
Thinking you need AI to forecast (start with spreadsheets).
Avoiding predictions out of fear of being wrong.
Ignoring user feedback when interpreting metrics.