Evergreen Demo Measurement Plan: 6 Metrics That Predict MRR (with Dashboards & Event Names)

Not every demo interaction matters equally. Track signals that have a direct, traceable path to conversion, retention, or expansion. The measurement plan below focuses on six high-signal metrics you can instrument quickly and monitor continuously to predict and protect MRR.

This is a research-forward workflow: define events, map them to business outcomes (trial start, conversion, expansion), build compact dashboards, and run lightweight cohort checks weekly. Follow the naming standard in section 4 so your funnels and cohorts remain stable as the product evolves.

1) Demo-to-qualified-rate (DQR): percent of booked demos that reach a qualification outcome (e.g., SQL or demo_success property = true). This isolates demo quality from booking volume and is the first-order predictor of demo-sourced MRR.

2) Time-to-first-action-after-demo (TFA): median time between demo end and the first high-intent action (trial start, contract uploaded, payment method added). Shorter TFA correlates with higher close rates and faster cash realization.

3) Demo-to-paid conversion (DPC) within 30/90 days: classic outcome metric. Track both 30-day (speed) and 90-day (maturity) windows to catch slow closes that still convert to MRR.

4) Net-new ARR per demo cohort (NARR): revenue attributed to demo cohorts (by demo week or rep) divided by demo volume — a revenue efficiency metric that normalizes for lead flow variability and surfaces high-performing reps or campaigns.

Create one compact ‘Demo Health’ dashboard with 6 panels that map directly to the metrics above: DQR, TFA (median), DPC (30/90d), NARR per demo, 14-day feature engagement, and demo drop-off by step. Use simple visual encodings: sparkline for trend, bar for cohort breakdown, and table for top/ bottom reps or campaigns.

Add two secondary dashboard views: a ‘Cohort Quality’ panel that slices demos by source/campaign and a ‘Revenue Attribution’ panel that shows MRR movement for demo cohorts (week-of-demo → MRR after 30/90/180 days). These views let you answer both “is demo volume good?” and “is demo quality changing?” quickly.

Use a simple object_action snake_case convention that works across Segment, Mixpanel, Amplitude, and warehouses. Each event name should be human-readable, stable, and accompanied by a short description plus required properties (user_id, demo_id, rep_id, source, demo_step, demo_outcome, revenue_tag). This minimizes ambiguity in funnels and cohort joins.

Example events (copy these exact names into your tracking spec): demo_booked, demo_started, demo_step_completed, demo_ended, demo_outcome_recorded, post_demo_trial_started. For each, include a demo_step (string) and demo_outcome (enum: qualified, disqualified, no_show, scheduled_followup).

Run these three lightweight cohort checks weekly: 1) Source mix by demo cohort (are any sources suddenly dominant?), 2) Demo outcome distribution by rep (are qualification rates drifting?), 3) Post-demo feature engagement per cohort (is new onboarding impacting usage?). These checks catch upstream problems early — for example, a new campaign sending low-intent leads will show up as rising demo volume with falling DQR and NARR.

For governance: designate a taxonomy owner, keep an event catalog (short descriptions + required props), and set a semantic versioning rule for breaking changes. Tools like Mixpanel Lexicon or a simple shared doc serve as the single source of truth so analysts and engineers can iterate without corrupting historical cohorts.