Demo ROI Estimator: A Fillable Model to Predict MRR Lift from One Playable

Your core goal is simple: measure how engagement with one playable demo changes conversion rates, then convert that lift into projected MRR. The spreadsheet should be organized into three blocks: instrumentation & cohort windows, conversion-to-revenue math, and sensitivity scenarios. Keep each block one screen tall so you can update numbers quickly during experiments.

Instrumentation must be event-first. Capture unambiguous events (example names below), the user or account identifier, and revenue properties (first_payment_amount, plan, billing_frequency). Use a 30‑day paid conversion window for self‑serve funnels and a 60–90 day window for sales‑assisted funnels; the window must match how long it typically takes a demo‑touched prospect to become a paying customer.

Use precise, consistent event names to avoid ambiguity between marketing, product, and sales data. Suggested canonical events: demo_shown (landing hero exposure or CTA click), demo_launch (user opened the playable), demo_completed (finished the interactive path), demo_to_contact (user submitted email/chat), and demo_to_trial (user activated a trial or microcheckout). Send account identifiers for B2B so you can attribute revenue at the account level.

Cohort windows matter more than you think. For self‑serve products define a 30‑day window from demo_launch for paid conversion. For sales‑assisted or enterprise prospects use a 60–90 day window and evaluate both trial->paid and demo_touched opportunity→win rates. Make the cohort definition explicit in the model so comparisons are apples-to-apples.

Step 1: measure baseline conversion rate (baseline_CR) for the same cohort window and population. Step 2: measure demo-exposed conversion rate (demo_CR). Relative lift = (demo_CR - baseline_CR) / baseline_CR. Step 3: projected_new_customers = demo_exposed_count * demo_CR. Step 4: projected_MRR_lift = projected_new_customers * average_monthly_revenue_per_customer (ARPC).

Include a sensitivity table with at least three scenarios (conservative, expected, optimistic) and a break‑even calculation that includes demo build cost amortized over a realistic customer lifetime or 12 months. This makes the ROI argument crisp for founders and for sales/marketing partners.

1) Randomized landing split (A/B): Route equal traffic to the existing landing vs landing that launches the playable demo. Measure demo_launch rate and downstream paid conversion within your cohort window. Use your analytics duration estimator or an MDE calculator to set a minimum test duration; practically most early tests run 2–6 weeks. If traffic is low, run a longer duration or use a pooled sequential analysis approach.

2) Demo completion nudges: For users who started but didn’t complete the demo, run a targeted email or in‑app nudge with a clear CTA (microcheckout or calendar). Measure whether nudged users convert at a higher rate than the non‑nudged cohort.

3) Microcheckout vs lead capture: Split traffic between a microcheckout (one‑click $1 / $0 trial) and an email capture flow. Measure demo_to_trial and trial_to_paid conversion to see if converting immediately raises trial quality and reduces sales cycle.

4) Rep‑assisted follow-up vs automation: For demo-completers, randomize follow-up strategy—automated drip vs quick sales outreach. Compare opportunity creation rate and win rate to understand whether the demo lowers discovery time enough to alter follow-up posture.

Beware novelty and selection effects: a fresh playable can temporarily spike engagement. Validate over a longer window or with staggered rollouts. If you see lift only in the first 2 weeks and not afterwards, consider novelty rather than sustainable product improvement.

Don’t confuse volume lift with quality erosion. An intervention that raises demo_shown or demo_launch but reduces deal size or win rate is net negative. Segment outcomes by ICP match, deal value, and retention so you can tell whether demos are attracting better customers or just more leads.