The Playable‑Proof Conversion Benchmarks Report: What Good Looks Like for Pre‑Build Experiments

A playable proof is a short, product‑like experience shipped before full development: think an interactive demo, simulated workflow, or constrained sandbox that shows value in minutes. The goal is not perfect UX — it’s a measurable user action that signals demand (email signups, deposits, demo requests, trial starts, or a clearly defined activation event).

For benchmarking and decision‑making, focus on four outcomes you can instrument reliably: landing‑page conversion (visitor → sign‑up/lead), deposit or commitment rate (visitor → paid deposit or refundable commitment), demo→trial conversion (booked demo leading to an activated trial), and day‑7 activation (new cohort returns and hits a key value event within 7 days). These are the metrics that predict whether a playable proof is worth building into a shipped product.

Benchmarks depend on channel, audience, and complexity of the playable proof, but useful anchors (for founders and indie builders shipping directly to early adopters) are: landing‑page conversion 5–15%, deposit/commitment 1–4% of visitors, demo→trial conversion 20–40% (from booked demo to activated trial), and day‑7 activation 7%+ for top performers in SaaS categories. Use these as starting targets, then segment by traffic source and use cohorts for clarity.

Why these ranges? A higher landing conversion (5–15%) is realistic when your page targets a tightly‑defined niche and the playable proof communicates value clearly. Deposit rates are lower because they require risk; 1–4% is a practical signal of real paying intent for pre‑orders or refundable deposits. For demo funnels, a 20–40% demo→trial conversion shows the demo successfully uncovers value and gets users into the product. Day‑7 activation at or above ~7% is a practical top‑quartile threshold reported in activation benchmark studies and correlates with improved trial→paid outcomes.

Before running any A/B or benchmark inference, calculate the sample size you need for the minimum detectable effect (MDE) you care about, your desired power (commonly 80%), and significance level (commonly 5%). For conversion‑rate metrics, use proportion/sample calculators such as Evan Miller’s or Optimizely’s to translate baseline conversion and target lift into required visitors or events.

Rule‑of‑thumb sample sizes: to detect a 20% relative lift on a 5% baseline (absolute +1pt), you’ll often need several thousand visitors per variation. For low‑base outcomes (deposits at 1–2%), detecting small relative lifts (<25%) becomes impractical at early scale — instead, aim to detect large signals (2–3x) or treat early experiments as directional. Use sequential stopping rules or platform calculators only if you understand their statistical assumptions.

Instrument every playable proof with at least these events: page_view, playable_start, playable_complete (or first core action), sign_up, deposit_made, demo_booked, trial_started, and key_activation_event. Tag each event with traffic_source, campaign, cohort_date, and experiment_id so you can pivot by channel and cohort.

Sample analytics queries (SQL‑style) you can adapt: 1) Landing conversion by source: SELECT traffic_source, COUNT(DISTINCT user_id) AS visitors, SUM(CASE WHEN event='sign_up' THEN 1 ELSE 0 END) AS signups, signups/visitors AS conversion_rate FROM events WHERE date BETWEEN x AND y GROUP BY traffic_source; 2) Deposit rate: SELECT SUM(CASE WHEN event='deposit_made' THEN 1 ELSE 0 END)/COUNT(DISTINCT user_id) FROM events WHERE playable_id='pp_v1'; 3) Day‑7 activation cohort: build daily cohorts by cohort_date and compute percent with key_activation_event within 7 days.

These queries expose both the numerator and denominator you need for sample‑size planning and statistical tests. Store raw event timestamps to calculate true time‑to‑activation and avoid optimistic rounding (e.g., counting same‑day activation as day‑0 instead of day‑1 when you mean day‑7).

Treat these benchmarks as decision thresholds, not gospel. If your playable proof hits landing conversion in the mid‑range and shows above‑benchmark demo→trial and day‑7 activation, you have evidence to prioritize a minimal MVP and invest in productized onboarding. If you see high signups but low deposits and low activation, the experiment uncovers a gap: interest exists but value is not proven — iterate the playable proof to surface clearer first‑value moments or reduce friction to commitment.

Operational checklist for founders: 1) Define the commitment signal you’ll treat as ‘go’ (e.g., deposit rate ≥ 1.5% or demo→trial ≥ 25%). 2) Pre‑register your analysis plan and MDE to avoid p‑hacking. 3) Run the test long enough to reach your calculated sample size, or accept that small samples are directional only. 4) Combine quantitative thresholds with qualitative feedback from session recordings and demo calls before final decisions.