SaaS Feature Adoption Paywall Forecaster

Model the incremental MRR, conversion yield, and adoption risk before moving a beloved free feature behind a paywall. Input free-tier MAUs, current feature adoption, expected upgrade conversion, and target ARPU to forecast the revenue corridor and churn watchouts.

Count of active free-tier accounts engaging with your product each month.
Share of free users who rely on the feature you plan to gate behind a paid plan.
Percentage of engaged users projected to convert based on experiments or benchmarks.
Monthly recurring revenue per newly upgraded customer on the target plan tier.
Defaults to 40.00% if blank to warn when a heavily used free feature could drive churn.

Use alongside cohort and retention analysis before changing packaging or pricing.

Examples

  • 120,000 free MAUs, 18.00% adoption, 12.00% conversion, $19.00 ARPU → incremental MRR $49,248.00, 2,592 new paying users, sensitivity range $36,936.00 – $61,560.00, adoption within comfort zone (18.00% ≤ 40.00% threshold).
  • 45,000 free MAUs, 32.00% adoption, 8.00% conversion, $29.00 ARPU, 35.00% threshold → incremental MRR $33,408.00, 1,152 upgrades, sensitivity $22,272.00 – $44,544.00, adoption within comfort zone.

FAQ

How should I choose the conversion rate?

Use experiment results, cohort analyses, or competitor benchmarks. Start conservative—raising the input instantly shows the upside when activation improves.

Can I include churn impact?

Export the results into your retention or NRR model to layer churn scenarios alongside the incremental revenue forecast.

What if ARPU varies by region?

Enter the weighted average ARPU for the users you plan to target. For more precision, run multiple scenarios per pricing region and compare the totals.

How do I model phased rollouts?

Run separate scenarios for the cohorts you plan to migrate each month, then stack the incremental MRR outputs to mirror a phased launch roadmap.

Additional Information

  • Incremental MRR multiplies newly converted users by the upgraded plan ARPU assuming monthly billing cadence.
  • Sensitivity range shifts conversion ±3 percentage points to preview best- and worst-case revenue bands.
  • Adoption threshold flags when too many free users rely on the feature, signalling potential backlash unless you grandfather or provide alternatives.
  • Use the adoption flag output to brief customer success and support teams before communicating packaging changes.

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