From Paywall to Proof: Validate Synthetic Results

You used Propensity Guru to pressure-test a pricing or packaging idea. You mapped short buyer reactions to intent, spotted upgrade triggers, and picked a winning variant. Now comes the important part: prove it in the real world.

This playbook shows how to take your synthetic read and validate it with live paywall experiments and targeted offers. We use Nami for this step because it handles no-code paywalls, audience targeting, and A/B or multivariate testing out of the box. That lets you move from “looks promising” to “lift confirmed” without waiting on an app release.

The playbook extends the findings from Large Language Model Synthetic Panel Benchmarks, translating synthetic signal into live monetization experiments.

The validation loop in plain English

1. Pick the winning concept from your synthetic run.
2. Translate it into a paywall with the same price, promise, and proof points.
3. Target the audience that matched the high-intent personas.
4. A/B test against your control to isolate lift.
5. Watch acceptance, ARPU, and retention.
6. Promote the winner and iterate a new test.

Nami’s paywall builder and testing features make this fast. You can launch and update paywalls, run A/B or multivariate tests, and segment who sees what without a new build.

Step-by-step: Wire Propensity Guru → Nami

1. Freeze the hypothesis. Export the top variant from Propensity Guru with plan name, price point, promise, proof points, objections, and persona qualifiers. That export becomes your paywall spec.
2. Build the matching paywalls. Recreate the concept in Nami’s builder: headline equals your promise, body copy mirrors the proof points, and the buttons map to the exact SKU and term. Use Smart Text for dynamic price and localization so currency and plan details track store settings. Create two paywalls—your control and the new challenger.
3. Target the right audience. Use campaign filters to mirror your high-intent personas—for example Device Region, Device Language, or App/SDK version. Targeting ensures the people who loved the concept in synthetic testing see it first.
4. Launch an A/B or multivariate test. Set traffic allocation (50/50 works great), publish, and let the test run to statistical clarity. Nami supports both A/B and multivariate designs and provides guidance on sensible minimum impression counts before you call a winner.
5. Read the results like a pricing team. Track views → purchase attempts → purchases, plan mix, ARPU, trial-to-paid, day-30 retention, and downgrade/refund rate. Compare the lift to your synthetic Top-2-Box and objection patterns. When the same trigger language shows up in the winning paywall, you have proof, not just a hunch.
6. Promote the winner and iterate. Use Nami’s workflow to promote the winning paywall to 100% of the targeted audience, archive the test, and queue your next hypothesis. Keep the weekly rhythm.

Example: Concierge onboarding concept

Synthetic result: “Setup done for you” at $179 drove high intent among SMB founders. Live test: a Nami A/B experiment filtered to small-team geos and English device language. Outcome: +18% paywall conversion and +9% ARPU at day 30.

Follow-up: run a new challenger that adds an annual-only variant with a 20% savings callout. Keep the loop running until the telemetry and the synthetic read move together.

Tips that save time (and arguments)

• Mirror the copy from the synthetic winner exactly in your first live test.
• Respect segments. Show the challenger only to the personas that loved it.
• Run long enough. Follow Nami’s guidance on minimum exposure before you call a winner so the lift is trustworthy.
• Localize with Smart Text so prices and currencies stay correct without manual edits.
• Change one meaningful element per round—price, promise, or proof—not all three.

Where Nami fits in your stack

• No-code paywalls you can launch and update quickly
• A/B and multivariate testing to measure conversion lift
• Audience targeting to align with your persona segments
• Analytics and CRM hooks to track subscriber outcomes and retention

Pair those capabilities with your Propensity Guru outputs and you have the exact machinery needed to validate synthetic pricing concepts at production speed.

Mentioned resources

• Nami ML for no-code paywalls, audience targeting, and testing
• Nami best-practice references for A/B and multivariate testing (available in the customer help center)
• Guidance on campaign targeting and Smart Text configuration for localized pricing experiences

Want help wiring the pieces together? We can push your Propensity Guru outputs straight into named Nami paywalls, create audience filters, open the test with a clean 50/50 split, and replay the telemetry with you.