Growth Autopilot helps you figure out which experiments to run based on your actual performance data and how similar apps in your market are doing. Instead of guessing what might work, you get specific recommendations for tests that are more likely to improve your results.

This article offers a transparent look at how Autopilot thinks — what data it uses, how it evaluates opportunities, and why certain recommendations appear. The goal is to help you feel confident using it as part of your growth workflow.

What Autopilot actually does

Autopilot analyzes your app and paywall metrics to find the experiments that are most likely to increase your revenue. It looks at:

• Your current setup: pricing, trials, products, and how well they convert
• Market patterns: how similar apps structure their offers and what they charge
• Growth potential: which changes are most likely to make a difference

Autopilot prioritizes tests based on their potential impact and turns them into experiments you can launch right away. You get a focused plan without having to research competitors or guess what to test next.

The data behind Autopilot

Each recommendation is built from three main data sources that work together.

Your app’s own data

Autopilot looks at how your app performs today:

• Conversion metrics across your paywalls
• Pricing and product structure

This gives Autopilot a baseline to work from before suggesting any changes.

Paywall analysis

Autopilot analyzes your paywall screenshot and compares its design against the established patterns used by top-performing apps in your category. It evaluates layout choices, copy, subscription breakdowns, and conversion-oriented elements like savings badges or review sections.

This analysis produces two types of recommendations:

• Benchmarked recommendations based on what top-performing apps do differently — each backed by a specific stat (for example, “Used by 72% of top-performing Education apps”).
• Visual analysis recommendations generated by AI from your screenshot, covering copy improvements, layout changes, and other design adjustments.

These recommendations feed directly into your growth plan as A/B test rounds.

Competitor data

Autopilot compares your setup with similar apps in your market using public information like pricing, subscription structures, and common patterns in your category. These comparisons are country-specific, since competitor pricing and structures vary across markets. The data is based on third-party and public sources, not private metrics from other Adapty clients.

This way, you’re testing strategies that already work for apps like yours, not just random ideas. When you see the analysis, you can compare your benchmarks and competitor prices side by side. If similar apps are doing better with different pricing or structure, that’s a good signal that the same approach could work for you too.

Industry benchmarks

Autopilot also uses category-level data to show how you compare to the industry average within a specific country. This data is anonymized and aggregated from Adapty’s own metrics, not tied to specific apps.

For example, your conversion funnel might be compared against the average for apps in your category and country. This helps you see if you’re underperforming, doing about average, or already ahead of the curve.

Geographic market data

Autopilot analyzes your top 5 markets by revenue (excluding your main country) to identify where regional pricing adjustments could unlock more revenue. For each market, it evaluates:

• Conversion rate: How each country’s install-to-paid rate compares to your global average. A higher rate may indicate room to increase prices; a lower rate may signal price sensitivity.
• Pricing potential: Categorized as High, Medium, or Low based on the Adapty Pricing Index, which uses real transaction data to compare purchasing power across countries.

You can act on these recommendations directly by adding them as geo-pricing rounds in your growth plan.

How Autopilot decides what to recommend

Autopilot creates a sequence of experiments to improve your paywall step by step. Instead of testing everything at once, it focuses on one change at a time so you can see what actually works.

Here’s how it works:

1. Find the biggest opportunity

   Autopilot reviews your pricing, products, and funnel performance, then compares them with industry patterns and similar apps. It looks for where you have the most room to improve, whether that’s adjusting your price, adding a trial, or changing your offer structure.

2. Pick the next experiment

   Each recommendation is part of a testing sequence. You might test new products, price points, trial configurations, or design changes, depending on what’s likely to have the biggest impact.

3. Run winner vs. challenger rounds

   After each experiment, the winner becomes your new baseline. The next recommendation builds on that result with a new challenger. Each round gets you closer to the best setup for your app.

4. Keep it practical

   Autopilot only suggests tests you can launch with your existing products and setup, or with small changes like creating a new product or adjusting a price. The goal is to keep testing fast and manageable.

5. Show you the reasoning

   For each recommendation, Autopilot provides a clear hypothesis that explains exactly why this test is worth running. You’ll see how your current metrics compare to competitors and industry averages, what the opportunity is, and which key metrics we expect to improve.

This turns experimentation into a repeatable process where each test teaches you something and moves you toward a more effective paywall.

What happens after you complete the experiments

Once you finish all the recommended experiments and see an increase in revenue, your work isn’t done. After some time with your new setup, you can rerun the analysis and start a fresh round of experiments. You might even choose to compete with more advanced competitors now that you’ve optimized your baseline. This iterative approach helps you keep maximizing your revenue as your app grows and the market evolves.