Most subscription apps don't lose users because of "bad pricing".
They lose them before the user ever sees the paywall.
Onboarding is the invisible bridge between installation and revenue. A clear, focused flow:
→ Helps users quickly understand what your app does
→ Filters in the right users (and filters out the wrong ones)
→ Makes the paywall feel like a natural next step, not a surprise
Below is a 9-step onboarding framework from first screen to paywall, based on patterns we consistently see in successful subscription apps.
Exact impact will always depend on your product and audience, so treat these as high-confidence starting points to test, not universal laws.
Step 1 – First Screen: Make a Clear Promise
The first screen should answer:
"What will this app help me achieve?"
Weak first screens talk about the product:
"All-in-one fitness app."
Stronger ones talk about outcomes:
"Sleep better in 7 days."
"Turn one selfie into 40 professional headshots."
Keep it simple:
→ One strong outcome-driven headline
→ One main CTA ("Get Started")
→ No navigation clutter or competing buttons
Step 2 – Context & Trust: Why You're Worth Their Time
Once a user taps "Get Started", they're curious but cautious.
A short second screen can:
→ Explain how you work ("Answer a few questions and we'll build a plan for you")
→ Add light social proof ("Trusted by 2M+ users", "Featured in...")
The goal isn't to tell your full story, but to make continuing feel safe and reasonable.
Step 3 – Goal Selection: Start With What They Want
A common pattern in high-performing apps: the first real question is about the user's goal.
Examples:
→ Fitness: "Lose weight / Build muscle / Get healthier"
→ Productivity: "Finish a project / Build a habit / Get organized"
This:
→ Makes users feel understood
→ Gives you a key variable to personalize later screens and paywall copy
Keep options clear, mutually exclusive, and in user language.
Step 4 – Profile & Constraints: Understand Their Reality
Next, ask a few high-signal questions about the user's situation:
→ Experience level (Beginner / Intermediate / Advanced)
→ Time available (days per week, minutes per day)
→ Context (home/gym, solo/with coach, etc.)
These answers should directly influence:
→ The plan you show
→ The default recommendations
→ The messaging on the paywall
Rule of thumb: If a question doesn't change anything in the experience, remove it from onboarding or move it later.
Step 5 – Micro-Progress: Make the End Visible
Even short quizzes can feel long if users don't know when they'll end.
Simple patterns that improve completion:
→ Step indicator ("Step 2 of 5")
→ Short positive feedback ("Got it", "Nice choice")
→ Occasional hints ("We're building your plan based on these answers")
The aim is to make onboarding feel finite and manageable, not endless.
Step 6 – Plan Preview: Turn Answers Into a Clear Plan
Before you ask for money, show what you're actually offering this specific user.
A typical plan preview might include:
→ Their goal and basic profile
→ Frequency and duration ("3× per week, 20 minutes per session")
→ A short summary of what the plan includes
This step proves you were listening and sets up the logic:
"This looks like a real plan tailored to me → paying for it might be worth it."
Step 7 – First Taste of Value: Show, Don't Just Tell
When possible, give users a small, real taste of your product:
→ Generate one AI headshot or filter
→ Show the first few days of their program
→ Let them try a basic version of a core feature
Many teams find that even a tiny "this actually works" moment increases willingness to pay.
Important: The exact effect is product-specific and should be A/B tested. If a live sample isn't feasible, use strong, clearly labeled examples instead ("Example result based on similar profiles").
Step 8 – Value Recap: Bridge to the Paywall
Rather than jumping straight from quiz to paywall, add a short value recap:
"To help you [goal], your plan includes:
→ [Benefit 1]
→ [Benefit 2]
→ [Benefit 3]"
Each bullet should connect a feature to an outcome.
This small step makes the paywall feel like a logical continuation:
"I see what I'm getting → now I decide if it's worth paying for."
Step 9 – Paywall: Structure, Framing & Exit
By the time users reach your paywall, they should:
→ Know their goal
→ See a plausible plan
→ Have at least a small sense that this might work
Your paywall's job is to help them commit, not to re-explain everything from scratch.
Common patterns in strong paywalls:
→ 2–3 plan options, not 6–7
→ Annual as the default (with clear savings: "Save 40% vs monthly")
→ Copy that re-uses onboarding context: "Best for busy beginners training 3×/week"
→ Clear trial framing if you offer one: "7 days free, then $X/month. Cancel anytime."
→ A respectful exit: "Maybe later" or "Continue with limited access"
Whether these patterns work for you depends on price point, category, and audience. Pay attention to your metrics: paywall view rate, trial start rate, and conversion after trial.
How to Apply This 9-Step Framework
You can treat this breakdown as a practical checklist:
1) Map your current flow
From first screen → last onboarding step → paywall.
2) Tag each screen
Which step is it trying to take? Are you skipping some (e.g., no plan preview) or merging too many into one?
3) Choose 1–2 steps to improve first
Typical "high-leverage" changes to test:
→ Add a clear goal selection screen
→ Add a plan preview before the paywall
→ Make paywall copy reflect user goals instead of generic text
4) Measure impact
Track: onboarding completion, day-0 paywall views, trial starts, and purchases.
The goal isn't perfection in one shot, but steady improvement over time.
Where PaywallPro Helps
Designing or optimizing onboarding is much easier when you can see how leading apps do it in the real world.
With PaywallPro, you can:
→ Browse real onboarding flows and paywalls from successful iOS subscription apps
→ Compare different categories (AI, health & fitness, productivity, and more)
→ Study how top apps move users through these 9 steps—from first screen to paywall—and how their designs evolve over time
Instead of guessing, you can ground your experiments in patterns that already appear in high-performing apps, then validate what works for your specific audience with your own data.
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