Regulatory pathway
Does this industry require approval or licensing before you can sell?
Skip or Ship — Validate by Industry
Validate your mobile app idea with retention and ASO-aware scoring — monetisation, discoverability, and Day-30 retention.
Does this industry require approval or licensing before you can sell?
How long from first contact to signed contract for a typical buyer here?
What stops a funded competitor copying this in six months, specifically here?
What do typical margin, CAC, and churn look like for this category?
How do buyers in this industry actually discover new vendors?
Mobile app ideas face a discoverability problem most other categories don't: app stores are closed ecosystems where organic discovery is nearly dead, and the monetisation model (ads, subscription, one-time purchase, freemium) has to be decided before you write a line of code.
This validator checks retention realism (most apps lose 75%+ of users by day 30 — is your model built to survive that), monetisation-model fit, and whether there's a discoverability plan beyond 'hope it goes viral'.
| Category | What's different for Mobile App |
|---|---|
| Distribution | App Store Optimisation (ASO) and a specific pre-launch audience are weighted — 'organic app store discovery' alone without a distribution plan scores poorly. |
| Monetisation | The monetisation model needs to match usage pattern — subscription for daily-use utility, one-time purchase for a single-task tool, ads only with genuinely high engagement volume. |
| Real Pain | Retention curve realism is checked — apps solving a one-time need (a converter, a calculator) score differently on retention expectations than daily-habit apps. |
| Competition | App store category saturation is checked against comparable apps' install counts and ratings, not just a Google search for competitors. |
| Metric | Typical value | Why it matters |
|---|---|---|
| Typical Day-30 retention (consumer apps) | ~25% (top quartile), 5–15% (median) | Model your growth assumptions against realistic retention, not best-case. |
| Typical CPI (cost per install) | £1–£4 (varies hugely by category) | Paid user-acquisition economics need to work against realistic LTV. |
| Freemium-to-paid conversion | 2–5% | Free-tier volume needs to be large to support meaningful paid conversion. |
| Typical time to MVP | 8–16 weeks | For a focused single-feature app, cross-platform frameworks can reduce this. |
Habit-tracking app with social accountability groups and streak mechanics
Strong retention hook via social accountability, but monetisation model and differentiation against established habit apps need sharpening.
Generic to-do list app with no unique mechanic
Extremely saturated category (Todoist, Things, TickTick, Apple Reminders) with no differentiation.
Niche fitness app for a specific training methodology with a coach marketplace
Clear buyer and defensible niche, needs a validated distribution channel before Ship.
The examples above show the pattern — now score your specific idea. Head to the free idea validation tool and describe your buyer, pain, distribution channel, and pricing model. You'll get a Ship, Fix, or Skip verdict with the same 10-category breakdown in under 30 seconds.
Direct answer — Validate a Mobile App idea
Validate your mobile app idea with retention and ASO-aware scoring — monetisation, discoverability, and Day-30 retention.
Describe your idea and the Skip or Ship engine returns a Ship, Fix, or Skip verdict with a full 10-category score breakdown — free, in 30 seconds, no signup.
Match it to usage pattern: subscription for daily-use utility apps, one-time purchase for single-task tools, ads only if you have genuinely high engagement volume (10+ sessions/week).
ASO (keyword-optimised title and description), a pre-launch audience from a related community, and press/blog outreach targeting app-review sites in your category. Organic app store search alone rarely generates meaningful volume for new apps.
Median consumer apps retain 5–15% of users by day 30; top-quartile apps hit around 25%. Model your unit economics against realistic retention, not best-case assumptions.
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