For mobile apps.
Installs, subscriptions, and LTV that hold up.
Paid and organic installs, subscriptions, ads, in-app purchases, retention, and LTV, the app economics investors expect to see.
Startup Suite is an AI planning workspace that helps app founders model installs, monetization, retention, and LTV vs CAC.
This page is formobile app, gaming, and media founders modelling installs, retention, and LTV.
- Subscriptions, ads & IAP
- Revenue per user (ARPDAU)
- Retention curves (D1/D7/D30)
- Paid user acquisition (CPI)
- Platform & infrastructure
- Content & live-ops
- Retention-cohort model
- LTV/CAC model
- App-economics pitch pack
- Forecast Engine
- Scenario Workbench
- AI mentors
You shouldn't need a finance team to answer this
The everyday questions this module makes simple.
Paid installs burn cash fast
User-acquisition spend scales quickly, and it's easy to outrun what it pays back.
Retention decays quietly
Installs look great until you see how few users are still around on day 30.
LTV/CAC is unclear
Blended monetization and shaky retention make it hard to know if growth is profitable.
Model your app's economics
Edit CPI and retention and watch LTV, payback, and LTV/CAC recompute, then test improving retention versus scaling paid acquisition.
Model installs and CPI, retention cohorts, and a blended monetization mix into an LTV/CAC you can defend to an investor.
- Assumptions
- Model
- Scenarios
- Mentor Review
- Outputs
AI mentorCurrent retention and monetization, LTV clears CAC with a seven-month payback.
Not a dashboard, a model that connects end to end.
Editable assumptions change the model, scenarios expose the trade-offs, an AI mentor explains the risk, and the result is an output you can defend. Example view · illustrative.
Edit CPI and D30 retention
Editable assumption
LTV, payback, and LTV/CAC recompute
KPI impact
Compare base, D30-up, and UA-channel mix
Base / upside / downside
Flags when UA spend outruns payback
AI risk insight
App-economics pitch pack
What you can defend
Illustrative, capabilities and typical targets, not guaranteed outcomes.
Why founders reach for this
Installs, subscriptions, and LTV that hold up.
Model paid and organic installs, and the cost behind each.
- Install mix
- Monetization
- Retention curves
Decisions you can make this week
Practical calls this module helps you get right, no finance background needed.
The capabilities that earn the work.
Install mix
Model paid and organic installs, and the cost behind each.
Monetization
Subscriptions, ads, and in-app purchases blended into revenue per user.
Retention curves
D1/D7/D30 retention that drives realistic lifetime value.
LTV vs CAC
See the point where lifetime value clears acquisition cost.
From signal to decision.
- 1
Set install sources
Paid and organic installs and their costs.
- 2
Model monetization
Subs, ads, and IAP into revenue per user.
- 3
Add retention
Retention curves shape LTV by cohort.
- 4
Check LTV/CAC
Confirm growth spend pays back.
More than a spreadsheet, more than a consultant
| Feature | Spreadsheetthe old way | Consultant$10k+ / model | Startup Suiteconnected workspace |
|---|---|---|---|
| Paid + organic installs with cost per source | Manual | ||
| Retention curves (D1/D7/D30) drive LTV | Hard | ||
| Monetization mix (ads / IAP / subs) | Tabs | On request | |
| AI mentor explains the LTV/CAC risk | Hourly | ||
| App-economics pitch pack output |
- Paid + organic installs with cost per source
- Manual
- Retention curves (D1/D7/D30) drive LTV
- Hard
- Monetization mix (ads / IAP / subs)
- Tabs
- AI mentor explains the LTV/CAC risk
- App-economics pitch pack output
- Paid + organic installs with cost per source
- Retention curves (D1/D7/D30) drive LTV
- Monetization mix (ads / IAP / subs)
- On request
- AI mentor explains the LTV/CAC risk
- Hourly
- App-economics pitch pack output
- Paid + organic installs with cost per source
- Retention curves (D1/D7/D30) drive LTV
- Monetization mix (ads / IAP / subs)
- AI mentor explains the LTV/CAC risk
- App-economics pitch pack output
Built for your specific model.
Subscription apps
Recurring in-app revenue, model trial conversion, churn, and LTV like a SaaS.
Output · Subscription LTV model
See the Subscription apps modelFree-to-play & gaming
IAP and ad monetization, model whale curves, ARPDAU, and retention by cohort.
Output · ARPDAU model
Ad-supported media
Impressions and fill rate, model ARPDAU from ad revenue against UA cost.
Output · Ad revenue model
Creator & media apps
Audience growth to revenue, model engagement, retention, and blended monetization.
Output · Engagement-to-revenue model
Questions founders ask
Do you model paid and organic installs?+
Yes, install sources and their costs are modelled separately, so blended CAC reflects reality.
Does it handle subscriptions, ads, and IAP?+
Yes. Monetization blends into revenue per user, and retention curves drive a defensible LTV.
What is a mobile app financial model?+
It models installs and cost per install, retention curves (D1/D7/D30), and a blended monetization mix into ARPU and LTV, then checks whether LTV clears CAC.
Is this just a spreadsheet template?+
No. Edit CPI or D30 retention and LTV and payback update live, scenarios compare side by side, and an AI mentor flags when UA spend outruns payback.
What outputs can I export?+
A retention-cohort model, an LTV/CAC model, and an app-economics pitch pack, every figure traceable to an assumption.
Adjacent capabilities.
Make this the last spreadsheet you build
Build your plan, connect your data, and see this working on your own numbers, no finance team required.
From idea to investor-ready · built for founders, not finance teams