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For mobile apps.

Installs, retention and LTV in one cohort model.

Paid and organic installs, subscriptions, ads and in-app purchases, modelled by cohort so you can see when each cohort pays back its install cost.

Model my app's economicsSee the investor pack outline
Model
Retention cohort → LTV
Scenario
CPI rises, D30 holds
Output
app-economics pitch pack
Trust
every number links to an assumption
Forecast Engine, illustrative interface

Startup Suite is an AI planning workspace that helps app founders model installs, monetisation, retention, and LTV vs CAC.

This page is for mobile app, gaming, and media founders modelling installs, retention, and LTV.

Revenue drivers
  • Subscriptions, ads & IAP
  • Revenue per user (ARPDAU)
  • Retention curves (D1/D7/D30)
Cost drivers
  • Paid user acquisition (CPI)
  • Platform & infrastructure
  • Content & live-ops
Key outputs
  • Retention-cohort model
  • LTV/CAC model
  • App-economics pitch pack
Relevant features
  • Forecast Engine
  • Scenario Workbench
  • AI mentors

Where app growth stops paying back

Paid installs burn cash fast

User-acquisition spend scales quickly, and it's easy to outrun what it pays back.

Most users are gone by day 30

Install counts keep rising while D30 retention falls, and paid installs stop paying back.

LTV/CAC is unclear

Blended monetisation and shaky retention make it hard to know if growth is profitable.

A founder working through installs, retention and ltv in one cohort model.

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.

Mobile app planning workspaceExample view, sample data
Scenario
Installs
42k / mo
Paid + organic
CPI
$1.80
Paid
D30 retention
18%
Cohort
ARPU
$2.40
Blended
LTV / CAC
2.6×
Payback 7 mo

Model installs and CPI, retention cohorts and a blended monetisation mix into LTV/CAC, with each figure traced to the cohort behind it.

  • Assumptions
  • Model
  • Scenarios
  • Mentor Review
  • Outputs

AI mentorCurrent retention and monetisation: LTV clears CAC with a seven-month payback.

Get early accessSee the investor pack outline

One model, connected from the first input to the final output

Change CPI or D30 retention and LTV, payback and LTV/CAC rerun. Better retention sits next to more paid spend, and the mentor flags when UA outruns payback.

Input

Edit CPI and D30 retention

Model

LTV, payback, and LTV/CAC recompute

Scenario

Compare base, D30-up, and more paid spend

Mentor

Flags when UA spend outruns payback

Output

App-economics pitch pack

Illustrative example.

Mobile app economics dashboard showing installs, retention, DAU, MAU, LTV, CPI, and payback modelling.

Where install growth meets payback

When each cohort of installs earns back what it cost to acquire.

Paid installs cost money on day one and earn it back over months. This model follows each cohort until it covers its install cost, or shows that it never will.

  • Install mix
  • Monetisation
  • Retention curves
Get early access

Spend and monetisation calls you can make

User-acquisition and monetisation calls, each checked against cohort payback rather than install counts.

How much to spend on UA
Scale acquisition only while LTV clears CAC.
Which monetisation mix
Compare revenue per user under different mixes of subscriptions, ads and IAP.
When LTV beats CAC
See the cohort and month growth becomes profitable.

What the mobile app model covers

Install mix

Model paid and organic installs, and the cost behind each.

Monetisation

Subscriptions, ads and in-app purchases combined into revenue per user.

Retention curves

Lifetime value calculated from D1, D7 and D30 retention by cohort, not an assumed lifetime.

LTV vs CAC

See the point where lifetime value clears acquisition cost.

From installs to LTV against CAC

  1. Step 1

    Set install sources

    Paid and organic installs and their costs.

  2. Step 2

    Model monetisation

    Subscriptions, ads and IAP into revenue per user.

  3. Step 3

    Add retention

    Retention curves shape LTV by cohort.

  4. Step 4

    Check LTV/CAC

    See whether growth spend pays back, and in which month.

More than a spreadsheet, more than a consultant

Spreadsheetthe old way
Paid + organic installs with cost per source
Manual
Retention curves (D1/D7/D30) drive LTV
Hard
Monetisation mix (ads / IAP / subs)
Tabs
AI mentor explains the LTV/CAC risk
App-economics pitch pack output
Consultantbilled per engagement
Paid + organic installs with cost per source
Retention curves (D1/D7/D30) drive LTV
Monetisation mix (ads / IAP / subs)
On request
AI mentor explains the LTV/CAC risk
Hourly
App-economics pitch pack output
Startup Suiteconnected workspace
Paid + organic installs with cost per source
Retention curves (D1/D7/D30) drive LTV
Monetisation mix (ads / IAP / subs)
AI mentor explains the LTV/CAC risk
App-economics pitch pack output

Pick the version closest to your business

Subscription apps

Recurring in-app revenue: model trial conversion, churn and LTV the way a SaaS company would.

Sample output: Subscription LTV model

See the Subscription apps model

Free-to-play & gaming

IAP and ad monetisation: model how spend concentrates in top payers, ARPDAU and retention by cohort.

Sample output: ARPDAU model

Ad-supported media

Impressions and fill rate: model ARPDAU from ad revenue against UA cost.

Sample output: Ad revenue model

Creator & media apps

Audience growth to revenue: model engagement, retention and blended monetisation.

Sample output: Engagement-to-revenue model

App economics questions

Do you model paid and organic installs?

It's built to. Install sources and their costs are modelled separately, so blended CAC shows how much of your growth is paid for.

Does it handle subscriptions, ads, and IAP?

It's designed to. Each revenue stream is modelled separately and combined into revenue per user, and LTV comes from your retention curves.

What is a mobile app financial model?

It models installs and cost per install, retention curves (D1/D7/D30) and a blended monetisation mix into ARPU and LTV, then checks whether LTV clears CAC.

Is this just a spreadsheet template?

No. Templates often assume a fixed user lifetime. Here LTV comes from your D1, D7 and D30 curve, so a 2-point drop in D30 changes payback for every cohort after it.

What outputs does it produce?

It's built to produce a retention-cohort model, an LTV/CAC model and an app-economics pitch pack. Every figure traces to an assumption.

Related business models

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Pairs well withAll business modelsStart with Forecast EngineConnect your data sources

Check that your app's growth pays back

Enter your CPI and D1, D7 and D30 retention, and see LTV and payback for each cohort.

Model my app's economicsSee the investor pack outline