Hey, Ivan here!

Today I'll cover:

  • why developers turn off profitable campaigns without knowing they're profitable

  • how to build analytics on par with $20M+ companies

  • how to increase revenue with simple changes

Let's dive in.

Quick wins from the Adapty report.

Adapty released their subscription apps report. Key takeaways:

  1. Weekly > Yearly by LTV. Weekly subscriptions generate more revenue per user.

  2. And weekly paywalls with trials outperform every other setup.

  3. Higher prices = more money. High-price apps earn 3× more. Conversion doesn't compensate for lower prices.

What to do: raise prices, add weekly subscriptions, and test trials.

Lots of useful data in there – check it out.

In partnership with Adapty

How to build corporate-level analytics

I built analytics for my app in just one week – and it's a game changer. Now I know exactly:

  • how trial conversions flow into paid subscribers

  • how long each cohort takes to pay back

  • what ROAS and CAC I need on days 3, 7, and 30 to hit my payback targets

  • how many days until a campaign breaks even

  • which countries are top performers

  • how payback KPIs differ by country

At my previous job, building this took 6-12 months, 2 analysts, and 1 data engineer.

Today I did it in 1 week by myself with Claude Code.

Step 1. Collect subscription data

Set up webhooks from RevenueCat / Adapty to your server. Store: user_id, event_name, revenue, date.

Also export raw event data for your entire history – you'll need it for cohort analysis.

What goes into the database: user_id, event type (trial started, subscription started, renewal, cancellation, refund), product_id, revenue, date.

Ask Claude Code to set up the DB and load everything.

Step 2. Connect Apple Ads API

If you're running Apple Search Ads, connect directly via API. If you have other traffic sources and an MMP – connect to the MMP.

You need: spend, impressions, taps, installs – broken down by campaigns, ad groups, and keywords. By day.

Step 3. Link ads to subscriptions

Adapty / Superwal / RevenueCat has attribution in webhooks – campaign_id, adgroup_id, keyword_id from Apple Search Ads.

Important: store attribution separately from events – one record per user.

Step 4. Build ROAS curves and target KPIs

This is the key step – without it you won't know if a campaign is profitable or not.

4.1. Build historical ROAS curves

Ask Claude Code to build a payback chart for monthly cohorts based on historical data – both paid and blended (with organic). The goal is to see how fast old cohorts paid back.

4.2. Calculate target KPIs

Ask Claude Code to generate a KPI curve from historical cohorts based on your target payback period.

Say you need payback in 180 days (ROAS 1.0x by d180). From my historical curves, ROAS grows like this:

  • d7 = 0.28x

  • d30 = 0.48x

  • d60 = 0.67x

  • d180 = 1.0x

So if a campaign shows ROAS 0.28x on d7 – it's on track. If it's 0.15x – it's a candidate for shutdown.

4.3. Compare current cohorts to KPIs

For each fresh cohort (and campaign) calculate ROAS d7/d30 and compare to target. Green – above KPI, red – below.

4.4. Predict for fresh cohorts

For cohorts younger than 60 days, predict final ROAS based on historical curves. This lets you avoid waiting 6 months to find out whether a campaign will pay back.

Step 5. Build a dashboard

Ask Claude Code to build a React dashboard. Mine shows:

  • Overview – KPI cards (revenue, spend, CAC, ROAS for current month), trend charts

  • Marketing – ROAS by campaign, top countries

  • Cohorts – LTV curves by month, retention matrix

  • ROAS Evolution – how each cohort's ROAS grows from d7 to d180

What this gives you in practice

Many developers turn off ad campaigns, thinking they're unprofitable, when in reality they're not.

This analytics helps you:

  • build payback KPIs

  • know within a week if a cohort will pay back or not

  • stop turning off profitable campaigns

  • scale winning campaigns (even if they don't pay back on day 1)

If I'd had this analytics 2 years ago, my business would be 10× bigger now.

Reply to this email if you need more details – I'll do a deep dive.

Worth checking out

🍎 Apple Ads Insights – Apple launched new dashboards and visualizations. Looks nice, but without revenue data – you're only seeing half the picture.

💰 From failure to $22k/mo – Max struggled with one failed product for 5 years. Now he has 30 apps and $22k/mo.

Before you go:

I need more hands-on experience with different apps at different scales, so I started doing audits and consulting (not cheap).

Questions? Reply to this email.

See you next week,
Ivan.