AppLovin integration

AppLovin in Polar: spend in, real signal out.

Connect AppLovin and programmatic display spend, impressions and reach flow into Polar's blended ROAS, MER and CAC, next to Meta, Google and the rest. Programmatic reach is read against real orders, so upper-funnel spend is judged on incremental revenue, not impressions.

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Connect AppLovin See the setup
Google Sheets → Polar

What you can bring in

Spend, blended
"Is AppLovin in my blended numbers?"
Programmatic display spend, impressions and reach folded into blended ROAS, MER and CAC alongside every channel.
spend + ROAS
Reach vs revenue
"Is my reach spend working?"
Programmatic reach read against real orders and incremental revenue.
reach vs orders
Deduplicated ROAS
"AppLovin over-credits itself."
AppLovin's ROAS after platform overclaiming is removed, blended with the stack.
deduped vs reported
Campaign detail
"Break it down the way I run it."
One row per campaign and line item, refreshed automatically.
by campaign
Incrementality
"Is AppLovin spend incremental?"
Geo-based incrementality testing proves AppLovin's incremental impact.
GeoLift holdout
True CAC
"What did AppLovin really cost per customer?"
AppLovin spend against real new customers, blended into CAC and margin.
blended CAC

AppLovin gives you AppLovin's number. Your business is one.

AppLovin reports its own performance in isolation, before dedup, and it cannot see the rest of your stack or tell you what is incremental. Polar can.

AppLovin on its own

  • ROAS is AppLovin's number, before dedup, so it overclaims.
  • No blend with Meta, Google, Shopify or email.
  • No read on whether the spend is incremental.
  • Nothing reconciles against your real P&L.

AppLovin with Polar

  • Blended ROAS and MER after overclaiming is removed.
  • Reach read against real orders and incremental revenue.
  • Geo-based incrementality proves the real lift.
  • Every dollar reconciled into blended CAC and margin.
Setup Guide

Connect AppLovin.

01 · What you need
Polar

A Polar account with Shopify

A Polar account with Shopify connected. This connector plugs in right next to your stack.

Connect your data →

A AppLovin account

Access to the AppLovin account(s) you want to link, so the data flows into blended reporting.

Open AppLovin →

The AI data engineer

AppLovin is ingested and blended; reach is read against real orders and incrementality.

02 · Connect AppLovin
1

Connect AppLovin talk to Polar

Spend into blended reporting.
Polar ingests your programmatic display spend, impressions and reach and folds it into blended ROAS, MER and CAC next to every other channel.
2

Read against orders ~2 min

Reach vs revenue.
Programmatic reach is read against real orders, so awareness spend is judged on incremental revenue, not impressions.
3

Prove the lift the payoff

Incrementality.
Run a geo-based incrementality test to prove how much of AppLovin's revenue is truly incremental, independent of the platform.
Get started

Judge AppLovin on the whole business, not its own dashboard.

Connect AppLovin for blended reporting and read it against real orders and incrementality.

Book a demo Connect your data ↗
The Gap

The connector is only half of it.

Once your AppLovin spend and performance are connected to Polar, you can use them wherever you already work. Ask Polar in plain language, pull them live through the Polar MCP, or schedule a snapshot into the report your team reads every morning.

AppLovin

spend, impressions, reach
Polar

Polar

clean, blended, attributed

Ask Polar

Polar MCP

Scheduled reports

Featured Use Case

Judged on the whole business.

Judged on the whole business

AppLovin looks however AppLovin wants it to look in its own dashboard: ROAS before dedup, credit for conversions other channels also claim, and no idea what is truly incremental. Polar folds AppLovin into blended ROAS, MER and CAC next to every other channel, reads programmatic reach against real orders, and lets you run a geo-based incrementality test to prove how much of the revenue you would not have earned anyway. You stop scaling on a platform's self-report and start scaling on causation.

What makes it real

Deduplicated ROAS. AppLovin after overclaiming is removed, blended with every channel.

Incrementality. Geo-based holdouts prove the real lift, independent of AppLovin.

Ask Polar, in plain language. “Show AppLovin's blended ROAS after dedup and my last incrementality read.” No SQL, no export.

See how the blended view works
Other Use Cases

Other use cases

Use case 01

Placeholder

Placeholder. The use case skill fills this. (rung: see it)

See the use case › Use case 02

Placeholder

Placeholder. The use case skill fills this. (rung: report it)

See the use case › Use case 03

Placeholder

Placeholder. The use case skill fills this. (rung: analyze it)

See the use case › Use case 04

Placeholder

Placeholder. The use case skill fills this. (rung: decide from it)

See the use case › Use case 05

Placeholder

Placeholder. The use case skill fills this. (rung: act on it)

See the use case ›
Your Prompt Library

AppLovin in plain language

A scannable wall of sample prompts for your AppLovin data inside Polar. Ask Polar, or Claude with the Polar MCP connected.
See it
Show my AppLovin spend and ROAS for the last 30 days.
Break down AppLovin spend by campaign.
Show AppLovin next to my other channels.
Analyze it
Compare AppLovin's reported ROAS to blended after dedup.
Show blended CAC with and without AppLovin.
Is AppLovin spend incremental, based on my geo test?
Decide from it
Should I scale AppLovin on a blended basis?
If I shifted budget to AppLovin, what happens to blended CAC?
Summarize how AppLovin performance changed since last month.
Get started · connect once · judged blended

Every AppLovin dollar, in one view.

Connect AppLovin for blended reporting, read it against real orders, run an incrementality test, and ask Polar what it actually did, all in.

Get started by booking a demo Connect your data in Polar
Phase 1 talk to PolarConnect AppLovinSpend folded into blended reporting.
Phase 2 ~2 minRead vs ordersReach against real orders.
Phase 3 the payoffProve the liftGeo-based incrementality, blended and deduped.