---
title: "Action games Case Study | Lemon AI"
description: "Action games case study: how Lemon AI addressed the team could not reliably find users likely to stay and make in-app purchases and measured +31% ltv on…"
canonical: "https://lemon-ai.com/cases/action-games"
markdown_url: "https://lemon-ai.com/cases/action-games.md"
language: "en"
image: "https://lemon-ai.com/og/case-action-games.png"
image_alt: "Rising LTV, retention, and ad-revenue outcomes for an anonymized action-games company."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Action games

gaming · Customer outcome

The starting point, work, and retained outcome record from an anonymized Lemon AI engagement. 

**Android, iOS**

NA, LatAm, Europe

Google Ads, Meta Ads, Unity Ads

$55k+ monthly

**Evidence note:** The customer identity is withheld. The operating context, actions, and outcome figures are retained from Lemon AI’s previously published case record and were approved for this migration. Results describe this engagement and are not a promise of identical performance for another app or market. 

## Operating context

550K+ installs 

90K+ MAU 

## Starting point

- The team could not reliably find users likely to stay and make in-app purchases

## Goal

- Raise ROAS
- Cut CAC

## What Lemon AI did

1. Connected Adjust, Firebase, and devtodev data for installs, purchases, session length, and engagement
2. Predicted Top 10% and Top 50% players by LTV, launched several campaigns
3. Set the budget mix for North America, Europe, and LatAm at 54%:32%:14%

## Measured results

| Outcome             | Result                    |
| ------------------- | ------------------------- |
| LTV on Day 30       | Android +31% · iOS +24%   |
| Retention on Day 14 | Android +24% · iOS +18%   |
| Ad revenue          | Android +28% · iOS +16,5% |

## How to interpret the result

The outcome should be read in the scope shown above: its platforms, region, acquisition sources, budget band, objectives, and measurement period. The result does not isolate a universal causal effect and should not be compared with another case without aligning its metric definition and time horizon. 

Read the [measurement and forecasting methodology](https://lemon-ai.com/methodology) for source, attribution, aggregation, reconstruction, and prediction limitations. 

Next step

## See the same workflow with your data

Bring one app and its acquisition questions to a product walkthrough.

[Book a demo](https://calendly.com/robert-lemon-ai) 

[← All case studies](https://lemon-ai.com/cases)

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