---
title: "Casual games Case Study | Lemon AI"
description: "Casual games case study: how Lemon AI addressed revenue varied by market, and the team needed to protect player experience and engagement and measured…"
canonical: "https://lemon-ai.com/cases/casual-games"
markdown_url: "https://lemon-ai.com/cases/casual-games.md"
language: "en"
image: "https://lemon-ai.com/og/case-casual-games.png"
image_alt: "Rising campaign-efficiency and ROAS outcomes for an anonymized casual-games company."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Casual games

gaming · Customer outcome

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

**Android, iOS**

Europe, MENA, APAC

Google Ads, Meta Ads

$100k+ 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

5M+ installs 

725K+ MAU 

## Starting point

- Revenue varied by market, and the team needed to protect player experience and engagement

## Goal

- Increase ROAS and ad revenue for a game with hybrid monetization (80% ad revenue, 20% in-app purchases)

## What Lemon AI did

1. Trained and integrated a model in 8 days
2. Predicted Top 10%, 20%, and 30% players by revenue
3. Created custom event for players who reached "level 10" and spent 200 "diamonds"

## Measured results

| Outcome                                    | Result                    |
| ------------------------------------------ | ------------------------- |
| campaign efficiency during the Lemon trial | +25%                      |
| ROAS                                       | Android +42% · iOS +37%   |
| 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)

## Structured data

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```