---
title: "Digital Content Case Study | Lemon AI"
description: "Engaging digital content (news & sports) case study: how Lemon AI addressed retention had stopped growing and engagement was low and measured -24% cut…"
canonical: "https://lemon-ai.com/cases/digital-content-news-sports"
markdown_url: "https://lemon-ai.com/cases/digital-content-news-sports.md"
language: "en"
image: "https://lemon-ai.com/og/case-digital-content-news-sports.png"
image_alt: "Rising subscriber and retention outcomes for an anonymized news and sports service."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Engaging digital content (news & sports)

media · Customer outcome

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

**Web (35%) + Mobile (Android) 65%**

Europe

Google Ads, Meta Ads

$70K+ 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

540K+ installs 

## Starting point

- Retention had stopped growing and engagement was low

## Goal

- Increase number of paid subscribers with CAC benchmark

## What Lemon AI did

1. Connected raw Google Analytics and Firebase data in 35 days
2. Predicted content consumption patterns, likelihood of subscription, and churn
3. Launched targeted marketing campaigns to increase retention

## Measured results

| Outcome                             | Result                      |
| ----------------------------------- | --------------------------- |
| cut analytics costs                 | \-24%                       |
| hours saved monthly for the UA team | +20                         |
| Paid subscribers                    | Android +23% · Web +41%     |
| Retention on Day 30                 | Android +19.5% · Web +35.2% |

## 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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