---
title: "Retail company Case Study | Lemon AI"
description: "Retail company case study: how Lemon AI addressed a wide product range was not translating into ltv, retention, or aov and measured +35% retention."
canonical: "https://lemon-ai.com/cases/retail-company"
markdown_url: "https://lemon-ai.com/cases/retail-company.md"
language: "en"
image: "https://lemon-ai.com/og/case-retail-company.png"
image_alt: "Rising customer outcomes for an anonymized retail company."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Retail company

ecommerce · Customer outcome

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

**Android, iOS**

MENA

Google Ads, Meta Ads

$700K+ 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

25M+ installs 

650K+ MAU 

## Starting point

- A wide product range was not translating into LTV, retention, or AOV

## Goal

- Put more budget behind high-value audiences
- Encourage repeat purchases
- Develop predictive dynamic offers

## What Lemon AI did

1. Analyzed Adjust and AWS data, identified patterns and segments, trained a model to predict buying habits and churn, and optimized UA and retention strategies
2. Personalized product recommendations using purchase history and user behavior
3. Targeted users with top 35% LTV within 60 days, and users with 3+ purchases within 30 days post-installation
4. After 3 months, CAC was cut by 17.9%

## Measured results

| Outcome           | Result                  |
| ----------------- | ----------------------- |
| Retention         | Android +35% · iOS +17% |
| AOV over 5 months | Android +42% · iOS +33% |
| LTV on Day 60     | Android +49% · iOS +32% |

## 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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```