---
title: "Online clothing store Case Study | Lemon AI"
description: "Online clothing store case study: how Lemon AI addressed 45% of purchasers did not return within six months and measured 14.3% increased purchases."
canonical: "https://lemon-ai.com/cases/online-clothing-store"
markdown_url: "https://lemon-ai.com/cases/online-clothing-store.md"
language: "en"
image: "https://lemon-ai.com/og/case-online-clothing-store.png"
image_alt: "Rising purchase and LTV outcomes for an anonymized clothing store."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Online clothing store

fashion · Customer outcome

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

**Android, iOS, web**

MENA

Google Ads, Meta Ads, TikTok Ads

$310K+ 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

15M+ installs 

100K+ MAU 

## Starting point

- 45% of purchasers did not return within six months

## Goal

- Increase purchase frequencies and AOV
- Acquire users who make 2+ purchases in 3 months

## What Lemon AI did

1. Linked users across platforms and identified shared behavior, such as adding an item in the app and completing the purchase on the website
2. Targeted users with 1+ purchase per month, and users with Top 25% LTV on Day 90
3. Introduced prediction for rejection rate

## Measured results

| Outcome             | Result                      |
| ------------------- | --------------------------- |
| increased purchases | 14.3%                       |
| Purchase Frequency  | Android +34.9% · iOS +19.8% |
| LTV on Day 60       | Android +33.4% · iOS +24.9% |

## 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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```