---
title: "Perfume by subscription Case Study | Lemon AI"
description: "Perfume by subscription case study: how Lemon AI addressed high churn rate: ~35% of users canceled subscriptions within 2 months and measured +22.5%…"
canonical: "https://lemon-ai.com/cases/perfume-subscription"
markdown_url: "https://lemon-ai.com/cases/perfume-subscription.md"
language: "en"
image: "https://lemon-ai.com/og/case-perfume-subscription.png"
image_alt: "Rising retention, ROI, and D90 LTV outcomes for an anonymized subscription app."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Perfume by subscription

beauty · Customer outcome

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

**iOS**

USA

Google Ads, Meta Ads, TikTok Ads

$345K+ 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

1.8M+ installs 

100K+ MAU 

## Starting point

- High churn rate: \~35% of users canceled subscriptions within 2 months

## Goal

- Attract users with over 4 months lifetime
- Decrease CAC

## What Lemon AI did

1. Found the products linked to longer subscriptions
2. Tracked popularity of various products across different US states, tailored subscription content for each region
3. Focused campaigns on users likely to subscribe for more than 4 months

## Measured results

| Outcome       | Result     |
| ------------- | ---------- |
| Retention     | iOS +22.5% |
| ROI           | iOS +31.2% |
| LTV on Day 90 | iOS +17.2% |

## 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)

## Structured data

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```