---
title: "Online pharmacy Case Study | Lemon AI"
description: "Online pharmacy case study: how Lemon AI addressed over 50% of users made 1 or 0 orders and measured +22.2% purchase frequency."
canonical: "https://lemon-ai.com/cases/online-pharmacy"
markdown_url: "https://lemon-ai.com/cases/online-pharmacy.md"
language: "en"
image: "https://lemon-ai.com/og/case-online-pharmacy.png"
image_alt: "Rising purchase-frequency and ROAS outcomes for an anonymized online pharmacy."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Online pharmacy

ecommerce · Customer outcome

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

**Android, iOS**

Europe

Google Ads, Meta Ads

$165k+ 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

3.2M+ installs 

220K+ MAU 

## Starting point

- Over 50% of users made 1 or 0 orders

## Goal

- Raise LTV and AOV
- Reach at least 3 orders over 4 months

## What Lemon AI did

1. Found the users most likely to order medications online
2. Tailored optimization strategies for users with over 3 orders in 4 months, raised CPA limit
3. Monitored peak ordering periods and products among users, increased ads accordingly, and prioritized showing frequently ordered medications

## Measured results

| Outcome            | Result                      |
| ------------------ | --------------------------- |
| Purchase Frequency | Android +22.2% · iOS +23.8% |
| ROAS               | Android +26.9% · iOS +19.7% |

## 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

```json
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```
