---
title: "Taxi app Case Study | Lemon AI"
description: "Taxi app case study: how Lemon AI addressed ios tracking was limited, and the first trip often happened 24 hours after install and measured +32% purchase…"
canonical: "https://lemon-ai.com/cases/taxi-app"
markdown_url: "https://lemon-ai.com/cases/taxi-app.md"
language: "en"
image: "https://lemon-ai.com/og/case-taxi-app.png"
image_alt: "Rising purchase-frequency and LTV outcomes for an anonymized taxi app."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Taxi app

travel · Customer outcome

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

**Android, iOS**

MENA

Google Ads, Meta Ads

$2.1M+ 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

100M+ installs 

8.2M+ MAU 

## Starting point

- iOS tracking was limited, and the first trip often happened 24 hours after install

## Goal

- Raise engagement and ride frequency
- Spend the acquisition budget more efficiently

## What Lemon AI did

1. Set up predictive dashboard in 9 days, including purchase frequency, preferred routes, and peak usage times
2. Predicted iOS user values within seconds of install
3. Increased conversion to first ride by 39%
4. Ran campaigns for first ride and the predicted event of 3 or more rides by Day 14
5. Targeted users with Top 30% LTV on Day 30

## Measured results

| Outcome                      | Result                      |
| ---------------------------- | --------------------------- |
| purchase frequency on Week 3 | +32%                        |
| cost per install             | \-7%                        |
| Purchase frequency           | Android +38.2% · iOS +27.8% |
| LTV                          | Android +26.2% · 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)

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