---
title: "Personal financial advice app Case Study | Lemon AI"
description: "Personal financial advice app case study: how Lemon AI addressed too few users converted from the free trial and measured +22% ltv on day 90."
canonical: "https://lemon-ai.com/cases/personal-financial-advice-app"
markdown_url: "https://lemon-ai.com/cases/personal-financial-advice-app.md"
language: "en"
image: "https://lemon-ai.com/og/case-personal-financial-advice-app.png"
image_alt: "Rising D90 LTV and trial-conversion outcomes for an anonymized finance app."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Personal financial advice app

finance · Customer outcome

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

**iOS**

Europe

Meta Ads

$50K+ 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

560K+ installs 

180K+ MAU 

## Starting point

- Too few users converted from the free trial

## Goal

- Raise free trial conversion

## What Lemon AI did

1. Trained model in 4 days
2. Found the paywalls that worked best for each behavior pattern
3. Built user segments and matching retargeting campaigns

## Measured results

| Outcome                    | Result |
| -------------------------- | ------ |
| LTV on Day 90              | +22%   |
| Free trial conversion rate | +27%   |

## 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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