---
title: "Delivery app B2C & B2B Case Study | Lemon AI"
description: "Delivery app B2C & B2B case study: how Lemon AI addressed the team could not reliably find users likely to place frequent, high-value orders and measured…"
canonical: "https://lemon-ai.com/cases/delivery-app-b2c-b2b"
markdown_url: "https://lemon-ai.com/cases/delivery-app-b2c-b2b.md"
language: "en"
image: "https://lemon-ai.com/og/case-delivery-app-b2c-b2b.png"
image_alt: "Rising conversion and LTV outcomes for an anonymized B2C and B2B delivery app."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Delivery app B2C & B2B

delivery · Customer outcome

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

**Android, iOS, web**

US, Europe

Google Ads, Meta Ads

$260K+ 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

3.9M+ installs 

420K MAU 

## Starting point

- The team could not reliably find users likely to place frequent, high-value orders

## Goal

- Raise ROAS
- Cut CAC

## What Lemon AI did

1. Trained model to predict users with higher order frequency and order value
2. Built a prediction model for couriers
3. Focused campaigns on the top 15% and 50% of users by D90 LTV
4. Reworked the page for couriers seeking full-time work
5. Expanded into three new countries

## Measured results

| Outcome                   | Result                      |
| ------------------------- | --------------------------- |
| CAC                       | \-34.7%                     |
| Employment conversion     | +48.8%                      |
| LTV on Day 90             | Android +57.7% · iOS +31.7% |
| Order frequency on Day 90 | Android +23.8% · iOS +23.8% |

## 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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