---
title: "Home appliances Case Study | Lemon AI"
description: "Home appliances case study: how Lemon AI addressed 45% of total orders were abandoned and measured +22.2% purchase frequency."
canonical: "https://lemon-ai.com/cases/home-appliances"
markdown_url: "https://lemon-ai.com/cases/home-appliances.md"
language: "en"
image: "https://lemon-ai.com/og/case-home-appliances.png"
image_alt: "Rising purchase-frequency and LTV outcomes for an anonymized home-appliance business."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Home appliances

ecommerce · Customer outcome

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

**WEB**

MENA

Google Ads, Meta Ads

$90k+ 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

40K+ average monthly sessions 

## Starting point

- 45% of total orders were abandoned

## Goal

- Increase LTV and AOV
- Reach benchmark: 75% paid orders

## What Lemon AI did

1. Consolidated fragmented data across Google BigQuery and internal systems
2. Focused on day180 LTV, targeted users with high purchase frequency
3. Predicted offline purchases with online signups
4. Changed targeting, raised orders within three months, and supported expansion into a new market

## Measured results

| Outcome            | Result |
| ------------------ | ------ |
| Purchase Frequency | +22.2% |
| LTV of 6 months    | +28%   |

## 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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```