---
title: "Individual tours Case Study | Lemon AI"
description: "Individual tours case study: how Lemon AI addressed low order conversion rate and aov and measured +42% processed requests per manager."
canonical: "https://lemon-ai.com/cases/individual-tours"
markdown_url: "https://lemon-ai.com/cases/individual-tours.md"
language: "en"
image: "https://lemon-ai.com/og/case-individual-tours.png"
image_alt: "Rising manager productivity and LTV outcomes for an anonymized tours business."
schema_types: ["BreadcrumbList","Organization","WebApplication","WebPage","WebSite"]
---

# Individual tours

travel · Customer outcome

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

**WEB**

USA

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

210K+ monthly visits 

## Starting point

- Low order conversion rate and AOV

## Goal

- Raise order frequency and LTV

## What Lemon AI did

1. Integrated BigQuery in 2 days, trained a model in 15 days to identify likely customers
2. Adjusted campaigns and reached the CAC target by Week 3
3. Shortened the tour request form and reduced the work required to process it

## Measured results

| Outcome                        | Result  |
| ------------------------------ | ------- |
| processed requests per manager | +42%    |
| LTV                            | +62.7%  |
| CAC                            | \-37.2% |

## 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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```