finance · Customer outcome
The starting point, work, and retained outcome record from an anonymized Lemon AI engagement.
Android, iOS, Web
MENA, APAC
Google Ads, Meta Ads
$125K+ 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
1.2M+ installs
355K+ MAU
Starting point
- Users had low LTV and retention, and few opened credit products
Goal
- Increase LTV
- Predict high-value users
- Improve engagement strategies
What Lemon AI did
- Launched segment-specific ad campaigns, offering targeted benefits (e.g. rewards points and cash back incentives)
- Ran dynamic creative tests and adjusted campaigns around the results
Measured results
| Outcome | Result |
|---|---|
| Number of cash loans in MENA (web) | +14% |
| Amount of overdue debt in MENA (web) | -26% |
| LTV on Day 30 (APAC) | Android +34% · iOS +29% |
| New cards with first transaction within 7 days (APAC) | Android +22% · iOS +19% |
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 for source, attribution, aggregation, reconstruction, and prediction limitations.
Next step
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