lifestyle · Customer outcome
Dating subscription app
The starting point, work, and retained outcome record from an anonymized Lemon AI engagement.
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
Starting point
- Retention and LTV had stopped growing
- Competitors were running large campaigns
Goal
- Raise retention and revenue
What Lemon AI did
- Integrated data from Appsflyer and proprietary solution in 6 days
- Found direct correlation between chat message frequency and LTV, improved accuracy of revenue prediction
- Targeted users with Top 30% LTV on Day 90, and users with top 20% Ad Revenue on Day 30
- Standardized A/B testing across channels and predicted campaign revenue on Day 90
Measured results
| Outcome | Result |
|---|---|
| inefficient budget allocation reduced | -37% |
| hours saved per week on analytical tasks | 140 |
| Retention | Android +34% · iOS +24% |
| Ad Revenue on Day 30 | Android +37% · iOS +22% |
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.