ML that cut
wellness app CPC by a third
Built ML models scoring users by conversion likelihood using behavioral data,
then fed signals directly into Google Ads bidding.
45%
Improved Campaign ROI
30%
Less Spent per Lead
60%
High-Value Customer Acquisition
3x
ML Models Built & Deployed
AI & Automation
About The Company
Their Google Ads spend was generating clicks, but not reliably converting them into the long-term, high-LTV subscribers that actually move the business.
The Challenge
Acquiring the wrong users
Company's campaigns were acquiring users but not the right ones. Without a way to identify high-LTV subscribers before they converted, budget was spread evenly across a very uneven audience.
Bidding without LTV signal
Standard Google Ads signals couldn't see what was visible in Welltech's data: that behavioral patterns and early engagement reliably predicted long-term subscriber value.
Data not connected to spend
Company had the user data. They didn't have a system that turned it into a bidding signal. Every euro was allocated without knowing whether the person on the other end was worth €5 or €500 in lifetime value.
Scaling before the model was right
In a high-competition health app market, more spend without smarter targeting just inflated CPC and diluted ROI. The goal wasn't more leads: it was the infrastructure to find the right ones, consistently, at scale.
The Approach
Model Development
Mapped behavioral signals, LTV forecasts, and user history. Built the predictive scoring layer before touching a single campaign.
- Behavioral and app history data mapped
- Multiple ML model architectures built
- LTV forecast model validated against historical data
Algorithm Testing
Stress-tested each model on real audience segments. Refined until prediction accuracy held consistently.
- Tested across 6 audience segments
- Parameters tuned against live outcomes
- Accuracy benchmarks locked before go-live
Campaign Optimisation
Model scores wired into Google Ads bidding. Higher bids on high-LTV users, lower on the rest, spend aligned to value for the first time.
- 30% CPC within 6 weeks
- +60% high-value customer acquisition
- +45% overall campaign ROI
System Architecture
User Behavioral Data
App history, engagement signals, session depth, purchase events.
ML Model (LTV Prediction)
Scores each user by conversion likelihood and predicted lifetime value.
Audience Scoring
Users ranked and segmented: high-value flagged, low-value filtered out.
Google Ads Bidding
Scores fed into bidding strategy: spend follows predicted value and not guesswork
The Results
45%
Improvement in campaign ROI across all active Google Ads campaigns.
- 30%
Reduction in cost-per-click as bidding became precision-targeted.
60%
Growth in high-value customer acquisition through targeted campaigns.
3 models
ML models built, tested, and deployed, each one compounding the next.
See what's leaking before you spend another euro.
Precision targeting is the whole game.