At a Glance
-
Practice: IVF clinic
-
Market: Chicago metro
-
Ad spend: $60k/month
-
Timeline: 120 days
-
Stack: MMM + scheduling data + geo-income overlays
-
Primary challenge: High no-show rate for consultations
Key Insight
The issue wasn’t ad volume — it was false signals from no-shows. Analytics revealed where true demand lived.
Problem
Despite strong click and booking volume, cycle starts remained flat. No-shows drained the pipeline. The front office was busy, but business appeared to be stalled.
Approach
QuantiMedia integrated scheduling data with MMM and income data by ZIP. Analysis revealed that no-show rates were highest in lower-income areas, creating false booking volume. Inquiries “looked good” but they weren’t turning into patients.
Results
Targeting was refined to higher-LTV ZIPs, resulting in 29% more cycle starts and a 42% drop in no-shows. The booking department load was reduced, calendar slots were opened, and overall cycle starts increased.
Benefits
1
Geo-Income Overlay for Smarter Targeting
By combining income data with MMM, we identified which ZIP codes produced no-shows and which produced cycle starts. This gave the clinic clarity on where to focus spend for the best patient quality.
2
Pipeline Accuracy That Improved Planning
Reducing no-shows didn’t just help conversions — it made forecasting more reliable. The clinic could better staff consultations and predict monthly cycle starts, smoothing out operations.
3
Better ROI Through Patient Quality
Higher-income ZIPs didn’t just show up more often — their cases were more likely to lead to full IVF cycles. This increased ROI per patient and made marketing spend significantly more effective.