Predictive · ROAS
ROAS in 7 Days.
What Predictive ROAS Does
An ML model trained on 90 to 180 days of account data predicts how ROAS will move over the next 7 days. When drift is detected, it triggers alerts. Pacing decisions become preemptive rather than reactive.
01
7 Days
Forecast Window
02
70-85%
Accuracy
03
Preemptive
Pacing Mode
Setup Requirements
- 90 to 180 days of clean account data
- BigQuery or equivalent data pipeline
- ML model (XGBoost, LightGBM, or pre-trained)
- Drift detection layer for model validity
- Alert pipeline for anomalies
Example
A high-spend account set up a predictive model and caught drift alerts before a ROAS drop in 4 out of 6 cases. Pacing adjustments were made preemptively, and ROAS stability improved.
When It Does Not Work
On smaller accounts below 30k EUR/month (too little data). For heavily seasonal brands (model carries seasonal bias). For brand launches (no training data). Predictive is a scaling tool, not a startup tool.
„Predictive is not magic, it is statistics. With the right data foundation, it is a genuine lever.”