Marketing Science

From Propensity Scores to Learning Systems: What Most CRM Teams Get Wrong

Most CRM organizations don't have a feedback loop. They have a broadcasting system. The shift from propensity to uplift modeling changes everything.

Cyril Noirot

March 2, 2026

Share on

8 min read

After working on multiple CRM and marketing analytics systems, one pattern keeps repeating.

Teams invest heavily in data science. They build propensity models. They segment customers. They deploy campaigns. And then nothing really improves.

Not because the models are wrong. But because the system around them is incomplete.

The uncomfortable truth is this: most CRM organizations don't have a feedback loop. They have a broadcasting system.

About the author

Cyril Noirot

Lead Data Scientist

Freelance data scientist. I design and ship decision systems — forecasting, pricing, marketing measurement, optimization.

ROI Is Not Enough. You Need Time-to-ROI.

Two initiatives can show the same ROI and still deserve very different decisions. The missing variable is time-to-ROI: how fast the initial investment is recovered.

Marketing Science6 min

Your MMM Should Not Predict Revenue

Revenue is a derived quantity. Marketing influences demand, units sold, not price. Modeling revenue directly entangles two mechanisms and produces misleading attribution.

Marketing Science5 min

MIP for Marketing Budget Allocation: A Complete Guide

Learn how Mixed Integer Programming (MIP) with discretized response curves solves marketing budget allocation problems using branch-and-bound methods.

Newsletter

Technical writing on forecasting, pricing, and decision systems. No fixed schedule, no spam.

Enter your email