Marketing measurement
Marketing mix modeling, built to allocate budget
Measurement that changes where the money goes. Bayesian MMM, incrementality, response curves and budget allocation, deployed as a system your team runs on a monthly cadence.
What this usually looks like
- Attribution and MMM disagree, and nobody can say which one to act on.
- The model reports ROI by channel, but the budget meeting still runs on last year plus ten percent.
- A model was delivered once, as a deck. Nobody has rerun it since.
- Finance and marketing are arguing about incrementality with no shared definition of baseline.
What you get
A calibrated MMM
Adstock and saturation fitted per channel, with priors that hold up when a channel goes dark. Uncertainty reported, not hidden.
An allocation you can act on
Marginal ROI equalised across channels under real constraints: minimum spends, contracted commitments, agency lead times.
A monthly operating cadence
Refresh, review, reallocate. The model earns its keep by being rerun, not by being right once.
The handover
Code, documentation, drift checks and the training to run it without me.
Selected work
Marketing Science
Consumer healthcare marketing mix model & budget optimizer
Proprietary Marketing Mix Model with budget optimization replacing intuitive allocation with data-driven decision making across multiple countries and touchpoints.
Strategic budget reallocation based on incremental response curves
Marketing Science
CLV-driven acquisition & budget allocation
Predictive CLV modeling combined with lookalike audience optimization to shift acquisition from volume metrics to value-based targeting.
From volume optimization to value-based acquisition
Marketing Science
Automated credit decisioning system
Machine learning-powered credit decisioning system leveraging alternative data from social media and digital footprints combined with traditional credit data to improve risk assessment accuracy and speed.
Decision speed ↑10x
Try the mechanics
Adstock Calculator
Model carryover effects in marketing using geometric decay.
Hill Function Explorer
Explore saturation curves from C-curves to S-curves.
Response Curve Explorer
Visualize marketing response curves and efficiency zones.
MROI Optimizer
Compare revenue maximization vs efficiency strategies.
Baseline vs Incremental Sales
Understand baseline demand vs marketing-driven lift.
Depth
Jun 18, 2026
For Activation, a Directional LTV Model Beats an Accurate One
A staff-level field guide to predicted LTV for marketing activation: why ranking beats accuracy, why a directional model can beat an accurate one for seeding audiences, and where the whole thing quietly goes wrong.
Mar 18, 2026
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.
Mar 2, 2026
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.
Jan 12, 2026
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.
How it starts
Diagnostic first, build second.
01 · Decision System Diagnostic
Three weeks, fixed scope. We establish the decision, the data that exists, and whether MMM is even the right instrument. You get an executive readout and a roadmap. Any team can execute it, including not me.
02 · Design and build
The model, the allocation logic, the pipeline, the review cadence. Scoped from the roadmap, priced on the engagement rather than by the day.
03 · Operate and hand over
Monitoring, drift controls, documentation. The goal is a system your team owns.