[ MARKETING MIX MODELLING ]
Marketing Analytics, End to End
Marketing Mix Modelling is how marketing teams allocate spend across channels with a model that survives audit, not a vendor dashboard that survives until the next sales call. Bayesian MMM gives you uncertainty estimates per channel, an explicit treatment of adstock and saturation, and a budget reallocator the CFO can defend.
[ THE LEVENT POINT OF VIEW ]
Attribution that survives audit. Reallocation that survives the boardroom.
Last-click attribution is comfortable and wrong. Multi-touch attribution is sophisticated and brittle. Bayesian MMM is unfashionable enough to still work, because it grounds in causal reasoning instead of platform telemetry. We ship the model with the explanatory layer marketing leaders need to take it to the CFO, not just the data scientists who built it.
[ WHAT THIS MEANS IN PRACTICE ]
Our MMM accelerator is in production for a national tourism authority in the GCC. The same architecture composes for any brand with the channel diversity to make MMM worth doing: a warehouse-grounded data layer, a Bayesian model that respects adstock and saturation per channel, and an optimisation agent that translates the posterior into budget reallocation recommendations.
Google Meridian is a credible alternative implementation. We deliver on either Meridian or our own Bayesian framework depending on the customer's data shape and operational preferences. The framework matters less than the discipline around it.
Incrementality testing is the discipline that backs up MMM. A model that estimates channel contribution is only useful if the marketing team can validate the estimates against held-out spend. We design the testing cadence (geo-experiments, holdout markets, scaled rollouts) into the engagement so the model stays calibrated against ground truth, not just historical correlation.
Platform choice matters less than most teams assume. The model framework runs on Python in either BigQuery or Snowflake. Visualisation runs in Looker, Tableau, or any modern BI surface. Our deliverables target the platform the customer already operates, not the one the consulting deck recommends. Switching costs on the data stack are real, and our job is to deliver value inside the existing stack, not migrate it.
The hardest part of MMM is rarely the model. It is the cadence of refresh, the change-management on budget recommendations, and the attribution conversation with the agency partners. We scope those into the engagement from day one because they are where MMM projects fail.
[ HOW WE DELIVER THIS ]
How we deliver this
Strategy frames the use case and the data preconditions. Build delivers the Bayesian model and the optimisation agent. Operate runs the refresh cadence and the model registry. Enable trains the marketing analytics team to read the posterior and trust the recommendation.
[ PROOF, NOT PROMISES ]
Accelerators that ship this in production today.
[ MARKETING ANALYTICS PLATFORM ]
Helios
ATI-driven marketing analytics across the full operating loop: attribution, reallocation, planning, campaign creation, measurement. Built to deliver attribution accuracy that survives audit scrutiny and reallocation that survives the boardroom.
See the accelerator →