Allocation AI
Getting commercial leaders to move marketing money where a model, not instinct, said it should go. A Bayesian budget-allocation system Heineken publicly credits with a 36% ROI improvement.
- Role
- Product lead
- Context
- Heineken · 2021 – 2026
- Domain
- Marketing effectiveness
- Approach
- Bayesian ML · Azure
What Allocation AI does.
Every year Heineken's operating companies face the same question: where should the next marketing euro go?
Five million on TV, or on trade promotions? More weight behind a global brand, or a local power brand? Historically these calls leaned on experience and last year's plan. Allocation AI answers with evidence. It builds a Bayesian response model per operating company, brand and channel, learning how sales actually react to investment.
The core output is a set of response curves (spend on one axis, expected volume or ROI on the other) that let teams test budget scenarios before committing a cent. A constraint-aware optimizer then reads those curves and proposes the allocation that maximises return within real strategic limits: brand-role rules, long-term brand-building floors, and short-term caps.
Moving money is a political act.
Heineken spends billions on marketing every year: roughly €2.9B, about 9.8% of net revenue. Historically that budget was split across markets, brands and channels by habit and last year's plan, not by predicted return. Data science could build response curves and calculate where the next euro pays back best. That was not the hard part.
The hard part was authority and trust. To act on the model, a commercial leader has to pull real money out of a channel they believe in, on the say-so of a system, and defend that to their market. A reallocation nobody executes is a slide, not a system. The blocker was never the optimizer: it was whether a person with budget authority would move the money.
Accountable for whether the money actually moved.
I was the founding product hire in the analytics group and the product owner for this system, from discovery to delivery: the problem framing, the scope, the roadmap, and the adoption path into the annual planning cycle.
I was accountable for whether operating companies actually reallocated budget, not just whether the model produced an answer. I was the standing bridge between the data scientists who built the models and the OpCo commercial leaders who had to change how they planned.
Three product bets that earned adoption.
Each one traded theoretical elegance for something a commercial leader would actually execute.
A decision tool, not a reporting dashboard.
The cheaper path was a BI dashboard showing historical ROI by channel. I argued against it: teams didn't need more charts, they needed a defensible answer to "where should the next euro go" that they could take into a planning meeting.
Exposed the curve and its uncertainty, not a single "optimal" number.
The tempting move was a one-click "here is your optimal budget" black box. I rejected it. A black box that silently reallocates millions gets ignored; showing the diminishing-returns curve, the marginal ROI, and the confidence band lets a leader see why the model wants to move money, and where it is unsure.
Made the optimizer respect strategy, not just maximise ROI.
I built in guardrails (brand role, long-term brand building) so the model couldn't starve a strategic brand to chase short-term return. Pure ROI-maximisation would have produced a higher number on paper and lost the room in one meeting. The human sets the objective and the constraints; the system searches within them.
Show the whole curve, not just a number.
A production system in the planning cycle.
Bayesian response-curve models built per operating company, brand and channel; a scenario simulator for testing budget splits; and a constraint-aware optimizer that weighs hundreds of ROI projections against strategic limits, all on Heineken's Azure platform. Piloted in a first set of markets, then scaled into the annual planning cycle.
The value of an optimizer isn't the optimum. It's whether a human with authority will execute it, so legibility and real-world constraints beat a higher theoretical number no one acts on.
The macro engine of a three-part system.
Allocation AI sets the sandbox (where each channel and brand gets budget) that Promo Advisor and Smart Flighting then execute against.
Sets the annual budget across markets, brands and channels.
Fills the calendar with the promo mechanics that actually pay back.
Read the case study ›
Times each budget week by week, so pressure lands where it still converts.
Read the case study ›