Getting commercial leaders to move marketing money where a model, not instinct, said it should go. A Bayesian budget-allocation system with a 36% ROI improvement across brands and touchpoints, publicly reported by Heineken.
36%
ROI improvement across brands and touchpoints, publicly reported by Heineken.
Public · Heineken newsroomEvery 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 optimiser 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.
Heineken spends billions on marketing every year: ~€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 optimiser: it was whether a person with budget authority would move the money.
Product designer and adoption lead on the reboot. I shaped the rebuilt allocator’s product language and its adoption path into the annual planning cycle. The original was consultancy-built; the models were built by our data scientists.
My focus was whether operating companies actually reallocated budget, not just whether the model produced an answer. I was the bridge between the data scientists who built the models and the OpCo commercial leaders who had to change how they planned.
Each one traded theoretical elegance for something a commercial leader would actually execute.
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.
The tempting move was a one-click “here is your optimal budget” black box. I pushed back on 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.
We 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.
Bayesian response-curve models built per operating company, brand and channel; a scenario simulator for testing budget splits; and a constraint-aware optimiser 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 optimiser 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.
Allocation AI sets the sandbox (where each channel and brand gets budget) that Promo Advisor and Smart Flighting then execute against.
Where to spend.
Sets the annual budget across markets, brands and channels.
You are hereWhat to promote.
Fills the calendar with the promo mechanics that actually pay back.
Read the case study → 03 · WhenWhen to spend.
Times each budget week by week, so pressure lands where it still converts.
Read the case study →Tell me where the money goes today and who overrides the model. I’ll tell you what I’d build first.