Case study · Wellness
Same budget, +21% media-driven volume with Marketing Mix Modeling.
A leading nutraceutical brand used AD cube's Marketing Mix Model to measure the real contribution of every channel, and to reallocate the same spend for maximum volume.
Project overview
Sector
Wellness, nutraceutical supplements.
Channels
TV, video on demand, product sampling, offline.
AD cube module
Marketing Mix Model.
Objective
More volume from the same budget, by allocating it better.
The decision challenge
Knowing which euro actually drives volume.
The brand invests across TV, video on demand, product sampling and offline channels. But sales are shaped by many forces at once: price, seasonality, brand strength, one-off events and competitor activity. That makes it hard to isolate the true, incremental contribution of each channel.
Standard reporting could show what correlated with sales, but not what caused them. The brand needed to separate genuine media effects from spurious signals, understand where each channel saturates, and answer a concrete business question: can we generate more volume from the same budget, simply by allocating it better?
The method
A causal-first Marketing Mix Model.
AD cube deployed its Marketing Mix Model with a causal-first approach. The goal was not just to predict sales accurately: every modeling choice was made to preserve interpretation, estimating the real incremental contribution of each channel on the business KPI, then optimizing the mix against saturation and carryover effects. The model was also built to quantify non-media variables, not just the media channels themselves.
Incremental contribution
The causal effect of each channel on the business KPI, not its correlation with sales.
Saturation and carryover
How fast each channel's return fades with extra spend, and how long its effect lasts.
Non-media factors
Price, seasonality and brand strength measured explicitly, outside the media numbers.
What makes this case different
The methodology, feature by feature.
This engagement is less about a single headline number and more about the analytical work behind it. Five capabilities of the AD cube MMM did the heavy lifting.
01
Telling real drivers from coincidences
The first job of the model is to separate what really drives sales from what simply moves alongside them. A web-analytics conversion metric looked closely tied to sales, but it was mostly an effect of TV and video-on-demand advertising, not a cause of its own. Treating it as a driver would have made TV and video look weaker than they truly are. For the same reason, the model set aside a few variables that follow sales rather than cause them, so the results reflect what actually moves the business.
02
Counterfactual analysis of key events
A study supporting the product and the launch of a new variant reshaped the brand's trajectory. By comparing the sales trend before and after each event, the model isolated two distinct effects: a lasting level shift in volume, sustained over the following two years, and a growth shift, with monthly sales rising by roughly 23-27%. The same counterfactual analysis was run across a broader set of one-off events and variables beyond the media mix, and fed back into the model to make it more robust.
03
Every channel has a saturation point
Two very similar sampling channels were delivering very different returns. The reason was saturation: past a certain point, every extra euro on a channel produces less and less. One channel still had room to grow, while the other was already pushed well beyond its best-performing point. That is why its return was lower, and why budget should move toward the channel with headroom.
04
What-if scenarios
The model can replay history under different budgets. The brand asked whether TV spend was better concentrated in a burst or distributed over a longer period. At the same budget, the distributed plan outperformed the burst plan by roughly 6.5% on the TV contribution alone, by avoiding the saturated region.
05
Budget optimization
Finally, the AD cube optimizer reallocated the full 2025 budget, unchanged, across channels and time, using the estimated saturation and lag curves. Total sellout rose by 1.76%. Because about 92% of sellout comes from non-media base factors (price, seasonality, brand strength), the real story is the media lever: media-driven volume grew by 21.3% on the same spend. Measuring that non-media base explicitly, rather than folding it into the media numbers, is what makes the +21.3% result trustworthy.
Results
The full 2025 budget, unchanged, reallocated across channels and time.
+21.3%
Media-driven volume
Media-attributable units on an unchanged budget, purely from smarter allocation.
+1.76%
Total sellout
Total volume grew. The effect is modest at total level because about 92% of sellout is non-media base.
+6.5%
TV volume via flighting
Distributing TV spend over time, rather than bursting it, outperformed the burst plan at the same budget.
+23-27%
Monthly growth from events
Two key events drove a lasting level shift in volume over two years and lifted monthly growth by that margin.
The business impact
More value from the same budget.
Beyond the numbers, the brand gained a model it can trust. By removing spurious and mediating variables, the MMM produces clean, causal read-outs of each channel's incremental effect: what actually drives sales, not just what tracks alongside them. Saturation and carryover analysis turned into concrete flighting guidance, and the what-if engine let the team pressure-test allocation decisions before committing spend.
The headline, in one line: same budget, about a fifth more volume from the media lever. The +1.76% on total business and the +21.3% on media describe the same result from two angles: the overall impact, and the efficiency of the media lever itself.