To present marketing mix model results so people act on them, lead with the decision you recommend, show the range of plausible outcomes rather than a single number, and say what evidence would change your recommendation. Then name one operational condition the plan depends on and one specific measurement you’ll run next. The decomposition charts, coefficient tables and fit statistics still matter, but they belong in the appendix, as support for the decision rather than the main event.
I’ve built marketing mix models for companies ranging from Monzo Bank to DTC brands like Breeo and Groove Life, and the readouts that go badly tend to share a shape: dozens of slides of model output, a chart of ROI by channel, and a room full of people who don’t know what they’re being asked to do. A useful MMM presentation helps a team make a decision. It doesn’t require every stakeholder to become a modeler, and it doesn’t hide uncertainty to make the chart look decisive. The structure below comes from the Vexpower course’s lesson on communicating a recommendation, and I’ll show you how to turn a model result into a paragraph you could send to a CFO.
Start by checking which question the model answered
Before you build a single slide, go back to the brief you wrote at the start of the project (if there wasn’t one, the measurement plan guide explains what it should have said). Has the model answered that question, a narrower one, or a different one? This matters more than it sounds, because a marketing lead often asks “what is our ROI?”, while the finance lead wants to know “what should we do with next quarter’s budget?”, and those are not interchangeable. A historical return describes the past at past spend levels, while a planning decision also needs the effect of changing spend, the uncertainty around it, costs and practical limits.
If the model only supports the narrower question, say so on the first slide. “We can estimate the historical contribution of each channel with reasonable confidence, but we can’t yet separate search and social at higher spend levels” is a perfectly good opening, and it’s much better than letting the audience assume the model answered the bigger question and discovering otherwise after the budget has moved. I find this one sentence does more for the credibility of a marketing mix model than any amount of diagnostic charts.
The five-part decision note
The course boils a recommendation down to five parts, and I’ve found it works as well for a one-page memo as for the first five slides of a deck.
Decision: what you recommend, over which period
Lead with the action. “Increase paid social by £30k next quarter as a bounded test, holding search at its agreed minimum” is a decision. “Social has the highest ROI” is an observation that leaves the reader to work out the decision themselves, and they’ll often work out a different one from you. Include the period, the owner and the full allocation, and check it respects every constraint the business set (total budget, channel minimums, delivery capacity), since a recommendation that breaks a constraint gets dismissed along with everything else in it.
Evidence: what supports it
Keep four kinds of support separate, because mixing them is the most common way a memo overstates its case. An observation is a fact visible in the data or a checked record (social spend doubled in March). A model-based calculation is a result conditional on named assumptions, with units (incremental revenue per £1 of additional social spend of 2.9 under the fitted curve). An assumption is something the recommendation needs but the evidence doesn’t establish (the curve holds at higher spend). Independent evidence is something outside the model, such as an incrementality test, that wasn’t already used to fit or calibrate it. If you don’t have independent evidence, say so rather than letting a calibrated prior pose as validation.
Range: what outcomes remain plausible
Give the range, not just the point, and say what kind of range it is. A 90 percent credible interval from a Bayesian MMM, a range across several defensible model specifications, and a set of optimistic and pessimistic stress scenarios are three different things, and only the first is a probability statement. The most useful addition is the breakeven: if your contribution margin is 40 percent, a revenue return below 2.5 loses money, so showing the range next to 2.5 tells finance immediately whether the downside is a smaller gain or an actual loss (the breakeven ROAS guide has the calculation).
Conditions: what has to hold
Name the operational limits, costs and causal assumptions the result depends on. I like to include exactly one operational condition in the headline, something practical a non-analyst can monitor, like “only if the creative team can supply four new ads a month” or “only while branded search impression share stays above 90 percent”. One condition is memorable, while ten get skimmed. The rest go in the body.
Learning plan: what you’ll measure next, and when you’d change course
Finish with the single next measurement action most likely to improve the decision, with an owner, a date and a threshold. “We’ll run a six-week geo holdout on the extra social spend and revert if the implied marginal return comes in below 2.5” is specific enough that someone can check whether it happened. A good conclusion can also be “we need this evidence before making that move”, as long as you can explain why.
Compare two versions of the same recommendation
The course uses a contrast I find useful to show teams. The first version reads: “Social ROI is 3.7, so move 20% of search spend.” The second reads: “Test a feasible increase in social while preserving the agreed minimum search presence. The model suggests additional social spend has more headroom near current levels, but channel separation is uncertain. Check contribution economics, monitor delivery and use a regional test to learn before the next larger move.”
The second version is less numerically theatrical and more actionable. It names the decision, the support and the unresolved issue, and it gives finance a reason to say yes to a smaller step rather than no to a bigger one. The first version invites the obvious question (“3.7 compared with what, and how sure are you?”) and has no answer ready.
The tool below writes the second kind of paragraph from your own numbers. Put in the marginal return range for the change you’re proposing, your contribution margin, one condition and the test you’d run next, and it will tell you whether the range clears breakeven, straddles it or misses it, and phrase the recommendation accordingly.
The defaults show the most common real-world case: a central return of 2.9 looks comfortably above breakeven at 2.5, but the low end of 1.9 would lose about £7k on a £30k increase. That’s a “test first” result, and the paragraph says so without anyone having to read a chart. Try moving the margin slider to 30 percent: breakeven jumps to 3.3, the central estimate now loses money, and only the top of the range pays back. That’s a conversation you want to have before the meeting, not during it.
Explaining MMM results to a CFO and a CMO
A CFO and a CMO will usually hear the same recommendation differently, and it helps to write for both. A CFO wants to know the downside, the units and whether this is profit or revenue. So talk in contribution after advertising (margin times incremental revenue, minus the media cost) rather than ROAS, state the margin you used and the costs you left out (fixed overhead, agency fees, tax), and put breakeven next to the range. “A revenue multiple does not automatically include product costs, returns, fulfilment, agency fees or overhead” is a line from the course worth keeping in your head. The definition should travel with the chart: “Incremental revenue per £1 of media spend, UK, net of returns, 13-week window” is a chart title a CFO can trust, while “ROI” on its own is not.
A CMO usually wants to know what changes in the plan, what is protected and what the team will learn. That means being explicit about minimum presence (brand spend you’re not touching), about the practical conditions (creative supply, agency capacity), and about the test. It also means being honest when the model can’t separate two channels that always ran together, which is a multicollinearity problem rather than a failure of the team. In both cases, invite the person to say what would make them change their mind, and build that condition into the next review. People are far more comfortable approving a step when they’ve helped define the reversal condition.
Chart mistakes to avoid when you present marketing mix model results
Most MMM charts are designed by the software that produced them, which means they show what the model computes rather than what the decision needs. These are the ones I’d cut or fix first.
Decomposition pie charts. A pie shows shares of a total at one moment. It hides how contributions change across the year, it can’t show a negative contribution (a price increase, a competitor launch), and it hands the biggest slice to “baseline” without explaining that baseline is everything the model didn’t attribute to marketing, including brand equity built by past marketing. It also implies a precision the model doesn’t have, since every slice is a point estimate. A stacked area over time, with a short table of channel contributions as ranges, says much more.
ROI bars without intervals. A bar chart of ROI by channel is the single most requested MMM chart and the one most likely to cause a bad decision, because it ranks channels on central estimates alone. Two channels with similar bars can carry very different uncertainty, and the ranking can reverse within the intervals.
In the right-hand panel, search is the only channel whose whole range sits above breakeven, which might well make it the safer place for the next pound even though its central estimate is lower than social’s. That’s a completely different conversation from the one the bar chart starts.
Response curves drawn past the data. Saturation curves are often drawn out to two or three times current spend with no indication of where the observations stop. Shade or cut the curve at the highest spend the channel actually ran at, because everything beyond that is extrapolation from the functional form, not evidence.
Fit charts as proof. A line of predicted sales hugging actual sales is reassuring, but it shows predictive fit, not that the channel credit is right. Two models with very different channel splits can fit equally well. If you show it, pair it with what actually supports the causal story: an out-of-sample check (see model validation) and any experiment the model was calibrated against.
Unlabelled “ROI”. Every return chart should say what the numerator and denominator are in full, including currency, window, geography and whether revenue is net of returns. If the definitions are missing from an existing report, that’s your next measurement question.
Review the reasoning before the meeting
Before a memo goes to the people who’ll decide, I’d score it against six dimensions, each 0 (missing or misleading), 1 (present but incomplete) or 2 (specific, correct and challengeable). Does it name the decision, period, owner and complete allocation within every constraint? Does it record the data issues found and how they were fixed? Does it keep observation, calculation, assumption and independent evidence apart? Does it use contribution margin correctly, subtract advertising once and compare plans under the same assumptions? Does it show the range and any sign change without calling stress scenarios probabilities? And does it propose a relevant test with an outcome, owner, review point and threshold? A high total doesn’t cancel a fundamental error, such as calling prediction causal proof, so fix those first.
Then have a short review conversation with a colleague. Ask them to explain your recommendation back to you without seeing the spreadsheet, ask which assumption matters most to the action (not which number is largest), ask for the strongest defensible counterargument, and revise one sentence to make the action or its reversal condition clearer. It takes fifteen minutes and catches most of the problems a skeptical CFO would find in the meeting. If the decision turns on comparing several plans, the scenario planning guide has a tornado chart for finding the assumption your reviewer should push on.
A decision memo template you can reuse
The template below generalizes the decision memo and review rubric from the Northstar case in the Vexpower MMM course into something you can use on a real model: seven short sections (decision, support, economics and range, conditions, what would change it, next measurement, evidence trail), followed by the rubric and the review conversation. If you want to see it filled in on a worked case first, the marketing mix modeling example walks through Northstar from brief to memo.
All this structure is there to make the recommendation easy to challenge and easy to act on, so that the meeting is spent on the one assumption that matters rather than on what the chart means.
