When a marketing mix model earns its cost

The Performance Marketing work group sets the tests a company should pass before it pays for a marketing mix model.

Updated October 13, 2026

Build one when a wrong channel split would cost more than the model, the spend history can separate channels, and finance and channel owners will change budgets on what it says.

Size the decision first. Mark Fiske funds a model when the next budget call is large, for example when a consumer business adds TV or other offline media. If the history, the budget choice or the buy-in is weak, he starts with matched-market tests and post-purchase surveys.
Check the spend history. Daniel Pearson plots weekly spend by channel against customers and margin. If Meta and Google always rose together, the model's split between them is hard to trust. If its recommendation flips when a period is left out, he holds off and runs a geo holdout.
Calibrate it with experiments. Daniel, Timothy Davis and Mike Jacobs check the model against holdouts and lift tests, and feed results back in as spend and creative change. Mike notes that in one analysis about half of the purchases driven by social had no click.
Tie it to cash. Mark wants every scenario to show incremental customers, contribution margin and payback. Micah Moreau multiplies the low ends of the customer and margin estimates, and caps the increase if that case breaks the cash limit.

Group chat with Timothy, Mark, Daniel, Mike, Micah — the Performance Marketing work group

Question:

When is a marketing mix model worth the effort for a growing company?

Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma

I would fund MMM when the next budget decision is large enough that a wrong channel allocation costs more than the model and the work of using it. A growing consumer business moving beyond search and social into TV or other offline channels is a strong candidate, especially once platform reports and company-level results start telling different stories. In a public conversation about measurement, I described our quarterly MMM refresh at Ancestry: a channel manager could discover their program was 20% less efficient than they thought, then need to reset targets as spend scaled. The hard part was getting that finding into the next planning cycle.

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My decision check has three parts: Do we have enough history and meaningful changes in spend across channels to estimate effects? Is there a real choice about where the next dollar goes? Will finance and channel owners use a common view of incremental return and payback to change the budget? If any answer is weak, I would start with matched-market tests for offline media and use post-purchase surveys alongside channel data while building the measurement process. For an enterprise-sales-led company where marketing plays a smaller role in growth, I would prioritize sales effectiveness first.

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Micah MoreauVP of Growth at OpenAI. Former VP of Growth Marketing at DoorDash

Before commissioning a model, I’d make every channel owner write down the incremental customers and contribution margin they expect from the next spend increase, plus what a holdout would have to show to change their mind. I’ve argued publicly that marketers need to demand causal, incremental return on spend and question reported results.

X That makes the MMM useful as a way to rank competing budget claims and identify which claim to test next. If the organization will keep funding a channel based on its reported conversions after a credible test disagrees, more modeling effort will not change the decision.

Daniel PearsonCo-founder of Bamboo. Advisor to HexClad Cookware

I’d make the decision based on the size of the allocation decision and how long it takes the business outcome to show up. For a smaller DTC brand with first-purchase profitability, I’d start with first-party financials and one-day-click attribution, then adjust the CAC assumptions as the financials come in. For a business with long payback periods and several meaningful channels, those signals arrive too slowly to guide every budget move.

An MMM earns its cost when it can change a material budget decision, and the team can check its estimates against experiments. I’d run holdouts on the highest-spend channels first, and require a holdout when expanding into a new channel. Then use those results to calibrate the model and revisit its estimates as spend changes. A fast model with inaccurate inputs can push a growing company toward an expensive mistake.

Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma

The model's output needs to fit the cash plan. A channel can bring in customers with strong eventual value while leaving a growing company short of cash to fund the next acquisition cycle. In my public piece on marketing budgets, I separate infancy-stage cash constraints from the growth stage, where companies can plan against LTV and choose a payback window. The budget process then ties leadership's revenue and CAC targets to channel-level scenarios and monthly spend. I would require any MMM scenario to show incremental customers, contribution margin and expected payback under each proposed allocation, so finance can make the actual investment decision.

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Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify

The model should have an ongoing check against measured lift, especially as audiences and creative change. For an earlier-stage company, I’d start with clean conversion tracking and a focused lift test where the spend supports it. In a public conversation, I said that even a platform incrementality test can be more work than the signal warrants below roughly $50K a month on that platform. That was a rough guide for that test, not an MMM spend cutoff. I also pointed out that attribution cannot tell you whether someone would have converted without the ad.

YouTube At ElevenLabs, I’ve described rerunning lift studies when performance changes, because the estimated lift can move enough to justify increasing or reducing investment.

YouTube Once the company has enough varied channel spend for an MMM, I’d use those experiments to check its recommendations before shifting a large budget.

Mike JacobsChief Growth Officer at Consortium Brand Partners. Former VP of Growth at Etsy

One reason this becomes urgent as channels multiply: the click stops being a reliable proxy for what caused a sale. In one analysis I described publicly, roughly half of the purchases genuinely driven by social had no click. A company allocating spend using click-based attribution would undercount those sales.

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I’d use MMM for the recurring cross-channel budget decision, with experiments to calibrate it. In that talk, I described platform and geo holdouts as ways to check the model’s estimates. Each experiment captures one place and time, so the team needs to feed those results back into the model as spend and creative change.

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Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma

I would set the decision cadence before buying the model. At Ancestry, the quarterly MMM refresh required resetting channel targets after changes in measured efficiency, which could create real tension with managers who used platform reports every day. In board discussions, I have pushed for attributed reports so budget decisions use the same measurement approach as the channel plans. The operator test is whether finance, the board and channel owners will agree on how model updates reset targets and trigger experiments each quarter. Without that agreement, the model's recommendations will sit apart from the budget.

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Daniel PearsonCo-founder of Bamboo. Advisor to HexClad Cookware

Mark’s cash-plan point matters especially when the MMM uses forecasted LTV. I’ve seen startups treat LTV forecasts as financial truth while newer cohorts monetized worse and marginal CAC rose with scale. The model can inform the next allocation, but I’d reconcile its projected payback with actual cohort cash returns each month and lower the spending plan if those cohorts miss.

Mike JacobsChief Growth Officer at Consortium Brand Partners. Former VP of Growth at Etsy

Mark’s finance point changes what I would ask the model to optimize. A model can correctly estimate incremental sales and still recommend the wrong allocation if one channel brings in discount-driven, low-margin orders while another brings in customers with higher expected value.

In my public talk, I described passing predicted lifetime value to the media system and looking at margin and average order value alongside conversion rate. A creative can convert more often and produce smaller orders.

YouTube Daniel is right to check predicted LTV against realized cohorts. I’d ask the MMM team to show how its channel ranking changes when the outcome moves from gross revenue to contribution margin, then check any added value assigned to new customers against actual payback before moving budget.

Micah MoreauVP of Growth at OpenAI. Former VP of Growth Marketing at DoorDash

I’d put two uncertainty ranges in the budget review: how many incremental customers the spend creates, and how much contribution margin each cohort generates before the cash deadline. I’ve called out the risk of judging CAC and breakeven payback with too little cohort history in a consumer business.

X Multiply the low end of the incremental customer estimate by the low end of realized cohort margin. If that case breaches the cash limit, cap the increase and reserve the rest for a controlled test. Daniel’s monthly reconciliation then tells you whether to release the reserved spend.

Timothy DavisGrowth at ElevenLabs. Former Senior Lead, Growth Marketing at Shopify

One more check before feeding regional conversion data into an MMM: can customers actually buy in those regions? At IBM, we were advertising broadly across Africa while charging in USD. Demand existed, but the currency setup made conversion harder. We narrowed our focus to South Africa and offered payment in rand; performance improved.

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If a company had modeled that earlier period as a regional media-efficiency problem, it might have cut spend in a market where the checkout experience was the constraint. I’d log changes to pricing, payment options, landing pages and availability alongside the media data, then resolve any obvious purchase blockers before using regional MMM estimates to set budgets.

Mark FiskeManaging Director at H.I.G. Capital. Former VP of Growth and Marketing at Credit Karma

Daniel's cohort check also needs to affect timing within the year. In a budget example I published, the same full-year CAC could produce more in-year revenue by moving spend into periods with faster payback, even after accounting for marginal returns. That is a useful test of MMM output: compare the proposed channel mix and spend schedule against actual cohort receipts, then revise monthly budgets if the projected cash arrives late. A model that recommends the right annual channel shares can still leave money on the table through poor flighting.

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Mike JacobsChief Growth Officer at Consortium Brand Partners. Former VP of Growth at Etsy

There’s a second operational test for MMM: can its findings change what the bidding systems optimize? In my public talk, I warned that a system trained on last-click conversions will find more last clicks, even when those clicks add little incremental value. I described feeding predicted customer value and margin signals into media buying so the system could pursue better customers.

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If an MMM shows a channel has more incremental value than its platform report suggests, I’d change the budget plan and investigate the value signals sent to the platforms. Otherwise the team may correct the quarterly allocation while its daily bidding keeps pursuing the same low-value conversions.

Daniel PearsonCo-founder of Bamboo. Advisor to HexClad Cookware

One failure mode I’d guard against: using the MMM to justify spreading spend across more channels simply because the model now has a line for each one. I’ve argued publicly that strong ROI in Meta or Google is a good reason to keep funding the winner, and that concentration alone is a poor reason to shift dollars away.

Substack For each proposed shift, I’d ask for the marginal incremental contribution after the move, its payback period, and the experiment that could prove the shift wrong.

Question:

Daniel, Mark says MMM needs meaningful changes in channel spend. How would you check whether a growing company's spend history can separate channel effects well enough to guide its next budget decision? Please draw on public work and cite public sources only.

Daniel PearsonCo-founder of Bamboo. Advisor to HexClad Cookware

I’d plot weekly spend by channel against total customers and contribution margin, then mark promotions, price changes, launches and seasonality. The useful question is whether any major channel changed meaningfully while the others stayed relatively steady. If Meta and Google always rose together, their separate effects will be hard to trust, even if the model gives each a precise-looking ROI.

I’d then ask the modeler to rerun the next-budget recommendation after reasonable changes to assumptions and after leaving out a period of data. If the recommended channel flips, I’d defer the big reallocation and create cleaner variation through a channel-level or geo holdout. I’ve publicly argued for testing the highest-spend channels first and calibrating MMM against experiments, because an inaccurate model can do more harm than good.

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Frequently Asked Questions

What should you check before adding budget to flat paid social?
How the channel is judged. Many plateaus are measurement or payback-target problems: a channel graded on platform reporting, or on blended CAC instead of marginal payback. Then widen lookalike bands and rerun lift tests.
How do you find out why paid social costs went up?
Find the step where the cost rises. A higher CPM with steady CTR points to the auction, a falling CTR to tired creative, clicks that stop converting to the audience, and several audiences stalling at one step to the offer or checkout.
How do you test whether paid ads bring in customers you wouldn't have gotten anyway?
Compare a group that sees the ads with one that doesn't, through a lift study, holdout or matched-market test, and divide the extra customers or revenue by spend. Then follow those customers until they pay back, and rerun the test as spend grows.

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Frequently Asked Questions

When a marketing mix model earns its cost

Performance Marketing

Frequently Asked Questions

What should you check before adding budget to flat paid social?

How do you find out why paid social costs went up?

How do you test whether paid ads bring in customers you wouldn't have gotten anyway?

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When a marketing mix model earns its cost

Performance Marketing

Frequently Asked Questions

What should you check before adding budget to flat paid social?

How do you find out why paid social costs went up?

How do you test whether paid ads bring in customers you wouldn't have gotten anyway?

Your ideas, super powered.