Base·Note Data

Marketing mix modeling consultant · New York City

An MMM your CFO will actually trust.

I'm Chelsea Cramer, an independent marketing mix modeling consultant. I build, audit and calibrate MMMs that show what each channel really drives, and turn the results into budget decisions.

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When you need an MMM consultant

Platform dashboards each claim credit for the same sale. Tracking keeps getting harder. And at some point the CFO stops trusting the numbers marketing brings to budget meetings. A marketing mix model (MMM, sometimes called media mix modeling) fixes that by estimating what each channel actually contributes to revenue, after accounting for seasonality, pricing, promotions and the broader economy.

Brands usually bring me in when they're:

  • Spending meaningfully on channels that are hard to track, like TV, CTV, audio, OOH or sponsorships
  • About to make a big budget decision and want evidence behind it
  • Choosing between an MMM vendor, an open-source model or building in-house
  • Sitting on a model nobody trusts or uses

What I've built

At Credit Karma, I built the TV measurement and marketing mix modeling programs and led the move from legacy attribution to a single MMM-based source of truth. I selected, onboarded and ran the MMM vendor, owned the data pipelines and model validation, and calibrated the model against incrementality experiments. That framework guided paid media as budget scaled from $100M to $300M, while ROI went up.

Today I'm building measurement for Robinhood that connects brand marketing like TV, sponsorships and events to revenue, using MMM alongside brand lift studies and geo experiments.

How I approach marketing mix modeling

  • Start with the decision. We agree up front on what the model needs to answer, whether that's next year's budget split, a channel's saturation point or the value of brand spend.
  • Get the data right. Most MMM problems are data problems. I build and QA the spend, outcome and control data before any modeling starts.
  • Use the right tool. I work with open-source frameworks like Meridian, Robyn and PyMC-Marketing, with commercial vendors, or with custom models, depending on your team and budget.
  • Calibrate with experiments. An MMM on its own is a set of estimates. Tying it to incrementality tests is what makes it defensible.
  • Make it usable. The output is a plain-language read on where to put the next dollar, what to cut and what to test next, built for both marketing and finance.

Engagements

I work with brands as an independent marketing mix modeling consultant on a project or fractional basis. Common starting points are an MMM audit of an existing model or vendor, a first build, or ongoing ownership of the model and its refreshes. I do the modeling myself in Python, R and SQL, so you work directly with me.

Common questions

FAQ

What does a marketing mix modeling consultant do?

An MMM consultant builds or audits the model that estimates how much each marketing channel contributes to sales, then turns the results into budget recommendations. That includes preparing the data, choosing the modeling approach, validating the results against experiments and presenting them to marketing and finance leadership.

Is marketing mix modeling the same as media mix modeling?

Yes. The two terms are used interchangeably, and both are shortened to MMM. "Marketing mix" sometimes signals that the model also covers non-media drivers like pricing and promotions.

How much data do you need for an MMM?

Typically two to three years of weekly data on spend by channel and your main outcome, like sales or sign-ups, plus factors such as seasonality and promotions. Less history can work if spend varies enough across channels and time.

Should we use an MMM vendor or build in-house?

It depends on your team, budget and how often you need answers. Open-source tools like Meridian and Robyn make in-house builds realistic, but they still need someone who can validate and maintain them. I help teams make that call and can support either path.

How is MMM different from attribution?

Attribution follows individual user paths and over-credits the channels it can track. MMM works from aggregate data, so it covers offline and hard-to-track channels and isn't affected by tracking loss. Most mature teams use both, and use experiments to check them.

Work with me

Tell me what's not adding up.

A 30-minute call is enough to tell whether I can help and what a first engagement would look like. I'm based in New York City and happy to meet in person.

Book a 30-min call