How To Make Sense Of Marketing Mix Modeling—and Make It Work For Your Brand
What you can (and can't) expect from MMM.
Marketing mix modeling is a macrofinancial analysis. It does not rely on customer journey insights to run the model, nor does it require user-level data such as impressions and clicks. MMM Analytics doesn't look at campaign-level tactics, nor does it consider whether one creative is performing better than another. It's also not about multi-touch attribution or final touch at the user journey level. It is simply a way of confirming or rejecting decisions made at the budget level.
Concerns about cost and data accuracy are among the most common limitations of marketing mix modeling. But many of these concerns are based on beliefs from earlier times that obscure the true value of the practice.
Here's what brands need to know about MMM's four biggest myths.
Myth 1: Marketing mix modeling tells you what to do with your budget.
Media mix models can enable optimized budget allocations between channels based on historical data. For example, you might tell a brand that it is underfunded by 15% for TV spend and 5% for paid social advertising, and how that share varies by region, for example West Coast and East Coast.
However, this data cannot predict which budget strategies and tactics may or may not work in the future. MMM results can be used to create forecasting models, but are not forecasts themselves. A brand may want to introduce new channels that weren't in the original model, or a business objective may change priorities so that the calculated mix of channels is no longer appropriate.
Myth 2: Building sustainable practices is detrimental to organizations.
Industry best practice recommendations often give marketers the impression that building MMM capabilities requires six-figure investments and terabytes of historical data in enterprise-grade data warehouses.
Here's the low point for most brands: If you have historical data on your spend (this can be exports from platforms like Google Ads, Meta, and Pinterest) or just conversion statistics, you can create a Model Marketing Mix. with a relatively small investment.
Myth 3: If I can't measure everything, I shouldn't measure anything.
Most brands don't need to boil the ocean to benefit from MMM studies. Instead of looking at every signal of competition, weekly product sales, or external market forces affecting your industry, you can simply look at the macro level of how much money is spent on media per week per channel and overlay the sales data. Regression models with simple data sets can produce a mixed basis against or in favor of previous budget decisions.
Myth 4: An AI-based platform is the answer to all my problems.
Advances in machine learning and artificial intelligence offer tremendous benefits for marketing mix modeling. But AI alone cannot solve all MMM problems.
Depending on the questions asked and the data set, some methods may work better than others. When selecting third-party tools and vendors, marketers must decide whether the method is the best fit for their data and goals and understand which model parameters can be adjusted to ensure the best possible fit and generalizability. .
A new type of data dependency
As marketers continue to evaluate the role that marketing mix modeling can play in their organizations, they need to take a deeper look at what it means to rely on data in the privacy era. The shift from user-level data collection to more macroeconomic analyzes like MMM tends to give the impression of the wrong choice: some marketers expect marketing mix modeling to solve all their budgeting problems. Others say it's too rugged and not tactical enough.
But don't let the perfect be the enemy of the good. The MMM train leaves the station. Marketers must adopt the right mindset and approach to meet future measurement challenges.
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