Marketing Mix Modeling: Prove That Marketing Delivers Value

MMM is designed to measure the impact of advertising and promotions across channels while controlling for external factors outside a brand’s control. The outputs from MMM are used in three ways:

1. As a scorekeeper, to show the overall incremental impact of marketing investments on the business

2. As a forecaster, to predict the outcome that raising or lowering marketing budgets will have on marketing’s contribution to the overall budget

3. As a coach, to suggest shifts to current marketing investments that improve performance

Marketing mix models go deep, providing answers to a wide range of questions — and these questions require a wide variety of input data. 

As findings get more detailed, a model’s scope will tend to increase. Knowing and ranking the insights your department is most likely to act on can secure early wins that justify further investment and identify opportunities to improve marketing spend. 

Once you’ve prioritized your most critical insights, be sure your data requests allow for enough specific detail to answer your prioritized questions — for example, sorting impressions by geography or campaign type. Then, create a modifiable roadmap of future insight objectives. 

When reviewing MMM, start with the question, “Is it actionable?” The answer will help ensure your mix yields the specific, high-quality data that can inform future model iterations — such as adjustments to marketing activities, shifts in frequency or changes to the channel mix.

Since marketing mix models attempt to quantify the incremental impact of all included marketing activities, they are sometimes used solely for that purpose. But at its core, the marketing mix is a predictive model. Far more than a scorekeeper, it’s a coach that provides guidance on future marketing activities. Successful CMOs spend considerable time evaluating how to use MMM predictions to optimize future marketing plans and even adjust spending on in-flight campaigns.

As CMOs face increased uncertainty, effective scenario planning is critical. Simulation interfaces or tools allow marketers and other stakeholders to game out different future states and test investment strategies by geography, product, channel mix, channel execution or level of marketing spend. These planning tools account for marketing elasticities, and the fact that each marketing activity is on a different part of a unique response curve — for example, the same change in marketing spend — can have noticeably different impacts across marketing activities, such as paid search versus display.

The level of supported planning guidance is getting more detailed and can be particularly valuable for brands that do a variety of promotions in nondigital channels, such as weekly newspaper inserts. This level of reporting can also help quantify halo effects where the promotion of one item also increases sales of a second, unpromoted item.

Beyond adjusting marketing levers, scenarios can also modify external factors. Many scenario planning tools use an optimization engine that reveals the optimal future marketing spend based on marketplace dynamics and budget constraints. For example, an automotive manufacturer could forecast how a competitor’s hypothesized increase in advertising spend or a drop in gasoline prices will affect marketing performance — and then assemble a plan for the factors that are most important to monitor to ensure the delivery of planned marketing outcomes.