ABCs of Media Mix Modelling

For consumer packaged goods (CPG) companies, implementing the results of a media mix model (MMM) means turning insights into clear budget and planning decisions.

The model highlights which channels generate the strongest return on investment, allowing marketers to reallocate spend toward those that drive the most incremental sales, whether that’s brand-building media like TV or shopper-focused tactics such as in-store promotions and retailer media networks.

MMM also help balance trade spend with advertising by showing the relative impact of discounts, coupons, and displays compared to traditional media.

Results can be broken down by retailer to inform joint business planning, and the insights should be turned into practical playbooks—guidelines that brand teams and agencies can apply for seasonal campaigns or ongoing investments.

Since consumer behavior and competition change, MMMs should be refreshed regularly so companies can validate past decisions and continue optimizing their mix over time.

In a media mix model, diminishing returns are identified by examining the response curve that links marketing spend to sales. At lower levels of investment, each additional dollar produces a strong lift, but as spending increases the curve begins to flatten, meaning the incremental sales gained from additional spend shrink.

This is captured by calculating the marginal ROI—the return from the next unit of investment—and determining the point at which it falls below a profitability threshold or the efficiency of other channels.

For CPG brands, this insight helps set spending caps on channels that saturate quickly, like retailer media, and guides budget shifts toward channels that still generate meaningful incremental sales.

For a CPG media mix model that updates regularly, you need a comprehensive set of inputs covering sales, marketing, distribution, pricing, competition, and external factors.

Sales data should be captured at the product, SKU, or category level and broken down by region, channel, or retailer, ideally over multiple years to account for trends and seasonality. Marketing inputs include spend, impressions, and promotional activity across all channels, while distribution data captures store availability, shelf space, and retailer-specific campaigns.

Pricing and competitive information, such as competitor promotions and price changes, help measure cross-elasticity effects, and external factors like holidays, weather, and economic indicators account for broader influences on demand. All data should be aligned to the same time scale, with consideration for lagged effects from specific channels, ensuring that the model can produce accurate, actionable insights each time it is refreshed.

When comparing media mix modeling (MMM) vendors, it’s important to evaluate them across methodology, data integration, accuracy, actionability, update frequency, and support.

Look at the modeling techniques they use—whether regression, Bayesian, or machine learning—and whether they can handle SKU-, category-, or retailer-level data while accounting for lagged effects and diminishing returns.

Assess their ability to integrate multiple data sources, including marketing spend, sales, pricing, distribution, and external factors, and how they handle missing or inconsistent data. Consider the accuracy of incremental sales measurement and whether they provide actionable insights like iROAS, and “what-if” scenario simulations.

Additionally, check how frequently the model can be updated and how automated the process is, as well as the level of ongoing support, training, and customization offered. Comparing vendors across these dimensions ensures you select a solution that aligns with your CPG business needs and delivers reliable, decision-ready insights.

Yes, MMMs have been around for decades—initially developed in the 1990s to measure the impact of traditional media like TV, print, and radio—but they remain highly relevant today because marketing has become far more complex and data-driven.

Modern MMMs have evolved to handle digital channels, e-commerce data, and advanced analytics, incorporating first-party and real-time data from DSPs, social media, and retailer platforms. They help marketers understand incremental impact across all touchpoints, account for diminishing returns, and guide budget allocation in an increasingly fragmented media landscape.

Essentially, while the core principle—linking marketing spend to sales—remains the same, modern MMMs are far more sophisticated, providing actionable insights in a world of omni-channel marketing, dynamic pricing, and rapid consumer behavior shifts.