What Is Marketing Mix Modeling (MMM)?

Marketing mix modeling (MMM), also known as media mix modeling, is a statistical method that analyzes time series data from sales and marketing to quantify the impact of various marketing initiatives on business outcomes and predict the results of future marketing strategies. The methodology analyzes historical data to evaluate the performance of different marketing channels—from traditional media like television and print to all manner of digital platforms—to help businesses understand how their marketing activities collectively drive sales and other key performance indicators.

How It Works

MMM analyzes aggregated market-level data to provide a holistic view of marketing performance across the entire customer journey. Unlike last-touch attribution models that focus exclusively on the final interaction before conversion, MMM considers the cumulative impact of all marketing touchpoints while accounting for external variables such as seasonality, competitive activity, macroeconomic trends, and events.

The methodology employs advanced statistical techniques to distinguish between sales that would have occurred organically and those directly attributable to a full array of marketing efforts, enabling marketers to identify the true incremental value of campaigns.

The success of each marketing channel can be understood by evaluating how much it contributes to incremental sales (i.e., sales that wouldn’t have happened if not for marketing efforts). MMM breaks this down into three main components:

• Effectiveness: Measuring sales brought in by each effort in a marketing channel; for example, if you post a new ad or increase spending, how much does this increase sales?
• Efficiency: Comparing sales generated to costs involved in the effort, ensuring that for every dollar spent on marketing, you realize the greatest impact on sales
• Return on Investment (ROI): The big-picture metric, considering both sales made and costs incurred to give you an overall understanding of the value gained from your marketing efforts

Key Benefits

Key Points

• Comprehensive channel assessment: MMM evaluates effectiveness across both digital and traditional channels within a unified measurement framework.
• Privacy-compliant measurement: MMM functions without relying on individual-level tracking, making it increasingly valuable in today’s privacy-first world.
• Budget optimization: MMM identifies cost curves, diminishing returns, and saturation points in marketing channels to prevent wasteful spend.
• Cross-team alignment: MMM bridges gaps between performance marketing, brand, and other teams by providing a common measurement framework.
• Long-term planning: MMM forecasts potential outcomes of different marketing scenarios, allowing for strategic budget allocation.

Required Inputs

Creating an MMM involves training a model using historical data from sales, conversions, and ad spend derived from marketing efforts, striking a balance between automated modeling tools processing large data sets and the detailed work of experienced data scientists. Multiple iterations go into developing the most accurate model.

Effective MMM implementation typically requires:

• Historical marketing data: Expenditures and activity metrics (impressions, GRPs, etc.) broken down by channel, typically spanning 1–3 years
• Sales or conversion data: Aggregated at daily or weekly frequency to serve as the dependent variable
• External factors: Economic indicators, competitor activities, seasonality patterns, events, and other relevant external variables
• Conversion funnel data: Information on customer journeys from awareness to purchase

Key Outputs

A well-executed MMM delivers actionable insights including:

• Channel attribution: Quantification of each marketing channel’s contribution to business outcomes
• Cost/saturation curves: Identification of diminishing returns thresholds for each channel
• Optimization recommendations: Data-driven guidance for budget allocation
• Forecasting projections: Predictions of expected outcomes from various marketing strategies

Evolution

Historically, the analytical process underlying MMM was tedious and time-consuming, demanding a data science team to scrutinize marketing campaign data over many months, delivering semiannual or quarterly updates at best.

Fortunately, this has dramatically changed. Modern analytics tools, such as machine learning algorithms and predictive modeling leveraging AI, can uncover complex relationships between marketing activities and outcomes, enabling marketers to analyze vast datasets more effectively. MMM now readily integrates data from various sources, including online and offline channels, CRM systems, and third-party data providers, providing a more comprehensive view of marketing performance.

Agile next-generation MMM solutions have significantly reduced implementation complexity through automation and machine learning. These platforms offer more frequent model updates—daily or weekly rather than quarterly or annually—providing highly actionable insights in near real-time while maintaining privacy compliance.

As user-level tracking capabilities diminish due to privacy regulations, MMM has gained renewed relevance as a future-proof measurement approach that leverages aggregated data rather than granular user-level data.

This table provides a concise summary of your choices when approaching MMM for your organization. Read further to learn about Kochava’s next-gen SaaS solution.

Next-Generation SaaS MMM Solution: AIM by Kochava

AIM (Always-On Incremental Measurement) by Kochava is a next-generation software-as-a-service MMM platform. AIM is designed specifically to address the challenges faced by UA marketers. Its unique approach leverages advanced, AI-powered machine learning that continuously incorporates new market information, updating your models every day with real-time campaign results, ensuring that the data guiding your decisions is always up to date.

Want to connect with Kochava about fully harnessing the potential of MMM via our next-gen solution? Please request a meeting.