Marketing Mix Modeling (MMM)

What is Marketing Mix Modeling (MMM)?:

Marketing Mix Modeling (MMM) is an analytical technique used to determine which marketing tactics most effectively drive sales by examining historical data. It also measures the Return on Advertising Spending (ROAS), helping businesses to plan successful future campaigns, optimize budget allocation, and predict the outcomes of various marketing strategies. According to Deloitte’s measurement research, C-level executives who prioritize marketing mix modeling are over twice as likely to surpass their revenue targets by 10% or more.

“C-level executives who prioritize marketing mix modeling are over twice as likely to surpass their revenue targets by 10% or more.“

Deloitte

Uses of MMM:

1. Measure the Incrementality Effect: Distinguish between base sales (happening without marketing) and incremental sales (driven by marketing).
2. Identify Effective Marketing Channels: Assess which channels drive the most sales and brand awareness.
3. Optimize Budgets through Forecasting: Determine the point of diminishing returns and allocate budgets efficiently.

Data Used in MMM:

Dependent Variables

• Sales or other KPIs (historical sales and revenue)

Independent Variables

• Media spend data (spending on various channels)
• Competitive data
• Macroeconomic data (GDP, unemployment rates)
• Consumer data (demographics, behaviors)

Methods Used in MMM:

In past practices, linear regression was a preferred method. Although linear regression offers a simpler and straight forward explanation for the relationship between the dependent and independent variables, it doesn’t address the non-linear nature of advertising, such as advertising saturation and the carry-over effect. With the advent of more ML algorithms, we are now able to model non-linear relationships with more accuracy and efficiency.

“With the advent of more ML algorithms, we are now able to model non-linear relationships with more accuracy and efficiency. “

Kaytics

Reliability of MMM:

Validate MMM accuracy by comparing model predictions to actual sales data and through real-world incremental experiments like conversion lift studies.

MMM Accessibility:

MMM has evolved from being manual and expensive to more automated and accessible with tools that facilitate data collection and analysis.

Future of MMM:

• Increased use of AI and machine learning.
• More embrace of the power of MMM
• Real-time MMM.

MMM vs. Multi-Touch Attribution: MMM aggregates historical data for macro-level conclusions, while attribution modeling assigns values to individual marketing touchpoints in real-time. Due to privacy regulations, MMM is becoming a more popular choice for understanding marketing effectiveness. We will explore MTA in another blog; it is too broad to cover it in one paragraph.

Getting Started with MMM:

1. Secure executive buy-in.
2. Define business questions.
3. Collect relevant data.
4. Run and iterate the model.
5. Use insights to inform decisions.

Best Practices:

• Start small and iterate.
• Ensure clean and comprehensive data.
• Collaborate with experts in the field.