Marketing Mix Modeling (MMM) is a powerful tool that blends the art of traditional marketing with data-driven science. Rooted in historical marketing practices, MMM has evolved to provide a comprehensive analysis of the 4Ps of the marketing mix. By considering factors like Adstock and using time-series regression, MMM offers insights into both immediate and long-term marketing impacts. With the ability to run ‘what if’ simulations, marketers can optimize their strategies, ensuring maximum ROI.
In addition, Marketing Mix Modeling (MMM) has struggled to adapt to the growth of digital marketing due to factors such as the complexity of digital channels, data fragmentation, lack of real-time analysis, inadequate attribution modeling, overemphasis on linear relationships, and slow adoption of new technologies like AI and ML. This failure to evolve could make MMM obsolete unless significant changes are made to align it with the intricate and dynamic nature of modern digital marketing.
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Marketing Mix Modeling (MMM) is a powerful tool that helps businesses predict outcomes through statistical analysis and multivariate regressions. The regressions analyze the contribution of various marketing tactics and spends to conversions and sales, enabling companies to make informed decisions about their marketing mix.
MMM works by collecting aggregated data from multiple sources over a multi-year period, taking into account external factors such as seasonality, economic data, weather, and promotions. This data is then used to develop a demand model that quantifies the historical impact of each marketing input on business outcomes such as sales and conversions.
For established brands with a wealth of data, MMM can provide valuable insights into their entire media portfolio, making it ideal for long-term strategic planning.
However, it has its limitations, particularly when it comes to making tactical or day-to-day decisions. MMM models are based on historical data and assumptions, so they may not accurately predict the impact of dynamic changes to marketing channels or business changes in recent periods.
MMM also relies on probability to estimate marketing impact on business outcomes, which can be subject to the correlation vs. causation dilemma. While well-built models can provide channel lift and forecasts, they are not designed to inform sub-channel-level tactical decision making and are challenged in identifying changes in recent periods.
Marketing Mix Modeling is a valuable tool for decision makers looking for high-level insights into their media portfolio.
However, it should be used in conjunction with other data sources and techniques to make informed decisions about their marketing mix and drive better business outcomes.
Historical Context of Marketing Mix Modeling
Marketing Mix Modeling (MMM) has its roots deeply embedded in the historical context of marketing. Traditional marketing practices were once considered an art form, with results being mysterious and untraceable. However, the advent of big data in the 1980s shifted this perspective, emphasizing the scientific aspect of marketing. Today, MMM stands as a testament to this evolution, blending the art of marketing with data-driven science.
🏈 Think of MMM like managing a football team. Just as a manager analyzes each player’s contribution to a match, MMM evaluates each marketing channel’s contribution to sales. Some channels might score the goals (direct sales), while others play a crucial role in assisting those goals (brand awareness, lead generation). Understanding this dynamic is key to optimizing your marketing strategy.
Here’s what our CEO has to say about the differences between MMM & MTA on the Data Gurus Podcast
“Prior to bottom-up multi-touch attribution marketers only had top-down marketing mix models.
If you were a progressive company you would get a new MMM model done every year and that model would tell you to put ‘X’ percentage of your budget to digital, ‘X’ to TV, ‘X’ to direct mail and then it was up to each agency to determine how to allocate.
With MMM, there are no clear-cut goals in terms of a ‘feedback loop’ if any of the media was actually hitting your numbers until you did another model next year.
And next year’s model would tell you to adjust the channel allocations, but there’s no feedback to the actual on the ground people in terms of telling them what to do.
So marketing mix modeling tells you where to allocate those dollars while multi-touch attribution used to tell you how to allocate within that specific channel and cross-channel at a very very granular level, that was until all of the privacy changes, including IOS changes and the upcoming cookie apocalypse“
4Ps and MMM
While many are familiar with the 4Ps of the marketing mix – Product, Price, Place, and Promotion, MMM takes this a step further. It not only considers these factors but also quantifies the success generated by each. By doing so, MMM provides a comprehensive view of how each element of the marketing mix contributes to overall business success.
Adstock and Time-Series Regression
An essential concept in MMM is the idea of ‘Adstock.’ This theory suggests that the impact of advertising is not immediate and diminishes over time. Through time-series regression analysis, MMM can account for this diminishing return, ensuring that marketing strategies are optimized for both immediate and long-term results.
Optimization and “What If” Simulations
Once the analysis is complete, the real power of MMM comes into play. With the data in hand, marketers can run ‘what if’ simulations to predict outcomes of different strategies. For instance, what would be the impact on sales if the budget for TV advertising was increased by 10%? These simulations provide actionable insights, allowing for informed decision-making.
Marketing Mix Modeling and Its Struggle to Adapt to Digital Marketing Growth
Marketing Mix Modeling (MMM), has been a cornerstone for marketers for years. However, as digital marketing has grown exponentially, MMM has faced significant challenges in adapting to this new environment. Here’s six reasons why MMM has lagged behind the growth of digital marketing:
1. Complexity of Digital Channels
Traditional MMM relies on analyzing historical data, such as TV ratings, print circulation, and sales figures. With the introduction of various digital platforms, including social media, search engines, and online advertising, the landscape has become much more intricate. MMM often fails to fully integrate these channels due to their dynamic and multifaceted nature.
2. Digital Data Fragmentation
Digital marketing involves several interconnected channels, each producing a vast amount of data. This data fragmentation hinders MMM’s ability to create a cohesive analysis. Digital platforms often use different metrics and have varying data privacy restrictions, creating obstacles in gathering and synthesizing information.
3. Lack of Real-Time Analysis
Digital marketing evolves rapidly, with trends and user behavior changing at an unprecedented pace. Traditional MMM, with its focus on analyzing historical data, struggles to keep up with these real-time changes. Its inability to provide timely insights makes it less effective in guiding current marketing strategies.
4. Inadequate Attribution Modeling
Attribution in digital marketing is multifaceted, as consumers often interact with multiple touchpoints before making a purchase. MMM has struggled to adapt to this complexity, as it typically relies on simplistic models that may not accurately attribute the success of a particular channel.
5. Overemphasis on Linear Relationships
MMM traditionally operates under the assumption of linear relationships between marketing inputs and outputs. Digital marketing often follows non-linear patterns due to the interplay of various channels and the unique nature of online consumer behavior. This mismatch between the linear model and the actual complex relationships contributes to MMM’s struggle to adapt.
6. Failure to Embrace New Technologies
Emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are playing vital roles in understanding and predicting consumer behavior online. MMM’s relatively slow adoption of these technologies has hindered its ability to evolve in tandem with the digital marketing ecosystem.
To remain relevant, MMM must undergo substantial changes. Integration with new technologies, adaptation to the non-linear nature of digital marketing, and the ability to handle the complexity and fragmentation of data are essential for its evolution.
What is Marketing Mix Modeling and how does it work?
Marketing Mix Modeling (MMM) is a statistical technique used to measure the effectiveness and ROI of marketing activities by analyzing their impact on sales and revenue. It uses a combination of data from various sources such as sales data, marketing spend data, and consumer behavior data to model the relationship between marketing activities and business outcomes.
What are the benefits of Marketing Mix Modeling for businesses?
Marketing Mix Modeling provides businesses with insights into the effectiveness of their marketing activities, helping them make informed decisions about where to allocate their marketing budget. It allows companies to evaluate the impact of specific marketing initiatives and provides a clear understanding of the return on investment for each marketing activity.
What are the key components of a Marketing Mix Model?
The key components of a Marketing Mix Model include:
- Sales data
- Marketing spend data
- Consumer behavior data
- Market data such as competitor and market share information
- Economic and demographic data
How is Marketing Mix Modeling different from other marketing analytics techniques?
Marketing Mix Modeling differs from other marketing analytics techniques in that it focuses specifically on the relationship between marketing activities and sales, rather than just analyzing marketing data in isolation. MMM takes into account multiple factors and provides a comprehensive view of the impact of marketing activities on sales and revenue, allowing for more informed and effective marketing decisions.