In the fast-paced world of marketing, understanding the impact of your efforts is crucial. Marketing leaders are constantly grappling with questions like:
- What’s the return on marketing investment?
- Where should we invest the next dollar?
- How much of marketing’s contribution is truly incremental?
- How do we adjust investments in real time?
- How much should we be investing in marketing?
- How confident are we about hitting our business goals?
To answer these questions, many marketers have traditionally turned to touch-based attribution models. However, as we’ll discuss in this article, these approaches have their limitations. Enter Marketing Mix Modeling (MMM), a robust statistical analysis that can provide invaluable insights into the effects of your marketing activities on sales performance.
Why Consider Marketing Mix Modeling?
Touch-based attribution has long been the go-to method for assessing marketing effectiveness. It assigns credit for conversions to the last touchpoint a customer interacts with before taking action. While this method offers a simple and seemingly clear way to measure marketing impact, it is highly limited and unreliable.
This is where Marketing Mix Modeling steps in. It stands out as one of the few marketing measurement methodologies grounded in proven statistical techniques. Through MMM, we can gain deep insights into which channels are driving the highest Return on Investment (ROI), allowing us to forecast the future performance of our campaigns and initiatives. Here’s why MMM is superior to touch-based attribution:
- Universal Applicability: MMM can tackle indirect marketing channels (e.g., paid social, video) and offline channels (e.g., TV) in addition to digital direct response (e.g., search engine advertising). It provides a holistic view of your marketing mix.
- Future-Proof: Unlike touch-based attribution, MMM is not significantly impacted by privacy changes or evolving tracking methods. It offers a stable foundation for measuring marketing effectiveness.
- Unbiased Insights: MMM does not favor any specific marketing channel. In contrast, touch-based attribution often gives undue credit to channels that generate digital touches, potentially leading to skewed allocation of resources.
Now that we understand why MMM is a game-changer, let’s dive into how it works.
How Does Marketing Mix Modeling Work?
At its core, Marketing Mix Modeling is an advanced statistical analysis. In recent years, machine learning techniques have further improved its strength and accuracy. Here’s a breakdown of how MMM operates:
1. Choosing the Right Target Metric
Selecting the right target metric is paramount. Your choice should:
Be directly influenced by marketing activities.
Show immediate correlation with marketing actions.
Accurately reflect how you measure marketing’s impact on the business.
Consider the length of your sales cycle and any lag between marketing activities and conversions when picking a target metric. For instance, if your sales cycle is long, choose a higher-funnel metric to reduce noise in your model.
2. Collecting the Right Data
Comprehensive and accurate data is the lifeblood of MMM. Collect data from various marketing channels, including paid, owned, and earned media. Start with one input metric per channel, but be open to merging or splitting them based on your marketing strategy.
Gather data on impressions and costs for each input metric. While impressions are crucial for analysis, costs are essential for ROI calculations. Consider including other variables that may impact your target metric, such as interest rates or price indices.
3. Choosing the Right Time Period
Selecting the appropriate time period for your data is essential. Think about whether you need multiple models for different years and whether specific timeframes may contain unrelated factors affecting your target metric.
Explore both daily and weekly data rollups, and choose the one that aligns best with your analysis goals and business needs.
4. Building, Interpreting, and Improving the Model
Building an MMM model is a technical task best handled by a data scientist or experienced marketing analyst. Before diving into model construction, perform checks on your source data. Look for multicollinearity, outliers, and other data anomalies that may affect your results.
Utilize data science techniques and open-source libraries to construct your MMM model. Assess its performance using metrics like Mean Absolute Percentage Error (MAPE). A high MAPE indicates room for improvement, so don’t stop there—continuously refine your model by analyzing residuals and considering additional variables.
How Is MMM Related to Experimentation?
It’s important to note that Marketing Mix Modeling provides correlational insights. In other words, it estimates incrementality and suggests potential causal relationships. These insights should be viewed broadly across time periods and not specific to a single point in time.
Experimentation, on the other hand, delivers truly causal insights. To gain a more precise understanding of incrementality, we recommend combining MMM with experimentation.
How to Get Started with MMM
By now, you might be intrigued by the potential of Marketing Mix Modeling but also aware of its specialized nature. While a DIY approach is possible, it demands significant investment and commitment. If you decide to embark on this journey, consider using open-source libraries mentioned in this article and collaborate with a data scientist, marketing analytics experts, and your marketing leadership team.
Conclusion
Marketing Mix Modeling offers a data-driven path to understanding the impact of your marketing efforts accurately. It goes beyond the limitations of touch-based attribution and provides a comprehensive view of your marketing mix. By following the steps outlined in this guide and leveraging MMM, you can make more informed decisions about your marketing strategy, optimize your investments, and confidently navigate the dynamic world of marketing.
