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What is marketing mix modeling - MMM explained

Last updated: 25/07/2022

Marketing mix modeling definition:

It is a powerful statistical analysis on sales and marketing data to estimate the impact of marketing activities on sales

 

Marketing mix modeling, or MMM for short, is an incredibly powerful statistical analysis that can identify which elements of your campaigns are driving overall performance most. 

 

In the episode of Funnel Tips, Alex breaks down the following:

  • What MMM is, exactly
  • Some analogies of how the analysis model works,
  • Why you should explore MMM

 

What is marketing mix modeling?

As Alex points out, MMM is a highly resilient, privacy-friendly, and data-driven statistical analysis that considers how various internal and external factors impact your marketing performance - be it sales or any other KPI. 

 

In a modern 360-degree campaign, you might employ broadcast advertising, Google Ads, paid and organic social media, public relations outreach, outdoor transit advertising (bus stops, billboards, etc.), webinars, co-selling partnerships, promotions, and more. 

 

That’s a lot to keep track of! Plus, the more initiatives and complexity that you add to your campaign, the more robust it becomes and the more impressions it achieves. 

 

However, it also becomes harder to track the contribution of each of these channels. If you aren’t able to measure contribution for your channels, you may not realize that some of your marketing spend is more efficient than others. 

 

An MMM analogy

To better illustrate this dynamic, Alex reaches into his past as a football (or soccer) player in the UK and US. With each team sending 11 players onto the pitch, it would be useful to identify that one player contributed 25 percent of the win from their individual performance. Especially if another player contributed only 4 percent of the win. 

 

It’s incredibly difficult to make this determination just by standing back and watching the game unfold. As Alex rightly points out, the player who scores the winning goal likely didn’t bring the ball up the field all by themselves to score. There were likely a series of passes or a critical defensive challenge. Other players were moving on the pitch, which could have created the perfect space for the scorer to use. 

 

It’s an entire micro-ecosystem that feeds off itself. Here, attribution is very hard to define. 

 

However, if the team manager identifies a set of core KPIs that will be tracked for each player — say, shots on goal, pass completion rate, assists, interceptions, clearances, etc. This gives us an opportunity to employ an MMM-style approach. 

 

Applying this MMM approach to one single game might not give you that much valuable data. In a campaign, that may be trying to define performance attribution based on a single days-worth of data. 

 

However, if we view these KPIs across an entire season (or several months to a year in the case of a campaign), we can start to see some attribution insights starting to rise to the surface. 

 

Now that we have the basics out of the way, let’s explore the variable you may want to track using an MMM approach with a campaign. 

 

What variables should I analyze for MMM?

The list of variables you can monitor is nearly limitless. However, we can group many of them together in a few categories. 

 

  1. Calendar-based variables
  2. Media activities
  3. External variables
  4. Internal variables

 

First, there are calendar-based variables of the market. Think of seasonal trends and major holidays that have an impact on your consumer’s buying patterns. 

 

Next, we have media activities. This category is a bit of a catch all for your advertising. It includes TV, print, outdoor, display, direct, search, social, etc. It can also include earned media mentions like those gained from your public relations efforts. 

 

Third, we should consider external effects. This is a sort of “force major” category. It’s all of the factors that are out of your control like macroeconomic conditions, weather, natural disasters, competitor activities and more.  

 

Finally, there are internal changes that arise from alterations in how you do business. This can include a change to your product distribution, changes to the product or service itself, price changes and sales process changes. This category is almost like the old 4 Ps of marketing (product, price, and place), with promotion being covered by our media activities. 

 

By measuring the variables in these categories that are critical to your business, an MMM approach can begin to identify which variable has the strongest contribution to changes in performance. And remember, the more data you feed the analysis, the more factors you can draw insights from. 

 

Why you should implement MMM

As modern digital marketers, there are an ever-increasing array of different tools and media that we can leverage to get the word out about our product or service. Some of those tools are easily trackable, but some are not. Plus, some tools lose a bit of their attribution tracking capacity as you blend data with other tools. 

 

This all means that marketers need a way to account for as many below- and above-the-line tactics as possible, while also digging into which is the most valuable for their campaign. 

 

Now, Alex does point out that the desire to precisely pinpoint exact attribution of each tactic is misguided. For instance, check out his video all about attribution. Especially with 3-party cookies falling out of style, even our digital tactics are becoming harder to track. 

 

With an MMM approach, though, marketers can gain a wider and more holistic view of their marketing ecosystem. With that larger, more bird’s eye view, they can get a better handle on which “levers” should be pulled at different times.

 

If we look back to our variable categories, just by monitoring external variables and our media activities, we can gain an understanding of how fluctuations in the consumer price index are influencing the effectiveness of targeted digital spend versus broadcast. 

 

Sounds useful, right? Here are 3 more analyses that can benefit from an MMM approach. 

 

  1. ROI analysis
  2. Forecasting
  3. Pricing

 

ROI analysis

Which marketing investment is giving us the best return. ROI analysis is probably the most common use for marketing mix modeling. It gives business leaders a quick summary of where our money is being best spent. Then, we can make decisions about how to shift our investment strategies to keep getting the best return.  

 

Forecasting

With forecasting and MMM, it’s less about using a crystal ball to see future sales and revenue, but instead about identifying future budgets and overall spend. After all, there are a lot of teams that will want to know what marketing spend will look like in the months or year ahead. 

 

While we won’t get exact future figures from MMM, it can be used to roughly predict what those budgets and spends will be. This is still quite useful, though, since it allows other parts of the business to plan accordingly. 

 

Pricing

If you drop the price of your product or service, will more customers flock to it? Will you steal market share from a competitor? Or, does a price decrease lower the perceived value of said product or service? These are all incredibly difficult and complex questions to ask, requiring equally complex analysis to arrive at a reliable conclusion. 

 

However, with the right data and analysis model, you can begin to make predictions about how the market may react to changes in your pricing structure. 

 

While MMM can be a complex topic, we’ve included a list of some of Alex’s favorite resources on the topic: 

Mass Analytics have a full in depth MMM course on Youtube that really gets into the weeds of the topic: Full course on Youtube

 


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