Marketing-mix modelling is a process that helps businesses understand how changes to their marketing mix will impact their sales and profits.

 By using mathematical models, marketers can simulate different scenarios and determine the best course of action. 

In this comprehensive guide, we will discuss what marketing-mix modelling is, the different elements measured, and its benefits and limitations.

What is Marketing Mix Modelling?

The marketing mix model, or MMM, is the process of taking any activities that might influence sales figures and using statistical modelling to show how marketing contributes to changes in sales volume. 

This scenario report breaks down your results by channel, so you can see which marketing activities are having the greatest impact on your ROI. 

Marketing mix modelling, unlike attribution modelling, attempts to account for the influence of non-measurable marketing channels such as television, radio, and newspaper media. 

Ultimately, you’ll need accurate data to back up your findings and demonstrate why you deserve a larger marketing budget. 

The marketing mix model combines all of your activities, and it displays the influence they’re having on your revenue; this might be anything from an offline campaign advertising a half-off offer to a piece of material that went viral on Facebook. 

The Marketing Mix Model allows you to track activity like:

  • Traffic
  • Conversion rates
  • Sales figures

You’ll have data linking your sales to your marketing activities when you use marketing mix modelling. You’ll be able to see exactly what your marketing victories and failures are, allowing you to develop and optimise future campaigns.

5 Elements Measured in Media Mix Modelling

Here are 5 different elements that are measured when using the media mix modelling method.

Sales Volume

There are two forms of sales volume to consider. MMM breaks down total sales into two categories: base sales and incremental sales.

  • Base Sales: this category of sales is driven by base parameters like pricing, long-term trends, seasonality, brand awareness, brand loyalty, and more. Essentially, these are economic factors that are set and can change seasonally.
  • Incremental Sales: these are influenced and driven by marketing and sales activities. The total additional sales are split into sectors influenced by each marketing activity. We can calculate the part of total sales driven by marketing efforts once we know how much of sales comes from each source. This gives us a clear picture into the efficacy of the activities being carried out.

Media and Advertising

The aim of media mix modelling is to assess the impact of various media and advertising on mediums such as television, publications, and online ads. 

The MMM findings might not always be clear-cut, but they may still provide useful information about how shifting ad techniques might influence sales; for instance, you can compare:

  • the effectiveness of 15-second vs 30-second execution
  • the outcome of running ads on multiple platforms
  • the effect of run times that are during the prime-time vs non-prime time

These findings can have a significant influence on a company’s ad spend choices, usually resulting in savings.


Changes in pricing structure may have a big impact on sales volume, and MMM can assist you with this. With knowledge of the percentage change in sales versus the percentage change in price, you can observe the impact of this adjustment. 

Positively, teams will be aware of the direct influence of price adjustments now, allowing them to improve their decisions more effectively.


The most important element of a successful return is distribution. A well-oiled distribution system has the ability to boost performance like nothing else.

As a result, MMM helps organisations see how changing distribution efforts may affect sales volume/value as a function of change in distribution initiatives. 

The overall holistic view of the influence of all distribution channels and their relative costs can assist marketers in determining which ones to invest in.


The goal of the launch of a new product is to create revenue, and it should have an influence on sales.

A successful launch might produce peaked results that can’t be explained by the model’s current variables; to capture the additional impact, special variables are required.

Benefits of Marketing Mix Modelling

We’re sure you already have approaches to extract insights from your marketing data. However, it’s never simple. 

Let’s take a look at 2 great situations where marketing mix modelling comes in useful.

Provides a Complete Analysis

You’ll be able to see the effects that each marketing channel has had. 

Not only will you be able to see the impact of non-marketing variables, but also you’ll be able to evaluate how well your campaigns are performing given changing industry conditions, such as seasonal changes or general user behaviour. 

Modelling the marketing mix is a mathematical procedure. So, you’ll have real data for your reports. It’s worth noting that you won’t have data on different ad spend across channels (we’ll discuss MMM’s limitations below).

Offers High Statistical Reliability

The statistical validity of MMM is another advantage. After all, it’s based on a large data set, and you’re applying well-known statistical modelling approaches like regression analysis.  

Marketing mix modelling is also an iterative process. So, you can keep improving your model by adding more data over time or by changing the way that you’re measuring certain variables.

Limitations of Marketing Mix Modelling

Unfortunately, there are certain factors that can limit the effectiveness of marketing mix modelling. 

As a result, it may not be the greatest option for your marketing plan. Here’s why:

Considers Traditional Marketing Channels

Previously, marketing mix modelling was used to examine the influence of marketing tactics such as TV advertising, magazine discounts, and newspaper advertisements. 

Marketing mix modelling does not take into account the many different messages and creatives tailored for distinct audiences.

MMM only gives a top-down perspective. Although a top-down view is useful, it will not provide you an accurate representation of the success of your campaign. 

Because MMM averages over time, it disregards peaks and troughs. 

For example, if your firm was praised in a major online publication, and witnessed a significant boost in traffic (which slowed down after a few days), the results would be averaged over the whole period and not reflect the influence of a single media mention at the time.

Because of uncommon occurrences, it may make particular channels appear to perform better on average than they normally do.

Not Appropriate For Small Teams

Unfortunately, not all organisations have the resources to employ marketing mix modelling. To be effective, it necessitates a thorough understanding of statistics.

If your firm is small or doesn’t contain anyone with statistical knowledge, in-house implementation probably isn’t worth it. 

You might work with a qualified external supplier, agency, or freelancer who would guide you through the procedure. However, this will cost you both time and money.

No Feedback Regarding Changes

Another disadvantage of MMM against other marketing research techniques is that you will only receive statistical data without any suggestions on how to make improvements. 

It’s worth considering if your time is being well spent on MMM, especially because marketers have access to technology that helps us attribute our efforts to income. 

So, using the tools you currently have to gain insight into what’s working would often be a more efficient use of your time in many situations.

Concluding Thoughts

Despite some limitations, you shouldn’t dismiss MMM entirely. If you conduct routine offline marketing operations, marketing mix modelling may still be used to evaluate your progress. 

However, if your major goal is to trace your customers’ digital footprints, there is an easier method to link your revenue back to your marketing activities. and you don’t even need a background in statistics either. 

Today, there are plenty of software tools available that can completely automate this process for you; where data can be displayed in real time, and account for all of the channels your business uses.