If you are an avid reader of this blog, you most probably know what attribution modelling is. I have written dozens of articles on attribution modelling on this blog.

But do you know what ‘marketing mix modelling’ is and is it the same or different to ‘attribution modelling’?

What is Marketing Mix Modelling?

Marketing mix modelling (MMM) is a set of statistical analysis techniques that are used to measure and forecast the impact of various marketing activities on sales and ROI.

It is used to measure the overall marketing effectiveness and determine optimal ad spend among various marketing channels.

The word ‘mix’ in MMM refers to the mix of the 4Ps of marketing (Product, Price, Place and Promotion). In MMM, we carry out data analysis with the aim to understand and find the optimal mix of these 4Ps.

Regression analysis is also carried out to forecast the impact of various marketing activities on sales.

What is the marketing mix model is made up of?

A marketing mix model can be made of the following types of data:

• Target audience data
• Product data (product price, product features)
• Competitive data
• Industry data
• Economic data
• Marketing data
• Conversion data (sales, profit, ROI)

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Marketing Mix Modeling vs. Attribution Modeling. What is the difference?

The following are the main differences between attribution modelling and marketing mix modelling (MMM):

1. Different objectives and focus
2. Implementing MMM is a lot more difficult than implementing attribution modelling
3. Attribution modelling provides much more control over optimising digital marketing channels for ROI.

#1 Different objectives and focus

Attribution modelling can be considered a subset of MMM where the focus is on understanding and finding the optimal mix of ‘digital’ marketing channels. Pay special attention to the word ‘digital’ here. 

We use attribution models to measure and understand the impact of digital marketing touchpoints in a conversion path and to determine the most effective marketing channels for investment.

So in the context of digital media, marketing mix modelling can be referred to as attribution modelling.

#2 Implementing MMM is a lot more difficult than implementing attribution modelling

Unlike MMM, implementing attribution modelling is pretty lightweight, in the sense that it usually does not involve direct and heavy use of statistics by an ‘end user’.

Whatever statistical analysis is carried out, is by the attribution model itself. So you don’t need a master’s degree in statistics to implement attribution modelling. This is one advantage of attribution modelling over MMM. 

The other advantage is that, since you do not heavily use statistics to create attribution models, your models are less prone to statistical errors (as long as you use high-quality data to feed your models).

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#3 Attribution modelling provides much more control over optimising digital marketing channels for ROI.

You may be wondering at this point that, marketing mix modelling has been around for decades and sound very much like attribution modelling.

Then why do we need attribution modelling? Why not just use marketing mix modelling?

The problem with MMM is that it is much older than the internet itself. The concept was first introduced when there were was no digital media, no search engines, no web browsers. And somehow it just couldn’t catch up with the digital age.

Now I am not implying that MMM is an outdated set of techniques. It is still very much relevant and has its own place. It just doesn’t work well when it comes to digital marketing mix modelling.

Unlike MMM, attribution modelling provides much more control over optimizing various digital marketing channels for ROI. This level of control comes because of immediate and real-time access to digital data at an individual user level. 

Unlike MMM models, attribution models natively integrate with your web analytics data. 

For example, the attribution models provided by Google Analytics natively integrate with GA.

The ‘Data Driven Attribution (DDA) Model’ provided by GA can be integrated with several Google and non-Google digital data sources. 

For example, the DDA model can be integrated with: Doubleclick Campaign Manager, Google Ads, Google Search Console, Google Play, Google BigQuery etc in addition to Google Analytics.

Such type of integration makes it possible for attribution models, to access users’ data in real-time which makes attribution modelling much more accurate and accountable.

MMM models are often built on outdated and highly aggregated data. The data is outdated in comparison to the real-time data which feeds an attribution model esp. algorithmic attribution models (like Data Driven Attribution Model).

As such, a traditional MMM model is not suitable for carrying out digital marketing mix modelling aka attribution modelling.

Get my best selling books on Attribution Modelling

• Learn to implement attribution modelling in your organisation
• Understand the customer purchase journey across devices
• Determine the most effective marketing channels for investment

 Click book covers to find out more

Integrating Attribution Modelling into Marketing Mix Modelling

If your business has both online and offline presence and you carry out both online and offline marketing then you are an ideal candidate for integrating Attribution Modelling into MMM.

For example, if you are a retailer who has got both physical and online stores and you are actively involved in both online and offline advertising then you are an ideal candidate for implementing both MMM and attribution modelling.

The main advantage of attribution modelling and MMM integration is that you can feed the attribution modelling data to your MMM model and can more accurately measure the overall marketing effectiveness.

You can also more accurately forecast the impact of both online and offline marketing activities on sales and ROI.

Remember, true multi-channel analytics is nonline i.e. it is neither purely online nor purely offline. So we can’t afford to measure online and offline customers purchase journeys in silos.

We need to learn to understand the complete customer purchase journey which takes both online and offline touchpoints into account.

We now know that both online and offline marketing campaigns and touchpoints impact each other.

We also know that, in the world of multi-channel marketing, no single marketing channel is solely responsible for generating sales. Different marketing channels work together to create sales and conversions. 

So in order to truly understand the overall marketing performance, we need to take both online and offline marketing touchpoints into account.

Other articles on Attribution Modelling

1. How to analyse and report the true value of your SEO Campaign
2. How to valuate Display Advertising through Attribution Modelling
3. Understanding Shopping Carts for Analytics and Conversion Optimization
4. 6 Keys to Digital Success in Attribution Modelling
5. Google Analytics Attribution Modeling Tutorial
6. How to Measure and Improve the Quality of SEO Traffic through Google Analytics
7. How to explain attribution modelling to your clients
8. Default and Custom Attribution Models in Google Analytics
9. Understanding Missing Touchpoints in Attribution Modelling
10. What You Should Know about Historical Data in Web Analytics
11. Model Comparison Report Explained in Google Analytics Attribution
12. Data-Driven Attribution Model in Google Analytics – Tutorial
13. Conversion Lag Report Explained in Google Analytics Attribution
14. Selecting the Best Attribution Model for Inbound Marketing
15. How to do ROI Analysis in Google Analytics
16. Conversion Credit Models Guide – Google Analytics Attribution
17. Introduction to Nonline Analytics – True Multi Channel Analytics
18. Conversion Types Explained in Google Analytics Attribution
19. Attribution Channels Explained in Google Analytics Attribution
20. Differences Between Google Attribution & Multi-Channel Funnel Reports
21. Introduction to TV Attribution in Google Analytics Attribution 360
22. Conversion Credit Distribution for Attribution Models in Google Analytics
23. Conversion Paths Report Explained in Google Analytics Attribution
24. Attribution Model Comparison Tool in Google Analytics
25. Touchpoint Analysis in Google Analytics Attribution Modelling
26. Attributed Conversions & Attributed Revenue Explained in Google Attribution
27. Which Attribution Model to use in Google Analytics?
28. Google Attribution Access and User Permissions – Tutorial
29. Conversion Path Length Report Explained in Google Analytics Attribution
30. How to set up a data-driven attribution model in Google Analytics
31. View-Through Conversion Tracking in Google Analytics
32. Offline Conversion Tracking in Google Analytics – Tutorial
33. How to Create Custom Attribution Model in Google Analytics
34. 8 Google Analytics Conversions Segments You Must Use
35. You are doing Google Analytics all wrong. Here is why
36. How to Use ZMOT to Increase Conversions and Sales Exponentially
37. Connected Properties Explained in Google Analytics Attribution
38. Marketing Mix Modelling or Attribution Modelling. Which one is for you?
39. How is attribution modelling helpful for ecommerce and non-ecommerce websites?
40. Conversion Time & Interaction Time Explained in Google Analytics Attribution
41. How to Allocate Budgets in Multi Channel Marketing
42. How Does Attribution Work?
43. Data-Driven Attribution Model Explorer in Google Analytics
44. Introduction to Attribution Beta – Attribution Project in Google Analytics