Marketing Mix Modelling or Attribution Modelling. Which one is for you?


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 same or different than ‘Attribution Modelling’?

Introduction to Marketing Mix Modelling

Marketing mix modelling (MMM) is a set of statistical analysis techniques which 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.

A marketing mix model can be made of 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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The Difference between Attribution Modelling and Marketing Mix Modelling

Attribution Modellingcan be considered as 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 touch points in a conversion path and to determine most effective marketing channels for investment.

So in the context of digital media, Marketing mix modelling can be referred to as ‘Attribution Modelling’.

However 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 that 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).

Why Do You Need Attribution Modelling?

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 the 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, DDA model can integrated with: Doubleclick Campaign Manager, Google Adwords, Google Search Console, Google play, Google Bigquery etc in addition to Google Analytics.

Such type of integration make 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.


Attribution Modelling in Google Ads and FacebookAttribution Modelling in Google Analytics and Beyond

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

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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 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 in Google Analytics

  1. Touch Point Analysis in Google Analytics Attribution Modelling
  2. 8 Google Analytics Conversions Segments You Must Use
  3. Default and Custom Attribution Models in Google Analytics
  4. Attribution Model Comparison Tool in Google Analytics
  5. Which Attribution Model to use in Google Analytics?
  6. How to create Custom Attribution Model in Google Analytics

  1. How to do ROI Analysis in Google Analytics
  2. Google Analytics Attribution Modelling – Complete Guide
  3. Guide to Data Driven Attribution Model in Google Analytics
  4. Conversion Credit distribution for Attribution Models in Google Analytics
  5. You are doing Google Analytics all wrong. Here is why

  1. Marketing Mix Modelling or Attribution Modelling. Which one is for you?
  2. Introduction to Nonline Analytics – True Multi Channel Analytics
  3. How to set up Data driven attribution model in Google Analytics
  4. How to valuate Display Advertising through Attribution Modelling
  5. Understanding Shopping Carts for Analytics and Conversion Optimization

  1. View-through conversion tracking in Google Analytics
  2. Understanding Missing Touch Points in Attribution Modelling
  3. Guide to Offline Conversion Tracking in Google Analytics
  4. How to explain attribution modelling to your clients
  5. 6 Keys to Digital Success in Attribution Modelling

  1. How to use ZMOT to increase Conversions and Sales exponentially
  2. How to Measure and Improve the Quality of SEO Traffic through Google Analytics
  3. How to analyse and report the true value of your SEO Campaign
  4. How to allocate Budgets in Multi Channel Marketing
  5. What You Should Know about Historical Data in Web Analytics

  1. Google Analytics Not Provided Keywords and how to unlock and analyze them
  2. Selecting the Best Attribution Model for Inbound Marketing
  3. Introduction to TV attribution in Google Analytics Attribution 360
  4. Cross Device Reports in Google Analytics via Google Signals
  5. Data-Driven Attribution Model Explorer in Google Analytics
  6. What is Attribution Modelling and why it is the ‘key’ to online business success?
  7. How Does Attribution Work?
  8. How is Attribution Modelling helpful for e-commerce and non-e-commerce websites?

What is the difference between Digital Analytics and Google Analytics?

99.99% of course creators themselves don’t know the difference between Digital analytics, Google Analytics (GA) and Google Tag Manager (GTM).

So they are teaching GA and GTM in the name of teaching Digital analytics.

They just copy each other. Monkey see, monkey do.

But Digital analytics is not about GA, GTM.

It is about analyzing and interpreting data, setting up goals, strategies and KPIs.

It’s about creating strategic roadmap for your business.

Digital Analytics is the core skill. Google Analytics is just a tool used to implement ‘Digital Analytics’.

You can also implement ‘Digital analytics’ via other tools like ‘adobe analytics’, ‘kissmetrics’ etc.

Using Google Analytics without the good understanding of ‘Digital analytics’ is like driving around in a car, in a big city without understanding the traffic rules and road signs.

You are either likely to end up somewhere other than your destination or you get involved in an accident.

You learn data analysis and interpretation from Digital analytics and not from Google Analytics.

The direction in which your analysis will move, will determine the direction in which your marketing campaigns and eventually your company will move to get the highest possible return on investment.

You get that direction from ‘Digital analytics’ and not from ‘Google Analytics’.

You learn to set up KPIs, strategies and measurement framework for your business from ‘Digital analytics’ and not from ‘Google Analytics’.

So if you are taking a course only on 'Digital Analytics’, you are learning to use one of the tools of ‘Digital analytics’. You are not learning the ‘Digital analytics’ itself.

Since any person can learn to use Google Analytics in couple of weeks, you do no get any competitive advantage in the marketplace just by knowing GA.

You need to know lot more than GA in order to work in digital analytics and marketing field.

So what I have done, if you are interested, is I have put together a completely free training that will teach you exactly how I have been able to leverage digital analytics to generate floods of news sales and customers and how you can literally copy what I have done to get similar results.

You can sign up for the free training here:

I truly hope you find it helpful.  

My best selling books on Digital Analytics and Conversion Optimization

Maths and Stats for Web Analytics and Conversion Optimization
This expert guide will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions, which can quickly improve the bottom-line of your online business.

Master the Essentials of Email Marketing Analytics
This book focuses solely on the ‘analytics’ that power your email marketing optimization program and will help you dramatically reduce your cost per acquisition and increase marketing ROI by tracking the performance of the various KPIs and metrics used for email marketing.

Attribution Modelling in Google Analytics and Beyond
Attribution modelling is the process of determining the most effective marketing channels for investment. This book has been written to help you implement attribution modelling. It will teach you how to leverage the knowledge of attribution modelling in order to allocate marketing budget and understand buying behaviour.

Attribution Modelling in Google Ads and Facebook
This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. It will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and determine the most effective marketing channels for investment.

Himanshu Sharma

Digital Marketing Consultant and Founder of

Himanshu helps business owners and marketing professionals in generating more sales and ROI by fixing their website tracking issues, helping them understand their true customers purchase journey and helping them determine the most effective marketing channels for investment.

He has over 12 years experience in digital analytics and digital marketing.

He was nominated for the Digital Analytics Association's Awards for Excellence.

The Digital Analytics Association is a world renowned not-for-profit association which helps organisations overcome the challenges of data acquisition and application.

He is the author of four best-selling books on analytics and conversion optimization:

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