Selecting the Best Attribution Model for Inbound Marketing

 

The following article is in conjunction with the post: Google Analytics Attribution Modeling – Complete Guide. Read that post if you have not already read it to understand the fundamentals of attribution modelling.

I am going to highlight the issues with existing attribution models like: ‘last touch attribution‘ and more recently the ‘first touch  attribution model’. I am also going to introduce you to my  ’Proportional Multi Touch Attribution Model‘ which is the best attribution model available to date for inbound marketing.

 

Attribution modeling

Attribution modeling is the process of understanding and assigning credit to the acquisition channels which eventually leads to conversions (goal completion).  The Acquisition channels can be:

  1. Digital Marketing Channels (like paid search, organic search, email marketing, direct traffic, referral traffic, display advertising etc.)
  2. Offline Marketing Channels (like radio, TV, Billboard, Magazines, Newspapers etc)

The purpose of attribution modeling is to understand the buying behavior of your website visitors:

Why people buy from your website?
What happens before they buy?
What prompted them to make a purchase or complete a predefined goal?
Which are the most effective acquisition channels for investment.

An attribution model can give answers to such questions.

Types of  Attributions

There are four types of attributions:

  1. Online-Offline Attribution
  2. Multi Device Attribution
  3. Multi Channel Attribution
  4. Real World Attribution (New)

 

Online-Offline Attribution

In case of ‘online-offline attribution’ we determine the impact of digital marketing channels on offline marketing channels and vice versa. We try to understand how online and offline campaigns work together to create conversions and how the credit for conversions should be distributed among different online and offline marketing channels.

Google Analytics support this type of attribution (to an extent) via Universal Analytics.

 

Multi Device Attribution

In case of ‘multi device attribution’ we determine the impact of multiple devices (Desktop, Tablets, Smart Phones, Smart TVs etc) on conversions. We try to understand how different devices work together to create conversions and how the credit for conversions should be distributed among different devices.

Google Analytics support this type of attribution (to an extent) via Universal Analytics.

 

Multi Channel Attribution

It is the most popular attribution and when marketers talk about attribution they are generally referring to this attribution.

In case of ‘multi channel attribution’ we determine the impact of multiple digital marketing channels (paid search, organic search, email marketing, direct traffic, referral traffic, display advertising etc) on conversions. We try to understand how different digital channels work together to create conversions and how the credit for conversions should be distributed among various channels.

Google Analytics support this type of attribution via Multi Channel Funnel Reports and Model Comparison Tool.

The multi channel attribution can be single touch or multi touch. Single Touch Attribution Models includes first and last touch attribution models. Whereas Multi Touch Attribution Models include ‘Time Decay’, ‘Linear’ and ‘Position Based’ attribution models.

Note: You can learn more about single touch and multi touch attribution models from this post: Google Analytics Attribution Modeling – Complete Guide

 

Real World Attribution

In the real world, customers don’t choose offline marketing channels over online marketing channels and vice versa. They don’t even choose one device like desktop over tablets or smart phones over smart TVs all the time.

In the real world, customers can go back and forth between online and offline marketing channels or they can go back and forth between tablet and desktop depending upon what stage they are in their purchase process, what they are buying, where they are, what device they own and what is their comfort level.

For example, some customers are more comfortable buying from store than buying online. Whereas some customers are more comfortable buying online than buying from store.Some customers don’t purchase high price items online. Some customers always make purchase offline and use online channels only for research work.

Some customers always make purchase from desktop or laptops. Some customers never make purchase from smart phones while some always do. So there can be ‘N’ types of buying behavior.

 

None of the existing attribution model (online-offline, multi device and multi channel) take this back and forth activities of customers between multiple devices both online and offline into account while distributing credit for conversions. So we can’t get the complete picture of the conversion path followed by customers.

The real world attribution is the hybrid of online-offline, multi device and multi channel attributions.  It takes into account the back and forth activities of customers between multiple devices both online and offline while distributing credit for conversions. Because of that property, the real world attribution model is much more complex and difficult to develop than any existing attribution model. But at that same time it is the only true attribution model.

Proportional Multi touch Attribution Model is an example of real world attribution model. I have developed this attribution model and will talk about it in great details in a moment.

 

Issues with Last Touch Attribution Model

In the last touch attribution modeling, 100% conversion is attributed to the last acquisition channel/touch point. For e.g let us consider that I followed the following conversion path:

attribution modelling

1.  I read a blog post on your website.

2.  After 3 days I saw your display ad on a website

3.  After 2 days I read a review of your product on some website.

4. After 4 days I decided to make a purchase. So I made a search using a non branded keyword and clicked on your PPC ad on Google.

5.  Just to make sure that I am going to get the best deal, I went to a product comparison site. Being satisfied with your product pricing I decided to make a purchase during weekend.

6. During weekend I again searched on Google but this time used a branded keyword and clicked on your organic search listing.

7. I made a purchase from your website.

 

Here I was exposed to multiple acquisition channels (blog post, display ad, product review, paid search etc). Each of these exposures is considered as touch.  So I was exposed to 6 different acquisition channels before I made a purchase.

Now according to last touch attribution model, the conversion (making a purchase) is attributed to organic search. Most analytics software by default use ‘last touch’ attribution so they will also report to you that I searched for your website on Google through a branded keyword and then made a purchase.

So acquisition channel responsible for your sale is ‘organic search’.  As you can see from the chart above, this is not true. 6 acquisition channels have played an important role in the conversion on your website.

Another issue with ‘Last touch attribution’ model is that it is not truly last touch as it doesn’t take into account those last touches which happened offline or on devices (like smart phone) where the online behavior/conversion can not be tied to the person who started the conversion process . 

So for example after clicking on the organic search result, I saw an ad in a Magazine and then made a purchase. The last touch attribution model in Google Analytics will still give credit for conversion to organic search as Google Analytics can’t associate me with the magazine ad.

 

Issues with First Touch Attribution Model

In the first touch attribution model, 100% conversion is attributed to the first touch (in my case blog post). So according to this model, I read your blog post and then made a purchase decision on that basis. This is also not true.

As I also saw your display ad, read a review of your product, clicked on your PPC ad, visited a product comparison website and clicked on your organic search listing before making a purchase. All these acquisition channels influenced my purchase behavior.  Just as a last touch attribution can lead to mis-allocation of resources, over crediting first touch can mislead as well.

Neither first touch nor last touch provides a good understanding of the buying behavior. 

 

Issues with the traditional Multi Touch Attribution Models

In multi touch attribution modeling, the conversion is attributed to multiple acquisition channels instead of just the first touch or last touch attribution.  Here the middle touches also come into picture. This model aligns well the real life situations as people rarely make a purchase through one or two acquisition channels.

For e.g. it is highly unlikely for someone to read your blog post and then just make a purchase. Similarly it is highly unlikely for someone to see your display ad on a website and then directly make a purchase.  He may read reviews of your product, go to couple of product comparison site before making a purchase.  So we need to take all of the touches into account.

Now the problem with the typical multi channel attribution modeling is the way credits for conversions are distributed to different marketing channels. For example

linear attribution model  gives equal credit to each marketing channel/touch point in a conversion path.

Position based attribution model gives more credit to first and last touch.

Time decay attribution model gives more credit to the touch points which occur closest in the time to conversions.

 

In the real world, not all acquisition channels are equally valuable. For example, in the example above, I read product review and went to a product comparison site before making a purchase. These two touches were more valuable to me than the exposure to the blog post, display ad and the PPC ad as they play a very important role in my purchase decision. Had I not been satisfied with the product review or pricing, I wouldn’t have made a purchase.  Consequently these touches should be given more credit.

 

Introducing Proportional Multi touch Attribution Model

Here the proportional multi touch attribution model comes into the picture. In this model values are assigned to touches in proportion to their contribution in a conversion.

The acquisition channel which assists the most gets the maximum credit for conversion and maximum resources are allocated to it regardless of it being a first touch, last touch or middle touch. All other touches would get credit in proportion to their contribution in the conversion.

For example:

In the chart above, product reviews and product comparison site played a very important role in my purchase decision. So under the proportional multi touch attribution model, ‘product reviews’ and ‘product comparison site’ will get maximum credit for conversions  regardless of  them being the first, middle or last touches.

Similarly, if clicks on the paid search ad had helped me most in my purchase decision then ‘paid search’ would get maximum credit for conversions even when it is neither the first touch or last touch. All other touches would get credit in proportion to their contribution.

 

Why Proportional Multi Touch Attribution Model is better than traditional models?

It is better because of the following two valid reasons:

1. Proportional Multi touch  is a real world attribution model. That means it takes into account the back and forth activities of customers between multiple devices both online and offline while distributing credit for conversions.

Because of that property it has the ability to provide truly complete picture of the conversion path followed by customers.  It is the first generation of truly multi channel analytics modeling tool.

 

2. It takes into account your business model, marketing objectives, sales cycle, customers’ activities and seasonality as it allows you to assign credit to different marketing channels/touch points in proportion to their contribution in the conversion process.

Thus it provides more flexibility than linear, position based and time decay models in terms of credit distribution.

 

Final Thoughts

One thing that you should not conclude from this post is that the traditional attribution models are flawed or useless. This is because attribution is driven by experiments and in order to increase ROI across mutiple marketing channels you have to test different types of attribution models all the time.

It is only through continuous testing you can determine the acquisition channels which deserve maximum credit for conversions in a particular point in time/product life cycle.

Remember ☛ Without testing different models you can’t use the Proportional Multi Touch Attribution model. At least not in the way which can produce optimal results.

So it is critical that you don’t sideline other attribution models (both single and multi touch) in favor of proportional multi touch model.

 

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Himanshu Sharma About the Author: is the founder of seotakeaways.com which provides SEO Consulting, PPC Management and Analytics Consulting services to medium and large size businesses. He holds a bachelors degree in ‘Internet Science’, is a member of 'Digital Analytics Association', a Google Analytics Certified Individual and a Certified Web Analyst. He is also the founder of EventEducation.com and EventPlanningForum.net.

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