Ok guys I am finally back with a post on web analytics. Those of you who have been requesting an analytics post from me may find this post to be the most informative of all the analytics posts I have written so far. I am going to talk about attribution models today and try to break the myth surrounding the widely used last touch attribution and more recently the ‘first touch’ attribution model. I am also going to introduce you to ‘Proportional Multi Touch Attribution Model’.
What is attribution modeling in Web Analytics?
Attribution modeling is the process of understanding and assigning credit to the acquisition channels which eventually leads to conversions (goal completion). Acquisition channels can be paid search, organic search, email marketing, direct traffic, referral traffic, display advertising 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 give answers to such questions.
What is Last Touch Attribution Modeling and why it is flawed?
In the last touch attribution modeling, conversions are credited to the most recent acquisition channel. For e.g let us consider the following conversion path:
1. A person say ‘Himanshu’ reads a blog post on your website
2. After 3 days he saw your display ad on a website
3. After 2 days he reads a review of your product on some website.
4. After 4 days he decided to make a purchase. So he searched using a non branded keyword and clicked on your PPC ad on Google.
5. Just to make sure that he is going to get the best deal, he went to a product comparison site. Being satisfied with your product pricing he decided to make a purchase during weekend.
6. During weekend he searched on Google using a branded keyword, clicked on your organic search listing.
7. He made a purchase from your website.
Here Himanshu was exposed to multiple acquisition channels (blog post, display ad, product review, paid search etc). Each of these exposures is considered as touch. So Himanshu was exposed to 6 different acquisition channels before he 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 a person 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.
What is First Touch Attribution Modeling and why it is flawed?
According to first touch attribution model, the conversion is attributed to the first touch (in our case blog post). The visitor to your website read your blog post and then he made a purchase decision on that basis. This is also not true. As the visitor also saw your display ad, read a review of your product, clicked on your PPC ad, visited a product comparison site and click on your organic listing before making a purchase. All these acquisition channels influenced the purchase behaviour. 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 conversions.
What is Multi Touch Attribution Modeling?
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 make a purchase later via organic search. Similarly it is highly unlikely for someone to see your display ad on a website and then later make a purchase via paid search. He may read reviews of your product or go to product comparison site before making a purchase. So we need to take all of the touches into account.
Introducing Proportional Multi touch Attribution Modeling
Now the problem with multi channel attribution modeling is assigning value to multiple touches. Not all acquisition channels are equally valuable. For example, in the example above, Himanshu reads product review and went to a product comparison site before making a purchase. These two touches seem more valuable than the exposure to the blog post, display ad and the PPC ad as they play a very important role in the purchase decision. Had Himanshu not satisfied with the product review or pricing, he wouldn’t have made a purchase. Consequently these touches should be given more credit.
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 value and maximum resources are allocated to it regardless of it being a first touch, last touch or middle touch.
For example:
In the chart above, if clicks on the paid search ad have helped the most in conversions over a period of time say 60 days, then ‘paid search’ should be allotted maximum resources even when it is neither the first touch or last touch. Similarly if viewing of a display ad has helped the most in conversions, then ‘display ad’ should be allotted maximum resources regardless of being the first, middle or last touch. All other touches should be allotted resources in proportion to their attribution.
