9 . 3 . 15

Attribution – Part 2

In my  previous post, I  gave an introduction to attribution, our approach and a general overview of what can be done.
Our study of attribution in the conversion optimisation team has led to detailed analysis for some of our clients who are able to better understand how customers find, interact with and purchase from their site. The results begin by showing first click, last click and linear attribution, each is briefly explained below.

  • First click: The potential lost revenue if this channel did not exist. This is because this channel was the first step in the user journeys towards the respective amount of revenue. You could argue if the user did not discover the site in the first place, they would not go onto convert.
  • Linear click: This is a more accurate representation of what the channel was worth to the business based upon all of the conversion paths and all sources used. It simply divides the total value of a conversion by the number of sources used leading to this conversion and gives each source the respective value.
  • Last Click: This is the typical view seen within Google Analytics, this reports upon the source which converted the user.

Our Approach & Analysis

What makes us different, and where the most compelling piece of analysis is, is where we begin to weight the attributed value of a channel depending upon its influence upon the conversion. This has many factors including the length of the path, the position within the path and the frequency of the source and value of conversion. We find significantly different results to the simple and typical ‘last click view’, which excluded around 50% of the true picture in one of our client examples.

Can you afford to only view 50% of where your sales are attributed towards?


Example data showing customer journeys to a conversion.  Anything previous to the final step is unaccounted for in typical reviews of channel performance.

What makes us different, and where the most compelling piece of analysis is, is where we begin to weight the attributed value of a channel depending upon its influence upon the conversion.

Time to Convert by Channel

Once we are able to establish the frequency and volume of sources used in each conversion, we can review the total involvement of each channel and how many steps are typically involved for users beginning their conversion path with an organic search visit. With this information, we can see the actual involvement of each channel and how quickly they convert the user, as can be seen in the example below:


Chart showing an example set of attributing sources towards conversions, the number of conversions they attribute towards at the first click and how many steps on average it takes to convert.

This level of analysis also allows for re-modelling and re-purposing the budget towards different channels and a visualisation of how this would have affected the bottom line revenue. It allows for the modelling of how, when and where customers could have been lost on their journey to a conversion or alternatively where we could be influencing further conversions, whether this be customers at the first interaction point or their last visit before purchasing, and everything in-between.

Associated Gains / Losses


There really is some outstanding results from this analysis, with one of our clients showing that re-purposing a small proportion of budget from one medium to another, assuming a related drop/gain in conversion involvement, would have seen double the losses they see at a last click view point. This would have been an expensive mistake costing thousands of pounds which would see many potential customers lost during the first interaction points and not returning to later purchase after their research stage.

We modelled the reduction in involvement of the reduced channel against the increase in the other based on the actual customer journeys that occurred over the year:

scenario - gains losses

Chart showing an example overview of the associated gains and losses through each channel after modelling how a scenario would have happened based upon the customer journeys. A slight change to a channel seemingly insignificant at last click can have a dramatic impact when modelling this change through the entire user journey of all conversions

Another example typically seen is the true role of search, display and affiliate channels. It is not uncommon to increase investment in these channels and not see any additional return when looking at a last click conversion perspective or even in overall revenue. However attribution modelling aims to uncover the true success or failing of this action.

There are too many other factors coming into play which affect why you may not see any results at the conversion end:

  • Did the increase result in a decline in another channel?
  • Is other activity, online of offline attributing to the results?
  • Has seasonality affected this and have you invested in the correct channel for the correct season?
  • Is the channel influencing sales from other mediums at a last click perspective?

Attribution modelling aims to answer these questions so we can establish the most efficient marketing solution and determine where your budget should be spent.


If you currently are investing in an attribution solution or are interested in how users are discovering, interacting and purchasing from your site then you should consider our solution. We can determine the correct model for you, tailor the attributed channels to where you invest your marketing budget and provide you with the bigger picture of how users are really purchasing from your site.

Maximize business opportunity with data-driven decision making. Find out more about our data team.

Phil Gawrylo is Senior Data & Insights Analyst in the data team at twentysix. He enjoys the challenge of unearthing actionable insights from data analysis to help inform marketing strategy.

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