Data-Driven Attribution (DDA)
This is the best of all attribution methods, as it tries to figure out the real contribution for every channel. Let us see how it works. Let us assume that the conversion journey includes these 3 channels.
The previous year’s report of these 3 channels is given below. So how should we change budgets this year?
Based on this data a lot of people might be tempted to remove Youtube’s budget allocation and give it to Facebook. Put a pin on that. Let us look at how the Data Driven Attribution Model can help us take this decision.
We first look at all the consumer journeys that led to a conversion.
And we also take into account all journeys that did not lead to a conversion as well. THis is the only attribution model that looks at non-converted journeys.
We then put all the journeys that are the same together. For example, below we have highlighted the journey : Search to Youtube to Facebook
When we put them together, we see that Team 1 (Search to Youtube to Facebook) has led to 2 converts and 3 non-converts.
The probability of conversion for Team 1 is: 2 / 5 = 40%
Similarly we put the probability down for other Consumer Journeys as well:
Now we see that the only difference between Team 1 (Search to Youtube to Facebook) and Team 2 (Search to Facebook) is that Youtube is missing in between. This shows that the presence of Youtube in the middle actually leads to an increase of 0.07 in probability.
Similarly by picking up other journeys we calculate this individual contribution of each channel to the probability of conversion of Team 1.
Now we can calculate the % contribution.
Search = 0.1/ (0.1 + 0.07 + 0.15) = 31%
Youtube = 0.07/ (0.1 + 0.07 + 0.15)= 22%
Facebook = 0.15/ (0.1 + 0.07 + 0.15) = 47%
Note that had this been a linear model, we would have given 33% to each of these channels.
Had this been first click, 100% to search, and 0% to YT an FB.
Had this been last click, 100% to FB and 0%to search and YT.
Data Driven Attribution here shows that based on past data, in this consumer journey (Search to Youtube to Facebook), Search should get 31% of the credit, Youtube 22% of the credit and Facebook should get 47% of the credit.
So now we know the exact contribution of each channel in a consumer journey. This has an effect on the total number of conversions that we attribute to a channel.
As you can see Facebook has come down from 10,000 conversions to 8,998 conversions. This has happened because for a lot of conversions like the one we saw in Team A (Search to Youtube to Facebook), Facebook was given full 100% credit, which we have now reduced to 47%.
Similarly Youtube and Search get credited for more than they had been previously.
This has led to the CPL of Facebook increase by 11% and that of Youtube decrease by 38%.
What comes out of this is that Youtube, while it is not leading to a lot of last clicks is certainly quite useful to get the consumer interested, and hence is setting up the entire game for the channels that follow.
We have put in a lot of money at the end of the funnel, but not enough at the top.
This can lead us to a conclusion that we actually should put in some more money in Youtube so that we expand the top of our funnel. We can do it at the cost of Facebook. It is a reasonable assumption that can lead to an increase in the efficiency of the campaign.
Just a few things to note about the Data Driven Attribution Model:
- The model does NOT have FIXED weights for each channel & position
- It depends on the data for your campaign and is dynamic. The weights change with EVERY new conversion that you get.
- It also factors in ABSENCE of a conversion. Thus, it also changes with every path NOT resulting in a conversion as well.