We have built a machine learning attribution model, by using a LSTM (Long Short-Term Memory) neural network. We input sessions (visits) and sequences (a visitors full journey) into the network and the network gives us a probability of purchase after each session.
We then take those probabilities and apply a weighted formula to them, to find out the effect each session had on the final decision to place an order.
Our neural network is self learning, which means the more data we give it, the smarter (and more accurate) it gets!
When we have put in all the data from visits and user journeys, the network analyzes 14 different factors from each, these are:
The network then outputs a probability of purchase score from 0-1 for each session in a sequence. The higher the number, the higher the probability of purchase. What typically happens is that the number steadily rises as each visit takes place, until it reaches a peak. After which a decrease will be observed, along with more unstablility thereafter. How much the probability changes after each visit varies a lot and is affected by each individual factor listed above.
The final step is to apply the weighted formula to the output data. Each session in a sequence will be given a weighted score. The sum of all the weighted scores from a sequence will always equal 1. The way the scoring works is that the higher the score, the more impactful that visit was on the final purchase.
As an example, if the probability of a purchase after the first visit is 20% and after the second visit 50%, these will be weighted so the first visit had 40% impact on the order and the second had 60% impact. To decide the actual value of each visit, this impact is then multiplied with the order value of the purchase. If an order of €200 was made, the value of the first visit was €80 and for the second it was €120.
To start with, standard attribution models only take the position of each visit into account. With a last click model, the last click get 100% of the value, with a first click model the first visit receives all the value and with any multi-touch model the value is simply distributed according to a fixed formula. Our Data Driven Attribution model not only takes the position of a visit in consideration, it also takes 14 different parameters into account when analyzing how much value a visit actually added.