Algorithmic or Probabilistic Attribution uses statistics and machine learning to determine the probability of conversion across marketing touchpoints. In other words, how much of a conversion should be attributed to each channel. In order to keep things simple, I randomly chose a few variables—out of dozens or more—that could go into our model. Let’s go through the rows one at a time.
Touchpoints – This is a mix that includes online as well as offline touchpoints.
Platforms – These are some of the platforms that can be used to collect the data for each touchpoint.
Cost – Cost is one of our most important variables since it helps us determine ROI. Just because something is effective does not meant it is efficient.
Frequency – How many times was our ad/content served to the prospect.
Action – Did the prospect take any action upon viewing our content (i.e. click on it, etc.).
Duration – What is the duration of an engagement? In this example, our prospect spent 6 seconds on landing page after clicking on a mobile ad. Also, we used technology to determine that the prospect looked at an OHH ad for 2 seconds.
Recency – When was the last engagement before conversation. The closer the recency between touch points the higher the weight to the precedent one. Here we see that the prospect conducted a product search within the hour after he/she engaged a social ad/content. Thus, “Social” would get a larger attribution.
Quality score – This a variable that you don’t see often that is extremely important. What is the quality of the ad/content? Was the ad place next to undesirable content? Was the engagement likely from a bot?
Halo – Was there a “halo effect?” That is, did the prospect take a secondary action of value to the brand. In this example, the prospect did a search for a related product and ended up buying that product in addition to the one advertised.
Data Lake / BI – This is the infrastructure needed to process the data and run the machine learning models.
Attribution – Self-explanatory.
