In a marketing context, Attribution involves identifying the set of user actions ("events" or "touchpoints") that contribute in some manner to a desired outcome, and then assigning a value to each of these events. Users might click on multiple ads before converting. This can make it challenging to assign proper credit to the different marketing channels. For example, should all the credit go to the last ad the user saw, or the first ad? Should all the ads share in the credit equally? Or should some other rule be used to determine how to distribute credit?

DDA attempts to algorithmically work out a fair weighting of credit among marketing channels. For example, a particular display ad might not convert immediately, but users who click the display ad might be much more likely to convert later on. In this case, the display ad should get credit, even though it may not be the first or last ad on a user's path to conversion.

Google Marketing Platform products already support DDA. This Fractribution package is a DDA algorithm that generates user-level fractional attribution values for each conversion. The advantage of user-level attribution is that the attribution values can later be joined with custom user-level data (e.g. transaction value, lifetime value etc). This can be useful when regulation or data policy prevents ecommerce/revenue events from being shared with the Google Marketing Platform.

Please see Fractribution_Slides.pdf file in this directory for more background on use cases and details on the DDA algorithm.

For more instructions, including a tutorial for running Fractribution over sample GA360 data from the Google Merchandise Store, from see py/README.md.

fractribution
├── README.md
├── py
├──── README.md
├──── main.py
├──── fractribution.py
├──── templates/
├──── Dockerfile
└──── start.py

Disclaimer: This is not an officially supported Google product.