The difficult problems in attribution modeling

The term “attribution modeling” can have different meanings to different people – sometime being used interchangeably with “attribution model”. To me, attribution model refers to things like “last touch”, “first touch” etc. – rules that specify how attribution should be done. Attribution modeling is about the process where attribution model is generated.  Attribution Modeling give us the model generation process, as well as the reasons and justification of the attribution model being derived.

It is not difficult to come up with an attribution model, in fact, we can make up one in seconds. What difficult is to determine which one is the right attribution model. Despite all the discussion and progress made over the last few years, there is no consensus about it. And the lack of industry consensus really hurt.

The question about right attribution model is perhaps miss-guided; for we all know that a model could be right for one business, say e-commerce, may be wrong for another, such as B2B. What is right may also depend on type of campaigns, type of conversions and even type of users (male vs female, adult vs teens).  The right question should be: what’s the right attribution modeling – the right process of how an attribution model is generated.

Each one of us can easily list 4 or 5 most commonly used attribution models. What about attribution modeling? How many different processes can attribution model be produced?

Last Click/Last Touch attribution models are examples where intuition is the modeling process.  It is not data driven.  You can argue about the good and bad conceptually.  On the other hand, data-driven approach holds the fundamental belief that the right attribution model should be derived from data.  Within data-driven approach, there are two slightly differing approaches: experimental design vs algorithmic attribution.  

You may ask, what about Google’s Attribution Modeling Tool in Google Analytics? It is not really an Attribution Modeling Tool in my use of the word, it helps you specifying attribution models, not creating any data-driven models. It does not tell you how to derive the “right” attribution model.

The data-driven approach is what we will focus here. There has been great progress in the “algorithmic attribution” approach, and significant business build on this (Adometry and VisualIQ to name a couple).  However, none is clear and transparent enough about their key technologies – as an industry, we left with a lot of confusions.  

The set of difficult problems are about that – the core technology of attribution modeling. We need to answer these questions so we can build upon a common ground and move on.  Here’s a list of the questions/problems:

1) Is attribution modeling the same as statistical conversion modeling?

2) What’s the right type of models to use: predictive modeling, descriptive modeling, causal modeling?

3) Does it matter if the model is linear regression, logistic or some bayesian network model?

stay tuned for more.