Analytics Strategist

April 11, 2012

Funny analogies of wrong attribution models

Few topics are near and dear to my heart as Attribution Modeling is.  I first bumped into it a more than 4 years ago; and my first written piece on attribution is a linkedin Q&A piece answering a question from Kevin Lee on duplication-rate  (in August 2007).  Since then, my interest in attribution gets real serious, resulting in a dozen’s attribution related blog posts.  The interest never died after that, although I have not written anything the last three years.

I am back on it with a vengeance! Consider this as my first one back.

I want to start on a gentle note though.  I am amused about people still debating about First Touch vs Last Touch attribution as viable attribution models, a bit out of the question in my opinion.  I want to share some funny analogies for what could go wrong with them.

Starting with Last Touch Attribution Model, a football analogy goes like this: “relying solely on a last click attribution model may lead a manager to sack his midfielder for not scoring any goals. Despite creating countless opportunities he gets no credit as his name isn’t on the score-sheet. Similarly a first click attribution model may lead the manager to drop his striker for not creating any goals, despite finishing them. – BrightonSEO presentation slides

There are a lot of good analogies like this that are derived from team sports.  This analogy is applicable not only to Last Touch, but to all single touch point attribution models.  The funniest one I heard is about First Touch Attribution, from none other than the prolific Avinash Kaushik: “first click attribution is like giving his first girlfriend credit for his current marriage.” – Avinash quote

Analogy is analogy, it does not do full justice to what’s been discussed.  However, what we should learn at least this much: if your attribution model is solely based on the sequencing order of touch points, you are wrong.  Those who propose Last, First, Even, Linear or whatever attribution models, watch out!

A good attribution model needs a disciplined development process, and better yet, a data-driven one.  The less the assumptions made about the values of touch points the better – we should learn to let empirical evidence speak for itself.

Do you have any interesting analogy, or thought?

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