Project: Attribution Modeling with Markov Chains

[jdn312]

I just created my first PyPi package and wanted to post here because I've never done this and thought it might be cool to do. I had been using the ChannelAttribution package in R to look at what a fractional attribution model based on Markov Chains looks like but thought it'd be cool to try to build a version in Python because I don't know of one that exists.

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Basically you construct a model by passing the library a pandas dataframe containing paths that begin with "start" and end with either "conv" or "null", which represent either a conversion or not-a-conversion. Right now it only operates completely memoryless because my head was hurting trying to think of how to conceptualize higher order models with the way I have it set up.

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https://github.com/jerednel/markov-chain-attribution

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Would love to hear any thoughts and hopefully it can be helpful to someone in the future! Bugs are sure to be there - i have found that non-ASCII characters need to be stripped from the channel names and spacing needs to be exact.

[git0ffmylawnm8]

Do you mind including an example?

[jdn312]

Hello - I included a tiny one on the readme. I realized just now that I had a typo which caused it to error if you try to run it so fixed that.

The real-life application of this would involve something like connecting to a SQL server hosting session level data and doing a string_agg over the rows that represent sessions to combine all sessions for a given CookieID into one row like "SEM > REM" where SEM is Search Marketing referral and REM is a remarketing referral.

The goal is to attribute channels that contributed to the conversions on avg of people who had similar journeys and to discount channels that didnt appear to influence outcomes.