7 Lessons From Building 20 Markov Chain Attribution Models on Real Datasets
We have built 20 Markov Attribution models for various companies across geographies in the last 2 yrs.
Here are the 7 lessons we learnt along the way.
Reality is different from toy examples:
Things don’t work as easily as illustrated on toy examples. Markov Attributions implementation on a Real data set is a different beast altogether.
It is all about the Paths:
Roughly, 60% of the Markov attribution projects is about Data pre-processing and data transformation (forming the paths).
Pay attention to the timestamp:
While aggregating the data, one needs to pay attention to the order of timestamp (both in terms of format and chronology). The chronology of path is extremely important.
Any mix up will result in wrong attributions.
It is good to break the problem:
Customers are more interested in which paths lead to conversion rather than which has not. Hence it makes sense to break your data set into two
Paths leading to conversion
Paths leading to No conversion
Apply the algorithm selectively:
Building up on the last point, apply the algorithm only on the ‘path s leading to conversion’. Applying the algorithm to the whole data set (sometimes having millions of records) can lead to intractable solution.
Don’t forget the removal effects:
The removal effect tells you how much is the contribution of a channel by removing that channel from the path and seeing how many conversions are happening without that channel. Applying Removal effects is crucial to get the right attributions.
Interpret Markov results through lens of Domain Experience:
The Markov results only show which paths lead to higher conversions. It does not quantify the value of each conversion per say.
For example path A may have resulted in maximum number of conversion relative to path B but path B though having less number of conversion could have resulted in the conversions with higher revenue.
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Website: https://www.arymalabs.com/
LinkedIn: Ridhima Kumar
