7 key steps to get your Multi Touch Attribution (MTA) right !!

7 key steps to get your Multi Touch Attribution (MTA) right

7 key steps to get your Multi Touch Attribution (MTA) right !!

We have built 30 Markov Attribution models for companies across geographies in the last 3 yrs.

Here are the 7 key steps to make your MTA project a success.

๐Ÿ“Œ Reality is different from toy examples:

Things don’t work as easily as illustrated on toy examples or tutorials. Markov Attributions implementation on a real data set is a different beast altogether. This expectation needs to be set.

๐Ÿ“Œ It is all about the Paths:

Roughly, 60% of the Markov attribution projects is about Data pre-processing and data transformation (forming the paths). It is pertinent to get this step right.

๐Ÿ“ŒPay 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.

๐Ÿ“ŒModularize 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

๐Ÿ“ŒApply 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.

๐Ÿ“ŒDon’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.

๐Ÿ“ŒInterpret 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 n.o of conversion relative to path B but path B though having lesser n.o of conversion could have resulted in the conversions with higher revenue.

Link to the case study where we helped client get 6% more lead conversion through MTA is under resources.

Resources:

Link to the case study: https://arymalabs.com/mta-case-study/

Facebook
Twitter
LinkedIn

Recommended Posts

Chebyshev’s Inequality for Marketing Mix Model Diagnostics

Chebyshev’s Inequality for Marketing…

At Aryma Labs, we constantly endeavor to add as much science as possible…

How to use Robyn’s…

In my last post (ICYMI link in resources), I talked about the similarities…

Similarities between Decomp RSSD and Bayesian Priors in Marketing Mix Modeling (MMM)

Similarities between Decomp RSSD…

Open source Marketing Mix Modeling (MMM) tools are great for democratizing MMM. But…

Scroll to Top