Explainable AI: Application of shapely values in Marketing Analytics

Explainable AI: Application of shapely values in Marketing Analytics

In this article, firstly we will talk about the specific challenges and their solutions related to current state of marketing analytics. Secondly, we will try to develop an intuition about the XAI and finally, we will implement XAI with some basic marketing dataset. So let’s begin with the challenges and the possible solutions.

Recently, I stumbled upon a white paper, which talked about latest in AI applications in Marketing Analytics. It specifically talked about the application of XAI(Explainable AI) in marketing mix modelling [source]. This caught my attention and I started exploring more about three things XAI, the current state of marketing analytics, and XAI’s potential applications in marketing analytics. After going through the available resources, I realized that it has huge potential to reinvent marketing analytics. In this article, firstly we will talk about the specific challenges and their solutions related to current state of marketing analytics. Secondly, we will try to develop an intuition about the XAI and finally, we will implement XAI with some basic marketing dataset. So let’s begin with the challenges and the possible solutions.

Challenges associated with current state in Marketing Analytics and its possible solutions:

There are many challenges but the three significant challenges associated with the current state of Marketing Analytics are related to accuracy of models used(GLMs: Generalized Linear Models), inherent non linearity in market response, and attribution; because of these challenges it becomes very difficult and cumbersome to identify the metrics mentioned below. I am specifically discussing below mentioned metrics because moving forward we will see how a different approach can address these issues.

  1. Channel Attribution

Existing Challenge: This is one of the biggest pain points for marketers. Since there are interactions between channels, so it becomes almost impossible to fairly distribute or assign the payoffs to the different channels.

*Possible Solution: *Shapely values from Cooperative Game Theory comes to rescue here. The Shapley value is a way to fairly distribute the total incremental gains to the collaborating players in the game. In our case the marketing channels are the players cooperating with each other to increase the metrics such as revenue, total conversions etc. Even Google Analytics use shapely values in their Data-Driven Attribution methodology.[source]

2. Interactions of different Marketing Channels

Existing Challenge: There are channels, which as a standalone, are not significant contributors ; however in combination with other channels could play a significant role. Therefore it is important for a marketer to know about the different combinations of the channels, which are interacting with each other. The number of interactions increases significantly as number of channels increases and it becomes very cumbersome to include all such interaction terms in GLMs.

Possible Solution: To address this challenge we will again use the shapely values but in a different way. We will use SHAP algorithm to study the interactions of channels at scale. SHAP algorithm is the implementation of shapely values in machine learning to explain and interpret any black box ML models. Since, we will be replacing GLMs with highly accurate tree based ensemble models in our example, therefore we will be using SHAP to interpret and explain our model.

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