Types of customers¶
We can determine 4 types of customers based on a response to treatment:
Do-Not-Disturbs (a.k.a. Sleeping-dogs) have a strong negative response to marketing communication. They are going to purchase if NOT treated and will NOT purchase IF treated. It is not only a wasted marketing budget but also a negative impact. For instance, customers targeted could result in rejecting current products or services. In terms of math: \(W_i = 1, Y_i = 0\) or \(W_i = 0, Y_i = 1\).
Lost Causes will NOT purchase the product NO MATTER they are contacted or not. The marketing budget in this case is also wasted because it has no effect. In terms of math: \(W_i = 1, Y_i = 0\) or \(W_i = 0, Y_i = 0\).
Sure Things will purchase ANYWAY no matter they are contacted or not. There is no motivation to spend the budget because it also has no effect. In terms of math: \(W_i = 1, Y_i = 1\) or \(W_i = 0, Y_i = 1\).
Persuadables will always respond POSITIVE to marketing communication. They are going to purchase ONLY if contacted (or sometimes they purchase MORE or EARLIER only if contacted). This customer’s type should be the only target for the marketing campaign. In terms of math: \(W_i = 0, Y_i = 0\) or \(W_i = 1, Y_i = 1\).
Because we can’t communicate and not communicate with the customer at the same time, we will never be able to observe exactly which type a particular customer belongs to.
Depends on the product characteristics and the customer base structure some types may be absent. In addition, a customer response depends heavily on various characteristics of the campaign, such as a communication channel or a type and a size of the marketing offer. To maximize profit, these parameters should be selected.
Thus, when predicting uplift score and selecting a segment by the highest score, we are trying to find the only one type: persuadables.
1️⃣ Kane, K., V. S. Y. Lo, and J. Zheng. Mining for the Truly Responsive Customers and Prospects Using True-Lift Modeling: Comparison of New and Existing Methods. Journal of Marketing Analytics 2 (4): 218–238. 2014.
2️⃣ Verbeke, Wouter & Baesens, Bart & Bravo, Cristián. (2018). Profit Driven Business Analytics: A Practitioner’s Guide to Transforming Big Data into Added Value.