Generating targeted campaigns based on transactional data using Uplift Modeling

Minna Reiman

Abstract

Uplift modeling is a technique to model the incremental effect of a campaign

or marketing offer. The incremental effect of a campaign is measured by comparing

two customer groups - one group receiving a campaign and another

group not receiving any. The goal of this technique is to reduce the cost of a

campaign by identifying the most optimal targets.

This study presents results from applying uplift models on customer data at

Klarna, with the goal of understanding how to target campaigns effectively. To

understand how targeting should be performed, a dataset has been compiled

based on eight transactional features. The data was then cleaned, preprocessed

and a feature selection was performed based on the Net Information Value.

Two different models were built - one random forest model based on the single

model approach, and a gradient tree boosting algorithm based on the class

variable transformation approach. To evaluate the models, the Qini coefficient

has been used, which is a measure of the discriminatory power of the

model. The conclusion of this study is that for this given dataset, the gradient

tree boosting algorithm performed more than four times better than the singe

model, and should therefore be applied to this problem to maximise the effect

of a campaign.