MultiCamp — Cost sensitive active learning algorithm for multiple parallel campaigns

Abstract

One of the challenges that companies face when launching a campaign to promote new services is selecting the 'right' customers for the campaign, i.e., customers with the highest probability of a positive response. Active learning can be used to efficiently identify this set of customers. It can also prevent approach to non-relevant customers and reduce the campaign's cost. The problem is more challenging when parallel campaigns for multiple new services are launched, given a constraint on the number of promotions that can be offered to the same customer during a defined period of time. The goal is to maximize the total net profit. In this paper we present MutiCamp, a new cost sensitive active learning based algorithm that uses the Hungarian Algorithm to find the optimal match between campaigns and customers. MultiCamp was tested on a real world dataset using a decision tree classifier. Results were compared to a random baseline, indicating the superiority of the proposed algorithm.