A Greedy Approach for Offering to Telecom Subscribers

TL;DR

This paper introduces a greedy algorithm for telecom customer retention that optimizes offers to subscribers, maximizing expected revenue while considering diverse offers and large subscriber bases.

Contribution

It presents a novel combinatorial algorithm specifically designed for offer optimization in telecom, addressing heterogeneous offers and large-scale subscriber data.

Findings

Algorithm is efficient for large subscriber bases.

Maximizes expected revenue under churn scenarios.

Handles diverse types of offers effectively.

Abstract

Customer retention or churn prevention is a challenging task of a telecom operator. One of the effective approaches is to offer some attractive incentive or additional services or money to the subscribers for keeping them engaged and make sure they stay in the operator's network for longer time. Often, operators allocate certain amount of monetary budget to carry out the offer campaign. The difficult part of this campaign is the selection of a set of customers from a large subscriber-base and deciding the amount that should be offered to an individual so that operator's objective is achieved. There may be multiple objectives (e.g., maximizing revenue, minimizing number of churns) for selection of subscriber and selection of an offer to the selected subscriber. Apart from monetary benefit, offers may include additional data, SMS, hots-spot tethering, and many more. This problem is known as offer optimization. In this paper, we propose a novel combinatorial algorithm for solving offer optimization under heterogeneous offers by maximizing expected revenue under the scenario of subscriber churn, which is, in general, seen in telecom domain. The proposed algorithm is efficient and accurate even for a very large subscriber-base.