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.

翻译：客户维系或防止流失是电信运营商面临的一项挑战性任务。有效的策略之一是为用户提供具有吸引力的激励措施、附加服务或现金优惠，以保持其活跃度并确保其长期留在运营商网络中。运营商通常会拨付一定金额的预算来开展优惠活动。该活动的难点在于，从庞大的用户群体中选择目标客户，并确定向每位用户提供的优惠金额，以实现运营商的业务目标。选择用户及为其定制优惠方案时可能存在多重目标（例如：最大化收入、最小化流失率）。除货币优惠外，优惠内容还可包含额外流量、短信、热点共享等。这一问题被称为优惠优化。本文提出了一种新颖的组合算法，通过最大化用户流失场景下的预期收入（该场景在电信领域较为常见）来解决异构优惠条件下的优惠优化问题。所提算法即使面对庞大的用户基数，仍能保持高效性与准确性。