Interest-free promotions are a prevalent strategy employed by credit card lenders to attract new customers, yet the research exploring their effects on both consumers and lenders remains relatively sparse. The process of selecting an optimal promotion strategy is intricate, involving the determination of an interest-free period duration and promotion-availability window, all within the context of competing offers, fluctuating market dynamics, and complex consumer behaviour. In this paper, we introduce an agent-based model that facilitates the exploration of various credit card promotions under diverse market scenarios. Our approach, distinct from previous agent-based models, concentrates on optimising promotion strategies and is calibrated using benchmarks from the UK credit card market from 2019 to 2020, with agent properties derived from historical distributions of the UK population from roughly the same period. We validate our model against stylised facts and time-series data, thereby demonstrating the value of this technique for investigating pricing strategies and understanding credit card customer behaviour. Our experiments reveal that, in the absence of competitor promotions, lender profit is maximised by an interest-free duration of approximately 12 months while market share is maximised by offering the longest duration possible. When competitors do not offer promotions, extended promotion availability windows yield maximum profit for lenders while also maximising market share. In the context of concurrent interest-free promotions, we identify that the optimal lender strategy entails offering a more competitive interest-free period and a rapid response to competing promotional offers. Notably, a delay of three months in responding to a rival promotion corresponds to a 2.4% relative decline in income.

翻译：免息促销是信用卡发卡机构吸引新客户的普遍策略，然而关于其对消费者和发卡机构影响的研究仍相对匮乏。选择最优促销策略的过程极其复杂，涉及确定免息期时长和促销可用窗口，同时需考虑竞争性优惠、波动的市场动态以及复杂的消费者行为。本文提出了一种基于智能体的模型，可促进在不同市场情景下探索各类信用卡促销活动。与以往基于智能体的模型不同，我们的方法聚焦于优化促销策略，并利用2019至2020年英国信用卡市场的基准数据进行校准，其中智能体属性源自大致同一时期英国人口的历史分布。我们通过典型事实和时间序列数据对模型进行验证，从而展示了该技术在研究定价策略与理解信用卡客户行为方面的价值。实验表明：在无竞争对手促销的情况下，发卡机构利润最大化所需的免息期约为12个月，而市场份额最大化则需要提供最长的免息期；当竞争对手不提供促销时，延长促销可用窗口既能实现发卡机构利润最大化，也能同时最大化市场份额。在同时存在免息促销的背景下，我们识别出发卡机构的最优策略是提供更具竞争力的免息期，并对竞争性促销要约做出快速响应。值得注意的是，对对手促销延迟三个月响应会导致收入相对下降2.4%。