The increase in retention of customers in gyms and health clubs is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused essentially on predictive analytics, neglecting the business domain. This work presents an actionable knowledge discovery system that uses the following pipeline (data collection, predictive model and retention interventions). In the first step, it extracts and transforms existing real data from databases of the sports facilities. In the second step, predictive models are applied to identify user profiles more susceptible to dropout, where actionable withdrawal rules are based on actionable attributes. Finally, in the third step, based on the previous actionable knowledge, some of the values of the actionable attributes should be changed in order to increase retention. Simulation of scenarios is carried out, with test and control groups, where business utility and associated cost are measured. This document presents a bi‐objective study in order to choose the more efficient scenarios.

中文翻译：

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在体育服务中提供可行知识的双目标程序

如今，增加在健身房和健身俱乐部中的客户保留率是一项挑战，需要采取具体和个性化的措施。传统的数据挖掘研究主要侧重于预测分析，而忽略了业务领域。这项工作提出了一个可操作的知识发现系统，该系统使用以下管道（数据收集，预测模型和保留干预）。第一步，它从体育设施的数据库中提取并转换现有的真实数据。在第二步中，应用预测模型来识别更容易退出的用户配置文件，其中可行的退出规则基于可行的属性。最后，在第三步中，基于先前的可操作知识，应更改可操作属性的某些值以增加保留。通过测试和控制组对场景进行模拟，在其中测量业务效用和相关成本。本文档提出了一个双目标研究，以便选择更有效的方案。