A bi‐objective procedure to deliver actionable knowledge in sport services

Abstract

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.

Performance Prediction in Major League Baseball by Long Short-Term Memory Networks

“…The data mining should not just present the result and center by data itself, but it should be useful for domain users and benefit organizations. Also, the actionable knowledge provided by domain-driven data mining could fit users' needs and close the gap between researchers and practitioners (45). That was the lesson we learned that it is more important to create actionable knowledge for the organization through data mining, so we do not only focus on the deep learning application but also consider its real usage in the baseball domain.…”

Fifth special issue on knowledge discovery and business intelligence

“…The sixth and last paper “A Bi‐objective Procedure to Deliver Actionable Knowledge in Sport Services,” by Pinheiro and Cavique (2020), focuses on a real‐world application, aiming to increase the retention of customers in gyms and health clubs. The work presents a data‐driven pipeline that is capable of providing actionable knowledge.…”