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- Regular sports services: dataset of demographic, frequency and service level agreementPublication . Pinheiro, Paulo; Cavique, LuísThis article describes a dataset of different services acquired by users during the period in which they are active in a sports facility as well as their behavior in terms of frequency of the sport facility itself and the type of classes they prefer to attend. Each observation in the dataset corresponds to one user, including the features of subscriptions and frequency. Data were collected between June 1st 2014 and October 31st 2019 from a database of an ERP solution operating in a sports facility in Lisbon, Portugal. From this database, it was possi- ble to perform operations of extraction, transformation and loading into the dataset. The dataset with real data can be useful for research in ar- eas such as customer retention, machine learning, marketing, actionable knowledge and others. Although we present real data from users of a sports facil- ity, in order to comply the GDPR legislation, the attributes that could identify the users were removed making the data anonymized.
- Uplift modeling using the transformed outcome approachPublication . Pinheiro, Paulo; Cavique, LuísChurn and how to deal with it is an essential issue in the telecommunications sector. Within the scope of actionable knowledge, we argue that it is crucial to find effective personalized interventions that can lead to a reduction in dropouts and that, at the same time, make it possible to determine the causal effect of these interventions. Considering an intervention that encourages clients to opt for a longer-term contract for benefits, we used Uplift modeling and the Transformed Outcome Approach as a machine learning-based technique for individual-level prediction. The result is actionable profiles of persuadable customers that increase retention and strike the right balance between the campaign budget.
- Telco customer churn analysis: measuring the effect of different contractsPublication . Pinheiro, Paulo; Cavique, LuísCustomer retention is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused on predictive analytics, neglecting the business domain. This work aims to present an actionable knowledge discovery based on specific, actionable attributes and measuring of their effects. It is common to use matching, and propensity score approaches in healthcare to evaluate causality. After performing matching using the actionable attributes in this analysis, the causal effect is quantified. This work concludes that the difference between having a yearly contract versus having a monthly contract affects the churn of around 34%.
- A bi‐objective procedure to deliver actionable knowledge in sport servicesPublication . Pinheiro, Paulo; Cavique, LuísThe increase in retention of customer 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 which uses the following pipeline (data collection, predictive model, retention interventions). In the first step, it extracts and transforms existing real data from databases of the sports facilities. In a 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 associate cost are measured. This document presents a bi-objective study in order to choose the more efficient scenarios.