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Uplift modeling using the transformed outcome approach
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Autores
Orientador(es)
Resumo(s)
Churn 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.
Descrição
EPIA 2022. Conferência Internacional, realizada em Lisboa, Portugal, de 31 de agosto a 2 de setembro de 2022.
Palavras-chave
Uplift modelling Causal effect Decision trees Transformed outcome approach