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ESTUDO DO PERFIL DE CLIENTE DE UMA COMPANHIA DE SEGUROS ANÁLISE DE CLUSTERS, CÁLCULO DO VALOR DO CLIENTE E PREVISÃO DA PROBABILIDADE DE CHURN PARA O DESENVOLVIMENTO DE CAMPANHAS DIRECIONADAS
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Ao longo das últimas décadas, verificou-se que o cliente, bem como as suas necessidades
e a sua satisfação, são cada vez mais o foco das empresas. Tentar satisfazer e reter os
clientes mais lucrativos para as companhias é um tópico com bastante interesse que tem
ganho relevância nos últimos tempos.
Desta forma, e tratando-se o Grupo Future Healthcare de uma empresa que comercializa
seguros e planos de saúde, é de extrema importância para a companhia estar
consciente do valor que cada cliente representa, assim como perceber qual a influência
que as suas estratégias de marketing têm na retenção dos seus clientes.
Assim, esta dissertação pretende numa primeira fase identificar os clientes mais lucrativos
para a empresa e caracterizá-los, com recurso à análise Recência, Frequência e Valor
Monetário (RFM).
Posteriormente, o objetivo passa pela segmentação dos clientes com base no algoritmo
agregação por K-Médios (K-Means), por forma a obter grupos de clientes com comportamentos
semelhantes, e daí concluir sobre o perfil do cliente, e quais as campanhas mais
eficazes para cada grupo.
Por fim, recorre-se à regressão logística para averiguar sobre a taxa de retenção, ou
seja, pretende-se analisar que grupos são mais suscetíveis de abandonar a empresa, de
modo a direcionar as estratégias desenvolvidas e a melhorar a satisfação destes clientes.
Over the last decades, it has been seen that more and more the customer is the focus, as well as their needs and satisfaction.Trying to satisfy and retain the most profitable customers for companies is a topic with a lot of interest that has been gaining relevance in recent times. Therefore, as Future Healthcare Group is a company that, among many other things, sells insurance and health plans, it is extremely important for the company to be aware of the value that each client represents, as well as to understand the influence that its marketing strategies have on client retention. Thus, this dissertation aims, in a first phase, to identify the most profitable customers for the company and characterise them using Recency, Frequency and Monetary Value (RFM) analysis. Subsequently, the goal is to segment customers based on the K-Means algorithm, in order to obtain groups of customers with similar behaviour, and then conclude on the customer profile and which campaigns are most effective for each group. Finally, logistic regression is used to find out about the retention rate, that is, to discover which groups are more likely to leave the company, so as to direct the strategies developed and improve customer satisfaction.
Over the last decades, it has been seen that more and more the customer is the focus, as well as their needs and satisfaction.Trying to satisfy and retain the most profitable customers for companies is a topic with a lot of interest that has been gaining relevance in recent times. Therefore, as Future Healthcare Group is a company that, among many other things, sells insurance and health plans, it is extremely important for the company to be aware of the value that each client represents, as well as to understand the influence that its marketing strategies have on client retention. Thus, this dissertation aims, in a first phase, to identify the most profitable customers for the company and characterise them using Recency, Frequency and Monetary Value (RFM) analysis. Subsequently, the goal is to segment customers based on the K-Means algorithm, in order to obtain groups of customers with similar behaviour, and then conclude on the customer profile and which campaigns are most effective for each group. Finally, logistic regression is used to find out about the retention rate, that is, to discover which groups are more likely to leave the company, so as to direct the strategies developed and improve customer satisfaction.
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Palavras-chave
Análise RFM Segmentação de Clientes Análise de Clusters Taxa de retenção