The power of data

: how can customer metrics predict desired intermediaries

Abstract

The world has been shaping itself for a digital overhaul, forcing many industries to radically adapt and find new and innovative procedures. In the health insurance industry, this transformation has pushed companies to expand from their traditional sales person methods to more sophisticated online networks, with readily available solutions. In addition, a shift in logic has increased the leveraging power of customers, becoming drivers of innovation through the datathat they provide to firms. Consequently, the main objective of this dissertation is to explorehow collecting customer satisfaction data can aid in the prediction of the distribution channel ofchoice when buying health insurance. By working closely with a real company, Saúde Prime,we delve into the current state of the industry, identify key roles in the service-ecosystem, suchas intermediaries, and examine how customers value health insurance services. To easily take advantage of this data, Machine Learning algorithms were used, due to their scalability and interpretability towards complex features. The predictive analysis introduces customer satisfaction metrics as a strong predictor of customer’s preferences in regard to the chosen channel - intermediary- for purchasing health insurance, connected to the level of technological literacy and the degree of importance given to the relationship with the mediator. Results indicate that most customers will opt for traditional channels, presenting a digital landscape still in its inception phase.

Date of Award	25 Jan 2021
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Omar El Nayal (Supervisor)