Connected Vehicle Data Applied to Feature Optimization and Customer Experience Improvement 2023-36-0109

In a recent time, which new vehicle lines comes with a huge number of sensors, control units, embedded technologies, and the complexity of these systems (electronics, electrical and electromechanical parts) increases in an exponential way. Considering these events, the expressive generated data amount grows in the same pace, so, consume, transform, and analyze all these data to better understand the modern customer, their needs and how they use the car features becomes necessary. Through that scenario, connected vehicles developed by Ford Motor Company has been generating opportunities to feature’s improvement and cost reduction based on data analysis. This growing quantity of data might be used to optimize feature systems and help engineering teams to understand how the features have been used and enhance the systems engineering design for new or existing features.

These opportunities come to the target of this work, even because features are designed to transform inputs into functional outputs that add perceived value to the customer. Therefore, focusing the customers, this work intends to deal with connected vehicles and bring results to Feature Systems Engineers in the following way: collect data from the Ford Motor Company databases, pre-process and model the data, analyze information applying a variety of methods, understand Customer Experience, add data knowledge based on feature usage, and improve Features: identifying new opportunities to enhance vehicle Features based on the customer behavior, targeting customer satisfaction.

The outcomes of all this work are to unveil patterns of features usage that were not clear enough for systems engineering teams until these techniques show results from data knowledge. Moreover, reaching out the understanding about the customer experience might deliver information resources for the Systems Engineering teams to redefine or improve Engineering products design.