Akansha Akansha and Stuart So, University of Exeter Business School, United Kingdom

Understanding the user segment is highly significant in the age of a highly competitive wearable Fitness Technology market. In this study, we leveraged a comprehensive dataset containing information on user interactions, activity logs and device usage records. For effective segmentation of the users, K-Means clustering was employed. The unsupervised Machine Learning algorithm helped us group the clusters of consumers based on their similarity in the usage of the device, activity levels and engagement patterns. The collaborative Filtering technique refines product recommendations by identifying user preferences based on past patterns. The analysis aims to uncover distinct user segments and provide insights into user behaviours and lifestyles to enhance Fitbit's Market Performance and improve user engagement, customer satisfaction and brand loyalty leading to higher customer retention. The findings of an extensive analysis conducted on Fitbit User data using K-Means Clustering and Collaborative filtering techniques are presented. To achieve sustainable growth in the highly competitive smart wearables market, Fitbit can improve its user experience by addressing the diverse needs of different user segments.

Fitbit, Segmentation, K-Means, Collaborative Filtering, Personalisation, Wearable Fitness