Real-world usage of a digital employee wellbeing platform: k-means clustering analysis

Employers now acknowledge the crucial role of employee wellbeing in promoting productivity, positive relationships, and engagement, as well as its impact on absenteeism and presenteeism. Consequently, there is an increasing need for affordable, evidence-supported, scalable innovative approaches to improve employee wellness. This paper presents usage analysis of a digital employee wellbeing platform, created by Inspire, a mental health social enterprise. The platform has several self-help components, including a chatbot that delivers mental health self-assessments, CBT -based e-learning modules, and a mood tracker. Analysis was conducted using the machine learning technique k-means clustering and descriptive analytics. Through the analysis of user tenure (i.e. the time interval between the first and last day of a user who engaged with the platform), total interactions, daily interactions, and number of unique days on the platform, K-means clustering successfully identified three user groups: short-term (95.5% of users), intermediate (3.4% of users), and long-term users (1.1 % of users). By using these analysis techniques, we can understand how employees utilize a digital employee wellbeing platform, helping to design more effective and personalized solutions.