Novel approaches to understanding client behaviour within an employee wellbeing service

In today’s ever-changing workplace landscape, organisations are increasingly recognising the pivotal role of employee wellbeing in fostering productivity, positive relationships and engagement, and how it impacts on absenteeism and presenteeism in the workplace. As such, there is a growing demand for cost-effective, evidence-based, scalable innovative solutions for employee wellbeing. This work presents an ongoing study with Inspire, a social enterprise that specialises in providing employee assistance programmes, including an online support platform, 24/7 helpline, and face-to-face counselling. We present a comprehensive, novel approach to understanding client behaviour within an employee wellbeing service, leveraging advanced analysis techniques including descriptive analytics, time-series analysis, association rule mining, clustering, and process mining. By applying association rule mining to an employee wellbeing digital platform dataset, it was possible to further understand the relationship between different components and resources utilised on the digital employee wellbeing platform. Prominent association rules were found to include sessions with chatbot or mood tracker events. An unsupervised machine learning technique called K-means clustering helped identify 3 distinct user groups, short-term, intermediate, and long-term users, by analysing user tenure, total interactions, daily interactions, and unique days on the platform. Time series analysis was used to examine patterns and trends in referrals, mental health scales, and client engagement with the employee wellbeing service. The majority of interactions on the employee wellbeing digital platform (80.47%) occurred between 9am and 5pm, peaking at 11am. Process mining offered a holistic view of a client’s journey through the employee wellbeing service and uncovered inefficiencies or bottlenecks which could impact on their experience, and their wellbeing, offering recommendations for improvement. These analysis techniques allow us to gain valuable insights into the client’s journey through all components of an employee wellbeing service, enabling the creation of more effective and personalised solutions.