A corporate wellness company had serious challenge with their engagement levels and were not able to identify reasons for members disengaging and how they could proactively improve engagement in their programs.
zakipoint Health incorporated all their program participation data, including challenges, campaigns, message content across different customers, to build a disengagement model and run analytics to understand key drivers of disengagement. By bringing all the data in one place, we were able to show analysis of typical drop-offs across different segments. We built a prospective disengagement model using machine learning science to proactively score members that were at risk of disengaging and reasons behind it.
The following outcomes were achieved after running the analytics using our z5 Platform:
- Clearer definition of engagement metric, engagement indicators, framework for analyzing engagement (i.e. looking at the length of the user in the system as opposed to pure snapshot which includes new and old members)
- A robust defection risk scoring model. This core capability allowed our client to continuously score the member base and identify high risk members to profile, analyze and work on as opposed to focusing purely by program or demographic group or on the entire base
- A better understanding of the optimal challenge design
- Better ways to monitor health of the business through a subset of engagement indicators with recommendations on right cut offs, for example, the number of pedometer day readings