Member risk and retention
Using machine learning (AI) to predict at risk members
ActiveXchange's Data Science Team used advanced predictive modelling, leveraging a wide set of data from across the sector. This predicts each member's relative risk of cancelling their membership at any point in time, using variables such as:
- Time since last visit
- Visit pattern and establishment of habits
- Engagement with the venue offer (programming)
- Price point and price sensitivity of the member (profiling)
- Time of use and peak time pinch points
- Venue performance and wider industry trends
- New local competition
- Travel time and convenience
- Gym buddy impact
The analysis output is provided in a data feed which is integrated into the operator's existing marketing CRM tools so multiple marketing systems aren't required.
Mitigating risk through targeted campaigns
As well as predicting risk at an individual member level, ActiveXchange's output provides optimal incentives to change the member's behaviour and mitigate the risk of cancellation. Outcomes from these are constantly tracked across hundreds of venues so optimal member and campaign insight is always utilised by each partner operator.