Churn Prediction Built from Individual Device QoS Profiles
Correlate longitudinal device experience with churn events — identify at-risk subscribers while you can still act with fixes, upgrades, or retention offers.
Reactive churn teams learn who left after the port-out. Predictive churn built on BSS usage alone misses the causal story subscribers tell themselves: repeated dropped calls in one neighborhood, a handset that overheats during video calls, radios that cling to weak bands despite better alternatives nearby.
What a device QoS profile captures
Across weeks and months the device exposes patterns invisible in monthly invoice data: stalled handovers, chronic low SINR at home or work, application stalls correlated with congestion, abnormal battery thermal states during tethering, or firmware quirks after an update. Stitching those into a per-subscriber QoS storyline makes churn less mysterious.
From signal to playbook
High-risk cohorts can receive different interventions:
- targeted network remediation where clusters appear
- device replacement or subsidy where hardware is clearly limiting experience
- personalized retention perks where experience is unfairly poor versus price paid
Value
Transforms churn from a lagging KPI into an addressable operational problem — with measurable uplift when interventions precede cancellations.
Model retention with device-grounded churn propensity scores. Request a demo.