Initial Situation
At a telecom-internet corporation, willingness to switch among DSL customers increased noticeably. Retention offers were deployed too broadly, campaign architecture was inefficient, win-back performance stagnated. A controllable, data-driven model was missing to identify churners early and win them back in a targeted manner.
Lever
A partner on our team developed a retention framework with AI-powered churn prediction: machine learning on existing customer data identifies attrition early. Built on top: multichannel win-back via telesales and online retention with dynamic offer logic, a KPI-driven testing and reporting structure for ongoing conversion optimization, and alignment of sales, CRM, product development, BI, and external partners.
Result
The win-back rate rose by 15% compared to the prior-year period, and churn in the DSL customer base fell by more than 10%. Additionally, a scalable retention and campaign model with strong C-level buy-in was established. Period: 6 months · Role: Senior Consultant (Product Management & CRM)
