Research findings from the UW-Stevens Point Center for Data Analytics show the costs of acquiring new customers are estimated to be 3-5 times higher than retention costs. UW-Stevens Point’s Sentry Endowed Chair Kurt Pflughoeft and data analytics alum Alick Corbley ’23 presented their research on customer retention at the World Conference on Data Science & Statistics June 17-19 during Data Science Week 2024 in Amsterdam, Netherlands.
The research demonstrated how Corbley helped Internet service provider Bug Tussel glean insight into customers who are more likely to leave. Analysis was done in both JMP Pro and R. Corbley started this research as an intern and is now a full-time data analyst with Bug Tussel in Green Bay.
The conference is one of the largest and the most technological annual events, bringing together the most innovative minds, enterprise practitioners, technology providers, start-up innovators, and academics, working with Data Science, Big Data, ML, AI, Data Management, Data Engineering, IoT and Analytics, in one place to discuss ways to accelerate AI-driven Transformation throughout companies, industries, and public organizations.
Determining Predictors of Churn for Fixed Wireless ISPs using Methodological Triangulation
Abstract: Customer attrition remains a significant area of concern in the telecommunications sector due to its high prevalence. As Fixed Wireless Access (FWA) gains traction, the field is witnessing heightened competition, especially with the integration of 5G technology that serves dual purposes – for cellular communications and fixed wireless access. This research delves into a comprehensive analysis of factors that influence attrition rates in this rapidly evolving landscape. By examining a range of determinants, including demographic profiles, socio-economic statuses, historical customer interactions, competitive dynamics, geographic regions, and specific service features, we establish key predictors of churn. Our methodological triangulation leverages Kaplan-Meier Survival Plots, Random Survival Forests, and Cox Regressions. The research also utilizes cross-validation tuning to search for the best out-of-sample model. The insights garnered provide a foundational basis for telecom providers to devise robust strategies to curtail customer attrition, ensuring sustained growth and customer satisfaction in the burgeoning FWA market.
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