تقنيات معتمدة على البيانات للاحتفاظ بالعملاء

In this course on Data-Driven Techniques for Customer Retention, you’ll learn how to use predictive models and monitoring systems to identify churn signals before they become major risks. You’ll see how behavioral, transactional, and feedback data combine to reveal patterns, and how metrics like CLV and sensitivity scores guide where to focus retention resources. You’ll also explore customer segmentation and continuous engagement monitoring to keep risks visible across the customer journey.

You’ll begin with churn prediction models that analyze data signals and assign risk levels, then link those outputs directly to retention workflows. From there, you’ll apply segmentation strategies that tailor retention campaigns to customer needs, supported by integrated dashboards that update in real time.

Finally, you’ll examine monitoring systems that detect journey drop-offs early and trigger timely interventions. By the end, you’ll know how to turn scattered data into actionable insights that reduce churn and strengthen long-term loyalty.