Many organisations possess the necessary data to comprehend customer attrition; however, they often lack the tools to sift through and interpret these insights for their customer success teams. Here are seven essential steps to utilise AI-driven data exploration to rapidly identify customer churn factors.
Select the churn indicator: Choose a representative value indicating customer churns, such as contract status or a defined time since the last purchase.
Drivers’ analysis: Analyse all data points to rank the impact of each attribute on churn outcomes.
Refined analysis: Remove fixed attributes and re-run the drivers’ analysis to identify deeper insights often missed through traditional analysis.
Unbiased understanding: An unbiased understanding of churn causes enables informed strategies to counter attrition.
Network graph analysis: Generate a network graph to reveal customer communities and relationships, visualising commonalities.
AI-generated customer profiles: Examine AI-generated profiles of identified communities to understand high-risk customer personas.
Proactive strategies: Proactively address at-risk clients with tailored strategies informed by AI-generated insights.