A company wants to reduce its high customer churn rate. As a data analyst, which metric is most important to focus on during your initial analysis?

Churn rate over time directly measures the percentage of customers leaving the company. Understanding its trends and patterns is critical for diagnosing the underlying causes and targeting solutions. Analyzing this metric helps prioritize efforts, such as identifying common factors among churned customers or evaluating the effectiveness of retention strategies. Option A : ARPU is important for profitability analysis but does not directly address churn. Option B : NPS reflects customer loyalty but might not pinpoint churn reasons effectively. Option C : CLV provides long-term value insights but is secondary to understanding churn causes. Option D : CSAT indicates satisfaction but does not necessarily explain customer departures.