Executive Development Programme in Customer Retention Analysis Methods
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⢠Customer Retention Analysis Fundamentals: Understanding the importance of customer retention, key metrics, and the basics of retention analysis.
⢠Customer Segmentation Techniques: Techniques for segmenting customers, including demographic, behavioral, and value-based segmentation.
⢠Customer Lifetime Value (CLV): Calculating and understanding the importance of CLV, its impact on customer retention, and strategies to optimize CLV.
⢠Churn Rate and Churn Analysis: Defining churn rate, conducting churn analysis, and identifying at-risk customers.
⢠Retention Strategies: Implementing effective retention strategies, including loyalty programs, personalization, and customer engagement.
⢠Customer Feedback Analysis: Collecting and analyzing customer feedback to improve retention, including net promoter score (NPS) and customer satisfaction (CSAT) surveys.
⢠Data Analysis Tools: Utilizing data analysis tools, such as SQL, Python, and R, to conduct retention analysis.
⢠Predictive Analytics for Customer Retention: Applying predictive analytics to customer retention, including machine learning algorithms and predictive modeling.
⢠Data Visualization Techniques: Presenting retention data effectively, including data visualization tools and best practices.
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