Customer Analytics with R and Tableau

Unlock actionable insights into customer behavior and enhance decision-making with advanced analytics using R, Tableau!

What you'll learn
The fundamentals of customer analytics and its practical applications.
Conducting market research and segmenting customers effectively.
Applying descriptive, predictive, and prescriptive analytics to real-world scenarios.
Tools and techniques for analyzing customer churn.
Creating visualizations and dashboards using Tableau to communicate insights.

Requirements
Basic understanding of R and Tableau. Familiarity with fundamental concepts in statistics and data analysis. An interest in customer behavior and data-driven decision-making.

Description
Course IntroductionUnderstanding customers is vital for any business aiming to thrive in today’s competitive market. This course introduces you to customer analytics, teaching you how to leverage R and Tableau to conduct market research, segment audiences, analyze customer churn, and make data-driven decisions. Through hands-on case studies, you'll master key techniques in descriptive, predictive, and prescriptive analytics to drive customer-centric strategies.Section-wise WriteupSection 1: IntroductionBegin your journey into customer analytics by understanding its significance and applications across industries. This section provides an overview of how R and Tableau can be used to derive insights from customer data and transform them into actionable strategies.Section 2: Market Research and AnalyticsDive into market research with practical examples, such as analyzing Net Promoter Scores (NPS) of banks. Learn to differentiate between customer exceptions and perceptions and explore market segmentation techniques, specifically for the airline industry. The section concludes with a summary of cluster groups and their relevance, along with insights into company performance metrics through descriptive and predictive analytics.Section 3: Telecom Churn and Case StudiesExplore a real-world application of customer analytics by analyzing telecom customer churn. Understand sensitivity and specificity in predictive modeling and leverage prescriptive analytics to address churn issues. This section culminates with engaging case studies that solidify your understanding of applying analytics to solve customer-related challenges.ConclusionThis course equips you with the skills and tools necessary to excel in customer analytics using R and Tableau. By the end of the course, you’ll be capable of conducting in-depth customer analyses, uncovering trends, and developing strategies to improve customer satisfaction and retention.

Who this course is for
Business professionals aiming to understand customer behavior and improve retention strategies.
Data analysts and aspiring data scientists interested in customer analytics.
Marketing and sales professionals seeking data-driven insights into customer preferences.
Students and researchers focusing on customer behavior and business analytics.