Customer Analytics with R and Tableau

Name: Customer Analytics with R and Tableau
Rating: 5.0 (1 reviews)

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

Created byEDUCBA Bridging the Gap

Last updated 12/2024

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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.

Course content

3 sections • 13 lectures • 1h 42m total length

Introduction to Customer Analytics using R and Tableau11:33
Explore the field of customer analytics and learn to turn customer data into informed business decisions through descriptive, predictive, and prescriptive analytics, using R and Tableau.

Market Research NPS of Banks8:13
Explore how market research measures a bank's net promoter score by analyzing survey data from 200 customers, linking convenience, staff, and digital channels to promoters and detractors.
Exception Vs Perception7:59
Use a regression model to identify drivers of the customer service index and Net Promoter score from survey data. Find convenience and staff as the most significant factors.
Market Segmentation for Airlines5:54
Explore market segmentation of airline customers through clustering and k-means on a frequent flyer data set, revealing personalized mileage offers for distinct segments.
Summary of Each Cluster Group7:40
Learn how k-means creates five clusters with cluster means and between-cluster variance. Explore hierarchical clustering with euclidean distance to cut a dendrogram into five groups for marketing insights.
Descriptive Analytics5:12
Explore descriptive analytics as the first stage of data processing that summarizes historical customer data, generates insights, and informs predictive and prescriptive analytics through Tableau dashboards.
Company Performance - Key Matrices8:14
Explore a profitability dashboard linking top customers and products to sales and region insights. Drill down with filters to examine regional profitability, shipping efficiency, and what-if forecasts for strategic planning.
Predictive Analytics7:13
Apply predictive analytics to understand and predict telecom churn using logistic regression and decision trees, enabling proactive retention strategies.

Telecom Churn8:37
Build a logistic regression model on a telecom churn dataset to predict churn using features like international plan, voicemail, and customer service calls, comparing to an 85% baseline.
Telecom Churn Continues7:25
Compare predicted and actual churn to assess model accuracy, with threshold 0.5 and logistic regression, 86% accuracy; explore classification and regression tree analyses to prioritize variables.
Sensitivity and Specificity8:49
Derive accuracy from the confusion matrix, compare logistic regression to a decision tree, and discuss sensitivity, specificity, and cost complexity pruning via cross-validation to reduce overfitting.
Prescriptive Analytics12:17
Explore prescriptive analytics in customer analytics, using market basket analysis and association rules to recommend actions, bundle products, and reduce churn with R-based tools.
Case Studies3:38
Explore how Netflix uses customer data to fuel a recommendation engine and data-driven content production. See how e-commerce and ride-hailing firms leverage offers to gain competitive advantage.

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 Introduction

Understanding 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 Writeup

Section 1: Introduction

Begin 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 Analytics

Dive 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 Studies

Explore 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.

Conclusion

This 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.

Customer Analytics with R and Tableau

What you'll learn

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Course content

Introduction1 lecture • 12min

Market Research and Analytics7 lectures • 50min

Telecom Churn and Case Studies5 lectures • 41min

Requirements

Description

Who this course is for: