Retail Data Analytics and Customer Insights
( 25 Modules )

Module #1
Introduction to Retail Data Analytics
Overview of the importance of data analytics in retail, course objectives, and expected outcomes

Module #2
Retail Data Sources and Collection Methods
Examining different data sources, including transactional, customer, and operational data

Module #3
Data Preparation and Cleaning
Importance of data quality, handling missing values, and data normalization techniques

Module #4
Retail Data Visualization
Introduction to data visualization tools and techniques for retail data analysis

Module #5
Customer Analytics:Understanding Customer Behavior
Analyzing customer demographics, purchasing habits, and loyalty patterns

Module #6
Customer Segmentation:Identifying High-Value Customers
Techniques for segmenting customers based on behavior, demographics, and other factors

Module #7
Customer Churn Analysis and Prevention
Identifying and addressing factors contributing to customer churn

Module #8
Product Analytics:Optimizing Product Offerings
Analyzing product performance, demand forecasting, and optimizing product mix

Module #9
Price and Inventory Optimization
Using data analytics to optimize pricing and inventory levels

Module #10
Supply Chain Analytics:Improving Operational Efficiency
Analyzing supply chain data to optimize logistics, shipping, and inventory management

Module #11
Marketing Attribution Modeling
Assigning values to marketing campaigns and channels to measure ROI

Module #12
Customer Feedback and Sentiment Analysis
Analyzing customer feedback, sentiment analysis, and Net Promoter Score

Module #13
Omnichannel Retailing:Integrating Online and Offline Channels
Analyzing customer behavior across online and offline channels

Module #14
Mobile Analytics:Understanding Mobile Customer Behavior
Analyzing mobile app and website data to understand mobile customer behavior

Module #15
Social Media Analytics:Monitoring Social Media Conversations
Analyzing social media data to understand customer sentiment and preferences

Module #16
Location Analytics:Understanding Store Performance
Analyzing location-based data to optimize store performance and layout

Module #17
Predictive Analytics in Retail
Introduction to predictive modeling techniques in retail, including regression and decision trees

Module #18
Retail Forecasting:Demand Forecasting and Sales Prediction
Using statistical models and machine learning algorithms for demand forecasting and sales prediction

Module #19
Big Data and Retail Analytics
Handling large datasets, using Hadoop, Spark, and NoSQL databases in retail analytics

Module #20
Retail Analytics Tools and Technologies
Overview of popular retail analytics tools, including Tableau, Power BI, and R

Module #21
Case Study:Retail Data Analytics in Practice
Real-world examples of retail companies using data analytics to drive business decisions

Module #22
Retail Analytics for Competitive Advantage
Using data analytics to gain a competitive edge in the retail industry

Module #23
Ethics and Privacy in Retail Data Analytics
Importance of ethical data practices, GDPR, and CCPA compliance in retail analytics

Module #24
Communicating Retail Insights to Stakeholders
Effective communication of data insights to non-technical stakeholders in retail

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Retail Data Analytics and Customer Insights career

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Retail Data Analytics and Customer Insights ( 25 Modules )

Retail Data Analytics and Customer Insights
( 25 Modules )