Module #1 Introduction to Predictive Analytics in Retail Overview of predictive analytics, its applications in retail, and importance in decision making
Module #2 Retail Industry Overview and Challenges Understanding the retail industry, its current challenges, and how predictive analytics can address them
Module #3 Types of Predictive Analytics in Retail Exploring different types of predictive analytics in retail, including regression, decision trees, clustering, and more
Module #4 Data Preparation for Retail Analytics Collecting, cleaning, and preprocessing data for predictive analytics in retail
Module #5 Exploratory Data Analysis (EDA) in Retail Using statistical and visualization techniques to understand retail data
Module #6 Retail Sales Forecasting Using predictive analytics to forecast sales, including time series analysis and machine learning techniques
Module #7 Customer Segmentation and Profiling Using clustering and other techniques to segment and profile customers for targeted marketing and improved customer experience
Module #8 Predicting Customer Churn and Loyalty Using predictive analytics to identify at-risk customers and develop strategies to improve loyalty
Module #9 Price Optimization and Markdown Management Using predictive analytics to optimize prices and manage markdowns for maximum profitability
Module #10 Product Recommendation Systems Building recommendation systems to personalize product offerings and improve customer experience
Module #11 Supply Chain Optimization Using predictive analytics to optimize supply chain operations, including inventory management and logistics
Module #12 Competitor Analysis and Market Research Using predictive analytics to analyze competitors and conduct market research for competitive advantage
Module #13 GEO-Analytics and Location-Based Analytics Using geospatial data and analytics to understand customer behavior and optimize store locations
Module #14 Retail Analytics Tools and Technologies Overview of popular retail analytics tools and technologies, including Excel, R, Python, and more
Module #15 Case Studies in Retail Predictive Analytics Real-world examples of predictive analytics in retail, including success stories and lessons learned
Module #16 Ethics and Responsibility in Retail Predictive Analytics Discussion of ethical considerations and responsible use of predictive analytics in retail
Module #17 Implementing Predictive Analytics in Retail Organizations Strategies for implementing predictive analytics in retail organizations, including organizational change management
Module #18 Measuring ROI and Impact of Predictive Analytics in Retail Methods for measuring the return on investment (ROI) and impact of predictive analytics in retail
Module #19 Advanced Topics in Retail Predictive Analytics Exploring advanced topics, including deep learning, natural language processing, and more
Module #20 Project Development and Consulting Guided project development and consulting on a real-world retail predictive analytics project
Module #21 Retail Analytics for Omnichannel Retailing Using predictive analytics to optimize omnichannel retailing, including online and offline channels
Module #22 Retail Analytics for Store Operations Using predictive analytics to optimize store operations, including labor scheduling and inventory management
Module #23 Retail Analytics for Marketing and Advertising Using predictive analytics to optimize marketing and advertising campaigns, including social media and email marketing
Module #24 Retail Analytics for Product Development Using predictive analytics to inform product development and improve product offerings
Module #25 Retail Analytics for Supply Chain Risk Management Using predictive analytics to identify and mitigate supply chain risks
Module #26 Retail Analytics for Category Management Using predictive analytics to optimize category management, including product categorization and assortment planning
Module #27 Retail Analytics for Customer Experience Using predictive analytics to improve customer experience, including sentiment analysis and Net Promoter Score (NPS)
Module #28 Retail Analytics for Warranty and Return Analysis Using predictive analytics to optimize warranty and return processes
Module #29 Retail Analytics for Competitor Intelligence Using predictive analytics to gather competitor intelligence and stay ahead in the market
Module #30 Course Wrap-Up & Conclusion Planning next steps in Predictive Analytics for Retail career
