Play all

Marketing Analytics

Lecture 01: Introduction to R programming

Lecture 02: Introduction to R programming (Contd.)

Lecture 03: Introduction to R programming (Contd.)

Lecture 04: Introduction to R programming (Contd.)

Lecture 05: Introduction to R programming (Contd.)

Lecture 06: Introduction to R programming (Contd.)

Lecture 07: What Consumers Want

Lecture 08: What Consumers Want (Contd.)

Lecture 09: What Consumers Want (Contd.)

Lecture 10: What Consumers Want (Contd.)

Lecture 11: What Consumers Want (Contd.)

Lecture 12: What Consumers Want (Contd.)

Lecture 13: Segmentation Targeting and Positioning

Lecture 14: Segmentation Targeting and Positioning(Contd.)

Lecture 15: Segmentation Targeting and Positioning(Contd.)

Lecture 16: Segmentation Targeting and Positioning(Contd.)

Lecture 17: Segmentation Targeting and Positioning(Contd.)

Lecture 18: Demand Forecasting and Pricing

Lecture 19: Demand Forecasting and Pricing (Contd.)

Lecture 20: Demand Forecasting and Pricing (Contd.)

Lecture 21: Demand Forecasting and Pricing (Contd.)

Lecture 22: Pricing

Lecture 23: Pricing (Contd.)

Lecture 24: Pricing (Contd.)

Lecture 25: Pricing (Contd.)

Lecture 26: Pricing (Contd.)

Lecture 27: Pricing (Contd.)

Lecture 28: Pricing (Contd.)

Lecture 29: Marketing Mix Models and Advertising Models

Lecture 30: Marketing Mix Models and Advertising Models (Contd.)

Lecture 31: Marketing Mix Models and Advertising Models (Contd.)

Lecture 32: Marketing Mix Models and Advertising Models (Contd.)

Lecture 33: Marketing Mix Models and Advertising Models (Contd.)

Lecture 34: Recommendation Engine and Retail Analytics

Lecture 35: Recommendation Engine and Retail Analytics (Contd.)

Lecture 36: Recommendation Engine and Retail Analytics (Contd.)

Lecture 37: Recommendation Engine and Retail Analytics (Contd.)

Lecture 38: Recommendation Engine and Retail Analytics (Contd.)

Lecture 39: Recommendation Engine and Retail Analytics (Contd.)

Lecture 40: RFM and Market Basket Analysis

Lecture 41: RFM and Market Basket Analysis (Contd.)

Lecture 42: RFM and Market Basket Analysis (Contd.)

Lecture 43: RFM and Market Basket Analysis (Contd.)

Lecture 44: RFM and Market Basket Analysis (Contd.)

Lecture 45: Customer Churn and Customer Lifetime Value

Lecture 46: Customer Churn and Customer Lifetime Value (Contd.)

Lecture 47: Customer Churn and Customer Lifetime Value (Contd.)

Lecture 48: Customer Churn and Customer Lifetime Value (Contd.)

Lecture 49: Customer Churn and Customer Lifetime Value (Contd.)

Lecture 50: Customer Churn and Customer Lifetime Value (Contd.)

Lecture 51: Text Mining and Sentiment Analytics

Lecture 52: Text Mining and Sentiment Analytics (Contd.)

Lecture 53: Text Mining and Sentiment Analytics (Contd.)

Lecture 54: Text Mining and Sentiment Analytics (Contd.)

Lecture 55: Text Mining and Sentiment Analytics (Contd.)

Lecture 56: Text Mining and Sentiment Analytics (Contd.)

Lecture 57: Text Mining and Sentiment Analytics (Contd.)

Lecture 58: Text Mining and Sentiment Analytics (Contd.)

Lecture 59: Text Mining and Sentiment Analytics (Contd.)

Lecture 60: Text Mining and Sentiment Analytics (Contd.)

Lecture 61: Social Network Analysis and Excel Dashboards

Lecture 62: Social Network Analysis and Excel Dashboards (Contd.)

Lecture 63: Social Network Analysis and Excel Dashboards (Contd.)

Lecture 64: Social Network Analysis and Excel Dashboards (Contd.)

Lecture 65: Social Network Analysis and Excel Dashboards (Contd.)

Lecture 66: Social Network Analysis and Excel Dashboards (Contd.)

Description:

COURSE OUTLINE : In this course we will combine various concepts of marketing and business analytics in storytelling and problem solving. Real life marketing problems are often solved through a sequence of quantitative approaches. Identifying that sequence in the context of various marketing problems is important. This course will help the students in building the same. We expect that the students of the course will be able to do the following at the end of the course: • Identify a marketing problem as a sequence of small questions • Identify the appropriate tools and datasets required to solve each small research question • Properly apply the various available tools and choose the best one • Create a marketing story out of the statistical and machine learning tools applied • Solve a marketing analytics project end to end

Marketing Analytics

NPTEL

Add to list

#Business #Marketing #Data Science #Data Analysis #Programming #Domain-Specific Languages (DSL) #R Programming #Strategic Management #Pricing Strategy #Consumer Behavior #Demand Forecasting #Digital Marketing #Marketing Analytics #Computer Science #Artificial Intelligence #Computer Vision #Image Processing #Segmentation #Targeting