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Course Curriculum

A brief marketing introduction
1.1 Marketing Mix 00:00:00
1.2 Physical and Online Retailers Similarities and Differences 00:00:00
1.3 Price Elasticity 00:00:00
1.4 Segmentation, Targeting, Positioning 00:00:00
SegmentationData
2.1 Getting to know the Segmentation Dataset 00:00:00
2.2 Importing and Exploring Segmentation Data 00:00:00
2.3 Standardizing Segmentation Data 00:00:00
Hierarchical Clustering
3.1 Hierarchical Clustering Background 00:00:00
3.2 Hierarchical Clustering Implementation and Results 00:00:00
K-Means Clustering
4.1 K-Means Clustering Application 00:00:00
4.2 K-Means Clustering Background 00:00:00
4.3 K-Means Clustering Results 00:00:00
K-Means Clustering based on Principal Component Analysis
5.1K-Means Clustering with Principal Components Application 00:00:00
5.2 K-Means Clustering with Principal Components Results 00:00:00
5.3 Principal Component Analysis Application 00:00:00
5.4 Principal Component Analysis Background 00:00:00
5.5 Principal Component Analysis Results 00:00:00
5.6 Saving the Models 00:00:00
Purchase Data
6.1 Applying the Segmentation Model 00:00:00
6.2 Getting to know the Purchase Dataset 00:00:00
6.3 Importing and Exploring Purchase Data 00:00:00
6.4 Purchase Analytics – Introduction 00:00:00
Descriptive Analyses by Segments
7.1 Brand Choice 00:00:00
7.2 Dissecting the revenue by segment 00:00:00
7.3 Purchase Analytics Descriptive Statistics Purchase occasion 00:00:00
7.4 Purchase Analytics Descriptive Statistics Segment Proportion 00:00:00
Modelling Purchase Incidence
8.1 Calculating Price Elasticities with Promotion 00:00:00
8.2 Calculating Price Elasticity of Purchase Probability 00:00:00
8.3 Comparing Price Elasticities with and without Promotion 00:00:00
8.4 Model Estimation 00:00:00
8.5 Prepare the Dataset for Logistic Regression 00:00:00
8.6 Price Elasticity of Purchase Probability Results 00:00:00
8.7 Purchase Incidence Models. The Model Binomial Logistic Regre 00:00:00
8.8 Purchase Probability by Segments 00:00:00
8.9 Purchase Probability Model with Promotion 00:00:00
Modelling Brand choice
9.1 Brand Choice Models. The Model Multinomial Logistic Regression 00:00:00
9.2 Cross Price Brand Choice Elasticity 00:00:00
9.3 Interpreting the Coefficients 00:00:00
9.4 Own and Cross-Price Elasticity by Segment 00:00:00
9.5 Own and Cross-Price Elasticity by Segment – Comparison 00:00:00
9.6 Own Price Brand Choice Elasticity 00:00:00
9.7 Prepare Data and Fit the Model 00:00:00
Modelling Purchase Quantity
10.1 Calculating Price Elasticity of Purchase Quantity 00:00:00
10.2 Preparing the Data and Fitting the Model 00:00:00
10.3 Price Elasticity of Purchase Quantity Results 00:00:00
10.4 Purchase Quantity Models. The Model Linear Regression 00:00:00
Deep Learning
11.1 Balancing the Dataset 00:00:00
11.2 Exploring the Dataset 00:00:00
11.3 How Are We Going to Tackle the Business Case 00:00:00
11.4 Introduction to Deep Learning for Customer Analytics 00:00:00
11.5 Obtaining the Probability of a Customer to Convert 00:00:00
11.6 Outlining the Deep Learning Model 00:00:00
11.7 Predicting on New Data 00:00:00
11.8 Preprocessing the Data for Deep Learning 00:00:00
11.9 Saving the Model and Preparing for Deployment 00:00:00
11.10Training the Deep Learning Model 00:00:00

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