Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Customer Analytics in Python
A Brief Marketing Introduction
Segmentation, Targeting, Positioning (7:03)
Marketing Mix (8:17)
Physical and Online Retailers: Similarities and Differences. (6:42)
Price Elasticity (7:48)
Segmentation Data
Getting to know the Segmentation Dataset (3:12)
Importing and Exploring Segmentation Data (14:00)
Standardizing Segmentation Data (3:12)
Hierarchical Clustering
Hierarchical Clustering: Background (3:46)
Hierarchical Clustering: Implementation and Results (7:09)
K-Means Clustering
K-Means Clustering: Background (3:31)
K-Means Clustering: Application (5:41)
K-Means Clustering: Results (8:10)
K-Means Clustering based on Principal Component Analysis
Principal Component Analysis: Background (1:53)
Principal Component Analysis: Application (4:17)
Principal Component Analysis: Homework
Principal Component Analysis: Results (4:50)
K-Means Clustering with Principal Components: Application (1:57)
K-Means Clustering with Principal Components: Results (8:14)
K-Means Clustering with Principal Components: Homework
Saving the Models (2:04)
Purchase Data
Purchase Analytics - Introduction (1:05)
Getting to know the Purchase Dataset (6:04)
Importing and Exploring Purchase Data (2:01)
Applying the Segmentation Model (4:50)
Descriptive Analyses by Segments
Purchase Analytics Descriptive Statistics: Segment Proportions (7:15)
Purchase Analytics Descriptive Statistics: Purchase occasion and purchase Incidence (5:13)
Purchase Analytics Descriptive Statistics: Homework
Brand Choice (5:51)
Dissecting the revenue by segment (7:34)
Modeling Purchase Incidence
Purchase Incidence Models. The Model: Binomial Logistic Regression (2:14)
Prepare the Dataset for Logistic Regression (1:22)
Model Estimation (4:06)
Calculating Price Elasticity of Purchase Probability (6:51)
Price Elasticity of Purchase Probability: Results (6:09)
Purchase Probability by Segments (7:40)
Purchase Probability by Segments - Homework
Purchase Probability Model with Promotion (2:58)
Calculating Price Elasticities with Promotion (2:13)
Calculating Prcie Elasticities without Promotion: Homework
Comparing Price Elasticities with and without Promotion (3:06)
Modeling Brand Choice
Brand Choice Models. The Model: Multinomial Logistic Regression (1:50)
Prepare Data and Fit the Model (3:04)
Interpreting the Coefficients (3:15)
Own Price Brand Choice Elasticity (5:31)
Cross Price Brand Choice Elasticity (6:55)
Own and Cross-Price Elasticity by Segment (6:57)
Own and Cross-Price Elasticity by Segment: Homework
Own and Cross-Price Elasticity by Segment - Comparison (6:11)
Brand Choice Models: Homework
Modeling Purchase Quantity
Purchase Quantity Models. The Model: Linear Regression (1:52)
Preparing the Data and Fitting the Model (9:37)
Calculating Price Elasticity of Purchase Quantity (4:43)
Calculating Price Elasticity of Purchase Quantity: Homework
Price Elasticity of Purchase Quantity: Results (2:20)
Improving the Model: Homework
Deep Learning
Introduction to Deep Learning for Customer Analytics (3:15)
Exploring the Dataset (7:41)
How Are We Going to Tackle the Business Case (1:01)
Balancing the Dataset (3:39)
Preprocessing the Data for Deep Learning (10:31)
Outlining the Deep Learning Model (3:23)
Training the Deep Learning Model (8:44)
Testing the Model (3:48)
Obtaining the Probability of a Customer to Convert (3:38)
Saving the Model and Preparing for Deployment (1:30)
Predicting on New Data (5:34)
K-Means Clustering with Principal Components: Results
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock