Customer Analytics Using Python Programming

Customer Analytics in Python is where marketing and data science meet. Data science and marketing are two of the key driving forces that help companies create value and stay on top in today’s fast-paced economy. This course is packed with knowledge, and includes sections on customer and purchase analytics, as well as a deep-learning model, all implemented in Python.

Duration

5 days

Module 1
A Brief Marketing Introduction

• Segmentation, Targeting, Positioning
• Marketing Mix
• Physical and Online Retailers: Similarities and Differences
• Price Elasticity

Setting up the environment

• Setting up the environment (Crucial)
• Why Python and Why Jupyter
• Installing Anaconda
• Jupyter Dashboard 
• Installing the sklearn package

Module 2

Segmentation Data

• Getting to know the Segmentation Dataset
• Importing and Exploring Segmentation Data
• Standardizing Segmentation Data

Hierarchical Clustering

• Hierarchical Clustering: Background
• Hierarchical Clustering: Implementation and Results

Module 3

K-means Clustering

• Principal Component Analysis: Background
• Principal Component Analysis: Application
• Principal Component Analysis: Results
• K-Means Clustering with Principal Components: Application
• Saving the Models

Purchase Data

• Purchase Analytics - Introduction
• Getting to know the Purchase Dataset
• Importing and Exploring Purchase Data
• Applying the Segmentation Model

Descriptive Analyses by Segments

• Segment Proportions
• Purchase Occasion and Purchase Incidence
• Brand Choice
• Dissecting the Revenue by Segment

Module 4

Purchase Incidence Model

• The Model: Binomial Logistic Regression
• Prepare the Dataset for Logistic Regression
• Model Estimation
• Calculating Price Elasticity of Purchase Probability
• Price Elasticity of Purchase Probability: Results
• Purchase Probability by Segments
• Purchase Probability Model with Promotion
• Calculating Price Elasticities with Promotion

Brand Choice Model

• Brand Choice Models. The Model: Multinomial Logistic Regression
• Prepare Data and Fit the Model
• Interpreting the Coefficients
• Own Price Brand Choice Elasticity
• Cross Price Brand Choice Elasticity
• Own and Cross-Price Elasticity by Segment

Module 5

Purchase Quantity Model

• Purchase Quantity Models. The Model: Linear Regression
• Preparing the Data and Fitting the Model
• Calculating Price Elasticity of Purchase Quantity
• Price Elasticity of Purchase Quantity: Results

Deep Learning for Conversion Prediction

• Introduction to Deep Learning for Customer Analytics
• Exploring the Dataset
• Why do We Need to Balance a Dataset
• Preprocessing the Data for Deep Learning
• Training the Deep Learning Model
• Testing the Model
• Predicting New Data