Customer Analytics in Python

Curriculum

• A Brief Marketing Introduction Free

  5 Lessons 37 Min

  In this course, you’ll get to learn theory, as well as acquire practical skills, applicable to a variety of customer analytics areas. You’ll get to meet your instructors and learn about the topics we’ll discuss during the course. This is the place where we’ll discuss the marketing fundamentals, such as the STP framework and Marketing Mix. They will provide us with motivation as to why we take advantage of certain models in the following sections of the course.

  Course Introduction Free Segmentation, Targeting, Positioning Free Marketing Mix Free Physical and Online Retailers: Similarities and Differences. Free Price Elasticity Free
• Setting up the environment Free

  2 Lessons 1 Min

  Here you will learn how to set up Python 3 and load up Jupyter. We’ll also show you what the Anaconda Prompt is and how you can use it to download and import new modules.

  Setting up the environment Free Installing the relevant packages Free
• Segmentation Data Free

  3 Lessons 16 Min

  We will begin with customer analytics, which is a major part of the course. We will perform some initial exploration of our segmentation data set. Using Python, we’ll visualize our data and standardize it to aid in future analysis.

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

  2 Lessons 11 Min

  Here, we discuss the theory behind hierarchical and flat clustering. Throughout the course we’ll implement both approaches. In this section, we’ll focus on hierarchical clustering and use it to determine the number of clusters in our data set.

  Hierarchical Clustering: Background Hierarchical Clustering: Implementation and Results
• K-means Clustering

  3 Lessons 18 Min

  We continue with segmentation, this time focusing on a flat clustering technique, namely K-means. We’ll divide our customers into clusters using K-means and determine the customer segments. This is a crucial part of the customer analysis, as it is where we discover the most important characteristics, which define our customer groups.

  K-Means Clustering: Background K-Means Clustering: Application K-Means Clustering: Results
• K-Means Clustering based on Principal Component Analysis

  6 Lessons 23 Min

  In our final section of customer analytics, we’ll introduce a dimensionality reduction algorithm: Principal Components Analysis (PCA). We’ll combine K-means and PCA to obtain even better clustering results and gain insight about our customers. In addition, we’ll perform the model deployment, which we’ll need in the following sections of our course

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

  4 Lessons 14 Min

  It’s time for purchase analytics. This is the second major part of the course. We’ll examine our purchase data set and see similarities and differences with the segmentation data. We’ll analyze and preprocess our data and ultimately segment the purchase customers into groups.

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

  4 Lessons 26 Min

  We’ll analyze our customers by filtering our data by brand and segment, and we’ll visualize the findings. In addition, we’ll compute the revenues by brand and total revenues for each segment. Through the descriptive analysis, we will form our hypotheses about our segments, thus ultimately setting the ground for subsequent modeling.

  Purchase Analytics Descriptive Statistics: Segment Proportions Purchase Analytics Descriptive Statistics: Purchase occasion and Purchase Incidence Brand Choice Dissecting the revenue by segment
• Modeling Purchase Incidence

  9 Lessons 36 Min

  We’re ready to create our first prediction model. We’ll use logistic regression and model purchase probability of our clients. We’ll compute the price elasticity of purchase probability and use it to gain insight into our average customers, as well as each of our customer segments. Furthermore, we’ll explore the effects of promotion on our clients’ purchase behavior.

  Purchase Incidence Models. 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 Comparing Price Elasticities with and without Promotion
• Modeling Brand Choice

  7 Lessons 34 Min

  Our goal is to understand the brand preferences of our clients. We’ll imagine we work for one of the brands and create marketing strategies for targeting customers to attract them to our own brand. We’ll compute price elasticity of brand choice for our brand, as well as calculate cross price elasticities for a competitor brand to try to stay ahead of the competition.

  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 Own and Cross-Price Elasticity by Segment - Comparison
• Modeling Purchase Quantity

  4 Lessons 19 Min

  We conclude the purchase analytics part of the course with a model for purchase quantity. Here, we’ll use linear regression to determine how many units of our product the customer likes to buy. We’ll examine the effects of promotion on purchase quantity and determine price elasticity of demand.

  Purchase Quantity Models. The Model: Linear Regression Preparing the Data and Fitting the Model Calculating Price Elasticity of Purchase Quantity Own Price Brand Choice Elasticity
• Deep Learning for Conversion Prediction

  11 Lessons 55 Min

  Machine learning and artificial intelligence are at the forefront of the data science revolution. We will take advantage of the TensorFlow 2.0 framework to create a feedforward neural network. This is the part where we will build a black-box model, essentially helping us reach 90%+ accuracy in our predictions about the future behavior of our customers.

  Introduction to Deep Learning for Customer Analytics Exploring the Dataset How Are We Going to Tackle the Business Case Why do We Need to Balance a Dataset Preprocessing the Data for Deep Learning Outlining the Deep Learning Model Training the Deep Learning Model Testing the Model Obtaining the Probability of a Customer to Convert Saving the Model and Preparing for Deployment Predicting on New Data