Customer Analytics in Python

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.

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Section 1

A Brief Marketing Introduction

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 why we take advantage of certain models in the following sections of the course.

Premium course icon Meet your instructors
Premium course icon Segmentation, Targeting, Positioning
Premium course icon Marketing Mix
Premium course icon Physical and Online Retailers: Similarities and Differences
Premium course icon Price Elasticity

Section 2

Setting up the environment

We will show you how to install the Jupyter Notebook (the environment we will use to code in Python) and how to import the relevant libraries. Because this course is based in Python, we will be working with several popular packages – NumPy, SciPy and scikit-learn.

Premium course icon Setting up the environment - Do not skip, please!
Premium course icon Why Python and Why Jupyter
Premium course icon Installing Anaconda
Premium course icon Jupyter Dashboard - Part 1
Premium course icon Jupyter Dashboard - Part 2
Premium course icon Installing the sklearn package

Section 3

Segmentation Data

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.

Premium course icon Getting to know the Segmentation Dataset
Premium course icon Importing and Exploring Segmentation Data
Premium course icon Standardizing Segmentation Data

Section 4

Hierarchical Clustering

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.

Premium course icon Hierarchical Clustering: Background
Premium course icon Hierarchical Clustering: Implementation and Results

Section 5

K-means Clustering

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.

Premium course icon K-Means Clustering: Background
Premium course icon K-Means Clustering: Implementation
Premium course icon K-Means Clustering: Results

Section 6

K-means Clustering based on Principal Components Analysis

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.

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

Section 7

Purchase Data

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 difference with the segmentation data. We’ll analyse and preprocess our data and ultimately segment the purchase customers into groups.

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

Section 8

Descriptive Analyses by Segments

We’ll analyse 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 the subsequent modeling.

Premium course icon Segment Proportions
Premium course icon Purchase Occasion and Purchase Incidence
Premium course icon Brand Choice
Premium course icon Dissecting the Revenue by Segment

Section 9

Purchase Incidence Model

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 behaviour.

Premium course icon The Model: Binomial Logistic Regression
Premium course icon Prepare the Dataset for Logistic Regression
Premium course icon Model Estimation
Premium course icon Calculating Price Elasticity of Purchase Probability
Premium course icon Price Elasticity of Purchase Probability: Results
Premium course icon Purchase Probability by Segments
Premium course icon Purchase Probability Model with Promotion
Premium course icon Calculating Price Elasticities with Promotion
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Premium course icon Comparing Price Elasticities with and without Promotion
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Section 10

Brand Choice Model

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 and attracting 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 and stay ahead of the competition.

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

Section 11

Purchase Quantity Model

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.

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

Section 12

Deep Learning for Conversion Prediction

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.

Premium course icon Introduction to Deep Learning for Customer Analytics
Premium course icon Exploring the Dataset
Premium course icon How Are We Going to Tackle the Business Case
Premium course icon Why do We Need to Balance a Dataset
Premium course icon Preprocessing the Data for Deep Learning
Premium course icon Outlining the Deep Learning Model
Premium course icon Training the Deep Learning Model
Premium course icon Testing the Model
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Premium course icon Obtaining the Probability of a Customer to Convert
Premium course icon Saving the Model and Preparing for Deployment
Premium course icon Predicting on New Data
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MODULE 4

Advanced Specialization

This course is part of Module 4 of the 365 Data Science Program. The complete training consists of four modules, each building up on your knowledge from the previous one. Module 4 is focused on developing a specialized industry-relevant skillset and students are encouraged to complete Modules 1, 2, and 3 before they start this part of the training. Here you will learn how to perform Credit Risk Modeling for banks, Customer Analytics for retail or other commercial companies, and Time Series Analysis for finance and stock data.

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