Customer Analytics in Python

Introducing you to Customer Analytics with Python. You will learn the fundamentals of marketing, as well as the practical skills to analyze customer data and predict the purchase behavior of clients.








Course description

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.


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


Setting up the environment

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.


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.


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.


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.


K-Means Clustering based on Principal Component 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


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


Descriptive Analyses by Segments

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.


Modeling Brand Choice

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.


Modeling Purchase Quantity

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.