Customer Analytics in Python

Name: Customer Analytics in Python Course
Price: 36 USD

with Nikolay Georgiev and Elitsa Kaloyanova

4.9/5

(382)

Blend retail marketing understanding with data analytics skills: Master customer segmentation and purchase behaviour modeling in Python

10 hours of content 7734 students

$99.00

Lifetime access

Buy now

14-Day Money-Back Guarantee

What you get:

10 hours of content
18 Interactive exercises
124 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Customer Analytics in Python

A course by Nikolay Georgiev and Elitsa Kaloyanova

$99.00

Lifetime access

Buy now

14-Day Money-Back Guarantee

What you get:

10 hours of content
18 Interactive exercises
124 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

$99.00

Lifetime access

Buy now

$99.00

Lifetime access

Buy now

14-Day Money-Back Guarantee

What you get:

10 hours of content
18 Interactive exercises
124 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What You Learn

Master K-means clustering to identify distinct customer groups based on common characteristics
Combine Principal Component Analysis (PCA) and K-means clustering for improved customer segmentation
Analyse purchase quantity elasticity to understand how pricing and other factors influence customer buying decisions
Model purchase incidence through probability of purchase elasticity to gain insights on how sensitive customers are to changes in marketing and sales strategies
Leverage deep learning for powerful predictions about the future behaviour of clients
Acquire domain-specific skills in retail analytics and differentiate your data scientist resume

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

Customer Analytics in Python is where marketing and data science meet. These are two of the key driving forces that help companies create value and stay on top in today’s fast-paced economy. In addition, 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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1.1 Course Introduction

7 min

1.2 Segmentation, Targeting, Positioning

7 min

1.3 Marketing Mix

8 min

1.4 Physical and Online Retailers: Similarities and Differences.

7 min

1.5 Price Elasticity

8 min

2.1 Setting up the environment

1 min

Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Curriculum

1. A Brief Marketing Introduction

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
7 min
Segmentation, Targeting, Positioning
7 min
Marketing Mix
8 min
Physical and Online Retailers: Similarities and Differences.
7 min
Price Elasticity
8 min
2. Setting up the environment

2 Lessons 2 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 Read now
1 min
Installing the relevant packages
1 min
3. Segmentation Data

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
3 min
Importing and Exploring Segmentation Data
10 min
Standardizing Segmentation Data
3 min
4. 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
4 min
Hierarchical Clustering: Implementation and Results
7 min
5. 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
4 min
K-Means Clustering: Application
6 min
K-Means Clustering: Results
8 min
6. 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
2 min
Principal Component Analysis: Application
4 min
Principal Component Analysis: Results
5 min
K-Means Clustering with Principal Components: Application
2 min
K-Means Clustering with Principal Components: Results
8 min
Saving the Models
2 min
7. 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
1 min
Getting to know the Purchase Dataset
6 min
Importing and Exploring Purchase Data
2 min
Applying the Segmentation Model
5 min
8. 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
7 min
Purchase Analytics Descriptive Statistics: Purchase occasion and Purchase Incidence
5 min
Brand Choice
6 min
Dissecting the revenue by segment
8 min
9. 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
2 min
Prepare the Dataset for Logistic Regression
1 min
Model Estimation
4 min
Calculating Price Elasticity of Purchase Probability
7 min
Price Elasticity of Purchase Probability: Results
6 min
Purchase Probability by Segments
8 min
Purchase Probability Model with Promotion
3 min
Calculating Price Elasticities with Promotion
2 min
Comparing Price Elasticities with and without Promotion
3 min
10. 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
2 min
Prepare Data and Fit the Model
3 min
Interpreting the Coefficients
3 min
Own Price Brand Choice Elasticity
6 min
Cross Price Brand Choice Elasticity
7 min
Own and Cross-Price Elasticity by Segment
7 min
Own and Cross-Price Elasticity by Segment - Comparison
6 min
11. 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
2 min
Preparing the Data and Fitting the Model
10 min
Calculating Price Elasticity of Purchase Quantity
5 min
Own Price Brand Choice Elasticity
2 min
12. 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
3 min
Exploring the Dataset
8 min
How Are We Going to Tackle the Business Case
1 min
Why do We Need to Balance a Dataset
4 min
Preprocessing the Data for Deep Learning
11 min
Outlining the Deep Learning Model
3 min
Training the Deep Learning Model
9 min
Testing the Model
4 min
Obtaining the Probability of a Customer to Convert
4 min
Saving the Model and Preparing for Deployment
2 min
Predicting on New Data
6 min

Topics

PythonFinancial AnalysisProgrammingData ProcessingMachine LearningData AnalysisCustomer AnalyticsK-Means ClusteringHierarchical ClusteringPrincipal Component Analysis (PCA)Deep LearningTensorflowData AnalysisIndustry SpecializationMachine and Deep Learning

Tools & Technologies

Course Requirements

You need to complete an introduction to Python before taking this course
Basic skills in statistics, probability, and linear algebra are required
It is highly recommended to take the Machine Learning in Python course first
You will need to install the Anaconda package, which includes Jupyter Notebook

Who Should Take This Course?

Level of difficulty: Advanced

Aspiring data scientists
Current data scientists who are passionate about acquiring domain-specific knowledge in retail analytics

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Nikolay Georgiev

Director of Data Science and Automation at

2 Courses

779 Reviews

17273 Students

Nikolay is a Director of Data Science and Automation at KBC Group. He has a solid background in marketing analytics, risk modeling, and research. A Master’s degree in Science and a Ph.D. in Economics and Business Administration have given Nikolay vast experience in the academic world. He spent over six years in the field of research at HEC Paris, BI Norwegian Business School, and the University of Texas at Austin, U.S. In addition, Nikolay has worked on numerous projects for Coca-Cola Hellenic and Shawbrook Bank (UK) that involved building highly accurate quantitative models and solutions for customer portfolio management, credit risk, social media marketing research, and psychological targeting.