Customer Analytics in Python

Beginner and Advanced Customer Analytics in Python: PCA, K-means Clustering, Elasticity Modeling & Deep Neural Networks

4.57 (1484 reviews)
Udemy
platform
English
language
Data & Analytics
category
instructor
15,371
students
5 hours
content
Aug 2023
last update
$109.99
regular price

What you will learn

Master beginner and advanced customer analytics

Learn the most important type of analysis applied by mid and large companies

Gain access to a professional team of trainers with exceptional quant skills

Wow interviewers by acquiring a highly desired skill

Understand the fundamental marketing modeling theory: segmentation, targeting, positioning, marketing mix, and price elasticity;

Apply segmentation on your customers, starting from raw data and reaching final customer segments;

Perform K-means clustering with a customer analytics focus;

Apply Principal Components Analysis (PCA) on your data to preprocess your features;

Combine PCA and K-means for even more professional customer segmentation;

Deploy your models on a different dataset;

Learn how to model purchase incidence through probability of purchase elasticity;

Model brand choice by exploring own-price and cross-price elasticity;

Complete the purchasing cycle by predicting purchase quantity elasticity

Carry out a black box deep learning model with TensorFlow 2.0 to predict purchasing behavior with unparalleled accuracy

Be able to optimize your neural networks to enhance results

Description

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.

Welcome to…

Customer Analytics in Python – the place where marketing and data science meet!

This course is the best way to distinguish yourself with a very rare and extremely valuable skillset.


What will you learn in this course?

This course is packed with knowledge, covering some of the most exciting methods used by companies, all implemented in Python.

Since Customer Analytics is a broad topic, we have created 5 different parts to explore various sides of the analytical process. Each of them will have their strong sides and shortcomings. We will explore both sides of the coin for each part, while making sure to provide you with nothing but the most important and relevant information!

Here are the 5 major parts:

1. We will introduce you to the relevant theory that you need to start performing customer analytics

We have kept this part as short as possible in order to provide you with more practical experience. Nonetheless, this is the place where marketing beginners will learn about the marketing fundamentals and the reasons why we take advantage of certain models throughout the course.

2. Then we will perform cluster analysis and dimensionality reduction to help you segment your customers

Because this course is based in Python, we will be working with several popular packages - NumPy, SciPy, and scikit-learn. In terms of clustering, we will show both hierarchical and flat clustering techniques, ultimately focusing on the K-means algorithm. Along the way, we will visualize the data appropriately to build your understanding of the methods even further. When it comes to dimensionality reduction, we will employ Principal Components Analysis (PCA) once more through the scikit-learn (sklearn) package. Finally, we’ll combine the two models to reach an even better insight about our customers. And, of course, we won’t forget about model deployment which we’ll implement through the pickle package.

3. The third step consists in applying Descriptive statistics as the exploratory part of your analysis

Once segmented, customers’ behavior will require some interpretation. And there is nothing more intuitive than obtaining the descriptive statistics by brand and by segment and visualizing the findings. It is that part of the course, where you will have the ‘Aha!’ effect. Through the descriptive analysis, we will form our hypotheses about our segments, thus ultimately setting the ground for the subsequent modeling.

4. After that, we will be ready to engage with elasticity modeling for purchase probability, brand choice, and purchase quantity

In most textbooks, you will find elasticities calculated as static metrics depending on price and quantity. But the concept of elasticity is in fact much broader. We will explore it in detail by calculating purchase probability elasticity, brand choice own price elasticity, brand choice cross-price elasticity, and purchase quantity elasticity. We will employ linear regressions and logistic regressions, once again implemented through the sklearn library. We implement state-of-the-art research on the topic to make sure that you have an edge over your peers. While we focus on about 20 different models, you will have the chance to practice with more than 100 different variations of them, all providing you with additional insights!

5. Finally, we’ll leverage the power of Deep Learning to predict future behavior

Machine learning and artificial intelligence are at the forefront of the data science revolution. That’s why we could not help but include it in this course. We will take advantage of the TensorFlow 2.0 framework to create a feedforward neural network (also known as artificial 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.


An Extraordinary Teaching Collective

We at 365 Careers have 550,000+ students here on Udemy and believe that the best education requires two key ingredients: a remarkable teaching collective and a practical approach. That’s why we ticked both boxes.

Customer Analytics in Python was created by 3 instructors working closely together to provide the most beneficial learning experience.

The course author, Nikolay Georgiev is a Ph.D. who largely focused on marketing analytics during his academic career. Later he gained significant practical experience while working as a consultant on numerous world-class projects. Therefore, he is the perfect expert to help you build the bridge between theoretical knowledge and practical application.

Elitsa and Iliya also played a key part in developing the course. All three instructors collaborated to provide the most valuable methods and approaches that customer analytics can offer.

In addition, this course is as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts, and course notes, as well as notebook files with comments, are just some of the perks you will get by enrolling.


Why do you need these skills?

1. Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. All B2C businesses are realizing the advantages of working with the customer data at their disposal, to understand and target their clients better

2. Promotions – even if you are a proficient data scientist, the only way for you to grow professionally is to expand your knowledge. This course provides a very rare skill, applicable to many different industries.

3. Secure Future – the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you’ve probably heard of the number of jobs that will be automated soon, right? Well, the marketing department of companies is already being revolutionized by data science and riding that wave is your gateway to a secure future.

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and let’s start our customer analytics journey together!


Content

Introduction

What Does the Course Cover

A Brief Marketing Introduction

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

Setting up the Environment

Setting up the Environment - Do not Skip, Please!
Why Python and Why Jupyter
Installing Anaconda
Jupyter Dashboard - Part 1
Jupyter Dashboard - Part 2
Installing the Relevant Packages
Installing the Relevant Packages: Homework
Installing the Relevant Packages: Homework Solution

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

K-Means Clustering

K-Means Clustering: Background
K-Means Clustering: Implementation
K-Means Clustering: Results

K-Means Clustering based on Principal Component Analysis

Principal Component Analysis: Background
Principal Component Analysis: Application
Principal Component Analysis: Homework
Principal Component Analysis: Results
K-Means Clustering with Principal Components: Application
K-Means Clustering with Principal Components: Results Homework
K-Means Clustering with Principal Components: Results
K-Means Clustering with Principal Components: Results Homework
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
Purchase Occasion and Purchase Incidence Homework
Brand Choice
Dissecting the Revenue by Segment

Modeling Purchase Incidence

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
Price Elasticity Quiz Questions
Purchase Probability by Segments
Purchase Probability by Segments - Homework
Purchase Probability Model with Promotion
Calculating Price Elasticities with Promotion
Calculating Price Elasticities (Without Promotion) - Homework
Comparing Price Elasticities with and without Promotion

Modeling Brand Choice

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 Homework
Own and Cross-Price Elasticity by Segment - Comparison
Own and Cross-Price Elasticity by Segment Homework 2

Modeling Purchase Quantity

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

Deep Learning for Conversion Prediction

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

Screenshots

Customer Analytics in Python - Screenshot_01Customer Analytics in Python - Screenshot_02Customer Analytics in Python - Screenshot_03Customer Analytics in Python - Screenshot_04

Reviews

George
October 15, 2023
This is an amazing course, it has been above my expectations. The material, content and instructors are just excellent. I highly reccomend this course.
AL
September 12, 2023
I have learnt useful skills and acquired new knowledge, but my main interests are in sections 8, 10, 11, 12 and 13.
Nachiket
August 28, 2023
The coding done is very fast, the field names are pretty confusing with the Python feature name, and the code written is explained in the ML lingo, This means you already need to know what ML and all features are, there are instances where the code may not work how do you expect to troubleshoot. I have some understanding of python and still, it was very difficult for me to grasp this. The content of course is good, but the execution is made difficult, and may be it seems intentionally!
Bogdan
August 17, 2023
I have opportunity become acquainted myself of better way of doing marketing, and find out key segments in everi business that make a difference.
Armin
August 13, 2023
Great concept, great content, broad coverage of central marketing topics, interesting data set, nice animations. It gives you a good idea of how to deal with similar practical challenges. The course is quite unique among the Udemy courses. Explanations could have been more accurate and coding is at very fast pace. I had some difficulties with some package versions within the environment and I would have appreciated a requirements file. By stopping and reviewing the video lessons several times and with the help of ChatGPT, I managed to finish the course. If you really want to understand what is going on and why, you'll have to use additional study materials (or have some previous experience with the analyses and at least with pandas and numpy libraries), but at the same time, the course is lean and invites you to dig deeper. All in all I enjoyed the course quite a lot. Thanks to the authors!
Nilesh
June 19, 2023
Outstanding explanation of concepts related to demand and elasticity that's what i love the most about the course. Thanks for sharing valuable insights.
Muudey
June 13, 2023
Thank you Teacher Welcome WIth Open hands We are your students that means everybody watching this video is student who need to learn more thats means every body who follwed this lesson is student why if iam not student Im never be Here thank you
Carlos
May 14, 2023
Excelent. The subtitle in spanish was very useful for me. I think you can add optimum revenue and profit analysis too.
Wen
May 7, 2023
it's tooooo fast! every coding part is way toooo fast, it's impossible to follow without pause the video every few second to type the code...
Juan
April 3, 2023
The course content itself is very interesting and engaging, and I definitely learned a lot on the theory side - but the code is buggy and messy. They do try to take a simplified approach which may make this easier for beginners, but in so doing violate a lot of coding best-practices (code reusability egregiously so) and the resulting code becomes extremely difficult to organize and generalize. Trying to follow along the code alone, without going video lecture to video lecture, becomes an exercise in frustration and running the notebooks as they come will sometimes either not work at all because the code is out of date/bugged or will straight up give different results than the original. On the conceptual side it's a great course - very well explained and with a lot of interesting and insightful content - but I'm here to learn to generalize and reapply these skills in my day to day work; and on that front I am less than satisfied with the course...
Vade
March 8, 2023
Really nice an informative course. I enjoyed the method of teaching, its not boring. Highly recommended course if you want to understand customer analytics.
Artem
March 1, 2023
Nice course overall. I fond the part with customer segmentation particulary well done. On the other hand, the part with the price elasticity modelling was not so clear and streight-forward, in my view. Also, a very nice insights into Tensor flow & Neural nets prediction in the end chapter.
Ayşe
November 13, 2022
I purchased this course after I finished 365's data analyst bootcamp, because I really enjoyed the data analyst bootcamp course it was very much in detail, and very professional. So I was expecting the same quality, but unfortunately my expectations weren't met with this one. The instructor doesn't go in detail, in some examples they give just a very superficial overview of the explanation. I asked 2 or 3 questions during the course and never received an answer (It's been over a month). After I was in the half way of the course, I asked them to get the Tableau course that was promised to be given for everyone who finishes half of the course, but again no answer. Also one of the things that I didn't like is that for each section's each lesson, they have separate jupyter notebooks, which really doesn't make sense because for the entire section they work on the same notebook - but you need to download each lesson's notebook separately. In total I was disappointed with this course.
Angelo
July 16, 2022
Yes. This course indeed gives a good outline on how to approach EDA and comes to the point quickly. If you are willing to spend some time you have a good fundamental course that you can easily expand upon.
Daquan
September 2, 2021
Absolutely great course. I continually come back to it. It’s one of the best I’ve purchased on Udemy!

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2643050
udemy ID
11/6/2019
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11/20/2019
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