Customer Analytics in Python 2022

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 76 lectures (5h 10m) | 1.50 GB

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

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.

What you’ll 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