Customer Analytics in Python

About Course

Learn how to leverage the power of Python for customer analytics and gain valuable insights into customer behavior. This comprehensive course covers a wide range of topics, including:

Customer segmentation using cluster analysis and dimensionality reduction techniques like K-means and PCA.
Descriptive statistics for exploring customer behavior and identifying patterns.
Elasticity modeling to understand the impact of price, brand, and quantity on customer purchasing decisions.
Deep learning with TensorFlow 2.0 to predict future customer behavior and achieve high accuracy.

This course is taught by experienced instructors with both academic and practical expertise, making it perfect for beginners and experienced professionals alike.

Enroll in this FREE course today and unlock the potential of customer analytics with Python!

This course is from Udemy, Udacity, Coursera, MasterClass, NearPeer and other platforms.

This course is completely free of cost.

Keywords: Customer Analytics, Python, Data Science, Marketing Analytics, Segmentation, Clustering, K-means, PCA, Descriptive Statistics, Elasticity Modeling, Deep Learning, TensorFlow, Neural Network, Free Courses, Online Courses, Udemy, Udacity, Coursera, MasterClass, NearPeer

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

Course Content

Introduction

A Message from the Professor
– What Does the Course Cover

06:59

A Brief Marketing Introduction

Setting up the Environment

Segmentation Data

Hierarchical Clustering

KMeans Clustering

KMeans Clustering based on Principal Component Analysis

Purchase Data

Descriptive Analyses by Segments

Modeling Purchase Incidence

– The Model Binomial Logistic Regression

02:14
– Prepare the Dataset for Logistic Regression

01:22
– Model Estimation

04:06
– Calculating Price Elasticity of Purchase Probability

06:51
– Price Elasticity of Purchase Probability Results

06:09
– Purchase Probability by Segments

07:40
– Purchase Probability Model with Promotion

02:59
– Calculating Price Elasticities with Promotion

02:13
– Comparing Price Elasticities with and without Promotion

03:06

Modeling Brand Choice

Modeling Purchase Quantity

Deep Learning for Conversion Prediction

– Introduction to Deep Learning for Customer Analytics

03:15
– Exploring the Dataset

07:41
– How Are We Going to Tackle the Business Case

01:01
– Why do We Need to Balance a Dataset

03:39
– Preprocessing the Data for Deep Learning

10:31
– Outlining the Deep Learning Model

03:23
– Training the Deep Learning Model

08:44
– Testing the Model

03:48
– Obtaining the Probability of a Customer to Convert

03:38
– Saving the Model and Preparing for Deployment

01:30
– Predicting on New Data

05:34

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

Student Ratings & Reviews

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About Course

What Will You Learn?

Course Content

Introduction

A Message from the Professor

– What Does the Course Cover

A Brief Marketing Introduction

– Segmentation Targeting and Positioning

– Marketing Mix

– Physical and Online Retailers Similarities and Differences

– Price Elasticity

Setting up the Environment

– Setting up the Environment

– Why Python and Why Jupyter

– Installing Anaconda

– Jupyter Dashboard

– Jupyter Dashboard

– Installing the Relevant Packages

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

KMeans Clustering

– K

– KMeans Clustering Implementation

– K

KMeans Clustering based on Principal Component Analysis

– Principal Component Analysis Background

– Principal Component Analysis Application

– Principal Component Analysis Results

– K

– K

– 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

– 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

– Purchase Probability by Segments

– Purchase Probability Model with Promotion

– Calculating Price Elasticities with Promotion

– 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

– Own and Cross

Modeling Purchase Quantity

– Purchase Quantity Models The Model Linear Regression

– Preparing the Data and Fitting the Model

– Calculating Price Elasticity of Purchase Quantity

– Price Elasticity of Purchase Quantity Results

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