Customer Analytics in Python

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

with Nikolay Georgiev and Elitsa Kaloyanova

Course Overview

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.

60 High Quality Lessons

5 Practical Tasks

5 Hours of Video

Certificate of Achievement

Skills you will gain

data analysisdata processingfinancial analysismachine learningprogrammingpythontheory

What You'll Learn

This course will teach you how to gain authentic insights from the customer's data, as well as how to leverage the power of machine and deep learning to perform customer analytics. This is a highly valuable and rare skillset to have both in data analytics and data science.

Perform K-means clustering

Apply principal components analysis (PCA)

Combine PCA and K-means for customer segmentation

Model purchase incidence through probability of purchase elasticity

Complete the purchasing cycle by predicting purchase quantity elasticity

Carry out a deep learning model with TensorFlow 2.0 to predict purchasing behavior

Curriculum

A Brief Marketing Introduction Free

5 Lesson 37 Min

Course Introduction Free Segmentation, Targeting, Positioning Free Marketing Mix Free Physical and Online Retailers: Similarities and Differences. Free Price Elasticity Free
Setting up the environment Free

2 Lesson 1 Min

Setting up the environment Free Installing the relevant packages Free
Segmentation Data Free

3 Lesson 16 Min

Getting to know the Segmentation Dataset Free Importing and Exploring Segmentation Data Free Standardizing Segmentation Data Free
Hierarchical Clustering

2 Lesson 11 Min

Hierarchical Clustering: Background Hierarchical Clustering: Implementation and Results
K-means Clustering

3 Lesson 18 Min

K-Means Clustering: Background K-Means Clustering: Application K-Means Clustering: Results
K-Means Clustering based on Principal Component Analysis

6 Lesson 23 Min

Principal Component Analysis: Background Principal Component Analysis: Application Principal Component Analysis: Results K-Means Clustering with Principal Components: Application K-Means Clustering with Principal Components: Results Saving the Models
Purchase Data

4 Lesson 14 Min

Purchase Analytics - Introduction Getting to know the Purchase Dataset Importing and Exploring Purchase Data Applying the Segmentation Model
Descriptive Analyses by Segments

4 Lesson 26 Min

Purchase Analytics Descriptive Statistics: Segment Proportions Purchase Analytics Descriptive Statistics: Purchase occasion and Purchase Incidence Brand Choice Dissecting the revenue by segment
Modeling Purchase Incidence

9 Lesson 36 Min

Purchase Incidence Models. 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

7 Lesson 34 Min

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 - Comparison
Modeling Purchase Quantity

4 Lesson 19 Min

Purchase Quantity Models. The Model: Linear Regression Preparing the Data and Fitting the Model Calculating Price Elasticity of Purchase Quantity Own Price Brand Choice Elasticity
Deep Learning for Conversion Prediction

11 Lesson 55 Min

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

“In this course, you will learn beginner and advanced customer analytics in Python: Marketing theory, PCA, K-means clustering, Elasticity modeling, and Deep neural networks. This is a learning journey that builds a bridge between marketing theory and practical implementation with Python.”

Nikolay Georgiev

Director at KBC Group

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