Customer Analytics In Python

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

Last updated 2022-01-10 | 4.5

- 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

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

* Requirements

* You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course
* Basic Python programming
* A willingness and enthusiasm to learn and practice

Description

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