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Customer Analytics in Python

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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

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What Will You 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

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

Modeling Brand Choice

Modeling Purchase Quantity

Deep Learning for Conversion Prediction

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HM
3 months ago
good

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