RA: Retail Customer Analytics and Trade Area Modeling.

EP3: Learn Python and apply Customer analytics, Churn prediction, Customer Segmentation and Trade Area Modeling.
4.57 (410 reviews)
Udemy
platform
English
language
Data Science
category
RA: Retail Customer Analytics and Trade Area Modeling.
13 837
students
15.5 hours
content
Nov 2022
last update
$69.99
regular price

What you will learn

Python.

Customer analytics

Learn How to work daily with Python

Learn how to benefit from data to increase Customer Engagement.

Use K-means for Customer Segmentation.

Use Trade area modeling for Location and Competitive analysis.

Use Recommendation systems to Propose Products To customers.

Use Market Basket analysis to Make recommendations and Promotional Bundles to customers.

Predict Customer lifetime value of customers

Course Gallery

RA: Retail Customer Analytics and Trade Area Modeling. – Screenshot 1
Screenshot 1RA: Retail Customer Analytics and Trade Area Modeling.
RA: Retail Customer Analytics and Trade Area Modeling. – Screenshot 2
Screenshot 2RA: Retail Customer Analytics and Trade Area Modeling.
RA: Retail Customer Analytics and Trade Area Modeling. – Screenshot 3
Screenshot 3RA: Retail Customer Analytics and Trade Area Modeling.
RA: Retail Customer Analytics and Trade Area Modeling. – Screenshot 4
Screenshot 4RA: Retail Customer Analytics and Trade Area Modeling.

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Our Verdict

Dive into the world of retail customer analytics with RA: Retail Customer Analytics and Trade Area Modeling. Boost your analytical skills by learning to work daily with Python and applying it for customer analytics, churn prediction, segmentation, and trade area modeling. While audio aspects could use enhancement and hands-on experience varies across students, the course provides invaluable insights into essential retail and marketing techniques.

What We Liked

  • Comprehensive coverage of retail customer analytics and trade area modeling
  • Detailed explanations for all topics with real-world examples
  • Provides a solid foundation in Python for Customer Analytics
  • Actionable insights for predicting customer churn, segmentation, and lifetime value

Potential Drawbacks

  • Audio quality and pronunciation need improvement in some sections
  • In-depth understanding of machine learning concepts expected from learners
  • Lack of captivating presentation style can make some modules less engaging
  • Handwriting samples are not easily readable
3877156
udemy ID
26/02/2021
course created date
15/04/2021
course indexed date
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