Descriptions
RA: Retail Customer Analytics and Trade Area Modeling., This masterclass is a comprehensive 15.5-hour program designed to teach you seven distinct customer analytics disciplines using Excel and Python. Starting with the fundamentals of retail data science and famous cases like Tesco’s Clubcard and Target’s pregnancy prediction model, you will immediately transition into building predictive models that drive real commercial decisions. The curriculum covers trade area modelling using the powerful Huff gravity-based model from scratch to determine store placement, customer segmentation using RFM analysis paired with K-means clustering, and predicting customer lifetime value (CLV) with decision trees and randomized search cross-validation.
Furthermore, you will build a complete churn prediction pipeline using logistic regression, log odds, and Lasso regularization to target and retain at-risk loyalty customers. The course also dives into purchase patterns with market basket analysis using the Apriori algorithm for promotional bundling, as well as e-commerce-style recommendation systems utilizing SVD collaborative filtering. Finally, you will learn to apply Principal Component Analysis (PCA) for dimensionality reduction to handle complex, overlapping customer features in your machine learning pipelines, providing you with a robust retail data science portfolio.
What you’ll learn
- Understand the commercial value of customer analytics: the Tesco, Walmart, and Andrew Pole cases as evidence for why prediction matters in retail
- Build and apply the Huff Model in Python for trade area analysis: calculate store attraction probabilities per customer community and answer the store location question
- Perform RFM analysis: calculate customer recency, frequency, and monetary value, rank and group customers, and create meaningful commercial categories
- Apply K-means clustering to RFM data: choose the optimal number of clusters with the elbow method, visualise centroids, and interpret segment profiles
- Predict Customer Lifetime Value (CLV): engineer features, calculate lifetime value, classify customers by LTV tier, and build a decision tree model with randomized search cross-validation
- Build a full churn prediction pipeline: data orientation, feature engineering, logistic regression, confusion matrix, precision and recall, log odds, Lasso regularization
- Apply market basket analysis with Apriori: identify association rules, build promotional bundles, and surface slow-moving items for clearance or repositioning
- Use Market Basket analysis to Make recommendations and Promotional Bundles to customers
- Apply PCA for dimensionality reduction: build a pipeline, decompose customer features, run hyperparameter tuning, and prepare data for downstream modelling
- Write Python from scratch for customer analytics: a complete crash course is included covering all data structures, pandas manipulation, joining, filtering, and aggregation
Who this course is for
- Retail analysts and customer intelligence professionals
- Retail managers and strategists
- Data scientists entering retail
- CRM and loyalty program managers
- Merchandisers and category managers
- Students and early-career data analysts
Specificatoin of RA: Retail Customer Analytics and Trade Area Modeling.
- Publisher : Udemy
- Teacher : Haytham Omar-Ph.D
- Language : English
- Level : All Levels
- Number of Course : 164
- Duration : 15 hours and 42 minutes
Content of RA: Retail Customer Analytics and Trade Area Modeling.
Requirements
- Basic retail knowledge is helpful but not required — Section 1 covers all the retail data and customer analytics context needed before any modelling begins.
- No Python experience needed — Sections 2 and 3 are a complete Python crash course and data manipulation foundation, included before any analytics section begins.
- No statistics or machine learning background required — every model is built from first principles with full explanation of the maths before any code is written.
- A computer with Anaconda installed (free) — setup is fully guided in Section 2. All Python libraries used are free and open-source.
Pictures
Sample Clip
Installation Guide
Extract the files and watch with your favorite player
Subtitle : English
Quality: 720p
Download Links
Password file(s): www.downloadly.ir
File size
6.35 GB
