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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

Description

“This is one of the three courses in the Retail Series by RA, each course can be taken independently.”

Master Retail management and analytics with Excel and Python

Retailers face fierce competition every day and keeping up with the new trends and customer preferences is a guarantee for excellence in the modern retail environment. one Keyway to excel in retail management is utilizing the data that is produced every day. It is estimated that We produce an overwhelming amount of data every day, roughly 2.5 quintillion bytes. According to an IBM study, 90% of the worldโ€™s data has been created in the last two years.

Retail analytics is the field of studying the produced retail data and making insightful data-driven decisions from it. as this is a wide field, I have split the Program into three parts. in this course, we focus on the customer analytics part of retail. Understanding the customer is key for maintaining loyalty and developing products to boost retail business and profitability.

RA: Retail Customer Analytics and Trade Area Modeling.

1- Understanding the importance of customer analytics in retail.

2- Manipulation of Data with Pandas.

3-Working with Python for analytics.

5- Trade area modeling

6- Recommendation systems

7-ย  Customer lifetime valueย  prediction

8- Market Basket analytics


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9- Churn prediction

Don’t worry If you don’t know how to code, we learn step by step by applying retail analysis!

*NOTE: Full Program includes downloadable resources and Python project files, homework and Program quizzes, lifetime access, and a 30-day money-back guarantee.

Who this Program is for:

ยท If you are an absolute beginner at coding, then take this Program.

ยท If you work in Retail and want to make data-driven decisions, this Program will equip you with what you need.

ยท If you are switching from Excel to a data science language. then this Program will fast-track your goal.

ยท If you are tired of doing the same analysis again and again on spreadsheets and want to find ways to automate it, this Program is for you.

Program Design

the Program is designed as experiential learning Modules, the first couple of modules are for retail fundamentals followed by Python programming fundamentals, this is to level all of the takers of this Program to the same pace. and the third part is retail applications using Data science which is using the knowledge of the first two modules to apply. while the Program delivery method will be a mix of me explaining the concepts on a whiteboard, Presentations, and Python-coding sessions where you do the coding with me step by step. there will be assessments in most of the sections to strengthen your newly acquired skills. all the practice and assessments are real retail use cases.

English
language

Content

Introduction
Introduction
Tesco and Andrew Pole
False Positives
Walmart
Notable mentions
Why Customer analytics
Curriculum
The retail Customer
types of retail customers
Types of retail customrs
Why we need customer analytics
types of retail Data
Sales Data Vs Market basket Data
Retail Data structre
Customer analytics and machine learning applications
Quiz on section 1
Summary
Installing Python
Python
Downloading Anaconda
Installing Anaconda
Spyder Overview
Jupiter Notebook Overview
Python Libraries
Python Programming Fundmentals
Intro
Data Frames
Arithmetic Calculations in Python
Lists
Dictionaries
Arrays
Importing Data in Python
Subsetting DataFrames
Conditions
Writing Functions
Mapping
For Loops
For looping a function
Mapping on Dataframe
For Looping a DataFrame
Summary
Assignment
Assignment Answer 1
Assignment Answer 2
Manipulation of Retail Data
Inro
Dropping Duplicates and NAs
Conversions lecture
Conversions
Filterations
Imputations
Indexing Tutorial
Slicing index
Manipulation lecture
Groupby
Slicing the Groupby
Dropping levels
The proper form
Pivot tables
Aggregate function in pivot table
Melting the Data
Left join
inner & outer join
Joining in Python
inner, left join and full join(outer)
Summary
Assignment
Assignment answer 1
Assignment answer 2
Assignment answer 3
Assignment answer 4
Assignment answer 5
Trade Area Modeling
Tade Area Modelling
Introduction
Different trade area modelling
Drive time and Zip codes
The huff model
Some considerations about trade area modeling
Summary of a Huff model
Huff Model
Example Demonstration
Scaling attractiveness
Developing Huff model
The winner
The Huff model in Python
Reading the data in python
Getting the upper term
Probability per Customer Community
Where should I locate my store ?
Assignment
Assignment Answer
Summary
Customer RFM analysis
Intro
RFM
Customer segmentation based on RFM analysis
Customer Recency in Python
Frequency and Monetary Value
Ranking
Grouping
Creating the Categories
Insights
RFM with Kmeans
Centroids visualization
Elbow Spree
Assignment
Assignment Kmeans
Customer Lifetime Value
Intro
CLV
Feature Engineering
Calculating lifetime value
Outliers and Classification of Ltv
Preparing the data for modeling
Decision tree without tuning
Randomized search CV
Conclusion
Conclusion Final
Assignment
Assignment Answer
Churn Prediction with Logistic Regression
Churn Prediction
Why is Churn Prediction important
Data Orientation
Odds and Odds ratio
Another example
Logistic Regression
Importing data in notebook
Feature Engineering
Visualization
Histograms
Preparing the data for modelling
Interpreting the logistic model
Confusion matrix
Precision and Recall
Interpreting the threshold
Log Odds
Fitting the model manually
Understanding Probability
Patsy
Interaction terms
Fitting the interaction model
Lasso Regression
Conclusion
Data Description for assignment
Churn answer
Market Basket Analysis
Market Basket
Lecture
Importing the data
Visualizing Baskets
Preparing the data for Market Basket
Apriori and Association rules
Slow moving items
Conclusion
Recommendation Systems- Collaborative Based Fiiltering
Intro
Collaborative Based filtering
Item to Item Vs User to User
SVD Alghoritm
Preparing the model
Training on full dataset
Prediction Customer rating
Assignment
Assignment answer
Dimensionality Reduction and model selection
Introduction
PCA
Pipeline
Preparing the Data
PCA decomposition
Importing Models
Hyper Parmeter Tuning