RA: Retail Customer Analytics and Trade Area Modeling.
RA: Retail Customer Analytics and Trade Area Modeling., available at $69.99, has an average rating of 4.59, with 163 lectures, 1 quizzes, based on 300 reviews, and has 13474 subscribers.
You will learn about 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 This course is ideal for individuals who are Retail Managers or Retail Analysts or Data Scientists or Data Analysts or Retail Strategist or Merchandizers It is particularly useful for Retail Managers or Retail Analysts or Data Scientists or Data Analysts or Retail Strategist or Merchandizers.
Enroll now: RA: Retail Customer Analytics and Trade Area Modeling.
Summary
Title: RA: Retail Customer Analytics and Trade Area Modeling.
Price: $69.99
Average Rating: 4.59
Number of Lectures: 163
Number of Quizzes: 1
Number of Published Lectures: 162
Number of Published Quizzes: 1
Number of Curriculum Items: 164
Number of Published Curriculum Objects: 163
Original Price: $84.99
Quality Status: approved
Status: Live
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
Who Should Attend
- Retail Managers
- Retail Analysts
- Data Scientists
- Data Analysts
- Retail Strategist
- Merchandizers
Target Audiences
- Retail Managers
- Retail Analysts
- Data Scientists
- Data Analysts
- Retail Strategist
- Merchandizers
“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 world’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- Customer lifetime value prediction
8- Market Basket analytics
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 resourcesand Python project files,homework andProgram quizzes,lifetime access, and a30-day money-back guarantee.
Who this Program is for:
· If you are an absolute beginner at coding, then take this Program.
· If you work in Retail and want to make data-driven decisions, this Program will equip you with what you need.
· If you are switching from Excel to a data science language. then this Program will fast-track your goal.
· 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 fundamentalsfollowed 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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Tesco and Andrew Pole
Lecture 3: False Positives
Lecture 4: Walmart
Lecture 5: Notable mentions
Lecture 6: Why Customer analytics
Lecture 7: Curriculum
Lecture 8: The retail Customer
Lecture 9: types of retail customers
Lecture 10: Types of retail customrs
Lecture 11: Why we need customer analytics
Lecture 12: types of retail Data
Lecture 13: Sales Data Vs Market basket Data
Lecture 14: Retail Data structre
Lecture 15: Customer analytics and machine learning applications
Lecture 16: Summary
Chapter 2: Installing Python
Lecture 1: Python
Lecture 2: Downloading Anaconda
Lecture 3: Installing Anaconda
Lecture 4: Spyder Overview
Lecture 5: Jupiter Notebook Overview
Lecture 6: Python Libraries
Chapter 3: Python Programming Fundmentals
Lecture 1: Intro
Lecture 2: Data Frames
Lecture 3: Arithmetic Calculations in Python
Lecture 4: Lists
Lecture 5: Dictionaries
Lecture 6: Arrays
Lecture 7: Importing Data in Python
Lecture 8: Subsetting DataFrames
Lecture 9: Conditions
Lecture 10: Writing Functions
Lecture 11: Mapping
Lecture 12: For Loops
Lecture 13: For looping a function
Lecture 14: Mapping on Dataframe
Lecture 15: For Looping a DataFrame
Lecture 16: Summary
Lecture 17: Assignment
Lecture 18: Assignment Answer 1
Lecture 19: Assignment Answer 2
Chapter 4: Manipulation of Retail Data
Lecture 1: Inro
Lecture 2: Dropping Duplicates and NAs
Lecture 3: Conversions lecture
Lecture 4: Conversions
Lecture 5: Filterations
Lecture 6: Imputations
Lecture 7: Indexing Tutorial
Lecture 8: Slicing index
Lecture 9: Manipulation lecture
Lecture 10: Groupby
Lecture 11: Slicing the Groupby
Lecture 12: Dropping levels
Lecture 13: The proper form
Lecture 14: Pivot tables
Lecture 15: Aggregate function in pivot table
Lecture 16: Melting the Data
Lecture 17: Left join
Lecture 18: inner & outer join
Lecture 19: Joining in Python
Lecture 20: inner, left join and full join(outer)
Lecture 21: Summary
Lecture 22: Assignment
Lecture 23: Assignment answer 1
Lecture 24: Assignment answer 2
Lecture 25: Assignment answer 3
Lecture 26: Assignment answer 4
Lecture 27: Assignment answer 5
Chapter 5: Trade Area Modeling
Lecture 1: Tade Area Modelling
Lecture 2: Introduction
Lecture 3: Different trade area modelling
Lecture 4: Drive time and Zip codes
Lecture 5: The huff model
Lecture 6: Some considerations about trade area modeling
Lecture 7: Summary of a Huff model
Lecture 8: Huff Model
Lecture 9: Example Demonstration
Lecture 10: Scaling attractiveness
Lecture 11: Developing Huff model
Lecture 12: The Huff model in Python
Lecture 13: Reading the data in python
Lecture 14: Getting the upper term
Lecture 15: Probability per Customer Community
Lecture 16: Where should I locate my store ?
Lecture 17: Assignment
Lecture 18: Assignment Answer
Lecture 19: Summary
Chapter 6: Customer RFM analysis
Lecture 1: Intro
Lecture 2: RFM
Lecture 3: Customer segmentation based on RFM analysis
Lecture 4: Customer Recency in Python
Lecture 5: Frequency and Monetary Value
Lecture 6: Ranking
Instructors
-
Haytham Omar-Ph.D
Consultant-Supply chain
Rating Distribution
- 1 stars: 6 votes
- 2 stars: 6 votes
- 3 stars: 26 votes
- 4 stars: 101 votes
- 5 stars: 161 votes
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