1-day Instructor-led Workshop

Includes 2 Hours of Mentoring and Project Advice

Jan 28 Sat

Batch 1 (Saturday)

Jan 28 to Jan 28

Jan
  • 28 Sat

    Session 1

    10:00 AM to 01:00 PM (EST)


    Session 2

    02:00 PM to 05:00 PM (EST)


Mar 04 Sat

Batch 2 (Saturday)

Mar 04 to Mar 04

Mar
  • 4 Sat

    Session 1

    10:00 AM to 01:00 PM (EST)


    Session 2

    02:00 PM to 05:00 PM (EST)


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Instructor

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Ajay Bhargava

Ajay specializes in transforming organizations to be more “analytics driven”. He has over 27 years of experience in data and analytics industry developing solutions and mentoring professionals from a large spectrum of domains ranging from students to Fortune 500 employees. Starting in 2010, he has incubated and grew a global big data and analytics organization serving insurance and healthcare customers of the 6th largest IT services company in the world.

Increase Cross Selling and Upselling of Products and Services

Instructor: Ajay Bhargava

  • Improve and optimize your promotions, product placements, and bundling of products
  • Instructor has over 27 years of experience and was the Global Head of Analytics and Big Data Practice for TCS Insurance and Healthcare
  • Provides real-world examples with case studies

Course Description

Retail industry has always been a pioneer in using Analytics to sell more, reduce cost of goods sold, and enhance customer experience. About 20 years ago, the retail industry in the U.S. started gravitating from a product-centric view of the world to a customer-centric view, and moved towards a more personalized, “segment of one” approach. It has taken leaps and bounds in providing exceptional customer experience at the same time, cutting costs dramatically. In this highly competitive landscape, and with dwindling margins, the need to cross and up sell to existing customers and figuring out what items to promote (or not) to attract new customers, has only grown stronger. This course will lay out one of many ways to understand customer purchase behavior by looking at past retail transactions (store and/or online) and the collection of items that come together (think “association”) in a market basket (think “receipt”). This Market Basket Analysis (also known as Affinity Analysis and the technique called Association Rule Mining) is used to determine the likelihood of these items occurring together. This discovery of products and services being purchased together is used to identify specific items to be sold to specific customers, and help in increasing the customer’s lifetime value (CLTV).

What am I going to get from this course?
  • Increase cross-sell and up-sell of products and services
  • Determine the right "Next Best Offer" to the right person and optimize marketing spend
  • Apply promotion programs 
  • Improve loyalty and retention of the customer 
  • Optimize product placement
  • Optimize product bundling
  • Optimize the supply chain

Prerequisites and Target Audience

What will students need to know or do before starting this course?
  • MS Excel or any other spreadsheet program
  • Programming Language R
  • An IDE for R called R Studio. R is free, and can be installed by downloading from (https://cran.r-project.org/). After installing R, a free version of R Studio can be downloaded from (https://www.rstudio.com/products/rstudio/download/).
  • Basic Knowledge of any programming language is required for one of the sections. R language knowledge is preferred, but not necessary.
  • System Requirements: Any Windows (7 or higher) or Mac machine with at least 4 GB of RAM (needed for the R example) is sufficient for this course.
  • Data Sets: All data sets for the examples will be provided in MS-Excel and CSV file.
Who should take this course? Who should not?
  • Marketing professionals in any industry
  • Advertising and promotional managers in the retail industry
  • Merchandising managers in the retail industry
  • Store managers
  • Consultants
  • Financial analysts

Curriculum

Module 1: 1. Course Overview and Objectives
Lecture 1 Lecture
Lecture 2 Objectives
Lecture 3 Learnings
Lecture 4 Hands-on Exercises
Lecture 5 R Code
Lecture 6 Assumptions and Constraints

Module 2: Retail Industry Overview
Lecture 7 State of the Industry
Lecture 8 Not All Retailers are Born Equal!
Lecture 9 Analytics in Retail - Business Needs

- Customer Experience - Customer Service - Product & Store Layout - Assortment Planning - Human Behavior - Supply Chain - Omni Channel, 360 View - Promotions

Module 3: Analytical Techniques in Retail
Lecture 10 Purpose
Lecture 11 Business Benefits
Lecture 12 Supervised & Unsupervised Learning Techniques
Lecture 13 Analytics in Retail - Analytical Techniques and Algorithms

- RFM - Clustering - Association Rule Mining (Market Basket Analysis) - Regression Analysis

Lecture 14 Types of AR - Mining Algorithms

- Apriori-most common & Popular - Eclat - FP-Growth

Module 4: Association Rule Mining
Lecture 15 Concepts

- Association Rule - Antecedent (LHS) - Consequent (RHS) - Item Set - Support - Confidence - Lift - Minimum Support - Minimum Confidence - Frequent Item Set - Affinities

Lecture 16 "Correlation does not Imply Causation"
Lecture 17 Mathematics Behind Association Rule Mining
Module 5: Excel Example
Lecture 18
Module 6: R Code
Lecture 19 Problem Definition
Lecture 20 Data Set
Lecture 21 Code Walk Through
Lecture 22 Combinatorics
Lecture 23 Visualizations
Lecture 24 Iteration - Science, Art, and Business of it
Lecture 25 Story Behind the Data
Module 7: Applications in Other Industries
Lecture 26 Telecom
Lecture 27 Fraud Detection
Lecture 28 Banks
Module 8: Summary and Next Steps
Lecture 29 Course Feedback
Lecture 30 Related Retail Course Topics
Module 9: References
Lecture 31
Module 10: Handouts
Lecture 32

Reviews

1 Review

Empty user
Xi Y

December, 2016

Would recommend this course to anyone in retail!