Retail Analytics (for MBA)

Course Duration : 50 hrs
15500
(20% discount for students)

About Retail Analytics

Retail analytics is the process of providing analytical data on inventory levels, supply chain movement, consumer demand, sales, etc. that are crucial for making marketing, and procurement decisions.
There are some strategic areas where retail players identify a ready use as far as it is data analytics. Here are a few of those areas:

  • Price Optimization
  • Future performance prediction
  • Demand prediction
  • Pick out the highest Return on Investment (ROI) Opportunities
  • Forecasting trends
  • Identifying customers
  • Discount Efficiency
  • Churn Rate Reduction

Course Overview

This course focuses on the following key areas of Retail Analytics:

  • Introduction to Analytics
  • Various Analytics tools and their use
  • Role of Analytics in Retail sector
  • Strategy for Generating the Business using Retail Analytics
  • Learning R, the popular Analytics tool
  • Retail data Analytics using R

What We Offer

Hands on experience through practical assignments
Real time industry problems and solutions (Case studies/Projects)
Industry Experienced Faculties
Exclusive Study Material
Interview preparation and placement assistance
State-of-Art Labs with latest Infrastructure

Introduction to Analytics

Chapter-1: Introduction to Analytics

1.1 Key concepts of analytics
1.2 Simple yet powerful techniques
1.3 Draw insights from data
1.4 Where the analytics used
1.5 Some real time examples

 

Chapter-2: Understanding difference between Analysis and Reporting

2.1 Analysis versus Reporting
2.2 Basic and Advance Analytics
2.3 Conducting an Analysis- Things to consider
2.4 Building an Analytic Team

 

Chapter-3: Analytical Approaches and Tools

3.1 The evolution of Analytic Approaches
3.2 The evolution of Analytic Tools
3.3 Categories of Analytic Tools
3.4 Some popular Analytical Tools (R/SPSS/SAS)
3.5 Comparison between Analytical Tools

 

About Marketing  Analytics

Chapter-4: The Role of Analytics in Retail sector

4.1 A Brief History of the Evolution of Analytics
4. 2 Why Retail Analytics is a business necessity
4.3 How Retailers Harness the Power of Retail Data Analytics
4. 4 How Analytical Software Solutions Support Data-Driven Decisions

 

Chapter-5: Strategy for Generating the Business using Retail Analytics

5.1 Identify markets and designing Propositions
5.2 Reading Retail Profit and loss
5.3 Key Retail Functions
A. Real Estate
B. Buying
C. Distributed
D. Store Design
E. Store Operation

 

About R Analytics Tool

Chapter-6: Getting Started with R

6.1 Learning objectives
6.2 Download and Install R and R Studio
6.3 Working in the R Windowing Environment
6.4 Install and Load Packages

 

Chapter-7: Basic Building Blocks in R

7.1 Learning Objectives
7.2 R as a Calculator
7.3 Work with variables
7.4 Understand Data Types
7.5 Store Data in Vectors
7.6 Call Functions

 

Chapter-8: Advanced Data Structures in R

8.1 Learning Objectives
8.2 Create and Access Information in Data Frames
8.3 Create and Access Information in Lists
8.4 Create and Access Information in Matrices
8.5 Create and Access Information in Arrays

 

Chapter-9: Reading Data into R

9.1 Learning Objectives
9.2 Reading CSV Files
9. 3 Understanding Excel is not easily accessible in R
9.4 Read Data files from other Statistical Tools

 

Chapter-10: Basics of Programming

10.1 Learning Objectives
10.2 The Classic “Hello World” Example
10.3 Basics of Function Arguments

 

Chapter-11: Data Munging

11.1 Learning Objectives
11.2 Repeating Matrix Operations – the apply function
11.3 Repeating List Operations
11.4 Combining Datasets
11.5 Joining Datasets

 

Chapter-12: Making Statistical Graphs

12.1 Learning Objectives
12.2 Using Datasets for creating Graphs
12.3 Making Histograms, Bar graphs, Line graphs, Scatterplots, Boxplots etc with Base Graphics
12.4 Creating Line plots
12.5 Control colour and shapes
12.6 Add themes to graphs

 

Chapter-13: Special Packages in R

13.1 Plotly package to generate dynamic graph
13.2 Swirl package to learn R
13.3 RPivottable package to generate Pivot table
13.4 plyr package to split and combine data

 

Chapter-14: Basic Statistics

14.1 Learning Objectives
14.2 Drawing numbers from Probability Distributions
14.3 Summary Statistics-Mean, Variance,SD,Correlation

 

Chapter-15: Linear Models

15.1 Learning Objectives
15.2 Fit simple Linear models
15.3 Exploring the Data

 

Retail Analytics using R

Chapter-16: Retail Data Analytics using R

16.1 Define the Goal of Your Analysis
16.2 Collect and Manage the data in R
16.3 Building the Predictive Model
16.4 Evaluate and Critic Model
16.5 Present result and Documentation
16.6 Deploy Model

Retail Data Analysis of a business with multiple stores

Context The Challenge – One challenge of modeling retail data is the need to make decisions based on limited history. Holidays and select major events come once a year, and so does the chance to see how strategic decisions impacted the bottom line. In addition, markdowns are known to affect sales – the challenge is to predict which departments will be affected and to what extent. Content You are provided with historical sales data for 45 stores located in different regions – each store contains a number of departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Stores Anonymized information about the 45 stores, indicating the type and size of store

By analyzing that data we will do folowing predictions
1. Predict the department-wide sales for each store for the following year
2. Model the effects of markdowns on holiday weeks
3. Provide recommended actions based on the insights drawn, with prioritization placed on largest business impact

Tania Chakraborty

In-house Faculty (R & SAS)

Tania, with a background in engineering, have 3+ years of hands on working experience on various Analytics tools, mainly SAS & R. She played a major role in the student data analysis of two entire countries, Dominica & St. Kitts, on a popular student management software “openSIS”.
Other than that she has worked on various other data analysis projects like, Data Analysis on US Economic Indices, Twitter Sentimental Analysis, GDP rates etc.
Simultaneously with project work, she provides training on Big Data analytics using Hadoop and R, Base SAS & Advanced SAS. She has already educated over hundred high profile MNC professionals on Data Analytics. She is the most junior but most appreciated faculty of our team.

Debajyoti Chakraborty

In-house Faculty (R & SAS)

Debajyoti, a Statistical Analyst, Member of Actuarial Society of India, Analytics Trainer on Statistical Softwares - SAS,R,Ms Excel with basic query language knowledge on SQL. Graduate in Statistics with Maths and Computer Science as other subjects. Having over 3yrs of work experience as Data Analyst. In Statistical Analyst role he has worked on multiple industry projects including dashboarding and analytics implementation for Retail and Healthcare projects. Also, as an Actuarial Analyst he assisted in Claim Analytics. As an Analytics Trainer, he is providing Analytics training to Industry Professionals and Academic Students on Statistical Software packages - SAS, R, MS Excel (Beginner to Advance) and SPSS, and overseeing Data Analysis projects undertaken by students and knowledge sharing for successful completion of projects on time.

Anindya Kundu

In-house Faculty/Consultant (SAS, Python,R,SPSS)

Anindya Kundu is into qualitative and quantitative analytics consultancy for more than half a decade. He has been involved in both analytics as a service and analytics product development projects. Anindya is a data obsessed person who loves generating insights from large quantities of data - clean, process, harness data to get hidden truth. He uses his SAS/R/SPSS/Python tool implementation capability to analyze data, and also perform automation. He is involved in analytics innovation, specializing in product development for population health management, health economics, insurance and mortgage, healthcare analytics and transportation – supply chain management.  He was also extensively involved in functional development of CRM (Customer retention module) application tool for a Fortune's Best 100 Companies. He has received his post graduate certification from IIM Ranchi.