Marketing Analytics (for MBA)

Course Duration : 50 hrs+ Case Study
(20% discount for students)

About Marketing Analytics

Marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI).
A thorough marketing analytics program will help you to:

  • Understand big-picture marketing trends
  • Determine which programs worked and why
  • Monitor trends over time
  • Thoroughly understand the ROI of each program
  • Forecast future results

Course Overview

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

  • Introduction to Analytics
  • Various Analytics tools and their use
  • Role of Analytics in Marketing
  • Analytical Tools and Techniques for Marketing Professionals
  • Learning R, the popular Analytics tool
  • Marketing 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 Marketing Sector

4.1 A Brief History of the Evolution of Analytics
4. 2 Why is Analytics Important to the Marketing Field?
4.3 Marketing Analytics Defined
4. 4 Market Basket Analysis- Understanding Customer Behavior
4.5 An Example of Association Rule Mining


Chapter-5: Analytical Tools and Techniques for Marketing Professionals

5.1 Key Systems of Record for HR Data
5.2 Software Tools(SAS/R/EXCEL)
5.3 Quantitative Techniques for Analysis
5.4  Data Visualization
5.5 Data Analytic Techniques


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


Marketing Analytics using R

Chapter-16: Market Basket Analysis using R

16.1 Define the Goal of Your Analysis
16.2 Collect and Manage the data in R
16.3 Analyzing the Results of Basket Analysis

Marketing Data Analysis of an Organization

In this assignment, we will analyze a dataset related to marketing/selling activities in an organization to predict the customers’ behavior for making future business strategy. The dataset we will be using, in this case, comes from UCI Machine Learning repository. The dataset is called “Online Retail” and can be found there. It contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered online retailer. By analyzing that data we will find out the following information.

By analyzing that data we will find out the following information.
What time do people often purchase online?
How many items each customer buy?
Who are the 10 top best sellers?
Association rules for online retailer

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.