D. Marketing Analytics

Learning outcomes:

Marketing environment has become very complex both online and offline. This complexity is also reflected in the variety and sophistication of analytical tools that attempt to dissect marketing data from various perspectives and construct appropriate models. This course attempts to simplify the selection and application of dissection and modeling process in differing circumstances; it attempts to bring these technologies at the door-steps of a common man. It is divided into two modules. Module I covers basic building blocks of marketing data analysis and some important statistical predictive tools, Module II takes the study to a higher level attempting to analyze marketing data generated in digital world. After pursuing this course, a student will be able to:

  • Get insights into data using fundamental building blocks of quantitative analysis of frequency distribution, cross-tabulation and hypothesis testing
  • Examine differences in responses between two or more groups, say of customers
  • Understand association between variables and build predictive models based upon this association
  • Make sense of data on the web and make use of one very popular analytical tool—Google Analytics

Course Objectives:

  • To appreciate the importance of data-based decision making in marketing
  • To understand and learn a set of certain generic techniques of marketing analytics
  • To understand marketing analytics practices in digital world
  • To understand the impact of various marketing tactics (marketing mix) on sales and then forecast the impact of future sets of tactics.
  • To learn to optimize advertising mix and promotional tactics with respect to sales revenue or profit.

Brief Course Contents

Module 3: Bidding for a contract (Lab session); Marketing a new product and strategy to be adopted (Lab session).
Module 1: Marketing Analytics-Fundamentals:

Introduction to Marketing Analytics, Scale Types and Appropriate Analysis; Introduction to SPSS, Data Entry (Lab Session); Confidence Interval, Hypothesis Testing, Non Parametric Tests – 1 sample KS, Binomial, Parametric tests – T tests (Lab sessions); ANOVA, (Lab session); Multiple Regression Analysis, (Lab session); Linear Discriminant Analysis, (Lab session); Factor Analysis/ Principal Component Analysis, (Lab session); Data Visualization and Instant Analytics using Tableau Public, (Lab sessions); Structured Equation Modeling;

Module 2: Digital Marketing Analytics:

Web analytics; Search Engine Optimization; Google Analytics; (Lab session); Social Media Analytics and Text mining; (Lab session);

Module 3: Customer Retention Modeling:

Bidding for a contract (Lab session); Marketing a new product and strategy to be adopted (Lab session).


Projects: Two projects: Analyze two separate Market Surveys one of general retail area and the other of Food and Beverages outlets in commercial area of Delhi Airport. The surveys were conducted recently in two independent studies.