18MBAMM304 Marketing Research& Analytics syllabus for MBA

Marketing Research Dynamics-

Introduction, Meaning of Marketing research, when marketing research is unnecessary, Nature and Scope of Marketing Research, Marketing Research in the 21st Century (Indian Scenario), limitations of Marketing Research, threats to marketing research. Introduction to marketing intelligence: what is marketing intelligence (MI), components, need for MI, Domains of MI. Ethics in marketing research.

Marketing Research and MIS:

Marketing Information System, Importance, Relevance of MkIS, Marketing Research (MR) and MkIS, The marketing information systems and its subsystems, four components: user interfaces, application software, databases, and system support. Advantages & disadvantages of marketing information systems. Internal reporting systems.

Decision Support System & Big Data:

Marketing Decision Support System-meaning, Use of Decision Support Systems in Marketing Research, Data base & Data warehousing. The three Vs: Volume, Velocity & Varity, The Fourth V: Value. Elements of data base, types of data base, using marketing data base for marketing intelligence, ways to gather consumer data, Data Mining, benefits of data mining, Big Data Analysis, Descriptive Analysis, Prescriptive Analysis, Key challenges of Big Data Integration.

Applications of Marketing Research:

Introduction, Consumer Market Research, Business-to-Business Market Research, Product Research, Pricing Research, Motivational Research, Distribution Research, Advertising Research, Media research, Sales Analysis and Forecasting.

Predictive analysis:

Meaning of predictive analysis, how good are models at predictive behavior, benefits of predictive models, and applications of predictive analysis, reaping the benefits, avoiding the pitfalls, Importance of Predictive model, Process of predictive analytics.

Predictive analytical process:

Project initiation, project requirements, Model building and business evaluation, duration of a predictive analytics project.

Building a predictive model:

Exploring the data landscape, Sampling and shaping the development sample, data preparation, creating derived data, understanding the data, data reduction, data transformation, modeling, validation, selling models into business.

PRACTICALCOMPONENTS:

• Choose 5 successful products or services and identify the insight behind them through a field survey.
• Do a comprehensive essay on the difference between consumers vs. trade vs. Competition insights & how best to exploit them.
• Take 5 recent digital innovations like twitter or face book and identify the insights.
• Running case with real data Dell, Comprehensive critical thinking case Baskin-Robbins.
• Data Analysis case with real data IBM.

COURSE OUTCOMES:

The student should be able to:

1. Comprehend the objectives of Market research & its application in solving marketing problems.

2. Appreciate the use of different data collection methods, sampling design techniques, measurement methods to analyze the data.

3. Generalize and interpret the data with the help of various measurement techniques.

4. To understand the emergence of new trends in research.

RECOMMENDED BOOKS:

• Marketing Research an Application Orientation-Naresk K Malhotra,6/e, Pearson, 2013.
• Essentials of Marketing Research – William G. Zikmund et.al. 4/e, Cengage Learning,2010.
• Predictive Analytics, Data Mining and Big Data- S. Finlay, Palgrave Macmillan Publishing.

REFERENCE BOOKS:

• Marketing Research: Methodological Foundations 8 th Edition by Gilbert A. Churchill & Dawan Iacobucci.
• Marketing Research: David AAker/V.Kumar/Robert PLeone,George S Day. Willey publication.11th edition.
• Essentials of Marketing Research – 4/e, Tony Proctor, PHI, 2005 Market Research Best Practice. 30 Visions of the Future – Peter Mouncey, et.al, 2007.