Master of Management Studies (M.M.S) Marketing Analytics Syllabus - Mumbai University 2026

Course objectives

• 1 To provide students with foundational knowledge of analytics techniques and tools applied to marketing.
• 2 To equip students with skills to analyze marketing data for strategic decision- making and performance measurement.
• 3 To enable application of predictive modeling, segmentation analysis, and customer lifetime value calculation.
• 4 To familiarize students with analytical tools and software commonly used in marketing analytics.
• 5 To cultivate critical understanding of data-driven decision-making processes in contemporary marketing practices.

Course outcomes

• CO1: Explain the Role of Analytics in Modern Marketing Decision-Making.
• CO2: Differentiate between Predictive, Classification, Clustering, and Segmentation Models in Marketing Analytics.
• CO3: Apply Statistical and Machine Learning Techniques such as Regression, Classification, and Clustering to Marketing problems.
• CO4: Evaluate Customer Lifetime Value (CLV) and its impact on Marketing Strategies.
• CO5: Develop Data-Driven Marketing Performance measurement frameworks.

Unit 1 CO1 · 4 hours

Introduction to Marketing Analytics - Definition, Scope, and Importance; Types of Data in Marketing- Structured vs. Unstructured, First-party, Second- party, Third-party Data; Role of Data in Customer Segmentation and Personalization; Overview of Analytical Tools and Platforms (Google Analytics, Power BI, Python/R Basics for Marketing)

Unit 2 CO2, CO3 · 10 hours

Prediction and Classification in Marketing Analytics Basics of Predictive Analytics; Regression Models and their Applications in Forecasting; Decision Trees & Random Forest for Customer Response Prediction; Classification Techniques (Naïve Bayes, SVM) for Lead Scoring and Customer Intent Analysis

Unit 3 CO2, CO3 · 6 hours

Clustering, Segmentation, and Text Analytics in Marketing- Basics of Clustering in Marketing Analytics; K-Means Clustering and Hierarchical Clustering for Customer Segmentation; Market Basket Analysis and Association Rule Mining for Consumer Behavior Insights; Text and Sentiment Analytics

Unit 4 CO4 · 6 hours

Customer Lifetime Value (CLV) and Retention Analytics-Concept and Importance of Customer Lifetime Value (CLV); Methods to Calculate CLV (Historical vs. Predictive CLV); Retention Analysis and Churn Prediction Models; Personalization Strategies and Customer Engagement Based on CLV Insights

Unit 5 CO5 · 4 hours

Measuring Marketing Performance - Key Marketing Performance Metrics (ROI, CAC, Conversion Rates, Churn Rate); A/B Testing and Experimental Design in Marketing Analytics; Attribution Modelling: First-Touch, Last-Touch, Multi-Touch Attribution; Dashboarding and Reporting for Marketing Metrics (Using Excel, Tableau, or Power BI

Textbooks

• 1 Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques – Mike Grigsby
• 2 Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python – Thomas W. Miller
• 3 Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die – Eric Siegel
• 4 Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst – Dean Abbott

Reference Books

• 1 Customer Segmentation and Clustering Using SAS Enterprise Miner – Randall S. Collica
• 2 Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS – Gokhan S. Yildirim, Gary A. Koppenhaver
• 3 Sentiment Analysis: Mining Opinions, Sentiments, and Emotions – Bing Liu
• 4 Managing Customers for Profit: Strategies to Increase Profits and Build Loyalty – V. Kumar & Werner Reinartz
• 5 Customer Analytics for Dummies – Jeffrey Strickland
• 6 Marketing Metrics: The Manager's Guide to Measuring Marketing Performance – Paul W. Farris, Neil T. Bendle, Phillip E. Pfeifer, David J. Reibstein
• 7 Cutting-Edge Marketing Analytics: Real-World Cases and Data Sets for Hands- On Learning – Rajkumar Venkatesan, Paul Farris, Ron T. Wilcox