AKTU Marketing and Web Analytics (BMBMK02) Syllabus PDF – MBA 3rd Semester
Marketing and Web Analytics BMBMK02
All syllabus, notes, question bank & PYQ for this subject
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- Semester 3
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Syllabus — Marketing and Web Analytics
Download PDFBMBMK02 · 3 credits · L-T-P 3-0-0
Course Objectives: The objective of this course is to:
- To introduce the concept, scope, and importance of Marketing Analytics in data-driven decision-making.
- To equip students with pricing, sales forecasting, and customer analysis techniques using quantitative models.
- To enable understanding and application of various retailing, advertising, and web analytics methods.
- To familiarize students with tools and frameworks used in social media and digital analytics platforms.
- To develop competence in interpreting qualitative insights from digital interactions through modern web tools and methods.
Unit 1: (4 Hours)
Introduction to Marketing Analytics: Meaning, Characteristics, Advantages & Disadvantages of Marketing Analytics, Market Data Sources: Primary & Secondary Market Sizing: Stakeholders, Applications, Top-down & Bottom-up Approaches, PESTLE Analysis & Porter’s Five Forces Model for market evaluation
Unit 2: (8 Hours)
Pricing Analytics: Pricing Policy & Objectives, Demand Estimation: Price Elasticity, Linear & Power Demand Curves, Optimizing Pricing and Subjective Demand Curves, Pricing Multiple Products ,Price Bundling: Pure vs. Mixed Bundling Nonlinear Pricing & Profit Maximization Strategies, Revenue Management: Markdown Pricing & Uncertainty Handling, Price Skimming and Sales Promotions
Unit 3: (7 Hours)
Sales Forecasting & Customer Analytics: Linear & Multiple Regression for Forecasting, Seasonality & Special Events Modeling, Ratio to Moving Average Methods-Curve Forecasting for New Products, Customer Lifetime Value (CLV): Concept, Estimation & Business Implication, Market Segmentation: STP Framework, Clustering for Segment Derivation, Describing Market Segments
Unit 4: (8 Hours)
Retailing, Advertising & Web Analytics Retail Analytics: Market Basket Analysis (2-way & 3-way lift), RFM Analysis, Retail Space & Sales Resource Allocation. Advertising Analytics:
Advertising Effectiveness, PPC, Online Ads.
Introduction to Web Analytics: Key Terms, Process, Offsite vs. Onsite Web Analytics, Web Analytics Tools & Metrics: Hits, Page Views, Bounce Rate, Traffic Source Google Analytics Overview KPIs: Need, Characteristics, Perspective & Usage Custom Campaigns & Content Reports
Unit 5: (9 Hours)
Social Media, Web Analytics Tools & Qualitative Analysis: Facebook Analytics: Demographics, Engagement & Post Performance, Social Campaign Analysis: Goals & Outcomes Analytics for Instagram, Twitter, LinkedIn, YouTube Organic vs. Paid Traffic, Benchmarking Web Analytics Tools: A/B Testing, Online Surveys, Web Crawling & Indexing, NLP Techniques for Micro-text Analysis Google Website Optimizer: Working & Implementation.
Qualitative Techniques: Heuristic Evaluation, Site Visits, Surveys (Post-visit & Online)Web Analytics 2.0 vs. 1.0,Competitive Intelligence, Website Traffic Trends & Overlap
Suggested Readings
- Marketing Analytics: Data-Driven Techniques with Microsoft Excel by Wayne L Winston, Wiley India Pvt. Ltd.
- Marketing Analytics: Strategic Models and Metrics by Stephan Sorger, Create Space Publishing
- Marketing Engineering and Analytics by Gary Lilen, Arvind Rangaswamy,and Arnaud De Bruyn, Decision Pro, Inc.
- Marketing Metrics by Dugar Anurag, SAGE Publishing India\
- Avinash Kaushik, Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity,
- Clifton B., Advanced Web Metrics with Google Analytics, Wiley Publishing, Inc.2nd ed.
- Kaushik A., Web Analytics 2.0, The Art of Online Accountability and Science of Customer Centricity, Wiley Publishing, Inc. 1st ed.
- Sterne J., Web Metrics: Proven methods for measuring web site success, John Wiley and Sons
- Annmarie Hanlon, Digital Marketing, SAGE Publishing India
Course Outcomes
| Course Outcome | Bloom’s Level | |
| CO1 | Understand the meaning, characteristics, and applications of marketing analytics and market evaluation tools such as PESTLE and Porter’s Five Forces. | K2 (Understand) |
| CO2 | Apply pricing analytics techniques including bundling, nonlinear pricing, and revenue management to solve pricing problems. | K3 (Apply), |
| CO3 | Use regression models and forecasting techniques to estimate sales and customer lifetime value. | K4 (Analyze), |
| CO4 | Analyze & Evaluate customer behavior through segmentation, clustering, and retail metrics like RFM and basket analysis. | K5 (Evaluate) |
| CO5 | Evaluate the effectiveness of online advertising and interpret web analytics metrics using tools such as Google Analytics. | K5 (Evaluate) |
| CO6 | Develop insights using social media analytics, A/B testing, and qualitative tools like heuristic evaluations and surveys. | K6 (Create) |
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