1. To understand the role of analytics in marketing decision-making and strategy development.
2. To learn various quantitative methods and statistical techniques used in marketing analysis.
3. To gain proficiency in using data analytics tools and software for marketing data analysis.
4. To explore different types of marketing data sources and collection methods.
5. To develop skills in data interpretation, visualization, and communication for informing marketing strategies.
6. To apply marketing analytics techniques to real-world marketing problems and challenges.

1. Introduction to Marketing Analytics
2. Data Collection Methods in Marketing
3. Descriptive Analytics: Exploring and Summarizing Marketing Data
4. Inferential Statistics for Marketing Analysis
5. Predictive Analytics: Forecasting and Predictive Modeling
6. Customer Segmentation and Profiling
7. Marketing Mix Modeling (MMM) and Attribution Modeling
8. A/B Testing and Experimentation in Marketing
9. Customer Lifetime Value (CLV) Analysis
10. Market Basket Analysis and Cross-Selling
11. Social Media Analytics
12. Web Analytics and Conversion Rate Optimization (CRO)
13. Marketing Dashboards and Reporting
14. Data Visualization Techniques for Marketing
15. Ethical and Privacy Considerations in Marketing Analytics

• Lectures: Online lectures covering key concepts, theories, and methodologies in marketing analytics.
• Case Studies: Analysis of real-world marketing analytics cases and applications.
• Hands-on Exercises: Practical exercises using marketing analytics tools and software for data analysis.
• Group Projects: Collaborative projects applying marketing analytics techniques to solve marketing problems or optimize marketing strategies.
• Guest Speakers: Inviting industry experts to share insights and experiences in marketing analytics.
• Online Discussions: Interactive discussions on marketing analytics topics, trends, and challenges.
• Data Analysis Projects: Conducting data analysis projects using marketing datasets and analytics tools.
• Case Study Analysis: Analyzing and presenting case studies of marketing analytics applications and solutions.
• Exams: Assessing comprehension of marketing analytics concepts, theories, and methodologies through quizzes, midterms, and a final exam.
• Presentations: Delivering presentations on marketing analytics topics or projects, demonstrating understanding and critical thinking.
• Participation: Active participation in online discussions, group activities, and feedback sessions.
• Research Papers: Writing research papers on specific marketing analytics topics or case studies.

Prerequisites: While specific prerequisites may vary depending on the institution, most Marketing Analytics courses in MBA programs require a foundational understanding of statistics and quantitative methods. Proficiency in using statistical software such as R, Python, or SPSS may also be beneficial.

Module 1: Introduction to Marketing Analytics

• Overview of Marketing Analytics
• Importance of Data-Driven Decision Making in Marketing
• Role of Marketing Analytics in Business Strategy

Module 2: Data Collection and Management

• Types of Marketing Data Sources
• Data Collection Methods: Surveys, Interviews, Observational Data, etc.
• Data Cleaning and Preprocessing Techniques
• Data Privacy and Ethics Considerations

Module 3: Descriptive Analytics

• Exploratory Data Analysis (EDA)
• Data Visualization Techniques: Charts, Graphs, Dashboards
• Measures of Central Tendency and Dispersion
• Customer Profiling and Segmentation

Module 4: Inferential Statistics for Marketing

• Hypothesis Testing: t-tests, ANOVA, Chi-Square Test
• Correlation and Regression Analysis
• Confidence Intervals and Significance Testing
• Practical Applications in Marketing Research

Module 5: Predictive Analytics

• Introduction to Predictive Modeling
• Regression Analysis: Linear Regression, Logistic Regression
• Time Series Forecasting
• Machine Learning Algorithms for Marketing Predictions

Module 6: Customer Lifetime Value (CLV) Analysis

• Understanding CLV and Its Importance
• CLV Calculation Methods
• Applications of CLV in Customer Acquisition and Retention Strategies
• CLV Optimization Techniques

Module 7: Marketing Mix Modeling (MMM) and Attribution Modeling

• Introduction to MMM and Attribution Modeling
• Multichannel Attribution Models: First-Touch, Last-Touch, Time Decay, etc.
• Using MMM to Optimize Marketing Budget Allocation
• Challenges and Limitations of Attribution Modeling

Module 8: A/B Testing and Experimentation

• Principles of A/B Testing
• Experimental Design and Hypothesis Formulation
• Conducting A/B Tests: Sample Size Calculation, Test Duration, Statistical Significance
• Interpreting A/B Test Results and Making Data-Driven Decisions

Module 9: Social Media Analytics

• Overview of Social Media Platforms and Metrics
• Social Media Listening and Sentiment Analysis
• Social Media Engagement Analysis
• Social Media ROI Measurement

Module 10: Web Analytics and Conversion Rate Optimization (CRO)

• Introduction to Web Analytics Tools: Google Analytics, Adobe Analytics, etc.
• Key Web Analytics Metrics: Traffic Sources, Bounce Rate, Conversion Rate, etc.
• Conversion Rate Optimization Techniques
• Using Web Analytics for Website Optimization and User Experience Improvement

Module 11: Marketing Dashboards and Reporting

• Designing Effective Marketing Dashboards
• Key Performance Indicators (KPIs) for Marketing Dashboards
• Automated Reporting Tools and Templates
• Communicating Insights to Stakeholders

Module 12: Advanced Topics in Marketing Analytics

• Text Analytics and Natural Language Processing (NLP)
• Predictive Segmentation Techniques
• Marketing Attribution Modeling Beyond Digital Channels
• Emerging Trends and Technologies in Marketing Analytics

Module 13: Case Studies and Applications

• Analysis of Real-World Marketing Analytics Case Studies
• Application of Marketing Analytics Techniques to Solve Business Problems
• Group Presentations on Case Study Analysis and Solutions

Module 14: Final Project

• Individual or Group Project Applying Marketing Analytics Techniques to a Marketing Problem or Opportunity
• Project Proposal, Data Collection and Analysis, Presentation of Findings and Recommendations
  
  ***Lecture can update and edit new course marteial***

1. Textbooks:

   • "Marketing Analytics: Data-Driven Techniques with Microsoft Excel" by Wayne L. Winston
   • "Marketing Analytics: A Practical Guide to Real Marketing Science" by Mike Grigsby
   • "Predictive Analytics for Marketers: Using Data Mining for Business Advantage" by Barry Leventhal
   • "Advanced Web Metrics with Google Analytics" by Brian Clifton
   • "Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python" by Thomas W. Miller

2. Online Courses and Tutorials:

   • Coursera: Various courses on marketing analytics offered by top universities and institutions.
   • edX: Marketing analytics courses provided by universities such as MIT and Columbia University.
   • Udemy: Marketing analytics courses covering topics such as data analysis, predictive modeling, and customer segmentation.
   • LinkedIn Learning: Courses on marketing analytics, data analysis, and predictive modeling techniques.

3. Blogs and Websites:

   • Marketing Analytics Blog by Moz: Offers articles, guides, and resources on marketing analytics topics such as SEO analysis, keyword research, and data-driven marketing strategies.
   • Kissmetrics Blog: Provides insights, case studies, and tutorials on marketing analytics, customer behavior analysis, and conversion rate optimization.
   • Google Analytics Blog: Offers updates, tips, and best practices for using Google Analytics and interpreting marketing data.
   • MarketingProfs: Publishes articles, webinars, and resources on marketing analytics, digital marketing, and marketing strategy.

4. Online Communities and Forums:

   • Reddit: Subreddits such as r/MarketingAnalytics and r/DataAnalytics offer discussions, insights, and advice from marketing analytics professionals and enthusiasts.
   • LinkedIn Groups: Join groups related to marketing analytics, data analysis, and predictive modeling to connect with professionals and participate in discussions.
   • Stack Overflow: Platform for asking and answering questions related to programming, data analysis, and statistical techniques used in marketing analytics.

5. Webinars and Podcasts:

   • The Digital Analytics Power Hour: Podcast covering topics related to digital analytics, data analysis, and marketing analytics trends and challenges.
   • Analytics Neat: Podcast discussing marketing analytics, data visualization, and digital marketing measurement strategies.
   • Webinars hosted by marketing analytics software providers such as Google Analytics, Adobe Analytics, and IBM Watson Analytics.

6. Industry Reports and Whitepapers:

   • Marketing Analytics Reports by Forrester Research: Provides research reports, whitepapers, and insights on marketing analytics trends, technologies, and best practices.
   • Gartner Marketing Analytics Reports: Offers research reports and analysis on marketing analytics platforms, tools, and vendors.
   • Deloitte Insights: Publishes articles and reports on marketing analytics, data-driven marketing, and customer insights.

Objective: To assess students' ability to apply marketing analytics techniques to real-world marketing data and derive actionable insights to inform marketing strategies.

Instructions: You are tasked with conducting a marketing analytics project for a fictitious company of your choice. The project should involve analyzing marketing data to address a specific business problem or opportunity. Follow these steps in completing your project:

1. Problem Identification: Identify a marketing-related problem or opportunity that the company is facing. This could include optimizing marketing campaigns, improving customer segmentation, maximizing marketing ROI, etc.

2. Data Collection: Collect relevant marketing data from various sources, including customer databases, website analytics, social media platforms, email marketing platforms, etc. Ensure the data is clean, organized, and ready for analysis.

3. Data Analysis: Apply appropriate marketing analytics techniques to analyze the data and derive insights relevant to the identified problem or opportunity. This may involve descriptive analysis, regression analysis, customer segmentation, predictive modeling, etc.

4. Insights and Recommendations: Based on your analysis, summarize the key findings and insights. Provide actionable recommendations for addressing the identified problem or capitalizing on the opportunity. Support your recommendations with evidence from the data analysis.

5. Presentation: Prepare a presentation summarizing your project findings, methodology, and recommendations. Your presentation should be clear, concise, and visually engaging, with supporting visuals such as charts, graphs, and tables.

Submission Guidelines:

• Submit a written report documenting your marketing analytics project, including problem statement, data sources, analysis methodology, findings, and recommendations.
• Submit your presentation slides in PowerPoint or PDF format.
• Include any code or scripts used in your analysis, along with explanations and comments where necessary.
• Your report and presentation should be well-organized, clearly written, and professionally presented.

Grading Criteria: The assessment will be graded based on the following criteria:

• Problem Identification and Scope (20%)
• Data Collection and Preparation (15%)
• Data Analysis Techniques Applied (25%)
• Insights and Recommendations (25%)
• Presentation Quality and Clarity (15%)
• Final submit can be requested and update

Due Date: [Insert due date by lecture]