Data Analytics and Marketing Metrics
( 25 Modules )

Module #1
Introduction to Data Analytics and Marketing Metrics
Overview of the course, importance of data analytics in marketing, and key concepts

Module #2
Marketing Metrics Fundamentals
Understanding key marketing metrics, including ROI, CPA, and CTR

Module #3
Data Collection and Storage
Introduction to data collection methods, data storage options, and data governance

Module #4
Data Preparation and Cleaning
Importance of data quality, data cleaning techniques, and data transformation

Module #5
Data Visualization Fundamentals
Introduction to data visualization, types of visualizations, and best practices

Module #6
Descriptive Analytics
Measuring central tendency, variability, and distribution of data

Module #7
Inferential Analytics
Making inferences from samples, hypothesis testing, and confidence intervals

Module #8
Regression Analysis
Introduction to simple and multiple regression analysis

Module #9
Experiments and A/B Testing
Designing and analyzing experiments, including A/B testing and multivariate testing

Module #10
Customer Segmentation
Segmenting customers based on demographic, behavioral, and transactional data

Module #11
Customer Lifetime Value (CLV)
Calculating and optimizing customer lifetime value

Module #12
Marketing Mix Modeling
Analyzing the impact of marketing campaigns on business outcomes

Module #13
Google Analytics Fundamentals
Setting up and using Google Analytics for website and app tracking

Module #14
Social Media Analytics
Measuring social media performance, including engagement, reach, and conversions

Module #15
Email Marketing Metrics
Measuring email marketing performance, including open rates, click-through rates, and conversions

Module #16
Paid Advertising Metrics
Measuring paid advertising performance, including CPC, CPA, and ROAS

Module #17
Content Marketing Metrics
Measuring content marketing performance, including engagement, leads, and conversions

Module #18
Data Storytelling and Presentation
Effective communication of insights and recommendations to stakeholders

Module #19
Marketing Attribution Modeling
Assigning credit to touchpoints along the customer journey

Module #20
Predictive Analytics
Introduction to predictive modeling techniques, including logistic regression and decision trees

Module #21
Machine Learning for Marketing
Introduction to machine learning concepts and applications in marketing

Module #22
Marketing Automation and AI
Using automation and AI to optimize marketing processes and decision-making

Module #23
Ethics in Data Analytics and Marketing
Responsible data collection, use, and analysis in marketing

Module #24
Case Studies in Data Analytics and Marketing
Real-world examples of data analytics and marketing applications

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Data Analytics and Marketing Metrics career

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