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• Introduction to Data-Driven Marketing:
o The role of data in modern marketing
o Key performance indicators (KPIs) and metrics
• Data Collection and Integration:
o Data sources (web analytics, social media, CRM)
o Data cleaning and preparation
• Data Visualization and Storytelling:
o Creating effective data visualizations
o Telling compelling data stories
• Statistical Modeling:
o Regression analysis
o Time series analysis
o Hypothesis testing
• Machine Learning for Marketing:
o Customer segmentation
o Churn prediction
o Recommendation systems
• Text Analytics and Sentiment Analysis:
o Extracting insights from text data
o Sentiment analysis of social media and customer reviews
• Predictive Modeling:
o Forecasting future trends and customer behavior
o Building predictive models using machine learning
• AI-Powered Marketing Automation:
o Marketing automation platforms
o AI-driven personalization and targeting
• Chatbots and Virtual Assistants:
o Designing and implementing chatbots
o Natural language processing for customer interactions
• Measuring Marketing Performance:
o Key performance indicators (KPIs) and metrics
o Marketing attribution models
• A/B Testing and Experimentation:
o Designing and running A/B tests
o Analyzing test results and drawing conclusions
• Marketing Optimization:
o Continuous improvement and optimization
• Ethical Implications of Data-Driven Marketing:
o Data privacy and security
o Bias and fairness in AI algorithms
• The Future of Marketing:
o Emerging trends and technologies
o The impact of AI on marketing
• Case Studies and Best Practices:
o Real-world examples of successful data-driven marketing campaigns
o Lessons learned and best practices for future success