Unit 1108: Marketing Analytics

1. Introduction to Marketing Analytics

• Definition and scope of marketing analytics
• Importance of data-driven marketing decisions
• Overview of marketing analytics tools and technologies
• The marketing analytics process lifecycle

2. Data Collection and Management in Marketing Analytics

• Types of marketing data (first-party, second-party, third-party data)
• Data sources: CRM, social media, web analytics, surveys
• Data quality and cleansing techniques
• Data storage solutions and management best practices
• Introduction to data privacy laws affecting marketing data

3. Marketing Metrics and KPIs

• Understanding marketing metrics vs KPIs
• Common marketing KPIs: CTR, conversion rate, customer acquisition cost, lifetime value
• Sales funnel metrics and their importance
• Setting SMART goals for marketing measurement

4. Customer Segmentation and Targeting

• Purpose and benefits of segmentation
• Segmentation bases: demographic, geographic, psychographic, behavioral
• Techniques for segmentation: clustering, RFM analysis
• Targeting strategies and personalization

5. Predictive Analytics in Marketing

• Introduction to predictive analytics concepts
• Common predictive models: regression, decision trees, machine learning basics
• Applications: churn prediction, lead scoring, sales forecasting
• Tools and software for predictive analytics

6. Marketing Attribution Modeling

• What is attribution modeling and why it matters
• Types of attribution models: first-touch, last-touch, multi-touch, algorithmic
• Challenges in attribution modeling
• Choosing the right attribution model for your marketing strategy

7. A/B Testing and Experimentation

• Fundamentals of A/B testing
• Designing experiments: hypothesis creation, control and test groups
• Metrics for evaluating test results
• Common pitfalls and best practices

8. Marketing Dashboards and Reporting

• Purpose and benefits of dashboards
• Key components of effective marketing dashboards
• Tools for dashboard creation (e.g., Tableau, Power BI, Google Data Studio)
• Reporting cadence and stakeholder communication

9. Ethical Considerations in Marketing Analytics

• Data privacy and consumer protection
• Ethical use of customer data
• Transparency and consent in data collection
• Avoiding bias in data analysis and decision-making
• Regulatory compliance overview (GDPR, CCPA)