Marketing Analytics

3.

The Marketing Analytics Process

3.1.

Problem Definition and Goal Setting

3.1.1.

Translating Business Objectives

3.1.2.

Formulating Analytical Questions

3.1.3.

Setting SMART Goals

3.1.4.

Stakeholder Alignment

3.1.5.

Success Criteria Definition

3.2.

Data Strategy and Planning

3.2.1.

Data Requirements Assessment

3.2.2.

Data Availability Analysis

3.2.3.

Data Collection Planning

3.2.4.

Resource Allocation

3.2.5.

Timeline Development

3.3.

Data Collection and Integration

3.3.1.

Data Sourcing Strategies

3.3.2.

Multi-Channel Data Integration

3.3.3.

Data Quality Assessment

3.3.4.

Real-Time vs Batch Processing

3.3.5.

Data Storage Considerations

3.4.

Data Preparation and Cleaning

3.4.1.

3.4.2.

Missing Data Handling

3.4.3.

Outlier Detection and Treatment

3.4.4.

Data Normalization

3.4.5.

Data Transformation

3.4.6.

Data Validation Techniques

3.5.

Exploratory Data Analysis

3.5.1.

Descriptive Statistics

3.5.2.

Data Distribution Analysis

3.5.3.

Correlation Analysis

3.5.4.

Trend Identification

3.5.5.

Anomaly Detection

3.6.

Analysis and Modeling

3.6.1.

Descriptive Analytics

3.6.2.

Diagnostic Analytics

3.6.3.

Predictive Analytics

3.6.4.

Prescriptive Analytics

3.6.5.

Model Selection and Validation

3.7.

Insight Generation and Interpretation

3.7.1.

Pattern Recognition

3.7.2.

Statistical Significance Testing

3.7.3.

Business Context Application

3.7.4.

Avoiding Interpretation Pitfalls

3.7.5.

Insight Prioritization

3.8.

Visualization and Communication

3.8.1.

Audience-Specific Reporting

3.8.2.

Visualization Best Practices

3.8.3.

Dashboard Design

3.8.4.

Storytelling with Data

3.8.5.

Presentation Techniques

3.9.

Implementation and Action

3.9.1.

Recommendation Development

3.9.2.

Implementation Planning

3.9.3.

Change Management

3.9.4.

Performance Monitoring

3.9.5.

Continuous Improvement

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2. Foundational Concepts and Metrics

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4. Data Sources and Collection Methods