Marketing Analytics Accelerator Bootcamp

• Business goals, North Star and funnel metrics
• Leading versus lagging indicators
• Writing a measurement plan
• Metric definitions and a shared dictionary

Practical output: Write a measurement plan with KPIs for a business.

• Data types, sources and quality
• First-party data and consent
• Privacy and Indian data protection basics
• Data cleaning principles

Practical output: Audit a data set for quality and consent issues.

• Data streams, events and parameters
• Key events and user properties
• Naming conventions
• Debugging and validation

Practical output: Configure GA4 for a sample site and validate events.

• Tags, triggers, variables and data layer
• E-commerce and lead tracking
• Cross-domain tracking and consent mode
• Server-side tracking overview

Practical output: Implement and document a tracking set-up.

• Acquisition, engagement and monetisation reports
• Funnel, path and segment explorations
• Comparisons, filters and sampling
• Answering business questions with GA4

Practical output: Build explorations that answer three business questions.

• Formulas, pivot tables and lookup methods
• Cleaning and joining data
• Charts that communicate
• AI help with formulas, verified

Practical output: Build an analysis workbook from raw channel data.

• Connectors and data sources
• Blended data and calculated fields
• Dashboard design for decision-makers
• Sharing and scheduling

Practical output: Build a multi-channel performance dashboard.

• Conversion funnels and drop-off diagnosis
• Cohort retention analysis
• Segmentation, including RFM
• Turning findings into hypotheses

Practical output: Run a funnel and cohort analysis and list opportunities.

• Spend, CPC, CPA, ROAS and contribution margin
• Reading Google Ads and Meta reports
• Breakdowns and diagnostics
• Budget efficiency and diminishing returns

Practical output: Evaluate a paid media account and propose budget shifts.

• Search Console performance analysis
• Content performance and decay
• Linking organic traffic to conversions
• Opportunity sizing

Practical output: Create an SEO and content performance report.

• Funnel and lifecycle stages
• Retention, churn and customer lifetime value
• Engagement metrics and their limits
• Lead quality and pipeline analysis

Practical output: Build a lifecycle dashboard with retention and value metrics.

• Revenue, margin and unit economics
• Product and category performance
• Basket and checkout analysis
• Pricing and promotion effects

Practical output: Analyse an e-commerce data set and recommend actions.

• Core SQL: select, filter, join, aggregate
• GA4 export structure
• Common marketing queries
• Cost awareness

Practical output: Write queries that answer marketing questions.

• Attribution models and their biases
• Conversion lift and holdout tests
• Marketing mix modelling overview
• Choosing a measurement approach

Practical output: Compare attribution views and design an incrementality test.

• A/B test design and significance
• Common statistical pitfalls
• Forecasting traffic, leads and revenue
• Scenario and budget planning

Practical output: Design a test and build a three-scenario forecast.

• Using AI assistants to explore data, with verification
• Regression and propensity concepts
• Customer segmentation approaches
• Risks: bias, overfitting and hallucination

Practical output: Run an AI-assisted analysis and check it against manual results.

• Choosing the right chart
• Narrative structure for decision-makers
• Executive summaries
• Presenting uncertainty honestly

Practical output: Turn an analysis into a five-slide story.

• Tracking audits and documentation
• Access control and data retention
• Ethical use of customer data and AI
• Building a maintenance routine

Practical output: Write a governance checklist for an analytics stack.

• Scoping the capstone engagement
• Data collection and analysis plan
• Review of interim findings
• Coaching on recommendations

Practical output: Complete the capstone analysis with trainer feedback.

• Presentation of findings and recommendations
• Questions and challenge
• Implementation roadmap
• Feedback and next steps

Practical output: Present the capstone and receive written feedback.