Role-Based Programme
RB1225

AI for Product Data & Analytics

Smarter Product Insights, Experimentation & Data-Driven Decisions

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Understand how AI can support Product Data & Analytics across metric definition, data interpretation, behavioural analysis, experimentation, and reporting.
  • Apply AI to analyse product usage, customer journeys, funnels, cohorts, retention, adoption, and performance trends.
  • Use AI to support KPI interpretation, hypothesis generation, experiment design, and product decision-making.
  • Develop practical skills for converting product data into actionable insights, recommendations, and management-ready reports.
  • Understand responsible AI use, data privacy, analytical bias, data quality, statistical caution, and human validation in product analytics.

Tools covered

Generative AI AssistantsProduct AnalyticsData InterpretationCustomer Behaviour AnalysisFunnel AnalysisCohort AnalysisExperimentation SupportKPI AnalysisInsight GenerationReporting & Presentation Tools

Who should attend

  • Product Analysts
  • Product Data Analysts
  • Product Analytics Professionals
  • Product Managers
  • Product Owners
  • Digital Product Analysts
  • Growth Product Professionals
  • Business Analysts
  • Customer Analytics Professionals
  • Product Operations Professionals
  • Product Insights Professionals
  • Data Analysts Supporting Product Teams
  • Experimentation & Growth Analysts
  • Product / Service Management Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of products, customers, or business data
  • Familiarity with metrics, spreadsheets, dashboards, or product reporting is helpful
  • Basic analytical and digital-tool skills
  • No programming or advanced statistical knowledge required
  • Prior AI-tool experience is helpful but not mandatory

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to Product Analytics
  • Exploring AI support across data interpretation, analysis, hypothesis generation, and reporting
  • Distinguishing Generative AI from BI platforms, analytics systems, and statistical tools
  • Understanding AI limitations, hallucinations, data-quality issues, and analytical risks
Practical activities
  • Identifying recurring Product Analytics activities suitable for AI assistance
  • Comparing traditional and AI-assisted approaches to an analytics task
  • Mapping AI opportunities across the product-data lifecycle

Scenarios

Product Funnel Drop-Off to Improvement Hypothesis

Product Goal → Funnel Data → Drop-Off Analysis → Segment Comparison → Behavioural Patterns → Hypotheses → Experiment Design → Success Metrics

Participants use AI to analyse a sample product funnel, identify significant drop-off points, generate evidence-based hypotheses, and design a basic experiment to test potential improvements.

Product Usage Data to Executive Decision Insight

Usage Data → KPI Review → Cohort Analysis → Retention & Adoption Patterns → Key Findings → Business Impact → Recommended Actions → Executive Summary

Participants use AI to analyse sample usage and retention data, identify meaningful product insights, and prepare a concise recommendation for product leadership review.

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