Role-Based Programme
RB1226

AI for Product Data & Analytics

Product Intelligence, User Behaviour & Data-Driven Decision-Making

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Develop practical AI capabilities for product analytics, user behaviour analysis, KPI interpretation, and product decision support.
  • Apply AI to analyse adoption, activation, engagement, conversion, retention, funnels, cohorts, and feature performance.
  • Use AI to identify product-performance gaps, user behaviour patterns, friction points, and growth opportunities.
  • Strengthen experimentation, metric selection, dashboard interpretation, insight communication, and product prioritisation.
  • Apply responsible AI practices related to customer data, privacy, statistical interpretation, bias, data quality, and human validation.

Tools covered

Generative AI AssistantsProduct AnalyticsAI Search & ResearchData AnalysisFunnel AnalysisCohort AnalysisCustomer Behaviour AnalysisProduct KPI AnalysisExperimentation SupportDashboard InterpretationInsight GenerationExecutive Summarisation

Who should attend

  • Product Data Analysts
  • Product Analysts
  • Product Analytics Managers
  • Product Managers
  • Product Owners
  • Data Analysts
  • Business Analysts
  • Growth Analysts
  • Digital Product Analysts
  • Customer Analytics Professionals
  • Product Operations Professionals
  • Product Strategy Professionals
  • Business Intelligence Professionals
  • Experimentation Professionals
  • Product & Service Management Leaders

Prerequisites & Participant Readiness

  • Experience in product management, product analytics, data analysis, business analysis, or digital products
  • Familiarity with product KPIs, funnels, customer journeys, dashboards, or performance reports
  • Basic familiarity with Generative AI tools is recommended
  • Basic understanding of quantitative data and business metrics
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, reasoning models, AI assistants, and analytical support
  • Exploring AI applications across product metrics, user behaviour, experimentation, and reporting
  • Understanding how AI differs from analytics platforms, BI tools, and statistical software
  • Recognising hallucinations, calculation errors, unsupported interpretations, and AI limitations
Practical activities
  • Mapping Product Analytics activities to practical AI applications
  • Identifying high-value versus high-risk analytical use cases
  • Comparing traditional and AI-assisted product analysis workflows

Scenarios

Product Funnel Drop-Off to Conversion Improvement

Product Data → Funnel Analysis → Segment Comparison → Drop-Off Identification → Behavioural Analysis → Root-Cause Hypotheses → Experiment Design → Conversion Plan

Participants use AI to analyse a simulated product funnel, identify high-friction stages, compare user segments, and develop evidence-based experiments to improve conversion.

Declining Retention to Product Growth Strategy

Cohort Analysis → Retention Trends → Feature Adoption → User Segmentation → Churn Signals → Opportunity Identification → Product Actions → Executive Recommendation

Participants use AI to investigate a simulated decline in product retention, connect behavioural and feature data, identify improvement opportunities, and develop a structured product-growth recommendation.

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