Role-Based Programme
RB1227

AI for Product Data & Analytics

Advanced Product Intelligence, Experimentation & Data-Driven Growth

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across product analytics, customer behaviour analysis, KPI design, experimentation, performance monitoring, and product decision support.
  • Use AI-assisted analysis to identify adoption patterns, conversion gaps, engagement behaviour, retention drivers, customer friction, and growth opportunities.
  • Apply AI to funnel analysis, cohort analysis, segmentation, experimentation, product performance reporting, and insight generation.
  • Translate complex product data into clear business insights, hypotheses, recommendations, and product priorities.
  • Build responsible AI-enabled Product Analytics workflows with strong data quality, privacy, validation, governance, and human oversight.

Tools covered

Generative AI AssistantsProduct AnalyticsAI Search & ResearchCustomer Behaviour AnalysisFunnel AnalysisCohort AnalysisRetention AnalysisExperimentation SupportKPI DesignProduct Performance DashboardsForecasting ConceptsVoice-of-Customer AnalysisWorkflow AutomationAI AgentsDecision-Support Tools

Who should attend

  • Product Analysts
  • Senior Product Analysts
  • Product Analytics Managers
  • Digital Product Analysts
  • Product Data Analysts
  • Product Managers
  • Growth Product Managers
  • Product Operations Professionals
  • Customer Analytics Professionals
  • Business Intelligence Professionals Supporting Product Teams
  • Data Analysts Supporting Product Teams
  • Product Owners
  • Experimentation Professionals
  • Product Strategy Professionals
  • Leaders Responsible for Product Performance & Insights

Prerequisites & Participant Readiness

  • Experience in product analytics, product management, business analytics, digital products, or data analysis
  • Familiarity with product metrics, customer journeys, funnels, dashboards, and basic analytical concepts
  • Basic awareness of Generative AI and common business applications
  • Comfort working with quantitative and qualitative product information
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, reasoning models, analytics support, automation, and AI agents
  • Exploring AI applications across product measurement, analysis, experimentation, and reporting
  • Distinguishing AI-assisted analysis from automated decision-making
  • Understanding hallucinations, data-quality risks, analytical bias, and human accountability
Practical activities
  • Mapping AI opportunities across the Product Analytics lifecycle
  • Identifying activities suitable for augmentation, automation, or continued human ownership
  • Creating an AI opportunity map for Product Analytics teams

Scenarios

Low Product Adoption to Data-Driven Improvement Plan

Product Usage → Funnel Analysis → Segment Behaviour → Customer Feedback → Adoption Gaps → Hypotheses → Experiment → Measurement → Product Action

Participants use AI to diagnose weak product adoption, combine behavioural and qualitative evidence, identify key friction points, design experiments, and create a measurable improvement plan.

High Acquisition, Low Retention to Product Growth Strategy

Acquisition → Activation → Cohort Analysis → Retention Curves → Feature Usage → Customer Feedback → Retention Drivers → Experiments → Growth Roadmap

Participants use AI to analyse a product with strong acquisition but weak retention, identify behavioural and experience gaps, and develop a data-driven product growth and retention roadmap.

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