Role-Based Programme
RB1224

AI for Product Data & Analytics

Smarter Product Insights, KPI Analysis & Data-Driven Decisions

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Duration
4 Hours
Level
Awareness
Delivery
Instructor-Led
Format
Awareness Session

Programme Objectives

  • Understand how AI can support Product Data & Analytics across KPI analysis, behavioural insights, product performance, and decision support.
  • Explore practical AI applications for analysing product usage, customer journeys, funnels, cohorts, and feature performance.
  • Apply structured prompting techniques to interpret product data, identify trends, generate hypotheses, and create management-ready insights.
  • Use AI to improve productivity across product reporting, metric reviews, performance analysis, and stakeholder communication.
  • Recognise data quality, privacy, bias, statistical limitations, confidentiality, and human-validation requirements when using AI for product analytics.

Tools covered

Generative AI AssistantsProduct AnalyticsKPI AnalysisCustomer Behaviour AnalysisFunnel AnalysisCohort AnalysisProduct Performance ReportingData InterpretationAI-Powered Visualization & Reporting Tools

Who should attend

  • Product Analysts
  • Product Data Analysts
  • Product Analytics Professionals
  • Product Managers
  • Product Owners
  • Digital Product Managers
  • Product Operations Professionals
  • Business Analysts
  • Data Analysts Supporting Product Teams
  • Growth Product Professionals
  • Customer Insights Professionals
  • Product Strategy Professionals
  • Product Performance Managers
  • Digital Analytics Professionals
  • Product Data & Analytics Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of products, customers, or business metrics
  • Familiarity with spreadsheets, dashboards, KPIs, or product-performance reports is helpful
  • Basic numerical and analytical skills
  • No programming or advanced statistical knowledge required
  • No previous AI-tool experience required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to product analytics
  • Exploring AI applications across KPI reviews, customer behaviour, funnels, feature analysis, and reporting
  • Understanding the difference between AI assistance, analytics platforms, statistical analysis, and analyst judgement
  • Recognising AI limitations, hallucinated calculations, false correlations, and unsupported conclusions
Practical activities
  • Identifying high-value AI applications across a typical Product Analytics workflow
  • Comparing a traditional product-analysis task with an AI-assisted approach

Scenarios

Product Usage Data to Customer Behaviour Insight

Product Usage Data → AI-Assisted Analysis → Segment Comparison → Funnel Gaps → Behaviour Patterns → Hypotheses → Product Insight

Participants use AI to analyse sample product-usage information, identify behavioural patterns and funnel gaps, and prepare structured hypotheses for further product-team validation.

KPI Movement to Product Decision Support

Product KPIs → Trend Analysis → Performance Variance → Possible Drivers → Feature / Journey Review → Recommendations → Management Summary

Participants use AI to review sample product KPIs, identify important performance movements, generate investigation hypotheses, and create a concise decision-support summary for product stakeholders.

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