Data-Driven Product Management

Learn via : Virtual Classroom / Online
Duration : 2 Days
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Description

    The Data-Driven Product Management training aims to help product managers balance intuition with data and make more effective, measurable product decisions. Throughout the program, participants explore the strategic use of data in modern product management—from performance metrics and user segmentation to A/B testing and AI-powered product tools.

    In addition, critical topics such as MVP development, iterative improvement through user feedback, pricing strategies, and data ethics are covered through practical examples. This program offers a comprehensive roadmap for professionals looking to strengthen their product management decisions with data.

    Audience

    • Business Analysts, Systems Analysts, Project Managers, Team Leaders/Managers, and Enterprise Architects
    • Individuals holding the Product Owner (PO) role in Scrum teams
    • Those looking to improve their knowledge and skills in Business Analysis and Project Scope Management, and enhance business performance
    • Mid to Senior-level IT Managers managing Business Analysis teams or processes
    • UX Specialists
    • Software Experts
    • Test Engineers and Quality Assurance Experts

Outline

Introduction to Data-Driven Product Management
• The role of data in product decision-making
• Data vs. intuition: finding the right balance
• Hypothesis-driven product development approach

Types of Data and Their Use in Digital Products
• Difference between quantitative and qualitative data
• Transactional, behavioral, and attitudinal data sources
• Web analytics, clickstream, heatmaps, session recording tools

Product Performance Metrics
• OMTM (One Metric That Matters)
• North Star Metric
• Funnel metrics and user journey analysis
• Metrics such as conversion, retention, churn, LTV, and adoption

Decision-Making Frameworks and Experimental Methods
• AARRR and HEART metric frameworks
• Hypothesis – Experiment – Learning cycle
• A/B testing, feature flagging, rollout strategies

Segmentation and Personalization
• Rule-based and data-driven segmentation
• Segmentation models such as RFM and k-means
• Designing product experiences based on segments
• Personalization strategies and dynamic content

Using Data in Campaign and Pricing Decisions
• Price elasticity and how to test it
• Monitoring promotional and campaign performance
• Optimizing offers and campaigns with A/B tests

AI-Powered Product Management
• Recommendation engines
• Search engine optimization (search relevance/ranking)
• Automated customer support systems (chatbots, smart FAQs)

Data-Driven MVP and Prototyping Processes
• What is an MVP and why is it important?
• Creating MVP hypotheses with data and defining success criteria
• Gathering early-stage user feedback in prototype testing
• Post-MVP data tracking and decision-making

Using Data in Prototype Testing
• Types of data used in UX research
• Qualitative and quantitative testing tools (e.g., Hotjar, Maze, user testing)
• Analyzing user behavior in prototypes and deriving insights

Using Data for Roadmapping and Prioritization
• Feature prioritization frameworks (ICE, RICE, WSJF)
• Data-driven roadmap decisions
• Aligning strategy with user feedback
• Prioritizing features using impact-effort matrices

Data-Based Feedback, Retrospective, and Improvement
• Interpreting post-feature performance metrics
• Analyzing experiment results and using dashboards
• Creating internal learning cycles
• Improving experiment velocity and test-do-learn speed

Data Ethics and Usage Boundaries
• Key considerations when working with user data
• Data usage in compliance with GDPR and KVKK
• Anonymization and obtaining user consent

Prerequisites