Advanced Product Management & Analytics

• Course objectives, scope, and deliverables
• How to get the most out of the course (hands-on projects, case studies)

• Evolution of product management (from project management to product-led organizations)
• Modern Product manager’s impact: balancing user needs, business goals, and technology feasibility
• Cross-functional leadership in high-growth or complex orgs

• Organizational alignment around product strategy
• The shift from feature factories to outcome-driven product development
• Product-led vs. sales-led vs. marketing-led growth strategies

• Product management career trajectory: Individual Contributor (IC) PM to Chief Product Officer
• T-Shaped skill sets: deep domain knowledge + broad cross-functional expertise
• Continuous learning and upskilling (analytics, UX, technical ac

• Case studies
• Opportunity Solution Tree (OST) for structured discovery

• Triangulation of market research, usage data, and competitor intelligence
• Advanced customer segmentation (cohort analysis, psychographics, firmographics for B2B)
• Evolving personas with data-driven insights

• Moving beyond “customer is king” to “ecosystem synergy”
• Strategic approaches to validating the entire product ecosystem

• Key metrics for discovery effectiveness (signal-to-noise ratio, discovery velocity)
• Tying discovery back to business impact

• Leveraging “why” to unify teams and stakeholders
• Incorporating emergent trends and future scenarios

• Understanding and articulating business moats (network effects, brand, economies of scale)
• Using advanced strategic frameworks (Blue Ocean Strategy, “7 Powers” approach)

• Advanced diagnostics for product-market fit (retention cohorts, Net Promoter Score trends, expansion metrics)
• PMF for multi-sided platforms and marketplaces

• Advanced competitor teardown: user experience, pricing, product positioning
• Strategic partnerships, ecosystem thinking, and open innovation

• Data-driven ideation (deep analytics, ML-driven insights)
• Ethnographic research, contextual inquiry, and direct user observation
• Trendspotting (emerging technologies, cultural shifts, macroeconomic changes)

System & design thinking at scale (e.g., advanced versions of Google Ventures Sprints)

• Evaluating engineering complexity early
• Building alignment across UX, Tech, and Business

• Build-learn-measure loop with robust instrumentation
• Driving continuous improvement via “Innovation Accounting” metrics

• Multivariate testing, Bayesian vs. frequentist approaches
• “Wizard of Oz,” “Concierge,” and “Fake Door” experiments at scale

• Formulating strong hypotheses with quantitative thresholds
• Using advanced analytics tools to gather actionable insights

• Combining qualitative (in-depth interviews, usability labs) and quantitative (A/B, funnel analysis)
• Rapid prototyping vs. production-run tests

• Establishing “kill criteria” and pivot triggers
• Regulatory compliance, data privacy, and ethical considerations in experimentation

• Beyond the basics: advanced interaction patterns, inclusive design, accessibility at scale
• Mapping user flows for complex multi-step journeys

• How to extract data using Prototypes and iterate on feedback (case studies)
• Advanced usability heuristics (Nielsen’s vs. specialized heuristics for AR, Spatial Interfaces.

• How design systems scale across enterprise-level products
• Guest speaker: Design Lead on bridging design, technology, and business goals

• Evolving JTBD frameworks for sophisticated product ecosystems
• Mapping micro-moments and friction points to drive deeper engagement

• Advanced frameworks: Cost of Delay (CoD), Weighted Shortest Job First (WSJF)
• Outcome vs. output mindset (OKRs, North Star metrics)

• Creating a layered release approach (MVP, MLP, then scale)
• Story Mapping advanced techniques for complex feature sets

• The Hook Model with deeper considerations for ethics and user well-being
• Advanced Gamification strategies (progressive challenges, dynamic reward schedules)
• Case studies

• Aligning stakeholders on a continuous, dynamic roadmap
• Balancing strategic bets with incremental improvements
• Value proposition canvas & business model innovation

Instrumentation plan (logging frameworks, event tracking, data warehousing)

• Pirate Metrics (AARRR) and beyond (HEART framework, REAN model)
• Using cohort analysis, LTV/CAC, churn analytics, retention curves at scale

• Predictive analytics and machine learning for user segmentation, churn prediction, personalization
• Building AI-driven product features (recommendation engines, NLP-driven interfaces)

• Translating insights into product decisions
• Aligning analytics with user experience improvements and business outcomes

• Introduction
• Downloading Power BI & adjusting settings
• Exploring the Power BI Desktop interface & workflow
• Helpful Power BI resources

• Connect to data sources
• Types of data connectors in Power BI Desktop
• Change the data source settings
• The Power BI Query editor (aka Power Query)
• Basic table transformations in Power BI
• Working with text tools
• Working with numerical values
• Working with date & time tools

• Shape and transform tables
• User-friendly naming conventions
• Resolving inconsistencies & nulls
• Evaluate and transform column data types
• Append queries
• Merge queries and expand table
• Merge queries with group by, and different types of joins
• Pivot & Unpivot
• Conditional columns
• Columns from examples
• Transpose
• Data profiling
• Resolve data import errors
• Files from folders
• Data loading with parameters

• What is a “Data Model”?
• Principles of data normalization
• Understanding data tables vs. lookup tables
• Creating table relationships in Power BI Desktop
• Design a data model that uses a “Star” schema
• “Snowflake” schemas in Power BI
• Managing & editing table relationships in Power BI Desktop
• Managing active vs. inactive relationships
• Understanding relationship cardinality
• Understanding filter flow
• Two-way filters in Power BI Desktop (USE WITH CAUTION!)
• Hiding fields from the Power BI report view
• Table and column properties
• Common date table (using DAX and Power Query)
• Hierarchies and data granularity
• Role playing dimensions
• Power BI data model best practices

• Intro to Data Analysis Expressions (DAX)
• Intro to DAX calculated columns
• Basic operators
• IF, BLANK and ISBLANK
• AND, OR and NOT
• SWITCH
• Measures -an introduction, with standard aggregations including COUNTBLANK
• Aggregation of calculations
• Other statistical functions
• Rounding functions
• Division functions – MOD and QUOTIENT
• SIGN (and use with SWITCH) and ABS
• COUNT functions (COUNTA, DISTINCTCOUNT, COUNTROWS), exponential function
• Text searching
• Text extraction and substitution
• Text conversion
• Other text functions

• ISERROR and LOOKUPVALUE
• Other information functions
• RELATED – Flatten out a parent-child hierarchy
• Context
• RELATEDTABLE and COUNTROWS
• ALL, FILTER, CALCULATE, ALLEXCEPT, ALLSELECTED
• Date and time functions, FIRSTDATE, LAST DATE
• Start of… and End of…, Previous… and Next…
• DATESINPERIOD, DATESMTD, DATESQTD, DATESYTD, TOTALMTD, TOTALQTD, TOTALYTD
• Opening Balance and Closing Balance
• Semi-additive measures
• SAMEPERIODLASTYEAR and PARALLELPERIOD
• Other Time Intelligence Functions
• Quick measures

• Exploring the “Report” view in Power BI Desktop
• Table and matrix
• Column charts, line charts and area charts
• Combo charts and treemaps
• Card, multi-row card, and gauge visual
• Pie chart, donut chart and funnel
• Scatter chart, waterfall chart and maps
• Customize and configure visuals
• Custom tooltips
• Configure report page
• Conditional formatting
• Applying slicing and filtering
• Sorting
• Synch slicers
• Using themes
• Drillthrough and drill downs
• Bookmarks
• Page navigation
• Edit interactions
• Decomposition tree visual
• Custom Visuals

• Product Sales Analysis
• Amazon Product Review Analysis
• Customer Cohort and Retention Analysis

• Logging into Power BI Service and a quick look around
• Power BI terminology
• Adding new users
• Datasets and Reports in Power BI Service
• Publish reports and export data
• Create a workplace
• Assign Workplace Roles
• Providing Access to Datasets
• Analyze in Excel feature
• Configure a Dataset Scheduled Refresh
• Configure Incremental Refresh
• Creating and sharing a new report
• Differences between dashboards and reports
• Manage tiles in a dashboard
• Dashboards: Options
• Pin a Live Report Page to a Dashboard
• Configure Data Alerts
• Configure and Update a Workspace App
• Power BI Mobile App
• Creating a Row-Level Security

• Moving beyond feature-based roadmaps to holistic impact-driven strategies
• Aligning with company-level and market-level objectives

• Incorporating ambitious, quantifiable Key Results tied to revenue, engagement, or strategic impact
• Linking product-level OKRs to broader organizational goals
• Avoiding common pitfalls: poorly defined metrics, misaligned incentives

• Cultivating an outcome-first mindset in cross-functional teams
• Communicating the importance of “value” over “volume” of features

• Examining diverse revenue frameworks (subscription, marketplace, SaaS, or hybrid)
• Understanding value creation, delivery, and capture across multiple channels

• Structuring pricing models (usage-based, tiered, dynamic)
• Implementing bundling, cross-selling, and premium upgrades
• Balancing near-term revenue gains with long-term market positioning

• Creating spreadsheets or financial models to project revenue under varying assumptions
• Conducting sensitivity analyses (feature releases, user growth, competitor moves)
• Calculating ROI, break-even points, and payback periods to justify product investments

• Connecting revenue goals to user satisfaction and retention targets
• Communicating business case outcomes to stakeholders, highlighting risks and trade-offs
• Adapting pricing decisions to shifting market or customer preferences

• Ongoing user and market research instead of one-time studies
• Embedding a “learn-first” attitude into regular product cycles

• Mapping product goals into opportunities, testable ideas, and potential solutions
• Quickly spotting high-impact vs. low-value branches

• Scheduling interviews, quick assumption tests, and lightweight prototypes
• Integrating discovery checkpoints into existing Agile or scaled frameworks

• Targeting the riskiest assumptions first
• Combining qualitative feedback (user interviews) with quantitative signals (analytics)

• Involving cross-functional squads (Design, Engineering, Analytics)
• Sharing insights transparently to shape priorities and reduce guesswork

• Zeroing in on key indicators (e.g., retention, revenue expansion) that mirror real success
• Filtering out vanity metrics that misrepresent or inflate progress

• Identifying signals that predict future outcomes (e.g., new user activation) vs. final performance measures (e.g., churn rate)
• Balancing immediate feedback loops with big-picture health checks

• Building robust pipelines (instrumentation, data quality checks)
• Setting weekly/monthly reviews to iterate swiftly based on changing metrics

• Sharing metrics dashboards across squads to foster collective ownership
• Avoiding “reporting theatre”: ensuring metrics are actionable, not just displayed

• Anchoring experiments to essential business drivers (revenue, churn, engagement)
• Defining robust guardrails (performance, cross-segment checks) and clear success benchmarks

• Avoiding bias, overlapping segments, and pre-test disparities
• Employing techniques like multi-armed bandit for nuanced feature rollouts

• Integrating sequential testing, Bayesian updates, or advanced analysis methods
• Respecting user privacy, minimizing disruption, and having a rapid rollback plan

• Monitoring significance and ROI thresholds
• Determining when to roll out, revert, or iterate—guided by impact, not just p-values

• Orchestrating multiple concurrent tests without data collisions
• Documenting outcomes in an accessible “experiment vault”
• Championing a culture where evidence-based learnings consistently shape product direction

• Evaluating SAFe, LeSS, Nexus for orchestrating large product ecosystems
• Adapting stand-ups, backlog refinements, reviews, and retros to multi-team contexts

• Establishing release trains or multi-squad increments with consistent sprint lengths
• Handling top-down vision (executive mandates) without undermining local autonomy

• Synchronizing sprints with discovery and experimentation results
• Allocating resources for quick pivots when tests reveal unexpected user/market feedback

• Structuring concise pitches that highlight key insights (retention curves, margin analysis)
• Using storytelling arcs to create emotional resonance and clarity for leadership

• Translating complex analytics (predictive models, segment flows) into clean, intuitive dashboards
• Choosing the right charts for multi-dimensional or time-series data

• Anticipating counterarguments, addressing stakeholder skepticism
• Reinforcing the product’s strategic direction with robust data evidence

Participant Showcases

• Integrating monetization planning, continuous discovery insights, rigorous experiments, scaling frameworks, and metrics into a cohesive product strategy
• Demonstrating advanced storytelling to communicate ROI, user impact, and roadmapping decisions