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PM Critique and Learn from Case Studies for these concepts, My Guts,…

- - - - Select * from table_name
      - select column_name,.. from table_name
      - Left Join and Joins
      - CTEs and filtering down
      - Where, and, or
      - group by and order by and having
- - - - Instead of asking “What features should I build?”, ask:
        “What’s the riskiest assumption that, if wrong, makes everything else irrelevant?”
      - List the Assumptions
        
        Score by
        
        Risk Level
        
        High Risk Level Means Validation Required
      - Decide what truth matters most to validate
    - - Use the MUST-SHOULD-COULD-WON’T or Slicing Framework
      - Once you know the truths to validate, cut everything that doesn’t help validate those.
      - ✅ Use the MVP Slicing Framework:
        
        Pain Validation
        
        Does this feature prove the user has the problem?
        
        Yes for MVP
        
        Solution Fit
        
        Does it prove your solution is valuable?
        
        Yes for MVP
        
        Scalable Infra
        
        Does it help at 10K users
        
        No for MVP
        
        Delight Layer
        
        Does it improve UX but not core function?
        
        No for MVP
        
        Internal Ops
        
        Can this be done manually for now?
        
        No for MVP - Automate later
      - Strip features not tied to learning
    - - For every MVP feature, write a “test card”: We believe users will [do this], To test that, we will [build this experiment], And measure success by [metric or signal], We’re right if [threshold]
      - If the feature doesn’t help you test a “test card,” it’s out of MVP.
    - - Scoping across stakeholders
  - - - An Iteration Cycle -After every sprint - Ask this question
        
        What is the biggest opportunity or problem to solve now?
        
        OST
        
        Keeps your team focused on outcomes and user problems, not features.
        
        Outcome → Opportunities → Solutions → Experiments
        
        Example:
        
        Outcome: Increase free-to-paid conversion
        
        Opportunities: “Users don’t understand course value on Day 1”
        
        Solutions: Add onboarding walkthrough, Show alumni testimonials
        
        Experiments: A/B test video vs static walkthrough
        
        📌 Great for mapping all current efforts, not just adding features randomly.
        
        Goal is to improve
        
        Funnel RCA
        
        methodical way to diagnose where and why a product is underperforming by tracing drops or leaks through a user funnel and investigating the root causes behind them.
        
        User funnel analysis — Tracking how users move (or drop) through key steps.
        
        Root cause analysis (RCA) — Asking “Why?” multiple times to find the underlying issue.
        
        Why conversion is dropping?
        
        Problem Fixing
        
        What experiment will address this?
        
        Problem types
        
        Known Problems
        
        Comes from Data
        
        Qual
        
        2 more items...
        
        Quant
        
        4 more items...
        
        Unknown Problems
        
        Talk to users
        
        Generate new ideas
        
        Unaware of Need
        
        Education content, explainer videos, 'why' emails, quiz gives realization, better onboarding
        
        Unaware of Solution
        
        Landing page walkthrough, "You can do X with us" video, demo day, Onboarding
        
        Fear
        
        Mock preview, testimonials, mentor guarantee, "Safe trial" framing
        
        No Reminder
        
        WhatsApp nudges, calendar integration, streak systems, Emails and notification
        
        Friction
        
        One-click flows, better UX, skip signup, prefilled onboarding
        
        No Perceived Value/Progress/Investment/Reward
        
        Skill score, progress bar, certificate preview, rewards
        
        Wrong Segment
        
        Repositioning, redirect to better-fit offering, qualification quiz
        
        What metric will tell us if it worked?
        
        North Star Metric + Input Metric Tree
        
        What can we cut or simplify while shipping this?
        
        Prioritisation
        
        Basic Value Prop Not Validated
        
        MVP Slicing Framework
        
        Basic Value Prop Not Validated
        
        KANO to identify Delighters (what surprises & retains)
        
        RICE to prioritize them based on Reach, Impact, Confidence, Effort
        
        What did we learn? What’s next?
  - - - Age
      - Gender
      - Income
      - Occupation
      - Education
      - Use case: Consumer tech (e.g., targeting young professionals for upskilling app).
    - - Country / Region
      - Climate / Urbanity
      - Time zone
      - Language
      - Use case: Push notification timing, regional pricing strategies, language localization.
    - - Feature Usage
        
        Power users vs. casuals
      - Engagement
        
        Daily active vs. monthly active
      - Purchase Behavior
        
        High-LTV vs. window-shoppers
      - Funnel Stage
        
        Signed up but never activated
      - Loyalty / Churn Risk
        
        Repeat buyer vs. silent drop-off
      - Use case: Retention strategies, upsell offers, lifecycle emails.
    - - Motivation
      - Risk tolerance
        
        Early adopters vs. skeptics
      - Learning style
        
        Visual vs. auditory learners
      - Values
        
        Sustainability, speed, quality
      - Use case: Positioning, brand messaging, emotional hooks in landing pages.
    - - Use case: Curriculum design, onboarding flows, custom nudges.
    - - Recency
        
        How recently a user purchased or engaged
      - Frequency
        
        How often they use or buy
      - Monetary
        
        How much theyâ€™ve spent
      - Use case: Lifecycle campaigns, win-back flows, loyalty rewards.
    - - What it is: Grouping users by when they signed up, activated, or performed a key event.
      - Sign-up cohort
      - Activation cohort
      - Feature cohort
      - Use case: Analyze long-term retention, feature impact, engagement over time.
    - - What it is: Prioritizing users by their value to the business.
      - High-LTV Customers
      - Influencers - who refer
      - Power Users
      - High Churn Risk
      - Use case: Prioritization, loyalty programs, white-glove onboarding.
    - - What it is: Data science-driven clusters + overlaying product/business logic.
      - Use K-Means or hierarchical clustering based on usage vectors.
      - Then tag the clusters based on domain insight.
      - Use case: Building recommendation engines, adaptive onboarding paths.
- - - - Your ability to prioritize features and understand user needs
      - How you balance business goals and user/stakeholder inputs
  - - - Use frameworks to break down problems.
      - Mention all possible directions first.
      - State your inclination with reasoning (e.g., user impact, revenue, feasibility).
    - - Show ability to weigh user needs, business goals, and technical constraints.
      - Justify why you picked one solution/approach over others.
      - Stakeholder Handling
    - - Situation: What was the context?
      - Task: What was your responsibility?
      - Action: What did you do?
      - Result: What was the outcome?
  - - - User Journey Mapping
      - RICE (Reach, Impact, Confidence, Effort)
      - MoSCoW (Must, Should, Could, Won’t)
      - Jobs-To-Be-Done
    - - 5 Whys (for root cause analysis)
      - First Principles Thinking.
      - Tree Diagram (mentioned by you) – branch out possibilities and narrow down.
    - - Break down into categories and use logical assumptions.
      - Use round numbers and show thought process clearly.
    - - • Customers: Market size, segments, buying behavior, perceived value
      - • Competitors: SWOT, market share
      - • Collaborators: Suppliers, partners, cross-sell opportunities
      - • Climate: External factors—COVID, regulation, tech trends
      - • Company (Internal): Strengths, weaknesses, culture, resources
- - - - Short vs Long Term
      - Big company vs small company
    - - https://docs.google.com/document/d/1fuOHGr4CFTziQFNGzvG8ND9Clr86LsDy88a0byxx150/edit?usp=sharing
  - - - (Who, What, When, Where, Why, How)
      - Use case: Understanding the scope of a vague problem or requirement.
      - Example:
      - Problem: “Our users aren’t finishing the course.”
        
        • Who is affected? → Paid users in Module 2 and beyond
        
        • What are they doing? → Dropping out after Module 1
        
        • When is this happening? → Typically 3–5 days post-purchase
        
        • Where is it occurring? → Across mobile web, mostly Android
        
        • Why is it happening? → Maybe content too hard / lack of motivation
        
        • How are they behaving? → Engagement drops sharply; no quiz attempts
      - 🔍 Insight: You now have a scoped path to run user interviews and analytics to confirm hypotheses.
    - - (Deconstruct the problem to its most fundamental truths, then reason up)
      - Use case: Problems that involve constraints, ambiguity, or new ideas.
      - Example:
      - Problem: “How do we build an AI PM course?”
        
        • Fundamental truths:
        
        • Users want interview prep that reflects real AI PM roles
        
        • AI PM involves prompt engineering, RAG, hallucination debugging
        
        • Most courses are theory-heavy, not case-heavy
        
        • New solution built from bottom-up:
      - → Make your course practical + simulation-based, not academic
      - 🔍 Insight: Don’t rely on analogies (e.g., copying other PM courses). Build from first principles.
    - - Use case: When a metric is dropping and you want to pinpoint where.
      - Example:
      - Problem: “Why are trial-to-paid conversions dropping?”
      - Break the funnel:
        
        • Leads → Trial Signups → Day 1 Activity → Day 3 Stickiness → Paid Conversion
      - Data shows:
        
        • Trial Signups: ✅
        
        • Day 1 Activity: ❌ 20% drop
      - → Focus on onboarding flow or motivation gaps on Day 1
      - 🔍 Insight: Narrow down the drop-off to one part of the funnel. Attack that with qualitative + quant.
    - - (Specific to product design/interview situations)
      - Use case: Product design, feature planning.
      - Steps:
        
        Comprehend the situation
        
        What
        
        Identify the customer
        
        Who
        
        Report the customer’s needs
        
        Cut through scope
        
        Why
        
        List solutions
        
        Evaluate trade-offs
        
        Summarize recommendation
        
        How
      - Example:
      - Prompt: “Design a dashboard for course progress tracking”
      - → Use CIRCLES to go from context to a feature map with prioritization.
    - - Use case: When something is broken or not performing and you want to drill into why.
      - Tools:
        
        • 5 Whys
        
        • Ishikawa (Fishbone) Diagram: Categories include: People, Process, Tech, Tools, External
      - Example:
      - Problem: “NPS dropped to 5 from 8”
      - 5 Whys:
        
        Why? → Users feel the course isn’t relevant
        
        Why? → Content seems outdated
        
        Why? → AI PM content didn’t mention RAG or agents
        
        Why? → Syllabus frozen 6 months ago
        
        Why? → No regular curriculum review
      - 🔍 Action: Set quarterly content review cadences + plug in expert contributors.
    - - Use case: When trying to deeply understand the root user problem or need.
      - Structure:
      - “When I [situation], I want to [motivation], so I can [desired outcome].”
      - Example:
      - “When I start a PM course, I want to know what to expect, so I can stay committed.”
      - → Add a pre-course explainer walkthrough and estimated time commitment section.
      - JTBD → Features Idea -> Prioritise → User Stories
        Why? - What? - How?
    - - Use case: You’re flooded with 5+ problems at once—how do you choose where to focus?
      - Here’s a quick method using 3 lenses:
        
        Impact
        
        How much does this affect users or business?
        
        High / Medium / Low
        
        Urgency
        
        How soon does this need to be solved?
        
        Immediate / Soon / Later
        
        Clarity
        
        Do we know exactly what the problem is?
        
        Clear / Vague
      - Example: You’re running a PM course and have the following 3 issues:
        
        NPS score dropped last week
        
        Impact: High
        
        Urgency: High
        
        Clarity: Medium
        
        🔥 Investigate this now
        
        Font size looks odd on mobile
        
        I - Low
        
        U: L
        
        C: H
        
        Backlog
        
        Students confused about how to book mock interviews
        
        I: M
        
        U: M
        
        C: H
        
        Next Sprint
      - Triage is about making fast, strategic calls—so you don’t treat “change font” with the same energy as “we’re losing users.”
  - - - JTBD
  - - - Critique: Functionality, understanding, delight, error feedback
      - Design: Clarify, identify personas, evaluate tradeoffs
- - - - https://docs.google.com/document/d/1Xrph3q57kog7LR2NjErjF0ZDTij_3bCXwLIFwRfR17k/edit?usp=sharing
- - - - Truths & PMF
    - - CTLV
  - - - SQL
      - Analytics
    - - GTM
- - - - Loops