Visual reference for the management-consulting skill — 42 practitioner-grade frameworks across 7 categories. Install the skill to get structured problem solving, evidence-labeled analysis, and visual deliverables in Claude Code, Codex, Gemini CLI, or any agent that reads SKILL.md files.
Decompose complexity into actionable, testable components — 6 frameworks
Issue Tree
Break a question into MECE sub-questions until you find the answer
When to useAmbiguous problems with multiple possible causes
When NOT to useSimple yes/no decisions — just decide
"Why did SaaS churn spike from 4% to 7%?" → decompose into voluntary vs involuntary, by segment, by cohort (DuPont-style layering)
How it works: Start with the question at the top. Split it into sub-questions where each level is MECE (no overlaps, no gaps). Keep splitting until you reach testable, data-answerable leaves. Then work bottom-up: color each leaf green (supports), red (contradicts), or gray (unknown). The answer emerges from which branches light up.
What are the 2-3 branches that drive 80% of the answer? (Ignore the rest)
At each split: is this genuinely MECE, or am I double-counting?
At the leaves: what single data point would answer this?
What conclusion can the tree support? "The problem is X, not Y, because branch A is red while branch B is green."
Hypothesis Tree
Each branch is a testable claim with a kill test — prove or disprove, don't just explore
When to useYou have a hypothesis and need to confirm or kill it fast
When NOT to useExploratory phases where you don't have a hypothesis yet
"H: No incumbent has a data moat → Kill test: Do they have proprietary datasets? → KILLED: Top 2 players have 10+ years of training data"
How it works: Unlike an issue tree (which explores), a hypothesis tree starts with a claim you believe is true and tries to kill it. For each hypothesis, define a single data point that would disprove it — the "kill test." Gather that data first. If the hypothesis survives all kill tests, you have a defensible recommendation. If it gets killed, you've saved weeks of analysis on a dead end.
What must be true for my recommendation to be correct? (List 3-5 conditions)
For each condition: what one number or fact would prove it wrong?
Which hypothesis, if killed, most changes the conclusion? (Test that first)
Am I anchoring on confirming evidence and ignoring disconfirming data?
Profit / Revenue Tree
Decompose financial performance into its mathematical components
When to useThe question is about quantified revenue, cost, or profit drivers
When NOT to useQualitative questions — culture, strategy, org design
"Where did the +12% EBIT growth come from?" → Volume drove revenue +8%, but raw material inflation (+15%) ate into gross margin — SG&A discipline saved the bottom line
How it works: Take a financial outcome (revenue growth, margin compression, profit decline) and decompose it mathematically. Revenue = Price x Volume. Cost = Fixed + Variable. Profit = Revenue - Cost. Build a waterfall chart showing each component's contribution. The visual immediately reveals which lever moved and by how much — cutting through narrative to the math.
Which component contributed the most to the change? (The tallest bar in the waterfall)
Is the driver sustainable or one-time? (Price increases stick; one-time cost cuts don't)
What happens if you stress the biggest driver by 20%? Does the story hold?
Are there offsetting effects that the headline number masks? (Revenue up but margins down)
MECE Principle
Mutually Exclusive, Collectively Exhaustive — the quality check for every decomposition
When to useAs a quality check on EVERY issue tree, hypothesis tree, or decomposition
When NOT to useDon't let MECE perfectionism delay analysis — "good enough" structure beats no structure
"4 growth options: organic, M&A, partnerships, new markets" — MECE because every growth dollar must flow through exactly one path
How it works: MECE is not a framework — it's the quality standard applied to every decomposition. After building any tree, test each level: (1) pick any item in the real world — does it fall into exactly one bucket? If it fits two, your buckets overlap (not ME). (2) Can you think of something that doesn't fit any bucket? If yes, you have a gap (not CE). Fix the structure until both tests pass.
Can I place every real data point into exactly one branch? (If not: overlaps)
Is there anything important that doesn't fit any branch? (If yes: gaps)
Am I splitting by a single, clear dimension at each level? (Mixed dimensions = overlap)
Would a skeptic accept this structure as complete and non-redundant?
Pyramid Principle
Answer first, then supporting arguments, then evidence — top-down communication
When to useAny executive communication — memos, decks, verbal updates
When NOT to useExploratory brainstorming where you genuinely don't have an answer yet
"We recommend expanding into Southeast Asia: the market is large (680M people, 7% CAGR), we have a right to win (existing distributor network), and returns exceed our hurdle (18% IRR, 3-year payback)."
How it works: Start with the answer (recommendation or conclusion), then group supporting arguments into 2-4 MECE buckets, then back each argument with specific evidence. The audience gets your conclusion in 10 seconds; they go deeper only if they want to challenge it.
What is my one-sentence answer to the question being asked?
What are the 2-4 independent reasons this answer is correct?
What specific data or evidence supports each reason?
If the audience reads only the first slide, do they get the full message?
SCQA
Situation, Complication, Question, Answer — narrative framing that compels action
When to useBoard presentations, investment memos, any persuasion requiring narrative
When NOT to useRoutine status updates — SCQA adds drama where none is needed
"(S) We are the market leader at 28% share. (C) But we lost 3 percentage points in the last 12 months to a new entrant. (Q) How do we stop the erosion and recapture lost ground? (A) Launch a loyalty program targeting our most at-risk segment by Q3."
How it works: SCQA creates a narrative arc. The Situation establishes common ground (what everyone agrees on). The Complication introduces tension (what changed or threatens). The Question crystallizes the decision at hand. The Answer delivers your recommendation. The power is in the C — without a real complication, there is no reason to act.
What is the stable, agreed-upon reality? (Situation)
What changed, or what threatens that stability? (Complication)
What is the single question the audience needs answered? (Question)
What is our recommended course of action? (Answer)
Strategy
Analyze industries, position within them, and choose where to compete — 11 frameworks
Five Forces
Is this industry structurally attractive? Five pressures determine long-run profitability
When to useEvaluating whether to enter an industry — is the structure favorable?
When NOT to useFor company-specific strategy — Five Forces is about the industry, not the firm
US ride-sharing: strong network effects deter new entrants, but HIGH buyer power (riders switch apps freely) and HIGH supplier power (drivers multi-home on both platforms)
How it works: Rate each of the 5 forces as High, Medium, or Low. High forces squeeze industry profits; low forces let profits flow to incumbents. The combination tells you whether the industry is structurally attractive. A key insight: an industry can be growing fast but still unattractive if the forces are hostile (e.g., ride-sharing has massive TAM but brutal competitive dynamics).
Which force is strongest? That's where profit leaks out of the industry.
Are any forces changing? (New regulation, technology shift, consolidation)
Can you position yourself to neutralize the strongest force? (That's strategy)
Would you invest in this industry knowing these dynamics? Why or why not?
Value Chain
Where in the process does value get created — and where does it leak?
When to useFinding where a company should play in the industry — and where to partner
When NOT to useMarket sizing or industry attractiveness — that's Five Forces / TAM
Apple captures most value in design + marketing + retail, while outsourcing manufacturing to Foxconn. The value chain shows where to own vs partner.
How it works: Map every activity from raw inputs to the customer's hands. For each activity, ask: does this create differentiation or cost advantage? Activities that do are "strategic" — own them. Activities that don't are "commoditized" — outsource or partner. The chain also reveals where margins accumulate: typically in activities closest to the customer (brand, distribution) or with the highest barriers (proprietary technology).
Which activities create the most value for the end customer?
Where do margins concentrate in this industry? (Upstream, midstream, or downstream?)
Which activities does the company own vs outsource? Is that the right split?
Where are the bottlenecks? (Whoever controls the bottleneck captures the margin)
Ansoff Matrix
Four growth strategies: what to sell and to whom
When to useChoosing between growth strategies — where to allocate the next dollar
When NOT to useYou've already decided on diversification — then use sector screening
Uber Eats = "Develop Product" (new offering to existing riders). Uber Freight = "Diversify" (new service + new B2B customers). Very different risk profiles.
How it works: Plot your growth options on the 2x2. Risk increases as you move from top-left (penetrate — known product, known market) to bottom-right (diversify — new product, new market). The matrix forces an honest conversation: is this growth initiative really "adjacent" or is it a disguised diversification? Most failed growth strategies are diversifications that the team convinced themselves were market developments.
Which quadrant is this initiative really in? (Be honest — adjacency bias is real)
Do we have the capabilities for this quadrant? (Diversification requires entirely new skills)
What's the failure rate in this quadrant? (Penetration ~20%, Diversification ~60-70%)
Are we spreading capital across too many quadrants instead of dominating one?
Three Horizons
Manage the present while building the future — three time frames, simultaneously
When to usePortfolio strategy — what to optimize now vs invest for later
When NOT to useSingle-product decisions — Three Horizons is about the portfolio, not one bet
Google: H1 = maximize Search/Ads (the cash cow), H2 = scale Cloud (the growth engine), H3 = explore Waymo/DeepMind (the long-term options)
How it works: Classify every initiative into one of three time horizons. H1 (now) generates the cash that funds H2 and H3. H2 (2-5 years) is the emerging business being scaled — it's proven but not yet at full potential. H3 (5+ years) is the exploration portfolio — small bets with uncertain payoff. The insight: all three must run simultaneously. Companies that only invest in H1 become cash cows that eventually decline. Companies that only chase H3 burn cash without a base.
Is H1 healthy enough to fund H2 and H3? (If not, fix H1 first)
Do we have at least one credible H2? (This is where future growth comes from)
Are H3 bets sized appropriately? (Small enough to lose, large enough to matter if they win)
Are we confusing H3 exploration with H2 scaling? (Different management approaches needed)
Blue Ocean / Strategy Canvas
Don't compete — create uncontested market space by redefining competing factors
When to use"Create new market space" is a plausible option — redefining, not just competing
When NOT to useIncremental improvements in an existing market — that's competitive analysis, not Blue Ocean
Cirque du Soleil vs Ringling Bros: Ringling invested in star performers and animal acts. Cirque eliminated both, raised the bar on artistry, theme, and comfort — a different value curve, not a worse one.
How it works: List 6-8 factors that the industry competes on (the horizontal axis). Rate each competitor and yourself on each factor (High/Low). Plot the lines. Your strategy canvas should look different from competitors' — not just higher on every factor (that's expensive and unsustainable), but a different shape. The Four Actions framework asks: what can you Eliminate, Reduce, Raise, or Create relative to the industry standard?
What factors does the industry take for granted that buyers don't actually value? (Eliminate)
What factors are over-delivered relative to what buyers need? (Reduce)
What factors should be raised well above the industry standard? (Raise)
What factors has the industry never offered that would create new demand? (Create)
VRIO Framework
Is a competitive advantage sustainable? Four sequential tests
When to useEvaluating whether a competitive advantage is durable — not just current
When NOT to useIndustry-level questions — VRIO is about the firm's specific resources
Apple's ecosystem is V (valuable — drives retention + premium pricing), R (rare — no rival has hardware + OS + services), I (inimitable — decades of integration) and O (organized — Apple controls the full stack to capture value)
How it works: Take a resource or capability and run it through four sequential tests. Each "Yes" upgrades the advantage; each "No" tells you what you have. The critical insight: most advantages fail at "I" (Inimitable). Competitors can copy most things given time and money. Only advantages rooted in history (path dependence), complexity (many interacting parts), or social embeddedness (culture, relationships) pass the inimitability test.
Valuable: does this help win customers or reduce costs? (If no: competitive disadvantage)
Rare: do fewer than ~3 competitors have it? (If no: parity — necessary but not sufficient)
Inimitable: would it take a competitor 5+ years and significant investment to replicate? (If no: temporary advantage)
Organized: is the company structured to fully exploit this resource? (If no: latent advantage — the resource exists but value is leaking)
7S Framework
McKinsey 7S — seven interdependent levers that must align for strategy to execute
When to usePost-merger integration, org redesign, strategy execution gaps
When NOT to useExternal competitive analysis — 7S is about internal alignment, not the market
"After acquiring a competitor, the new strategy is clear — but structure still reflects the old org, systems aren't integrated, and staff don't share values. 7S reveals four of seven elements are misaligned."
How it works: Assess all seven elements independently, then check alignment between each pair. Hard elements (Strategy, Structure, Systems) are explicit and manageable. Soft elements (Skills, Staff, Style, Shared Values) are harder to change but often determine success. The framework forces you to ask: even if the strategy is right, is the organization configured to execute it?
Is the strategy clear, and do Structure and Systems support it?
Do the soft elements (Skills, Staff, Style) match what the strategy demands?
What are the specific misalignments between pairs of elements?
Which misalignment is the binding constraint on execution?
Growth-Share Matrix
BCG matrix — classify business units by market growth and relative share to allocate capital
When to useCapital allocation across a multi-business portfolio
When NOT to useSingle-business strategy — the matrix requires multiple units to compare
"A consumer goods company maps its brands: premium skincare (Star — high growth, high share), legacy soap (Cash Cow — low growth, dominant share), new energy drinks (Question Mark — high growth, low share), and declining home cleaning (Dog)."
How it works: Plot each business unit as a circle (sized by revenue) on two axes: market growth rate (vertical) and relative market share (horizontal). Stars need investment to maintain leadership. Cash Cows fund the portfolio. Question Marks require a bet-or-fold decision. Dogs should be divested unless they serve a strategic purpose. Capital should flow from Cows to Stars and selected Question Marks.
Which units generate cash vs consume it?
Are we funding Question Marks that will never become Stars?
Do our Cash Cows have enough life to fund the growth portfolio?
Which Dogs are we keeping for emotional rather than strategic reasons?
Nine-Box Matrix
GE/McKinsey — prioritize business units by industry attractiveness and competitive strength
When to useMulti-division portfolio prioritization with nuance beyond BCG's 2x2
When NOT to useWhen you lack data to score attractiveness and strength — garbage in, garbage out
"A conglomerate scores its 8 divisions: semiconductor unit (high attractiveness, strong position — invest), legacy hardware (low, weak — divest), and services arm (medium, medium — manage for earnings while monitoring)."
How it works: Score each business unit on two composite dimensions: industry attractiveness (market size, growth, profitability, competitive intensity) and business unit competitive strength (market share, brand, cost position, capabilities). Plot on the 3x3 grid. Green zone = invest and grow. Yellow zone = selective investment, hold. Red zone = harvest cash or divest. More nuanced than BCG because each axis is a weighted composite, not a single metric.
What factors define "attractiveness" in our context, and how do we weight them?
Where does each unit honestly sit — are we inflating scores for favorites?
Are any units in the yellow zone drifting toward red?
Do we have the capital to fund all green-zone units simultaneously?
Wardley Mapping
Map your value chain by evolution stage — see where to build, buy, or outsource
When to useTechnology strategy — deciding what to build vs buy vs outsource
When NOT to usePure financial portfolio decisions — Wardley is about capability evolution, not returns
"A cloud infrastructure team maps its stack: compute and storage are commodities (use AWS), the API gateway is a product (buy off-the-shelf), but the ML model is custom (build in-house) — and the novel database in genesis phase is the real risk."
How it works: Map every component in your value chain on two axes. Y-axis = visibility to the end user (user needs at top, deep infrastructure at bottom). X-axis = evolution stage from Genesis (novel, uncertain) through Custom Built and Product to Commodity (standardized, utility). Draw dependency lines. Components in Genesis need experimentation; Commodity components should be outsourced. The power is in spotting components being treated as custom when they should be commodity — or vice versa.
What are the user needs at the top of the chain, and what depends on what?
Which components are we building custom that are already commodity elsewhere?
Where is evolution creating strategic opportunity (or destroying our advantage)?
What would a competitor's map look like — are they ahead on any component?
Scenario Planning
Two key uncertainties create four plausible futures — stress-test your strategy against all of them
When to useLong-term strategy under deep uncertainty — 5-15 year horizons
When NOT to useShort-term operational decisions — too slow and abstract for next-quarter planning
"An energy company identifies two key uncertainties — oil price trajectory and regulation intensity — creating four worlds: Fossil Boom, Green Premium, Race to Bottom, and Transition Squeeze. The strategy that works in 3 of 4 scenarios wins."
How it works: Identify the two most impactful, most uncertain drivers (not trends you can predict). Cross them to create four distinct, plausible futures. Name each scenario vividly. Assign rough probabilities. Then test your strategy: does it survive in all four worlds? The winning strategy is not the one optimized for the most likely scenario — it is the one that is robust across scenarios, or that positions you to pivot quickly as uncertainty resolves.
What are the two biggest uncertainties we cannot control or predict?
Is our current strategy a bet on one scenario, or robust across all four?
What early signals would tell us which scenario is unfolding?
What low-cost options can we take now that pay off in multiple scenarios?
Innovation
Discover unmet needs, design business models, and create new market space — 4 frameworks
Financial / Sizing
Quantify markets, decompose financials, and build the numbers case — 5 frameworks
Revenue Tree
Decompose revenue by segment, product, channel, and pricing
When to useRevenue diagnosis, growth planning, identifying underperforming segments
When NOT to useEarly-stage companies with a single product and no segmentation
A SaaS company sees flat revenue. The tree reveals Enterprise platform sales grew 20% but SMB churn offset it entirely — the problem is retention, not demand.
How it works: Break total revenue into mutually exclusive, collectively exhaustive branches — typically Segment → Product → Channel → Pricing. Each node is a lever you can size and act on.
Which segments contribute the most revenue? Which are growing fastest?
Within each segment, which products drive disproportionate value?
Are certain channels more profitable than others after acquisition cost?
Where does a 10% improvement have the largest absolute impact?
TAM / SAM / SOM
Size the opportunity: total, serviceable, and obtainable market
When to useMarket entry, investor decks, growth ceiling analysis, M&A target screening
When NOT to useMature businesses with known market share; markets with no meaningful segmentation
An EV charging startup sizes TAM at $120B (all global EV charging), narrows SAM to $18B (North American DC fast-charging), and estimates SOM at $1.4B based on planned station rollout and utilization rates.
How it works: Start with the broadest possible market (TAM), filter by geography, technology, and customer fit (SAM), then estimate realistic capture given resources, competition, and go-to-market (SOM). Each layer requires different data and assumptions.
What is the total global spend on the problem you solve (TAM)?
Which segments can you actually serve with your current product and reach (SAM)?
Given competitive dynamics, what share can you realistically capture in 3–5 years (SOM)?
Are you sizing top-down (industry reports) or bottom-up (unit × price × customers)? Do they converge?
Unit Economics
LTV, CAC, payback period, and contribution margin per customer
When to useSubscription/recurring businesses, growth investment decisions, pricing optimization
When NOT to useOne-time transaction businesses; very early stage with <100 customers (data too noisy)
A subscription fitness app spends $800 to acquire a customer (CAC) who pays $67/month for an average of 36 months (LTV = $2,400). LTV:CAC of 3.0× with 12-month payback — healthy enough to scale marketing spend.
How it works: Calculate the full lifetime value of a customer (revenue × gross margin × average lifespan), compare against the fully loaded cost to acquire them (CAC), and derive the payback period. A healthy business typically targets LTV:CAC > 3× and payback < 18 months.
What is the average revenue per user per month, and what is your gross margin?
What is the average customer lifespan (or monthly churn rate)?
What is the fully loaded CAC including sales salaries, ad spend, and onboarding?
How do unit economics differ by acquisition channel or customer cohort?
Waterfall / Bridge
Show incremental contributions that explain the change from A to B
When to useYoY variance analysis, budget vs actual, explaining a metric change to stakeholders
When NOT to useWhen the components don't sum to the total; when there are 10+ small drivers (use Pareto instead)
A national retailer grew revenue from $8.2B to $9.2B. The bridge shows same-store sales (+$0.5B) and new stores (+$0.8B) drove growth, partially offset by closures (−$0.4B) and FX headwinds (−$0.2B).
How it works: Start with the base value, add positive increments (green) and subtract negative decrements (red) to arrive at the end value. Every bar must be additive — the components must sum exactly to the total change. Order bars by magnitude or logical grouping.
What are the 4–6 largest drivers of the change from period A to period B?
Which drivers are organic (same-store, pricing) vs. inorganic (acquisitions, new markets)?
Are negative drivers temporary (FX, one-time charges) or structural (market decline)?
Does the bridge balance? Do all components sum exactly to the net change?
Profit Tree
Decompose profit into Revenue (Price × Volume) and Costs (Fixed + Variable)
When to useProfitability diagnosis, cost reduction cases, margin improvement strategy
When NOT to useWhen the problem is revenue growth (use Revenue Tree); when cost structure is unknown
An airline's operating profit dropped 15%. The profit tree reveals revenue held steady (higher yields offset lower load factors), but variable costs surged — fuel up 30% YoY was the dominant driver.
How it works: Profit = Revenue − Costs. Revenue splits into Price × Volume. Costs split into Fixed + Variable. Each branch decomposes further into operational levers specific to the industry. Isolate which branch changed most to identify the root cause of a profit shift.
Is the profit change driven more by revenue or by costs?
On the revenue side, is it a price problem (yield, ASP) or a volume problem (units, load factor)?
On the cost side, are fixed costs (leases, overhead) or variable costs (materials, fuel) the driver?
Which single leaf-level lever has the largest impact if improved by 5–10%?
Decision Making
Structure decisions, assign ownership, and stress-test choices — 5 frameworks
RAPID
Clarify who does what in every decision
When to useCross-functional decisions with unclear ownership
When NOT to useSimple decisions one person can make alone
The leadership team needs to pick a new CRM. Sales recommends, Legal agrees on compliance, IT performs the rollout, Finance provides budget input, and the COO decides.
How it works: Assign exactly one role per person per decision. The Decider is a single individual — never a committee. The Recommender drives the process, the Agreer has veto power, Inputters are consulted, and Performers execute.
Who is the single Decider — and does everyone know it?
Who has true veto power (Agree) vs. advisory input?
Is the Recommender empowered to drive the timeline?
Are Performers looped in early enough to flag feasibility?
Decision Matrix
Score options against weighted criteria to pick the best fit
When to useComparing 3+ options across multiple criteria
When NOT to useBinary yes/no decisions or when one option clearly dominates
Evaluating three software vendors. Cost (30%), Features (25%), Support (25%), and Integration (20%) are weighted by priority. Each vendor is scored 1–10 per criterion, then multiplied by weight. Vendor C wins with 7.95.
How it works: List your options as columns and your criteria as rows. Assign a percentage weight to each criterion (must total 100%). Score each option 1–10 on every criterion. Multiply score by weight, sum the weighted scores, and compare totals.
Have stakeholders agreed on the criteria and weights before scoring?
Are scores based on evidence (demos, references) or gut feel?
Does the winner still feel right — or did the weights distort the outcome?
What happens if you change the top weight by ±10% — does the winner flip?
Pre-Mortem
Assume failure happened — then figure out why
When to useBefore launching any major initiative or project
When NOT to useAfter the decision is already made and irreversible
Before a product launch, the team imagines it's 6 months later and the launch flopped. Each person independently writes down the most likely reason. Top causes: no user validation, key talent attrition, and uncontrolled scope expansion — all preventable.
How it works: Gather the team and announce: "It's one year from now. This project has failed spectacularly. Write down the single biggest reason why." Collect responses independently (avoids groupthink), cluster themes, then build mitigations for the top 3–5 failure modes.
What is the single most likely cause of failure — and who owns mitigating it?
Which risks are we ignoring because they feel uncomfortable to name?
Are there single points of failure (one person, one vendor, one assumption)?
What early warning signal would tell us this failure mode is materializing?
Second-Order Thinking
Think past the obvious — consequences have consequences
When to useStrategic decisions with ripple effects across stakeholders
When NOT to useRoutine operational decisions with contained impact
A company raises prices 20%. First-order: revenue jumps, some customers leave. Second-order: churned customers boost competitors, remaining customers demand more value, margin funds R&D. Third-order: better product wins back share — or a price war erodes everyone's margins.
How it works: For any proposed action, ask "and then what?" at least three times. Map each consequence to its own consequences. Look for feedback loops (where effects amplify or dampen the original action) and unintended side effects. The goal is not to predict perfectly but to find risks and opportunities invisible at first glance.
What happens immediately — and who is affected first?
How will those affected parties react — and what does that trigger?
Are there feedback loops that amplify or reverse the initial effect?
Which second-order effect is most likely to surprise us?
Inversion
Flip the question — avoid failure instead of chasing success
When to useWhen "best practices" feel generic and you need concrete guardrails
When NOT to useWhen you already have a clear, specific plan to evaluate
A team planning to enter a new market flips the question: "How would we guarantee failure?" Answers are immediate and specific — ignore local regulations, burn cash on unproven channels, price without knowing costs, skip local partnerships. Now avoid each one.
How it works: Instead of asking "how do we succeed?", ask "how would we guarantee failure?" The failure list is always more specific and actionable than the success list. Once you have the failure modes, invert each one into a concrete safeguard. Popularized by Charlie Munger: "All I want to know is where I'm going to die — so I'll never go there."
If we wanted to guarantee this fails, what would we do?
What are the "obvious" mistakes in our domain that everyone knows but still makes?
Which item on the failure list are we closest to doing right now?
For each failure mode — do we have a specific, named safeguard in place?
Operations / Organization
Lead change, map experiences, and measure loyalty — 6 frameworks
Here are the 6 Operations/Organization framework cards:
ADKAR
Five sequential building blocks for individual change adoption
When to useManaging individual adoption through a specific change initiative
When NOT to useBroad cultural transformation with no defined change event
A company migrating from spreadsheets to a new CRM builds an ADKAR plan: town halls explain why (Awareness), managers address "what's in it for me" (Desire), training labs teach the new system (Knowledge), sandbox environments let reps practice (Ability), and leaderboards plus manager check-ins sustain usage (Reinforcement). Adoption hits 90% within 8 weeks.
How it works: ADKAR treats change as a sequential, individual-level process. Each letter represents a milestone that every affected person must reach. Diagnosing where people are stuck (e.g., they have awareness but no desire) lets you target interventions precisely rather than blanketing the organization with generic communications.
At which ADKAR stage are most employees currently stuck?
What specific barriers prevent people from moving to the next stage?
Who are the right sponsors to drive awareness and desire?
What reinforcement mechanisms will prevent regression to old behaviors?
Kotter's 8-Step Change
Structured sequence for leading large-scale organizational transformation
When to useLarge-scale transformation requiring broad organizational buy-in
When NOT to useSmall tactical changes or emergency pivots requiring speed over consensus
A mid-size insurer launches digital transformation: the CEO presents claims-processing data showing 3x competitor speed gaps (urgency), assembles a cross-functional digital council (coalition), defines a "zero-paper claims by 2027" vision, road-shows it across branches (communicate), removes legacy approval bottlenecks (empower), pilots auto-adjudication in one region to show 40% faster cycle times (quick wins), rolls out nationally (build), and ties digital KPIs to promotion criteria (anchor).
How it works: Kotter's model sequences transformation into three phases: creating the climate for change (steps 1-3), engaging and enabling the organization (steps 4-6), and sustaining the transformation (steps 7-8). The key insight is that skipping early steps — especially urgency and coalition — is the primary reason transformations fail. Each step builds momentum for the next.
Is the urgency genuine and data-driven, or manufactured and fragile?
Does the guiding coalition include enough informal influencers, not just senior titles?
Can you articulate the vision in under 5 minutes and have it be repeatable?
What quick wins can you engineer in the first 90 days to build credibility?
Lewin's Change Model
Unfreeze the status quo, transition, then refreeze the new state
When to useOrganizational or cultural changes requiring a clear break from past norms
When NOT to useContinuous improvement environments where constant change is the norm
A manufacturing firm shifts from individual performance bonuses to team-based incentives. Unfreeze: share data showing how individual bonuses create hoarding behavior and quality defects. Change: pilot team metrics on two production lines with coaching support. Refreeze: update the compensation policy, train all supervisors on team reviews, and celebrate early wins publicly so the new system becomes "how we do things."
How it works: Lewin's model treats organizations as systems in equilibrium held in place by driving forces (push for change) and restraining forces (resistance). Unfreezing weakens restraining forces by creating dissatisfaction with the status quo. The Change phase introduces new behaviors while people are receptive. Refreezing locks in the new state through policies, norms, and reinforcement so the organization doesn't drift back.
What are the strongest restraining forces holding the current state in place?
Have we created enough dissatisfaction to unfreeze, or will people snap back?
What support structures (training, coaching, tools) exist during the transition?
What formal mechanisms (policies, incentives, rituals) will refreeze the new state?
Influence Model
Four levers that drive mindset and behavior change in organizations
When to useDiagnosing why a change initiative is stalling or planning behavior change interventions
When NOT to useTechnical or process changes with no significant behavioral component
A retail bank wants to shift from product-pushing to needs-based selling. Role modeling: branch managers visibly use discovery questions in client meetings. Understanding: workshops show how needs-based selling increases wallet share by 2x. Confidence: reps practice in role-play labs and get real-time coaching. Reinforcement: commission plans shift from product volume to client satisfaction scores and cross-sell depth. Within two quarters, NPS rises 15 points.
How it works: The Influence Model holds that sustainable behavior change requires all four levers working simultaneously. Most change programs over-index on understanding (communications, town halls) while neglecting the other three. The model forces leaders to ask: even if people understand the change, do they see it modeled? Do they feel capable? Do the formal systems reward it? Weakness in any single lever undermines the others.
Which of the four levers is weakest in our current change approach?
Are leaders visibly modeling the behaviors they're asking of the organization?
Do people have the skills and confidence to act differently, or just the knowledge?
Do formal incentives, processes, and metrics reinforce or contradict the desired behaviors?
Customer Journey Map
Visualize the end-to-end customer experience across touchpoints and emotions
When to useIdentifying friction points, aligning teams around the customer experience
When NOT to useInternal operational problems with no direct customer-facing impact
An e-commerce company maps the journey from first ad impression to post-purchase referral. They discover checkout is the worst moment: 68% cart abandonment driven by surprise shipping costs and forced account creation. By adding transparent pricing earlier (Consider stage) and guest checkout (Purchase stage), they cut abandonment to 41% and increase NPS from 32 to 51 within one quarter.
How it works: A customer journey map plots every interaction a customer has with the organization across stages (awareness through advocacy), layering in emotional state, touchpoints, pain points, and moments of truth. The power is in making the invisible visible: teams that build the checkout flow rarely see the ad that set expectations, and marketing rarely sees the support tickets. The map forces end-to-end accountability and reveals where investment in experience will have the highest ROI.
Where is the biggest drop in customer emotion, and what causes it?
Which touchpoints are owned by different teams with no shared accountability?
What are the "moments of truth" where the experience is won or lost?
How does the actual journey differ from the one we designed?
NPS / Loyalty
Measure customer loyalty with a single question: "How likely are you to recommend us?"
When to useTracking customer loyalty over time and benchmarking against competitors
When NOT to useDiagnosing specific product or service issues (NPS tells you what, not why)
A B2B software company surveys 2,000 accounts quarterly. NPS drops from +38 to +22 after a pricing change. Drilling into verbatims, detractors cluster around "price increase without new features." The company responds with a feature roadmap webinar and grandfathered pricing for long-tenure accounts, recovering to +31 within two quarters. They also discover that accounts with a dedicated CSM have NPS of +55 vs. +12 for self-serve — making the ROI case for expanding the CS team.
How it works: NPS asks one question: "On a scale of 0-10, how likely are you to recommend us?" Responses split into Promoters (9-10), Passives (7-8), and Detractors (0-6). The score is simply % Promoters minus % Detractors, ranging from -100 to +100. NPS is powerful as a tracking metric and conversation starter, but it must be paired with follow-up questions ("why did you give that score?") to be actionable. The real value is in the segmentation and trend, not the absolute number.
What is our NPS by segment (enterprise vs. SMB, tenure, region)?
What are the top 3 themes in detractor verbatims — and are they fixable?
How does our NPS trend correlate with retention and expansion revenue?
Are passives closer to becoming promoters or detractors — what would tip them?
Innovation
Discover unmet needs and design business models — 4 frameworks
Jobs to Be Done
What "job" does the customer hire this product for?
When to useDiscovering unmet needs, product innovation, repositioning existing products
When NOT to useCommodity markets where the job is obvious and undifferentiated
Christensen's milkshake study: commuters "hired" milkshakes for a boring drive — not hunger. Competing against bananas and bagels, not other shakes. Led to thicker shakes with mix-ins for the morning commute job.
How it works: Observe what customers actually do (not what they say), identify the functional, emotional, and social dimensions of the job, then design the offering around the full job.
What is the customer trying to accomplish in this situation?
What workarounds are they currently using?
What are the emotional and social dimensions beyond the functional task?
Who or what are the real competitors for this job?
Business Model Canvas
Map all 9 building blocks of a business on one page
When to useNew venture design, business model pivots, communicating strategy to stakeholders
When NOT to useDeep-dive on a single component (use specialized tools instead)
Airbnb: Key Partners = homeowners; Value Prop = affordable unique stays; Customer Segments = budget travelers & hosts seeking income; Revenue = service fees on both sides; Channels = platform & mobile app.
How it works: Fill in each of the 9 blocks to create a holistic snapshot of how a business creates, delivers, and captures value. Iterate by testing assumptions in each block.
What is the core value proposition and who are the target segments?
What key resources and activities are required to deliver?
How do the cost structure and revenue streams balance?
Which block is the weakest — where does the model break first?
Value Proposition Canvas
Fit your product to customer jobs, pains, and gains
When to useProduct-market fit analysis, feature prioritization, go-to-market messaging
When NOT to useWhen you lack direct customer insight — the canvas requires real data, not assumptions
Slack vs email: Customer pains = email overload, lost threads, slow decisions. Slack's pain relievers = channels, search, integrations. Gain creators = transparency, speed, async flexibility.
How it works: Map the customer profile (jobs, pains, gains) on the right, then design the value map (products, pain relievers, gain creators) on the left. Achieve fit when every major pain and gain is addressed.
Which customer pains are most severe and underserved?
Which gains would delight customers beyond their expectations?
Do our pain relievers and gain creators map directly to the top pains and gains?
Where is the fit weakest — what gaps remain?
Advantage Matrix
Classify industries by the size and number of competitive advantages
When to useIndustry analysis, market entry strategy, understanding competitive dynamics
When NOT to useFirm-level strategy — the matrix classifies industries, not individual companies
Restaurants are fragmented (many ways to differentiate but no single player dominates). Semiconductors are volume (few ways to compete but massive scale advantages for winners like TSMC).
How it works: Plot industries on two axes — number of approaches to achieve advantage vs. the potential size of that advantage. Each quadrant implies a different strategy: niche focus, scale, differentiation, or cost discipline.
How many distinct ways can firms in this industry differentiate?
How large is the profit gap between leaders and laggards?
Is the industry trending toward consolidation (volume) or fragmentation?
What quadrant-specific strategy should we pursue?
Pricing
Set prices that capture value and drive growth — 5 frameworks
Value-Based Pricing
Price to perceived value, not cost
When to useDifferentiated products, B2B solutions with measurable ROI, premium brands
When NOT to useCommodity markets where perceived value is uniform across suppliers
Enterprise software: Salesforce prices based on revenue lift and productivity gains for sales teams — not server costs. A $150/user/month price is justified by thousands in incremental deal value.
How it works: Quantify the economic value your product creates for the customer, then capture a share of that value as your price. The gap between cost and value is the pricing power zone.
What is the measurable economic value the customer receives?
How does the customer perceive value relative to the next best alternative?
What share of created value can we credibly capture?
Can we segment customers by willingness to pay?
Cost-Plus Pricing
Cost + target margin = price
When to useManufacturing, government contracts, regulated utilities, stable-cost industries
When NOT to useMarkets where perceived value far exceeds cost (leaving money on the table)
Manufacturing: a furniture maker calculates wood ($30), labor ($25), shipping ($10), adds a 20% margin → sells at $81. Simple, transparent, and defensible in contract negotiations.
How it works: Sum all direct and indirect costs of producing a unit, then add a fixed percentage or dollar margin. Straightforward to calculate and explain, but ignores what customers will actually pay.
Do we have accurate, fully-loaded cost accounting?
Is our target margin competitive for this industry?
Are we leaving value on the table by ignoring willingness to pay?
How do volume changes affect unit costs and the resulting price?
Competitive Pricing
Price relative to the competitive set
When to useMature markets with transparent pricing, commoditized products, market entry
When NOT to useHighly differentiated products where competitive price anchors are misleading
Consumer electronics: a new smartphone brand maps competitors' prices ($299–$999), then positions at $449 — undercutting flagships while signaling quality above budget tier.
How it works: Map the competitive price landscape, then choose a positioning — discount (volume play), parity (compete on features), or premium (signal superiority). Price communicates brand positioning.
What is the full range of competitor prices and what does each signal?
Where are the price gaps that represent positioning opportunities?
Does our cost structure support the chosen price point sustainably?
How will competitors react to our pricing move?
Dynamic Pricing
Real-time price adjustment based on supply, demand, and context
When to usePerishable inventory, variable demand, real-time data availability
When NOT to useTrust-sensitive markets where price fluctuations feel unfair (healthcare, essentials)
Ride-sharing: Uber's surge pricing multiplies fares 1.5–3x during peak demand (rain, events, holidays), then drops to base rates in off-peak hours — balancing driver supply with rider demand in real time.
How it works: Continuously monitor supply, demand, competitor prices, and contextual signals. Adjust prices algorithmically to maximize revenue or utilization. Requires real-time data infrastructure and customer acceptance.
Do we have the real-time data infrastructure to support dynamic adjustments?
How price-sensitive is our customer base — will dynamic pricing erode trust?
What are the floor and ceiling prices to prevent PR disasters?
How quickly do supply and demand shift in our market?
Freemium
Free base tier drives adoption; premium features drive revenue
When to useLow marginal cost products, network effects, large addressable markets
When NOT to useHigh marginal cost per user, small markets where 2–5% conversion cannot sustain the business
Spotify: 615M total users, ~240M paying subscribers (~39% conversion). Free tier is ad-supported; Premium removes ads, adds offline play and higher quality. Free users drive word-of-mouth and network effects.
How it works: Offer a genuinely useful free tier to maximize adoption. Design premium features that power users need but casual users can live without. The free tier is a marketing channel, not a cost center.
Is the free tier valuable enough to drive organic adoption and word-of-mouth?
Is the premium upgrade compelling enough to convert 2–5% of free users?
Can the business sustain the cost of free users until conversion scales?
Where is the right feature boundary between free and paid?