alexwox/offer-pillar-discovery

Offer Pillar Discovery Skill

Pattern: Routed Playbook (Pattern 3). The user's starting point (avatar only, industry only, both, neither, or comparison mode) changes which frameworks and methods to run. Selected over Linear Pipeline because 5 distinct entry points require different workflows, and over Configured Playbook because the domain doesn't need overlays or per-unit templates.

Purpose

Surface deep, non-obvious, high-value offer pillars. Goes beyond surface-level frameworks to find the "gold nugget" — structural problems with large economic gravity.

Without systematic problem discovery, 42-43% of startups fail by picking the wrong problem (CB Insights 2024, 431 post-mortems). The average startup pivots 1.8-2.5 times before finding product-market fit (WinSavvy 2025). This skill aims to compress that to 0-1 pivots by front-loading discovery.

Slash command: /offerpillar

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Trigger

Invoke this skill when the user asks:

• "What are good offer pillars for [industry]?"
• "Help me find dream outcomes for [avatar]"
• "What problems should I solve in [market]?"
• "Apply the Dream Outcome framework to..."
• "What are unsolved high-value problems in [industry]?"
• "Is this problem worth solving?"
• "Help me find product-market fit"

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Step 0 — Collect Inputs

| Input | Why It Matters | Default If Not Provided | |---|---|---| | Industry / market | Determines which frameworks and discovery methods to prioritize | — (must ask) | | Avatar (specific role) | Determines dream outcome framing and buyer identification | Infer from industry if possible | | Stage | Greenfield exploration, hypothesis refinement, or pillar comparison | Greenfield | | Existing data | Prior interviews, market research, revenue data, customer feedback | None | | Depth | Quick scan (1 pass) or deep discovery (multi-framework + web research) | Quick scan | | Geography | Determines regulatory context and market boundaries | Global |

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Step 1 — Route to Discovery Workflow

Select exactly one route based on the user's starting point.

IF user has neither avatar nor industry (only a technology or capability):
  → Route 0: Pre-Discovery
  Flag the "solution-first" anti-pattern. Route to high-cagr-market-discovery or deep-web-research
  to identify an attractive market first, then return to Route B.

ELIF user has a specific avatar but no industry insight:
  → Route A: Avatar-First Discovery
  Run: Dream Outcome Framework (Phase 1) → Workflow Decomposition + Shadow Process Mining → Quality Scorecard

ELIF user has an industry but no specific avatar:
  → Route B: Industry-First Discovery
  Run: Framework Selection (Phase 3) → Discovery Methods (Phase 4) → Identify avatars from pain → Dream Outcome Framework → Quality Scorecard

ELIF user has both avatar and industry and wants to validate a specific pillar:
  → Route C: Pillar Validation
  Run: Quality Scorecard (Phase 2) directly → Gap analysis → Targeted frameworks only for weak criteria

ELIF user wants to compare multiple pillars:
  → Route D: Pillar Comparison
  Run: Quality Scorecard for each candidate → Composite scoring → Rank and recommend

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Phase 1 — Dream Outcome Framework (Hormozi)

The goal is to construct an offer in the form: "Get X without Y in Z time" — with X being quantifiable.

Procedure: Fill in the Dream Outcome Worksheet

Complete each row before moving to the next:

1. Painful Current State

Fill in: "Right now, ___ [avatar] at ___ [company type] wastes ___ [time/money] on ___ [specific task] because ___ [root cause]."

Rule: Pain must be at least one of — expensive, urgent, or embarrassing. If it's none of these, it's a "nice to have" that won't generate budget.

2. Dream Outcome

Fill in: "In 12 months, they want to be able to say: '___.' The metric that proves it: ___ [specific number]."

3. Objections to Remove

For each objection, name the removal:

| Objection | Removal mechanism | |---|---| | "It takes too long" | ___ [specific timeframe promise] | | "It's too much effort" | ___ [done-for-you / automated / templated] | | "I'm not sure it'll work" | ___ [guarantee / proof / case study] | | "It's too complex" | ___ [no technical team / turnkey / guided] |

4. Quantification

Fill in: "This saves/generates $___ per ___ [period]. The calculation: ___ [show the math]."

• Bad: "improve efficiency"
• Good: "reduce vendor risk assessment time from 40 hours to 4 hours per quarter, saving $18K/quarter at blended consultant rates"

5. Perfect Outcome Test

Fill in: "If this worked perfectly, the customer would say: '___'"

That sentence is usually the true dream outcome — more honest than anything you'll write in a positioning document.

6. Offer Statement

Formula: Get [quantified result] without [primary objection] in [timeframe]

Advanced: [Result] + [Mechanism] + [Objection Removal] + [Timeframe]

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Phase 2 — Pillar Quality Scorecard

A pillar is strong if it scores well on all six criteria. This is the primary evaluation gate.

Procedure: Fill in the scorecard for each candidate pillar

| Criterion | Evidence required | Your evidence | Score (0-2) | |---|---|---|---| | Money attached — someone already spends money solving this | Name the budget line item: "___ currently spend $___ on ___" | ___ | ___ | | Urgency — problem cannot be ignored | Name the forcing function: "They must act because " | ___ | ___ | | Quantifiable impact — you can say "this saves $X" | Show the math: "$ per ___ [period]" | ___ | ___ | | Clear buyer — you know who signs the check | Name the title: "The budget owner is ___ [title] at ___ [company type]" | ___ | ___ | | Repetition — problem happens repeatedly | Frequency: "This occurs ___ times per ___ [period]" | ___ | ___ | | Executive gravity — CEO/CFO talks about it | Board-level evidence: "This appears in ___ [earnings calls / board decks / annual reports]" | ___ | ___ |

Scoring: 0 = no evidence, 1 = indirect evidence or assumption, 2 = direct evidence (interview data, public data, budget confirmation)

Thresholds:

• 10-12/12 = Strong pillar — proceed to validation
• 7-9/12 = Promising — identify which criteria are weak and research them
• <7/12 = Weak — consider pivoting or combining with another pillar

The Budget-Owner Test (Critical)

The most underappreciated failure mode: everyone feels the pain, but nobody owns the budget.

In enterprise sales, "no decision" is a larger loss category than losing to competitors combined (Minoa, 2025). A champion without budget authority will stall the deal for months and eventually abandon it.

Fill in: "The person who can sign a check for this is ___ [specific title]. Their budget authority is $___ [range]. They report to ___ [who]. The business case they would make to their boss: '___'"

If you cannot fill this in, the pillar needs narrowing to a role that owns budget.

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Phase 3 — Framework Selection

Instead of running all 7 frameworks, select based on context:

| Framework | When to use | Best for | |---|---|---| | Profit Driver Analysis | Always run first — universal | Any industry, any avatar | | Boardroom Conversation Analysis | Enterprise / B2B targets | Finding executive-gravity problems | | Structural Friction Zones | Complex, multi-party systems | Finding coordination failures | | $1M Spreadsheet Signal | Process-heavy industries | Finding automation opportunities | | Regret Problems | High-stakes industries (healthcare, finance, security, legal) | Finding guaranteed-budget problems | | Theory of Constraints | User already has a business to optimize | Finding the single bottleneck | | Hidden Tax Framework | Large organizations with invisible overhead | Finding cost-reduction opportunities |

Framework 1 — Profit Driver Analysis

Every business optimizes five levers. Pillars touching these are automatically high value.

Procedure: For each lever, identify the avatar's specific problem:

| Lever | Avatar's specific problem | Current cost | Who owns it | |---|---|---|---| | Revenue (increase sales) | ___ | ___ | ___ | | Cost (reduce expenses) | ___ | ___ | ___ | | Risk (avoid catastrophic losses) | ___ | ___ | ___ | | Capital (improve return on capital) | ___ | ___ | ___ | | Speed (accelerate growth) | ___ | ___ | ___ |

Framework 2 — Boardroom Conversation Analysis

Procedure: For the target industry, fill in: "In their quarterly board meeting, the CEO/CFO argues about ___ because ___ . The KPI that's underperforming: ___."

Framework 3 — Structural Friction Zones

Where systems naturally break down:

| Zone | Industry-specific example | Economic cost | |---|---|---| | Markets: pricing inefficiencies | ___ | ___ | | Organizations: communication breakdown | ___ | ___ | | Processes: manual workflows | ___ | ___ | | Information: data fragmentation | ___ | ___ | | Incentives: misaligned goals | ___ | ___ |

Framework 4 — The $1M Spreadsheet Signal

Wherever you find giant spreadsheets, manual analysis, extensive human review — there is a pillar hiding. Humans compensating for broken systems = opportunity.

Procedure: Search for "[industry] spreadsheet problem" or "[role] manual process" in forums, Reddit, LinkedIn. Document what you find:

| Manual process found | Who does it | Frequency | Estimated cost | |---|---|---|---| | ___ | ___ | ___ | ___ |

Framework 5 — Regret Problems

Problems where failure leads to regret have guaranteed budgets. Nobody gets fired for preventing disaster.

| Domain | Regret problem | Budget signal | |---|---|---| | Finance | Investment mistakes, compliance failures | ___ | | Healthcare | Missed diagnoses, treatment delays | ___ | | Security | Breaches, data loss, fraud | ___ | | Legal | Missed deadlines, malpractice exposure | ___ |

Framework 6 — Theory of Constraints

Every system has one limiting factor. Find it:

Procedure: Map the value chain: ___ → ___ → ___ → ___ → ___. Which stage has the lowest throughput? That's the constraint. Everything upstream is starved; everything downstream is waiting.

Framework 7 — Hidden Tax Framework

Invisible costs that drain businesses without appearing on any line item:

Procedure: For the target industry, estimate: "The hidden coordination overhead costs approximately $___ per employee per year because ___."

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Phase 4 — Discovery Methods

Use these when frameworks alone don't surface specific enough problems. Select based on what information is missing.

| Method | When to use | What it reveals | |---|---|---| | Workflow Decomposition | You know the job but not the pain points | Step-by-step breakpoints and manual steps | | Shadow Process Mining | You suspect workarounds exist | Unofficial systems that reveal broken official ones | | Expert Complaint Mining | You need external signal fast | What practitioners actually complain about | | Regulation Mapping | Regulated industry | Forced processes with guaranteed demand | | Budget Line Item Method | You need to confirm money flows | What companies already pay for | | Decision Bottleneck Method | Enterprise/complex decisions | Where data is messy and decisions slow | | Failure Postmortem Method | Mature industry with documented failures | Structural weaknesses that recur | | Information Asymmetry Method | Multi-party markets | Where one side has more info than the other | | Pain Multiplier Method | High-frequency, low-intensity problems | Small pain x massive scale = massive opportunity | | Adjacent Market Transfer | Looking for novel solutions | What works in industry A but doesn't exist in B |

For deep discovery, run deep-web-research on the top 2-3 methods that are most relevant.

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Pillar Scoring — Composite Formula

For comparing multiple pillars, use this composite score:

Pillar Score = Quality Scorecard (0-12)
             + Budget Growth Signal (0-3)
             + Opportunity Gap (0-3)

Budget Growth Signal:
  3 = budget line item growing >15% YoY
  2 = growing 5-15% YoY
  1 = flat or growing <5%
  0 = shrinking

Opportunity Gap (Ulwick-inspired):
  3 = high importance + low satisfaction (Opportunity Score >12)
  2 = high importance + moderate satisfaction (Score 10-12)
  1 = moderate importance + low satisfaction (Score 6-10)
  0 = low importance or high satisfaction

Maximum: 18. Pillars scoring 14+ are strong candidates. 10-13 need more evidence. <10 should be deprioritized.

Sources: Ulwick Opportunity Score (Strategyn, 2005); Budget growth signals (Gartner IT Spending 2026).

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Worked Examples

Example 1 — Slack: Accidental Pillar Discovery Through Dogfooding

Industry: Team communication Discovery method: Internal usage observation (Shadow Process Mining)

Tiny Speck spent 3 years building Glitch, a multiplayer online game. The distributed team built an IRC-based communication tool to coordinate development. When the game failed, they noticed the internal tool was the thing nobody could stop using — a stronger market signal than any external validation.

Pillar Quality Scorecard: | Criterion | Evidence | Score | |---|---|---| | Money attached | Companies paying for email, Slack alternatives, IT coordination | 2 | | Urgency | Team communication is daily, mission-critical | 2 | | Quantifiable impact | Reduced email volume by 48.6% (internal data) | 2 | | Clear buyer | IT Director / Head of Engineering | 2 | | Repetition | Used every working hour | 2 | | Executive gravity | "Why is cross-team coordination so slow?" | 2 | | Total | | 12/12 |

Outcome: 10M+ DAU by 2019, $27.7B Salesforce acquisition. (TechCrunch 2019, founded.com)

Key lesson: The highest-value pillar was hiding inside the team's own workflow. The "can't-stop-using" signal is an organic proxy for the Sean Ellis "very disappointed" test.

Example 2 — Instagram: Pillar Discovery Through Usage Analytics

Industry: Social media / photo sharing Discovery method: Behavioral analytics on existing users (Workflow Decomposition)

Burbn was a Foursquare-style check-in app with 100 users. Systrom and Krieger observed that users were primarily using the photo-sharing feature, ignoring check-ins entirely. Instead of adding features, they subtracted everything except photo sharing.

The filters breakthrough came from Systrom's wife saying she wouldn't share photos "because they don't look good enough" — direct customer feedback resolving a specific adoption barrier.

Outcome: 100K users in week 1, $1B Facebook acquisition in 18 months. (Startup Archive, TechCrunch 2010)

Key lesson: The winning pillar was already present in the existing product — it just needed isolation. Usage data beats opinion data. This maps to Hormozi's "find the thing they're already paying for with time/attention."

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Near-Miss Counter-Example — AI Search Tool: Perfect Scores, Zero Adoption

Company: Unnamed B2B SaaS (documented by Richard Ewing, product advisor, 2025) Source: Built In, 2025

What looked right: The team built an AI-powered search tool achieving 0.92 nDCG relevance scores (vs. 0.65 for legacy search). State-of-the-art RAG pipelines, vector databases, latest LLMs. If you scored this on a pillar quality checklist, it would pass on most criteria — the problem (finding information in proprietary data) is real, urgent, repeated daily, and executives talk about it.

Where it failed — the Value Test: The tool created "homework" (prompting, editing, copy-pasting) rather than removing labor. Users had to stop their workflow, open a sidebar, type a prompt, wait, then manually integrate the answer. It was a destination when it should have been a background utility.

The three business tests it failed:

1. Value Test: Created work instead of removing it
2. Margin Test: At $0.08/query on flat-rate $29/month, power users cost more to serve than they paid
3. Retention Test: When the feature went down for maintenance, nobody called support

Outcome: Usage flatlined after launch. "The most technically impressive piece of software I have ever shipped. Also the biggest business failure of my career."

Why this is a near-miss: The pillar (unlocking proprietary data with AI) passes 5/6 on the quality scorecard. It fails on clear buyer — not because nobody wants it, but because the implementation created the wrong value delivery. The problem was real; the pillar formulation was wrong. A better pillar: "Automatically surface the right document at the right moment in the existing workflow, without the user asking."

Minimal fix: Reframe from "search tool" (user-initiated, creates homework) to "ambient intelligence" (system-initiated, removes steps). Same underlying technology, different pillar formulation.

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Benchmarks

Why This Matters — The Cost of Skipping Discovery

| Statistic | Value | Source | Year | Confidence | |---|---|---|---|---| | Startups failing due to wrong problem | 42-43% | CB Insights (431 post-mortems) | 2024 | Observed | | Startups that pivot at least once | 92% | WinSavvy (aggregated data) | 2025 | Inferred | | Average pivots before PMF | 1.8-2.5 | WinSavvy | 2025 | Inferred | | Time to PMF without systematic discovery | 18-24 months | WinSavvy | 2025 | Inferred | | Startups that scale prematurely | 70% | Startup Genome (3,200 startups) | 2012 | Observed | | Premature scalers that break $100K MRR | 7% | Startup Genome | 2012 | Observed | | Segment's cost of skipping discovery | $500K and 18 months wasted | Peter Reinhardt, YC Blog | 2017 | Observed |

PMF Validation Thresholds

| Framework | Threshold | What it means | Source | |---|---|---|---| | Sean Ellis PMF Score | ≥40% "very disappointed" | Strong product-market fit | Ellis, tested across hundreds of startups | | Ulwick Opportunity Score | >12 | Significant underserved opportunity | Strategyn (Observed, self-reported 86% success rate) | | Interview validation | 8/10 describe problem unprompted | Problem is validated | knowledgelib.io 2026 (Inferred) |

Where Budgets Are Growing Fastest (2025-2026)

| Category | YoY growth | Source | Confidence | |---|---|---|---| | AI-optimized servers | ~100% | Gartner 2025 | Observed | | AI-native software | +94% | Tropic 2025 | Observed | | Cloud security | +29% | Gartner 2026 | Observed | | Compliance automation | +25% | BusinessofGRC 2025 | Inferred | | GRC platforms | +12% CAGR | BusinessofGRC 2025 | Observed | | Information security (total) | +13% | Gartner 2026 | Observed |

Use these as a "money flow" heuristic: pillars aligned with growing budget categories have structural tailwind.

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Anti-Patterns

| Anti-pattern | Why it fails | Fix | |---|---|---| | Solution-first discovery | Building backward from a technology ("I have AI, what problem can it solve?") instead of a validated pain. 42-43% of startup failures trace to this (CB Insights 2024). | Start with pain, not capabilities. Phase 1 begins with the customer's current state, not your technology. | | Confusing "interesting" with "valuable" | Academic problems fascinate but don't generate purchase orders. A problem is valuable only if someone already allocates budget to it. | Phase 2 criterion 1 (Money attached) is the first filter. If no budget line item exists, the problem is interesting but not valuable. | | Framework dumping | Running all 7 frameworks + 10 methods produces a long list of plausible-sounding pillars but no prioritized recommendation. | Use the Framework Selection table (Phase 3) to pick 2-3 relevant frameworks. Run the scorecard to rank. | | Ignoring the buyer/user distinction | The person who feels the pain is not always the person who pays. Engineers feel deployment pain; VP Engineering holds the budget. | Phase 2 criterion 4 (Clear buyer) requires naming the budget-owning title, not just the pain-feeling role. | | Falling in love with a pillar before validation | Emotional attachment to an idea makes you interpret ambiguous signals as validation. Segment spent 18 months and $500K this way. | Set a kill criterion before investigating: "I will abandon this pillar if ___ [specific evidence] is found." | | Pillar too broad | "Companies need better analytics" is true but useless — it describes a market, not a pillar. | A pillar must name: who, what specific problem, and what specific outcome. "Mid-market SaaS CFOs need real-time churn prediction to prevent revenue surprises" is a pillar. |

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Quality Gates

Do not recommend a pillar for development unless all gates pass:

• [ ] Pillar Quality Scorecard completed with evidence, not assumptions
• [ ] Score ≥10/12 (or explicit plan to strengthen weak criteria)
• [ ] Budget owner identified by title and authority level
• [ ] At least one quantified economic impact ($ or time saved per period)
• [ ] At least one real example or case study showing the problem exists
• [ ] Offer statement compressed into "Get X without Y in Z time" format
• [ ] At least one counterargument considered: "This pillar might fail because ___"

If any gate fails, add a Research Gaps section specifying what evidence is missing.

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Handoff Rules

• Downstream — stakeholder-discovery: When the pillar needs human validation through interviews. The pillar becomes the hypothesis input for stakeholder-discovery. This is the default next step — pillars should be interview-validated before building.
• Downstream — lead-magnet-creation: When the pillar is interview-validated (via stakeholder-discovery) and needs market demand testing at scale. Route to lead-magnet-creation. Skip stakeholder validation only if the user has strong existing evidence (customer data, prior interviews, revenue from the problem).
• Downstream — consult-hormozi: For detailed offer design, pricing, and the Grand Slam Offer framework. Route to consult-hormozi.
• Upstream — high-cagr-market-discovery: If the user needs to identify which market to enter before selecting a pillar. Route to high-cagr-market-discovery.
• Adjacent — deep-web-research: For pillar-level competitive intelligence, budget signal research, and market size estimation. Route to deep-web-research.
• Adjacent — constructing-a-funnel: When a validated pillar needs an acquisition funnel built around it. Route to constructing-a-funnel.

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Output Templates

Template A — Single Pillar Discovery

## Offer Pillar: [Name]

**Route used:** [A/B/C/D]
**Frameworks applied:** [which ones and why]
**Discovery methods used:** [which ones and what they revealed]

**Avatar:** [specific role / company type]
**Painful Current State:** "___ [avatar] at ___ [company type] wastes ___ on ___ because ___"
**Dream Outcome:** "___ [quantified future state]"
**Offer Statement:** "Get ___ without ___ in ___ "

### Quality Scorecard
| Criterion | Evidence | Score |
|---|---|---|
| Money attached | ___ | ___/2 |
| Urgency | ___ | ___/2 |
| Quantifiable impact | ___ | ___/2 |
| Clear buyer | ___ | ___/2 |
| Repetition | ___ | ___/2 |
| Executive gravity | ___ | ___/2 |
| **Total** | | **___/12** |

### Budget Owner
"The budget owner is ___ [title] at ___ [company type]. Their budget authority: $___. Business case: '___'"

### Objections Removed
| Objection | Removal |
|---|---|
| ___ | ___ |

### Counterargument
"This pillar might fail because ___. Mitigation: ___."

### Next Steps
1. ___
2. ___
3. ___

Template B — Pillar Comparison

## Pillar Comparison: [Industry/Market]

| Dimension | Pillar A | Pillar B | Pillar C |
|---|---|---|---|
| Avatar | ___ | ___ | ___ |
| Core pain | ___ | ___ | ___ |
| Quality Score | ___/12 | ___/12 | ___/12 |
| Budget Growth Signal | ___/3 | ___/3 | ___/3 |
| Opportunity Gap | ___/3 | ___/3 | ___/3 |
| **Composite Score** | **___/18** | **___/18** | **___/18** |
| Biggest risk | ___ | ___ | ___ |

**Recommendation:** Pillar ___ because ___.
**Next step:** [stakeholder-discovery / lead-magnet-creation / deep-web-research]

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Reference Sources

1. CB Insights — "Top Reasons Startups Fail." 2024 (431 post-mortems)
2. WinSavvy — "Startup Pivot Rates." 2025
3. Startup Genome — "Premature Scaling Report." 2012 (3,200 startups)
4. Reinhardt — "Finding Product-Market Fit." YC Blog, 2017
5. Ellis — "Product-Market Fit Survey." ~2010, validated across hundreds of startups
6. Vohra/Superhuman — "PMF Engine." First Round Review, 2018
7. Ulwick/Strategyn — "Outcome-Driven Innovation." Marketing Journal, 2017
8. Gartner — "IT Spending Forecast." 2025-2026
9. Tropic — "SaaS and AI Buying Trends." 2025
10. BusinessofGRC — "GRC Market Size." 2025-2026
11. Ewing/Built In — "AI Product Business Test." 2025
12. TechCrunch — "Slack Origin Story." 2019
13. Startup Archive — "Instagram Pivot." 2023