# White Space Analysis Use this when the user asks to find market white space, identify untapped positioning, discover uncontested product areas, or run a "blue ocean" analysis for a company. This workflow produces a structured white space report across 4 analytical phases. **Trigger phrases:** "white space", "blue ocean", "uncontested", "market gap", "find positioning", "find the white space", "find uncontested" **Prerequisite:** This workflow requires a domain as input. Unlike market map workflows that produce competitive landscapes of *existing* players, white space analysis produces a structured assessment of *absence* — where competition is thin, fragmented, or non-existent. > **Data disclosure:** This orchestrator delegates to 4 phase workflows (saturation map, adjacency gap radar, feature survival matrix, ICP expansion grid). Each phase sends organization/product IDs and feature descriptions to the AlphaLens API. Confirm with the user before running on more than 20 organizations. --- ## What This Workflow Produces | Phase | Output | LLM Reasoning Task | |---|---|---| | 1 — Saturation Map | Competitor density heatmap per product | Classify clusters as Red/Yellow/Green ocean | | 2 — Adjacency Gap Radar | Upstream/downstream workflow coverage | Judge handoff fragmentation and gap severity | | 3 — Feature Survival Matrix | A/B/C combinatorics survival table | Identify table stakes vs. differentiators | | 4 — ICP Expansion Grid | Mechanics × ICP competitive density | Synthesize lateral white space opportunity | The final output is a single HTML report with 4 sections, one per phase, with an LLM-generated executive summary and white space opportunity narrative at the end. --- ## How to Prompt **Basic:** - `Find the white space around ramp.com` - `Run a blue ocean analysis for company.com` - `Find uncontested positioning for zendesk.com` **With Phase 3 (Subtract & Swap) specificity:** - `Run white space analysis on ramp.com and test whether adding AI-based expense categorization would be uncontested` - `Find positioning gaps for company.com using the subtract and swap method` **With Phase 4 (ICP Translation) specificity:** - `Run a white space analysis on ramp.com and test whether the same mechanics apply in the healthcare vertical` - `Find lateral white space for company.com across the legal and compliance ICPs` --- ## Execution Order ``` 1. Resolve domain → fetch products → score and select top 3 2. Phase 1: Saturation Map (per product) → read [white-space-phase1.md] 3. Phase 2: Adjacency Gap Radar → read [white-space-phase2.md] 4. Phase 3: Feature Survival Matrix → read [white-space-phase3.md] 5. Phase 4: ICP Expansion Grid → read [white-space-phase4.md] 6. LLM Synthesis: Executive summary + white space narrative ``` Load each phase file when you reach that step — not all at once. The phase files contain the detailed API calls and LLM reasoning tasks. --- ## Step 0 — Resolve Domain and Score Products This step is identical to [`market-map-product.md` Step 0 and Step 1](market-map-product.md). Always start here before any white space analysis — the white space findings are anchored to the company's specific products. ### Step 0a — Resolve the anchor company ```bash WORKDIR=$(mktemp -d) API="https://api-production.alphalens.ai" curl -s -H "API-Key: $KEY" "$API/api/v1/entities/organizations/by-domain/{domain}" # → get organization_id, active_domain, logo_url ``` ### Step 0b — Fetch all products ```bash curl -s -H "API-Key: $KEY" "$API/api/v1/entities/organizations/{organization_id}/products?limit=20" # → returns array of { id, product_name, product_description, is_primary_product, product_target_audiences, product_categories, product_references, key_features } ``` ### Step 0c — Score and select top 3 products Apply the scoring formula to each product: ``` +40 is_primary_product: true +10 per product_reference URL that is NOT the company homepage +5 per distinct entry in product_target_audiences 0 baseline -50 product_name contains: api, rest, extension, bulk, integration, crm, webhook, export, sdk -30 product_categories contains 'services' -20 ALL product_references point only to the company homepage -15 a key_feature overlaps with a higher-scoring product ``` **Decision rules:** 1. Compute score for every product 2. Keep only products with **score ≥ 20** 3. Cap at **3 products maximum** — take top 3 by score 4. **Pairwise distinctiveness check**: if two products share >70% overlapping `product_target_audiences`, merge them (same market map) 5. **Job-to-be-done sanity check**: surviving products should represent different buyer actions > **If no products pass threshold (score < 20):** Fall back to org-level similarity only. The company likely lacks product-level indexing. Run a single saturation map on the org. --- ## Phase 1 Read [`white-space-phase1.md`](white-space-phase1.md) — Saturation Map (The Mirror Test). --- ## Phase 2 Read [`white-space-phase2.md`](white-space-phase2.md) — Adjacency Gap Radar. --- ## Phase 3 Read [`white-space-phase3.md`](white-space-phase3.md) — Feature Survival Matrix (Subtract and Swap). --- ## Phase 4 Read [`white-space-phase4.md`](white-space-phase4.md) — ICP Expansion Grid (Lateral Move). --- ## Step 5 — LLM Synthesis: Executive Summary and White Space Narrative After all 4 phases are complete, run a final LLM reasoning pass to synthesize the findings into: ### 5a — Per-Product White Space Summary For each product, synthesize: 1. **Saturation verdict** (Red/Yellow/Green from Phase 1) 2. **Biggest adjacency gap** (most severe open/empty adjacency from Phase 2) 3. **Key differentiator** (which feature is the real differentiator from Phase 3) 4. **Top ICP expansion opportunity** (highest confidence lateral white space from Phase 4) ### 5b — Overall White Space Opportunity Narrative Structure the narrative around: - **The core insight**: What is the single most actionable white space finding? - **Confidence level**: How strong is the signal? (based on Phase 1-4 confidence indicators) - **Strategic options**: Given the findings, what are the 2-3 possible strategic responses? - Option A: Enter the adjacency gap with a new product - Option B: Defend the current position against the identified differentiator threat - Option C: Lateral move into the validated ICP white space - **Risk factors**: What could invalidate the white space finding? (incumbents pivoting, capital flooding in, regulatory changes) ### 5c — Render Executive Summary Section ```html

White Space Analysis — Executive Summary

{Anchor Company} ({Domain})

Overall White Space Verdict: {RED/YELLOW/GREEN}

Confidence: {High/Medium/Low}

Strategic Options

  1. Option A: {Name} — {2 sentence description}
  2. Option B: {Name} — {2 sentence description}
  3. Option C: {Name} — {2 sentence description}

Risk Factors

``` --- ## Output File Save the complete white space report as: ``` {anchor}-white-space.html ``` ### Design Rules (non-negotiable) Follow these consistently — they are the same standards used in all other AlphaLens workflows: - **Light mode only** — `background: #f7f8fc`, dark text. Never dark mode. - **No emojis** in section headers, table labels, or button text. - **Favicon handling**: fetch via Google favicon service and base64-encode inline. If a favicon curl returns empty, use a CSS letter avatar (coloured circle with the company's initial). Never leave a company without a visual representation. - **Anchor company**: if the anchor appears in any result set, give it a gold border + subtle glow (`border: 2px solid #d69e2e; box-shadow: 0 0 10px rgba(214,158,46,0.3)`). - **Pending companies** (just triggered indexing): `opacity: 0.65`, dashed border, italic name. - **Indexed companies**: full opacity, solid border. - **Cluster/classification naming**: use buyer-outcome language, not technology labels. E.g., "Automated Compliance Routing" not "Workflow Automation Tool". ### HTML Structure ```html

Phase 1 — Saturation Map

Phase 2 — Adjacency Gap Radar

Phase 3 — Feature Survival Matrix

Phase 4 — ICP Expansion Grid

Executive Summary

``` ### PDF Export The HTML is fully self-contained (favicons embedded as base64), so `html2pdf.js` works without any proxy or CORS issues: ```js html2pdf().set({ margin: 12, filename: '{anchor}-white-space.pdf', image: { type: 'jpeg', quality: 0.98 }, html2canvas: { scale: 2, useCORS: false, allowTaint: false, backgroundColor: '#f7f8fc', windowWidth: Math.max(document.documentElement.scrollWidth, 1560), scrollX: 0, scrollY: 0 }, jsPDF: { unit: 'mm', format: 'a2', orientation: 'landscape' }, pagebreak: { mode: 'avoid-all' } }).from(document.body).save(); ``` Set `visibility: hidden` (not `display: none`) on the export button before rendering — hides it from the PDF without shifting layout. --- ## Common Pitfalls - **Phase 1 — Mistaking horizontal for vertical competition:** A search returning generic tools (Zapier, Excel) doesn't mean the vertical is crowded — it means the handoff is unspecialized. Always judge by vertical specificity. - **Phase 2 — Defining upstream/downstream too broadly:** If your upstream is "all business software", you'll get meaningless results. Keep workflows specific to the product category. - **Phase 3 — Feature decomposition too granular:** 3-5 features max. If you decompose into 20 sub-features, the combinatorial queries become noise. - **Phase 4 — ICP extraction losing the core mechanic:** Strip industry jargon but don't strip the core innovation. "AI-powered" may be generic — "ML-based classification routing" is the mechanic. - **Phase 5 — Overclaiming white space:** Absence from AlphaLens ≠ absence from the market. Always qualify findings with "AlphaLens-indexed" and note that private companies, pre-revenue startups, and internal tools may not appear.