Positioning Map

Positioning Map

Positioning isn't marketing copy — it's a product decision about who you say no to. This skill makes that choice visible by mapping competitors on 5 signal axes, locating the empty space, and forcing you to pick between 3 candidate moves rather than defaulting to the first one that sounds good.

Founders consistently underweight four things on competitor positioning: hero copy is the most-curated surface (so trust it least), pricing reveals who they actually sell to, what they ship reveals what they think matters, and who they hire reveals where they're going next quarter (the leakiest signal of all). Customer logos reveal who they actually catch, which often contradicts who they pitch.

Frameworks this skill draws from

This skill is the operational version of three positioning frameworks, executed against real public data:

1. **April Dunford — Obviously Awesome (5 components): competitive alternatives, unique attributes, value (and proof), target market characteristics, market category. Dunford's #1 insight: start with competitive alternatives, not with what you do.** This skill operationalizes that by always grounding "the empty quadrant" against the actual competitor set, not against an abstract market.
2. **Geoffrey Moore — Crossing the Chasm (positioning statement template):** "For [target customer] who [need], the [product] is a [category] that [benefit]. Unlike [primary alternative], our product [primary differentiation]." This is the format of the deliverable — the one-sentence positioning move.
3. **Ries & Trout — Positioning: The Battle for Your Mind (mental real-estate):** you can only own ONE word/concept in the customer's mind. The "empty quadrant" framing inherits from this: the position you can own is the one nobody else is claiming AND that customers care about.

Adjacent frameworks worth knowing but not directly encoded here: Treacy & Wiersema's three value disciplines (product leadership / operational excellence / customer intimacy — pick one, do the others adequately) and Ulwick's outcome-driven JTBD (positioning aligned to unmet desired outcomes). If the founder uses these in their own thinking, fold them into the axis choices.

When this skill applies

• Founder asks where the positioning gap is
• Pre-launch — choosing what to say no to
• Pre-fundraise — proving positioning is defensible
• Post-customer-pain-mining — synthesizing what you found with what competitors actually claim

What you need (inputs)

1. 3–5 competitors with URLs — fewer than 3 isn't a map; more than 5 is noise.
2. The job-to-be-done — one line. Without it, "axis" has no meaning.
3. Customer pain themes — REQUIRED, not optional. Output from customer-pain-mining. Positioning without pain context is just rearranging marketing copy. The gap lives where pain ≠ competitor claims. If the founder hasn't run pain-mining, refuse to produce a positioning sentence — produce only a descriptive map and flag this in the executive summary.
4. (Optional) Founder's current positioning — if doing a repositioning exercise.

If you don't have item 1, run competitor-discovery. If you don't have item 3, run customer-pain-mining first.

Tools

Exa MCP (primary for axes 1, 2, 4 — modern SaaS marketing sites are JS-rendered SPAs):

• mcp__claude_ai_Exa__web_fetch_exa(urls) — pull homepage / pricing / customers / changelog pages. Batch multiple URLs per call.
• mcp__claude_ai_Exa__web_search_exa(query, numResults) — find case studies, changelog blogs, "X review" comparison posts.

Anysite MCP:

• mcp__claude_ai_Anysite__execute(source="linkedin", category="company", endpoint="company", params={"company": ""}) — company entity + URN + employee count + specialities[] + short_description.
• mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_posts", params={"keywords": " ", "date_posted": "past-month", "count": 10}) — what they're posting and what's being said about them in the last month. Use this for the "recent shipping" axis; it covers the same ground as linkedin/company/company_posts but with richer engagement filtering via query_cache.
• mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_jobs", params={"keywords": "", "count": 20}) — open roles. Filter response by company.alias matching the actual target (keyword search can catch namesakes — e.g. a keyword search for one competitor may return a different competitor's roles that mention it in the JD).
• mcp__claude_ai_Anysite__execute(source="sec", category="search", endpoint="search_companies", params={"entity_name": "", "forms": ["10-K","S-1","D"], "count": 5}) — only for late-stage / public competitors. For early-stage consumer SaaS, returns nothing useful.
• mcp__claude_ai_Anysite__execute(source="webparser", category="parse", endpoint="parse", params={...}) — only for static-HTML competitor sites. Modern SaaS marketing pages (Next.js / Vercel) return empty; use Exa instead.

Budget: ~3 Anysite calls per competitor (LinkedIn company + post search + jobs search) + ~2 Exa fetches per competitor. For 5 competitors: ~15 Anysite + ~10 Exa.

How to run

For each competitor, run the same 5 axes. Capture as a row in a growing table.

Axis 1 — Hero copy (who they pitch)

mcp__claude_ai_Exa__web_fetch_exa(urls=["https://.com"])

Extract: H1 headline, sub-headline, the verb (build / write / cite / extract / scrape / etc.), explicit audience callout if any ("for academic researchers," "for indie devs," "for teams 10–500", "for AI agents").

Validated on the web-scraping niche: a single batched Exa fetch on ["brightdata.com", "apify.com", "firecrawl.dev", "scrapingbee.com", "anysite.io"] returns hero + subhead + pricing summary + customer-logo trust bar for all 5 competitors in one call. The hero is the most curated surface — trust it least for "what the product actually does" and most for "who the company is currently pitching."

Webparser tends to fail on JS-rendered SPAs (Next.js / Vercel marketing sites) — Exa fetch is the reliable primary.

Axis 2 — Pricing (who they actually sell to)

mcp__claude_ai_Exa__web_fetch_exa(urls=["https://.com/pricing"])

If /pricing returns CRAWL_NOT_FOUND (it sometimes does for Next.js sites), the pricing is usually on the homepage from Axis 1 — search for "$" in the body text.

Extract: tier names, lowest paid tier (in $), highest published tier, gating (seats / usage / features / credits). Convert everything to monthly USD for comparability. The lowest tier reveals their floor; the highest reveals ambition.

Web-scraping API niche pricing spread (validated): Firecrawl $0 → $16 Hobby → $83 Standard → $333 Growth (credit-based, 5x multiplier on extract). Apify $5 free → $49 Starter (compute-unit). Bright Data product-by-product: Crawl $1/1K req; Unlocker $1/1K; Browser API $5/GB; Web Scraper $0.001/record; effective $499/mo for 510K records. ScrapingBee $49/mo Freelance → $99 Startup → $599 Business+. The whole category is in pricing turmoil — the dominant pain in customer-pain-mining is the credit-multiplier surprise, so the pricing axis has positioning leverage right now.

Axis 3 — LinkedIn company entity (the org's verbatim self-positioning)

mcp__claude_ai_Anysite__execute(source="linkedin", category="company", endpoint="company", params={"company": ""})

Returns: short_description, description, employee_count, founded_on, specialities[], headquarter_location.

What to extract:

• short_description — often a single positioning sentence the company curates for LinkedIn (different from their homepage hero — useful contrast).
• specialities[] — explicit, comma-separated list of what they claim to do. Rare verbatim self-positioning. In Dunford's terms, this is their stated "unique attributes" list.
• employee_count — sets the stage size (e.g. Bright Data=355, Apify=231, Firecrawl=48, ScrapingBee=17).
• founded_on — for stage context.

Validated on the web-scraping niche: Bright Data's specialities[] is 13 terms long, dominated by use-cases (price intelligence, brand monitoring, market research, AI Agents). Apify's is 8 terms, platform-shaped ("Web scraping, Browser automation, AI agents, API integration, Data pipelines, No-code tools, Actor marketplace, Developer platform"). Firecrawl + ScrapingBee both ship empty specialities[] (small pages, content-marketing-led) — that absence is itself a signal.

Skip company_employee_stats for competitors under ~200 employees — the endpoint returns empty arrays for small organizations.

For the "what did they ship lately" axis, prefer linkedin/search/search_posts from Axis 4 over linkedin/company/company_posts because Axis 4's keyword + mentioned[] filter gives richer engagement-based filtering via query_cache.

Axis 4 — Recent shipping / signal (what they think matters now)

What did they ship or signal in the last 30 days?

mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_posts", params={"keywords": " ", "sort": "recent", "date_posted": "past-month", "count": 10})

Filter for signal — many results will be SEO listicles. Keep posts where (a) the author is the company itself (mentioned[] contains the company), or (b) comment_count + sum(reactions[].count) >= 10. Use query_cache.

Alternative: Exa for changelogs / feature blog posts:

mcp__claude_ai_Exa__web_search_exa(query=" new feature release 2025 — changelog blog post — what's new", numResults=5)

Then fetch the top URL if promising. Some products have explicit /changelog, /whats-new, /blog pages.

What to extract: count feature-release posts vs customer-story posts vs marketing posts in the last 30 days. The ratio tells you the company's stage and what they think the buyer cares about.

Validated on the web-scraping niche: Firecrawl ships fastest on AI-agent affordances (Onboarding-Skill flow, CLI launch, MCP server, "Highlights and Question formats are now live"). Bright Data ships compliance/enterprise content (DataDome partnership, Web MCP "now free"). Apify ships marketplace breadth (native MCP highlight, Creator Growth program, LangChain/n8n integrations). The recent-shipping axis reveals which company believes the buyer cares about which thing this quarter.

Axis 5 — Hiring signal (where they're going next quarter)

mcp__claude_ai_Anysite__execute(source="linkedin", category="search", endpoint="search_jobs", params={"keywords": "", "count": 20})

Hiring is the leakiest competitive signal. Companies can curate hero copy, hide pricing, lawyer up customer logos. They cannot hide that they just opened 5 enterprise sales reqs.

What to extract: count of open roles filtered by company.alias matching the actual target (keyword search can catch namesakes — a keyword search for one competitor can return roles at another company that mentions it in the JD). Then group by function:

• Eng / AI / R&D → product surface expansion incoming
• Growth / Performance Marketing / SEO → optimizing the paid funnel; scaling existing model
• Enterprise Sales / AE / Customer Success → moving upmarket
• Partnerships / Channel / Solutions → going institutional / channel-led
• Product Marketing / Product Enablement → revenue-stage execution, not net-new product
• Developer Relations / Creator Growth → developer-ecosystem / platform play

The function MIX, not the count, is the signal. Validated on the web-scraping niche: Bright Data has 20+ open roles skewed Enterprise Sales (Overlay Sales Director, AI Account Executive UK, BDR NYC) + CS + global Solutions Architects → scaling enterprise GTM + global presence. Apify has 20+ skewed developer-platform (Senior Backend for proxy/unblocking, Fraud Prevention Eng, Product Marketing, Creator Growth Lead) → scaling developer + creator/Actor-publisher side. Firecrawl shows 0 results on LinkedIn jobs → small (~48 employees) and content-marketing-led, not hiring publicly. ScrapingBee shows 1 (Sr PM Vilnius) → stable, lean.

Mark "no signal" for very-small competitors ( customer case study OR testimonial 2024 2025 — ", numResults=5)`

Axis 7 (skip unless competitor is public / late-stage) — SEC filings

mcp__claude_ai_Anysite__execute(source="sec", category="search", endpoint="search_companies", params={"entity_name": "", "forms": ["10-K","S-1","D"], "count": 5})

Only meaningful when the competitor has filed (e.g. Grammarly has Form D filings under CIK 0002033975). Returns canonical positioning language they've committed to in legal filings — rigid, lawyered, but unedited.

For early-stage / private consumer SaaS this returns nothing useful. Skip.

Synthesize — the positioning table

Build a markdown table with axes × N competitors + the founder's own product as the last column. The point of including yourself is to make the gap visible relative to where you stand now.

Cross-reference with pain themes (the load-bearing step)

This is what makes positioning real rather than wishful: only positions that map onto a top-3 customer pain theme are worth considering. Take the pain themes from customer-pain-mining and rank them by signal density (upvotes, reactions, mention count). For each empty quadrant you spot in the table, check: is there a pain theme that backs this position? If no, drop it.

Validated on the web-scraping niche: the top pain theme was "scrapers break every time the website changes" (2,045-like LinkedIn launch post for Scrapling + cross-platform Reddit + Exa-blog confirmation). The empty quadrant "agent-native + predictable pricing" maps directly onto this pain combined with the credit-multiplier complaints from cluster 2. That alignment is what makes the move defensible.

Generate three candidate positioning moves (not one)

April Dunford's empirical observation: founders who write ONE candidate positioning statement default to the first one that sounds good. The fix is to write 3 and force a choice. For each candidate, use Moore's template:

> "For [target customer] who [need], [product] is a [category] that [benefit]. Unlike [primary alternative], our product [primary differentiation]."

Pick 3 candidates that genuinely differ on competitive alternative (per Dunford). Concretely:

• Candidate A — position against the obvious primary competitor.
• Candidate B — position against the "do nothing / DIY" alternative.
• Candidate C — position against a category-adjacent tool (something not in the named competitor set but that customers actually use).

For each candidate, evaluate on 3 criteria:

1. Pain coverage — does it map to a top-3 pain theme from customer-pain-mining?
2. Defensibility — does the founder have a structural advantage to hold this position (data, team, integrations, customer base)? Or is it just marketing copy?
3. Empty-quadrant evidence — how many of the 5 axes show this position as unclaimed?

Score each 1–3, pick the highest total. State why the other two lose. Show the work.

Output

Markdown report, ~900–1100 words:

# Positioning Map — 

## Executive summary
3 sentences: dominant positioning pattern, the empty quadrant the founder could own, the single positioning move that creates the most contrast (the WINNER of the candidate-3).

## Comparison table

|  |  |  |  |  |
|---|---|---|---|---|
| **Hero copy** | "..." (verbatim H1) | "..." | "..." | "..." |
| **Pricing floor / ceiling** | $X / $Y per mo | ... | ... | ... |
| **LinkedIn specialities (top 3)** | A, B, C | ... | ... | ... |
| **Last 30 days shipped** | 3 features + 2 case studies | 1 feature + 5 SEO posts | ... | ... |
| **Hiring (function mix)** | 4 Eng / 2 Sales / 1 CS — moving upmarket | "no signal" (early-stage) | ... | ... |

## The empty quadrant
1–2 paragraphs naming the position no one currently occupies. Quote evidence from at least 2 of the 5 axes AND tie it to a named pai

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## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [anysiteio](https://github.com/anysiteio)
- **Source:** [anysiteio/agent-skills](https://github.com/anysiteio/agent-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.