Prospecting

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B2B manufacturing proactive prospecting. Search Google Maps for potential customers based on existing client profiles, enrich leads with business details, score and rank them, and output actionable CSV + JSON lead lists with custom sales openers. Includes chain store strategy: local call → identify procurement decision chain → escalate to corporate. Use when: finding new customers, prospecting, lead generation, searching for potential clients, building a call list, or when user mentions existing customers they want to find more like. Triggers: prospect, find customers, lead gen, call list, 获客, 找客户, 搜客户, 潜在客户, 主动获客

Install

openclaw skills install prospecting

Prospecting — B2B Lead Generation from Existing Customers

Overview

Turn existing customers into a search template → find similar businesses on Google Maps → enrich → score → output actionable call lists.

One line: Known customer → profile → Maps search → enrich & rank → CSV call list + JSON index

When to Use

User gives a customer name + location and asks to find similar businesses
User asks to build a prospect/call list
User wants to find new clients in a specific industry (auto body, manufacturing, HVAC, etc.)

Input Required

Field	Required	Notes
Company name	✅	Core search term
Location (city/state)	✅	Search center point
Product purchased	❌	Helps with profiling

Even minimal input ("Bob's Auto Body, Orange CA") can start the full flow.

Execution Flow

Step 1: Profile the Existing Customer (8-step fixed process)

Read references/profiling.md for the full 8-step process. Key actions:

Google Maps deep dive — Use agent-browser to search [company name] [location], extract: address, phone, rating, review count, business type, hours, website, photos, chain status
Review sampling — Sample reviews with keyword filtering (not all reviews). Generic keywords: new, expand, equipment, upgrade, install, moved, bigger + industry-specific keywords (e.g., for auto body: paint booth, insurance, fleet, dealer)
Social/web enrichment — Only for 🔴 chain (FB+LinkedIn+website) or 🟡 mid-tier (FB+website). Skip 🟢 small (no website)
Output a Profile Card — Standard format saved to prospect-data/{batch}/profile-{name}.json

Tier detection (determines enrichment depth):

🔴 Chain/large: name contains chain markers OR >200 reviews
🟡 Mid-tier: has website, 50-200 reviews
🟢 Small: no website, <50 reviews

Step 2: Maps Batch Search (agent-browser automated)

Read references/search-strategy.md for the complete search framework.

Key principles:

Multi-center: Large cities (>2M) use 4-6 search centers (e.g., Houston: Downtown, Katy, Sugar Land, The Woodlands, Baytown, Cypress)
Keyword matrix: 4-6 keywords per center (core + service + equipment + brand + scene)
Pagination: Scroll and load 3 times per search to get 20-30 results
Deduplication: Cross-center, cross-keyword deduplication

Search execution:

For each center point × each keyword: open Google Maps, extract listings, paginate 3x
Collect: name, phone, address, rating, review count, business type, website status, chain markers
Dedup: same name + same address = duplicate
Remove: permanently closed, non-target industry

Save to: prospect-data/{batch}/candidates-raw.txt (raw extraction log) + candidates.json (deduplicated)

Step 3: Auto-Tier Candidates

Based on Maps data, assign tiers. Chain stores are NOT excluded — they are valid prospects with a different approach strategy.

Tier	Criteria	Next action
🔴 Chain/large	Chain name OR >200 reviews	Deep enrichment + chain procurement strategy
🟡 Mid-tier	Has website, 50-200 reviews	Medium enrichment
🟢 Small	No website, <50 reviews	Skip enrichment

Chain store prospecting strategy — Read references/chain-strategy.md for the full three-call approach:

Call 1: Local store — NOT to sell, but to identify procurement decision chain
Call 2: Regional/corporate — pitch to the person who can approve multi-location deals
Call 3: Follow-up with proposal

Key principles:

Chain stores have large, stable equipment needs — one deal can cover multiple locations
Local store manager is the entry point, not the decision-maker (usually)
Key question: "Is equipment purchasing handled locally, or should I speak with your regional/corporate procurement team?"

Step 4: Enrich by Tier

Tier	Action	Tools	Time
🔴 Chain	Website deep + LinkedIn + news search + chain procurement mapping	agent-browser + agent-reach (Exa)	3-5min each
🟡 Mid	Website basics + FB	agent-browser	1-2min each
🟢 Small	Skip — Maps data sufficient	—	0

Chain enrichment with agent-browser:

agent-browser open "[website URL]"
agent-browser snapshot -i → extract Services, About, Staff, Contact
Check for Portfolio/Cases and News/Blog pages for expansion signals
For chains: Look for corporate/region procurement contacts, preferred vendor programs, and expansion news

Chain news search with agent-reach:

mcporter call 'exa.web_search_exa(query: "[company name] expansion OR new location OR equipment", numResults: 5)'

Chain procurement mapping (chains only) — See references/chain-strategy.md for full approach:

Identify: local manager → regional operations manager → VP of operations / procurement director
Sources: LinkedIn, corporate website "careers" or "partners" page, news about leadership changes
Goal: find the person who can approve equipment purchases for multiple locations

Step 5: Score & Rank

Match each candidate against the profile card:

Factor	Rule	Points
Buy signal	Expansion / new service / new equipment	+5 (strong) / +3 (medium) / +1 (weak)
Industry match	Business type matches profile	+3
Scale match	Review count / bays similar to profile	+2
Service overlap	Same services as profile	+2
Geo similarity	Similar area type	+1
Business age	Similar years in operation	+1
Chain multiplier	Chain store (multiple locations = bulk potential)	+3
EV/high-end certification	EV Certified / LUXE / premium line	+4

Tie-breaking: buy signal strength → chain (bulk potential) → has phone → closer scale match

Total score	Priority	Action
10+	🔴 High	Call within 48h
6-9	🟡 Medium	Call this week
<5	🟢 Low	Call when available

Step 6: Generate Custom Sales Openers

Not templates — custom for each prospect based on their data.

Opener must accomplish 3 things: (1) prove you know them, (2) state your purpose, (3) invite dialogue.

Data source	How to use in opener
Buy signal	"Saw you just added [service related to your product]"
Similar customer	"We supplied [product] to [similar customer] in your area"
Business type	"Since you do [their business type]..."
Key clues	"As an [industry certification] shop..." / "Working with [their key client]..."
Tier	High→emphasize quality & custom, Mid→value, Low→entry-level
Chain store	Key opener question: "Is equipment purchasing handled locally, or should I speak with your regional/corporate procurement team?"
Premium/certified line	Reference their specialization: "As an EV-certified shop, you need [specific configuration] — we've done those."

Step 7: Output (3-layer structure)

Save to prospect-data/{batch}/:

prospect-data/{area}-{date}/
├── index.json          ← Lightweight index, instant search
├── P001.json           ← Full detail for first prospect
├── P002.json           ← Full detail for next prospect
└── call-list.csv       ← 11-column CSV for calling

See examples/ for sample output files.

Then export CSV from index + P###.json files for calling.

index.json — Search/filter only (few KB):

{
  "batch_id": "orange-ca-2026-05-19",
  "source_customer": "ABC Auto Body",
  "generated": "2026-05-19",
  "search_areas": ["Orange CA"],
  "product": "Customizable per industry",
  "chain_strategy": "Chain stores included — call local first to identify procurement decision chain, then escalate to regional/corporate",
  "prospects": {
    "P001": {
      "name": "Bob's Auto Body",
      "city": "Orange CA",
      "priority": "高",
      "tier": "中高端-独立",
      "status": "待联系",
      "tags": ["[industry]", "[business type]"],
      "file": "P001.json"
    },
    "P013": {
      "name": "Crash Champions Orange",
      "city": "Orange CA",
      "priority": "高",
      "tier": "连锁-中高端",
      "status": "待联系",
      "tags": ["collision", "chain", "Crash Champions"],
      "file": "P013.json"
    }
  }
}

P001.json — Full detail (all collected data + contact log):

{
  "id": "P001",
  "name": "Bob's Auto Body",
  "phone": "(714)555-1234",
  "city": "Orange CA",
  "tier": "Mid-high-Independent",
  "priority": "High",
  "buy_signal": "Added new [service]",
  "similar_customer": "Customer A",
  "business_type": "[industry service type]",
  "key_clues": "[specific observations from data]",
  "email": "bob@bobscorp.com",
  "chain_brand": null,
  "opener": "We supplied [product] to [similar customer] in your area — saw you recently added [service]. What [product type] are you currently using?",
  "status": "Pending",
  "contact_log": [],
  "tags": ["[industry]", "[business type]", "[certification]"],
  "maps_url": "https://maps.google.com/...",
  "rating": 4.5,
  "reviews_count": 87,
  "has_website": true,
  "website_url": "https://bobscorp.com",
  "raw_notes": "Reviews mention...",
  "source_customer": "Customer A"
}

P013.json — Chain store example:

{
  "id": "P013",
  "name": "[Chain Brand] [City]",
  "phone": "(714)555-5678",
  "city": "Orange CA",
  "tier": "Chain-Mid-high",
  "priority": "High",
  "buy_signal": "National chain with stable equipment needs across locations",
  "similar_customer": "Customer A",
  "business_type": "[Industry] Chain",
  "key_clues": "[Chain brand] national chain + [city] location + online booking",
  "email": "",
  "chain_brand": "[Chain Brand]",
  "opener": "Hi, I'm with [company] — we manufacture [product]. [Chain brand] has a location here, and I'd like to learn about your equipment purchasing process. Is that handled locally, or should I speak with your regional/corporate procurement team?",
  "status": "Pending",
  "contact_log": [],
  "tags": ["[industry]", "chain", "[chain brand]", "online booking"],
  "maps_url": "https://maps.google.com/...",
  "rating": 4.6,
  "reviews_count": 120,
  "has_website": true,
  "website_url": "https://www.chainbrand.com",
  "raw_notes": "National chain. Key question: local manager vs regional purchasing.",
  "source_customer": "Customer A"
}

CSV export — 11 columns, ready to call:

优先级,店名,电话,城市,档位,购买信号,相似客户,业务类型,关键线索,邮箱,开场白

CSV columns map 1:1 to P###.json fields (priority→tier, etc.). CSV is a projection of the JSON, not a separate data source.

Status tracking (in P###.json, not CSV):

待联系 → 已联系 → 意向 / 无意向 / 回访中
                 ↘ 无人接听 → 再试

Step 8: Update contact status

When user reports call results, update P###.json:

"contact_log": [
  {"date": "2026-05-20", "action": "电话", "result": "无人接听", "next": "明后天再试"}
]

And update index.json status field accordingly.

Re-export CSV filtered by status when user needs a new call list.

Critical Rules

Every step must execute — skip only if data source has nothing (no website = skip website enrichment)
Review sampling, not all — use tiered sampling + keyword filtering per profiling reference
Social media by tier only — 🔴 chain gets full search, 🟢 small gets nothing
Opener is custom — never use generic templates, always tailor to prospect's specific data
Output is 3-layer — index.json for search, P###.json for detail, CSV for calling
CSV is a projection — all data lives in JSON; CSV is just 11 columns exported on demand
Chain stores ARE valid prospects — do NOT exclude them. Include with a different strategy: local call first → identify procurement decision chain → escalate to regional/corporate buyer. One chain deal can equal many independent deals.
Tier labels include chain distinction — use "独立" (independent) or "连锁" (chain) suffix in tier: e.g., "中高端-独立", "连锁-中高端"
Chain opener must ask about procurement — "Is equipment purchasing handled locally, or should I speak with your regional/corporate procurement team?"
Specialized/certified prospects are high priority — certifications (EV, ISO, specific industry standards) indicate higher equipment requirements and justify premium positioning
DATA INTEGRITY — NO FABRICATION — All data in outputs MUST come from actual agent-browser searches, web_fetch calls, or other real data sources. NEVER invent, infer, or hallucinate business details. If a field cannot be verified from real data, mark it as "unknown", "not found", or "pending verification". If a search returns no results or fails due to network issues, report this honestly to the user instead of generating placeholder data.
TRANSPARENCY ON DATA GAPS — If Google Maps returns restricted view (limited details), if agent-browser fails to load, or if a business has no visible phone/address/rating, document this in raw_notes and adjust the priority accordingly. Do not fill gaps with assumptions.
VERIFICATION REQUIRED — Before marking any prospect as "ready to call", confirm that the phone number was actually extracted from a live page (not a template). If the number is a placeholder or unverified, flag it explicitly: "phone_status": "unverified_placeholder".