Sales Research
---
name: sales-research
description: This skill provides methodology and best practices for researching sales prospects.
---
# Sales Research
## Overview
This skill provides methodology and best practices for researching sales prospects. It covers company research, contact profiling, and signal detection to surface actionable intelligence.
## Usage
The company-researcher and contact-researcher sub-agents reference this skill when:
- Researching new prospects
- Finding company information
- Profiling individual contacts
- Detecting buying signals
## Research Methodology
### Company Research Checklist
1. **Basic Profile**
- Company name, industry, size (employees, revenue)
- Headquarters and key locations
- Founded date, growth stage
2. **Recent Developments**
- Funding announcements (last 12 months)
- M&A activity
- Leadership changes
- Product launches
3. **Tech Stack**
- Known technologies (BuiltWith, StackShare)
- Job postings mentioning tools
- Integration partnerships
4. **Signals**
- Job postings (scaling = opportunity)
- Glassdoor reviews (pain points)
- News mentions (context)
- Social media activity
### Contact Research Checklist
1. **Professional Background**
- Current role and tenure
- Previous companies and roles
- Education
2. **Influence Indicators**
- Reporting structure
- Decision-making authority
- Budget ownership
3. **Engagement Hooks**
- Recent LinkedIn posts
- Published articles
- Speaking engagements
- Mutual connections
## Resources
- `resources/signal-indicators.md` - Taxonomy of buying signals
- `resources/research-checklist.md` - Complete research checklist
## Scripts
- `scripts/company-enricher.py` - Aggregate company data from multiple sources
- `scripts/linkedin-parser.py` - Structure LinkedIn profile data
FILE:company-enricher.py
#!/usr/bin/env python3
"""
company-enricher.py - Aggregate company data from multiple sources
Inputs:
- company_name: string
- domain: string (optional)
Outputs:
- profile:
name: string
industry: string
size: string
funding: string
tech_stack: [string]
recent_news: [news items]
Dependencies:
- requests, beautifulsoup4
"""
# Requirements: requests, beautifulsoup4
import json
from typing import Any
from dataclasses import dataclass, asdict
from datetime import datetime
@dataclass
class NewsItem:
title: str
date: str
source: str
url: str
summary: str
@dataclass
class CompanyProfile:
name: str
domain: str
industry: str
size: str
location: str
founded: str
funding: str
tech_stack: list[str]
recent_news: list[dict]
competitors: list[str]
description: str
def search_company_info(company_name: str, domain: str = None) -> dict:
"""
Search for basic company information.
In production, this would call APIs like Clearbit, Crunchbase, etc.
"""
# TODO: Implement actual API calls
# Placeholder return structure
return {
"name": company_name,
"domain": domain or f"{company_name.lower().replace(' ', '')}.com",
"industry": "Technology", # Would come from API
"size": "Unknown",
"location": "Unknown",
"founded": "Unknown",
"description": f"Information about {company_name}"
}
def search_funding_info(company_name: str) -> dict:
"""
Search for funding information.
In production, would call Crunchbase, PitchBook, etc.
"""
# TODO: Implement actual API calls
return {
"total_funding": "Unknown",
"last_round": "Unknown",
"last_round_date": "Unknown",
"investors": []
}
def search_tech_stack(domain: str) -> list[str]:
"""
Detect technology stack.
In production, would call BuiltWith, Wappalyzer, etc.
"""
# TODO: Implement actual API calls
return []
def search_recent_news(company_name: str, days: int = 90) -> list[dict]:
"""
Search for recent news about the company.
In production, would call news APIs.
"""
# TODO: Implement actual API calls
return []
def main(
company_name: str,
domain: str = None
) -> dict[str, Any]:
"""
Aggregate company data from multiple sources.
Args:
company_name: Company name to research
domain: Company domain (optional, will be inferred)
Returns:
dict with company profile including industry, size, funding, tech stack, news
"""
# Get basic company info
basic_info = search_company_info(company_name, domain)
# Get funding information
funding_info = search_funding_info(company_name)
# Detect tech stack
company_domain = basic_info.get("domain", domain)
tech_stack = search_tech_stack(company_domain) if company_domain else []
# Get recent news
news = search_recent_news(company_name)
# Compile profile
profile = CompanyProfile(
name=basic_info["name"],
domain=basic_info["domain"],
industry=basic_info["industry"],
size=basic_info["size"],
location=basic_info["location"],
founded=basic_info["founded"],
funding=funding_info.get("total_funding", "Unknown"),
tech_stack=tech_stack,
recent_news=news,
competitors=[], # Would be enriched from industry analysis
description=basic_info["description"]
)
return {
"profile": asdict(profile),
"funding_details": funding_info,
"enriched_at": datetime.now().isoformat(),
"sources_checked": ["company_info", "funding", "tech_stack", "news"]
}
if __name__ == "__main__":
import sys
# Example usage
result = main(
company_name="DataFlow Systems",
domain="dataflow.io"
)
print(json.dumps(result, indent=2))
FILE:linkedin-parser.py
#!/usr/bin/env python3
"""
linkedin-parser.py - Structure LinkedIn profile data
Inputs:
- profile_url: string
- or name + company: strings
Outputs:
- contact:
name: string
title: string
tenure: string
previous_roles: [role objects]
mutual_connections: [string]
recent_activity: [post summaries]
Dependencies:
- requests
"""
# Requirements: requests
import json
from typing import Any
from dataclasses import dataclass, asdict
from datetime import datetime
@dataclass
class PreviousRole:
title: str
company: str
duration: str
description: str
@dataclass
class RecentPost:
date: str
content_preview: str
engagement: int
topic: str
@dataclass
class ContactProfile:
name: str
title: str
company: str
location: str
tenure: str
previous_roles: list[dict]
education: list[str]
mutual_connections: list[str]
recent_activity: list[dict]
profile_url: str
headline: str
def search_linkedin_profile(name: str = None, company: str = None, profile_url: str = None) -> dict:
"""
Search for LinkedIn profile information.
In production, would use LinkedIn API or Sales Navigator.
"""
# TODO: Implement actual LinkedIn API integration
# Note: LinkedIn's API has strict terms of service
return {
"found": False,
"name": name or "Unknown",
"title": "Unknown",
"company": company or "Unknown",
"location": "Unknown",
"headline": "",
"tenure": "Unknown",
"profile_url": profile_url or ""
}
def get_career_history(profile_data: dict) -> list[dict]:
"""
Extract career history from profile.
"""
# TODO: Implement career extraction
return []
def get_mutual_connections(profile_data: dict, user_network: list = None) -> list[str]:
"""
Find mutual connections.
"""
# TODO: Implement mutual connection detection
return []
def get_recent_activity(profile_data: dict, days: int = 30) -> list[dict]:
"""
Get recent posts and activity.
"""
# TODO: Implement activity extraction
return []
def main(
name: str = None,
company: str = None,
profile_url: str = None
) -> dict[str, Any]:
"""
Structure LinkedIn profile data for sales prep.
Args:
name: Person's name
company: Company they work at
profile_url: Direct LinkedIn profile URL
Returns:
dict with structured contact profile
"""
if not profile_url and not (name and company):
return {"error": "Provide either profile_url or name + company"}
# Search for profile
profile_data = search_linkedin_profile(
name=name,
company=company,
profile_url=profile_url
)
if not profile_data.get("found"):
return {
"found": False,
"name": name or "Unknown",
"company": company or "Unknown",
"message": "Profile not found or limited access",
"suggestions": [
"Try searching directly on LinkedIn",
"Check for alternative spellings",
"Verify the person still works at this company"
]
}
# Get career history
previous_roles = get_career_history(profile_data)
# Find mutual connections
mutual_connections = get_mutual_connections(profile_data)
# Get recent activity
recent_activity = get_recent_activity(profile_data)
# Compile contact profile
contact = ContactProfile(
name=profile_data["name"],
title=profile_data["title"],
company=profile_data["company"],
location=profile_data["location"],
tenure=profile_data["tenure"],
previous_roles=previous_roles,
education=[], # Would be extracted from profile
mutual_connections=mutual_connections,
recent_activity=recent_activity,
profile_url=profile_data["profile_url"],
headline=profile_data["headline"]
)
return {
"found": True,
"contact": asdict(contact),
"research_date": datetime.now().isoformat(),
"data_completeness": calculate_completeness(contact)
}
def calculate_completeness(contact: ContactProfile) -> dict:
"""Calculate how complete the profile data is."""
fields = {
"basic_info": bool(contact.name and contact.title and contact.company),
"career_history": len(contact.previous_roles) > 0,
"mutual_connections": len(contact.mutual_connections) > 0,
"recent_activity": len(contact.recent_activity) > 0,
"education": len(contact.education) > 0
}
complete_count = sum(fields.values())
return {
"fields": fields,
"score": f"{complete_count}/{len(fields)}",
"percentage": int((complete_count / len(fields)) * 100)
}
if __name__ == "__main__":
import sys
# Example usage
result = main(
name="Sarah Chen",
company="DataFlow Systems"
)
print(json.dumps(result, indent=2))
FILE:priority-scorer.py
#!/usr/bin/env python3
"""
priority-scorer.py - Calculate and rank prospect priorities
Inputs:
- prospects: [prospect objects with signals]
- weights: {deal_size, timing, warmth, signals}
Outputs:
- ranked: [prospects with scores and reasoning]
Dependencies:
- (none - pure Python)
"""
import json
from typing import Any
from dataclasses import dataclass
# Default scoring weights
DEFAULT_WEIGHTS = {
"deal_size": 0.25,
"timing": 0.30,
"warmth": 0.20,
"signals": 0.25
}
# Signal score mapping
SIGNAL_SCORES = {
# High-intent signals
"recent_funding": 10,
"leadership_change": 8,
"job_postings_relevant": 9,
"expansion_news": 7,
"competitor_mention": 6,
# Medium-intent signals
"general_hiring": 4,
"industry_event": 3,
"content_engagement": 3,
# Relationship signals
"mutual_connection": 5,
"previous_contact": 6,
"referred_lead": 8,
# Negative signals
"recent_layoffs": -3,
"budget_freeze_mentioned": -5,
"competitor_selected": -7,
}
@dataclass
class ScoredProspect:
company: str
contact: str
call_time: str
raw_score: float
normalized_score: int
priority_rank: int
score_breakdown: dict
reasoning: str
is_followup: bool
def score_deal_size(prospect: dict) -> tuple[float, str]:
"""Score based on estimated deal size."""
size_indicators = prospect.get("size_indicators", {})
employee_count = size_indicators.get("employees", 0)
revenue_estimate = size_indicators.get("revenue", 0)
# Simple scoring based on company size
if employee_count > 1000 or revenue_estimate > 100_000_000:
return 10.0, "Enterprise-scale opportunity"
elif employee_count > 200 or revenue_estimate > 20_000_000:
return 7.0, "Mid-market opportunity"
elif employee_count > 50:
return 5.0, "SMB opportunity"
else:
return 3.0, "Small business"
def score_timing(prospect: dict) -> tuple[float, str]:
"""Score based on timing signals."""
timing_signals = prospect.get("timing_signals", [])
score = 5.0 # Base score
reasons = []
for signal in timing_signals:
if signal == "budget_cycle_q4":
score += 3
reasons.append("Q4 budget planning")
elif signal == "contract_expiring":
score += 4
reasons.append("Contract expiring soon")
elif signal == "active_evaluation":
score += 5
reasons.append("Actively evaluating")
elif signal == "just_funded":
score += 3
reasons.append("Recently funded")
return min(score, 10.0), "; ".join(reasons) if reasons else "Standard timing"
def score_warmth(prospect: dict) -> tuple[float, str]:
"""Score based on relationship warmth."""
relationship = prospect.get("relationship", {})
if relationship.get("is_followup"):
last_outcome = relationship.get("last_outcome", "neutral")
if last_outcome == "positive":
return 9.0, "Warm follow-up (positive last contact)"
elif last_outcome == "neutral":
return 7.0, "Follow-up (neutral last contact)"
else:
return 5.0, "Follow-up (needs re-engagement)"
if relationship.get("referred"):
return 8.0, "Referred lead"
if relationship.get("mutual_connections", 0) > 0:
return 6.0, f"{relationship['mutual_connections']} mutual connections"
if relationship.get("inbound"):
return 7.0, "Inbound interest"
return 4.0, "Cold outreach"
def score_signals(prospect: dict) -> tuple[float, str]:
"""Score based on buying signals detected."""
signals = prospect.get("signals", [])
total_score = 0
signal_reasons = []
for signal in signals:
signal_score = SIGNAL_SCORES.get(signal, 0)
total_score += signal_score
if signal_score > 0:
signal_reasons.append(signal.replace("_", " "))
# Normalize to 0-10 scale
normalized = min(max(total_score / 2, 0), 10)
reason = f"Signals: {', '.join(signal_reasons)}" if signal_reasons else "No strong signals"
return normalized, reason
def calculate_priority_score(
prospect: dict,
weights: dict = None
) -> ScoredProspect:
"""Calculate overall priority score for a prospect."""
weights = weights or DEFAULT_WEIGHTS
# Calculate component scores
deal_score, deal_reason = score_deal_size(prospect)
timing_score, timing_reason = score_timing(prospect)
warmth_score, warmth_reason = score_warmth(prospect)
signal_score, signal_reason = score_signals(prospect)
# Weighted total
raw_score = (
deal_score * weights["deal_size"] +
timing_score * weights["timing"] +
warmth_score * weights["warmth"] +
signal_score * weights["signals"]
)
# Compile reasoning
reasons = []
if timing_score >= 8:
reasons.append(timing_reason)
if signal_score >= 7:
reasons.append(signal_reason)
if warmth_score >= 7:
reasons.append(warmth_reason)
if deal_score >= 8:
reasons.append(deal_reason)
return ScoredProspect(
company=prospect.get("company", "Unknown"),
contact=prospect.get("contact", "Unknown"),
call_time=prospect.get("call_time", "Unknown"),
raw_score=round(raw_score, 2),
normalized_score=int(raw_score * 10),
priority_rank=0, # Will be set after sorting
score_breakdown={
"deal_size": {"score": deal_score, "reason": deal_reason},
"timing": {"score": timing_score, "reason": timing_reason},
"warmth": {"score": warmth_score, "reason": warmth_reason},
"signals": {"score": signal_score, "reason": signal_reason}
},
reasoning="; ".join(reasons) if reasons else "Standard priority",
is_followup=prospect.get("relationship", {}).get("is_followup", False)
)
def main(
prospects: list[dict],
weights: dict = None
) -> dict[str, Any]:
"""
Calculate and rank prospect priorities.
Args:
prospects: List of prospect objects with signals
weights: Optional custom weights for scoring components
Returns:
dict with ranked prospects and scoring details
"""
weights = weights or DEFAULT_WEIGHTS
# Score all prospects
scored = [calculate_priority_score(p, weights) for p in prospects]
# Sort by raw score descending
scored.sort(key=lambda x: x.raw_score, reverse=True)
# Assign ranks
for i, prospect in enumerate(scored, 1):
prospect.priority_rank = i
# Convert to dicts for JSON serialization
ranked = []
for s in scored:
ranked.append({
"company": s.company,
"contact": s.contact,
"call_time": s.call_time,
"priority_rank": s.priority_rank,
"score": s.normalized_score,
"reasoning": s.reasoning,
"is_followup": s.is_followup,
"breakdown": s.score_breakdown
})
return {
"ranked": ranked,
"weights_used": weights,
"total_prospects": len(prospects)
}
if __name__ == "__main__":
import sys
# Example usage
example_prospects = [
{
"company": "DataFlow Systems",
"contact": "Sarah Chen",
"call_time": "2pm",
"size_indicators": {"employees": 200, "revenue": 25_000_000},
"timing_signals": ["just_funded", "active_evaluation"],
"signals": ["recent_funding", "job_postings_relevant"],
"relationship": {"is_followup": False, "mutual_connections": 2}
},
{
"company": "Acme Manufacturing",
"contact": "Tom Bradley",
"call_time": "10am",
"size_indicators": {"employees": 500},
"timing_signals": ["contract_expiring"],
"signals": [],
"relationship": {"is_followup": True, "last_outcome": "neutral"}
},
{
"company": "FirstRate Financial",
"contact": "Linda Thompson",
"call_time": "4pm",
"size_indicators": {"employees": 300},
"timing_signals": [],
"signals": [],
"relationship": {"is_followup": False}
}
]
result = main(prospects=example_prospects)
print(json.dumps(result, indent=2))
FILE:research-checklist.md
# Prospect Research Checklist
## Company Research
### Basic Information
- [ ] Company name (verify spelling)
- [ ] Industry/vertical
- [ ] Headquarters location
- [ ] Employee count (LinkedIn, website)
- [ ] Revenue estimate (if available)
- [ ] Founded date
- [ ] Funding stage/history
### Recent News (Last 90 Days)
- [ ] Funding announcements
- [ ] Acquisitions or mergers
- [ ] Leadership changes
- [ ] Product launches
- [ ] Major customer wins
- [ ] Press mentions
- [ ] Earnings/financial news
### Digital Footprint
- [ ] Website review
- [ ] Blog/content topics
- [ ] Social media presence
- [ ] Job postings (careers page + LinkedIn)
- [ ] Tech stack (BuiltWith, job postings)
### Competitive Landscape
- [ ] Known competitors
- [ ] Market position
- [ ] Differentiators claimed
- [ ] Recent competitive moves
### Pain Point Indicators
- [ ] Glassdoor reviews (themes)
- [ ] G2/Capterra reviews (if B2B)
- [ ] Social media complaints
- [ ] Job posting patterns
## Contact Research
### Professional Profile
- [ ] Current title
- [ ] Time in role
- [ ] Time at company
- [ ] Previous companies
- [ ] Previous roles
- [ ] Education
### Decision Authority
- [ ] Reports to whom
- [ ] Team size (if manager)
- [ ] Budget authority (inferred)
- [ ] Buying involvement history
### Engagement Hooks
- [ ] Recent LinkedIn posts
- [ ] Published articles
- [ ] Podcast appearances
- [ ] Conference talks
- [ ] Mutual connections
- [ ] Shared interests/groups
### Communication Style
- [ ] Post tone (formal/casual)
- [ ] Topics they engage with
- [ ] Response patterns
## CRM Check (If Available)
- [ ] Any prior touchpoints
- [ ] Previous opportunities
- [ ] Related contacts at company
- [ ] Notes from colleagues
- [ ] Email engagement history
## Time-Based Research Depth
| Time Available | Research Depth |
|----------------|----------------|
| 5 minutes | Company basics + contact title only |
| 15 minutes | + Recent news + LinkedIn profile |
| 30 minutes | + Pain point signals + engagement hooks |
| 60 minutes | Full checklist + competitive analysis |
FILE:signal-indicators.md
# Signal Indicators Reference
## High-Intent Signals
### Job Postings
- **3+ relevant roles posted** = Active initiative, budget allocated
- **Senior hire in your domain** = Strategic priority
- **Urgency language ("ASAP", "immediate")** = Pain is acute
- **Specific tool mentioned** = Competitor or category awareness
### Financial Events
- **Series B+ funding** = Growth capital, buying power
- **IPO preparation** = Operational maturity needed
- **Acquisition announced** = Integration challenges coming
- **Revenue milestone PR** = Budget available
### Leadership Changes
- **New CXO in your domain** = 90-day priority setting
- **New CRO/CMO** = Tech stack evaluation likely
- **Founder transition to CEO** = Professionalizing operations
## Medium-Intent Signals
### Expansion Signals
- **New office opening** = Infrastructure needs
- **International expansion** = Localization, compliance
- **New product launch** = Scaling challenges
- **Major customer win** = Delivery pressure
### Technology Signals
- **RFP published** = Active buying process
- **Vendor review mentioned** = Comparison shopping
- **Tech stack change** = Integration opportunity
- **Legacy system complaints** = Modernization need
### Content Signals
- **Blog post on your topic** = Educating themselves
- **Webinar attendance** = Interest confirmed
- **Whitepaper download** = Problem awareness
- **Conference speaking** = Thought leadership, visibility
## Low-Intent Signals (Nurture)
### General Activity
- **Industry event attendance** = Market participant
- **Generic hiring** = Company growing
- **Positive press** = Healthy company
- **Social media activity** = Engaged leadership
## Signal Scoring
| Signal Type | Score | Action |
|-------------|-------|--------|
| Job posting (relevant) | +3 | Prioritize outreach |
| Recent funding | +3 | Reference in conversation |
| Leadership change | +2 | Time-sensitive opportunity |
| Expansion news | +2 | Growth angle |
| Negative reviews | +2 | Pain point angle |
| Content engagement | +1 | Nurture track |
| No signals | 0 | Discovery focus |
