--- 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 |