From Claudient — SDR
- User provides a company name and LinkedIn URL and asks to "research this account," "build a dossier," "find decision-makers," or "extract pain signals" - User needs to understand who owns budget, wh
How this skill is triggered — by the user, by Claude, or both
Slash command
/claudient-sdr:company-intelligenceThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- User provides a company name and LinkedIn URL and asks to "research this account," "build a dossier," "find decision-makers," or "extract pain signals"
Every company dossier is built by stacking these layers. Higher tiers require all five; lower tiers require three.
Goal: Identify three role types at the company:
Sources to check:
Decision logic:
Goal: Find the last 90 days of company activity that creates urgency or context.
Sources to check (in order):
Scoring:
Goal: Identify what they use, what they don't use, and what's broken.
Sources to check (in order):
Decision logic:
Goal: Extract implicit problems from job postings and user reviews.
Methodology:
Job Posting Pattern Matching:
G2 Review Pattern Matching (filter for your company size/industry):
Scoring: Count pain signals. 3+ distinct signals across reviews + job postings = strong qualification.
Goal: Understand how visible and active the decision-makers are; what they care about.
Sources to check:
Scoring:
All tiers use the 5-layer model, but research intensity and output detail differ.
When to use: High-value account (named deal, enterprise ACV >$100k, C-list target, strategic partnership) Research depth:
Output: Full Account Dossier (template below) Time estimate: 18–22 minutes (4–5 min per layer + 2 min synthesis)
When to use: Mid-market account (ACV $20k–$100k), account list, early prospecting Research depth:
Output: Abbreviated dossier (1 page) Time estimate: 8–11 minutes
When to use: High-volume list research, quick qualification, social selling Research depth:
Output: One-paragraph company snapshot Time estimate: 2–4 minutes
Use this exact format for Tier 1 research. Adapt for Tier 2/3 by dropping sections marked [T1 only].
## [COMPANY NAME] — Account Intelligence Dossier
### Company Overview (2 sentences)
[1 sentence on what they do + market]
[1 sentence on recent traction or context that matters to your pitch]
### Decision-Maker Map
[Format: Name (Title, Last LinkedIn Activity) — Role & Influence]
**Economic Buyer:** [Name], [Title]
- P&L owner: [specific function: Sales, Engineering, Finance, Ops]
- Last active on LinkedIn: [date]
- Signal: [brief context, e.g., "Posted about hiring for team expansion" or "No activity in 60 days"]
**Champion:** [Name], [Title]
- Uses your solution category daily
- Last active on LinkedIn: [date]
- Signal: [job posting evidence or review where this role described the pain]
**Influencer:** [Name], [Title]
- Can block/accelerate: [why: CTO, Chief Product Officer, peer leader in their function]
- Last active on LinkedIn: [date]
- Signal: [recent activity that proves relevance: post about tech choices, hiring, M&A]
[T1 only] **Sponsor (optional):** [Name], [Title]
- Bridge to economic buyer (if company >1000 headcount)
### Layer 2: Recent Events (Momentum Signals)
[3–5 events, most recent first, with dates and links]
- **[Date, Event Type]:** [What happened] → Implication for your pitch
- Source: [Link]
### Layer 3: Tech Stack & Gaps
[List current tools; identify gaps and aspirations]
**Current Stack (verified):**
- [Category]: [Tool 1], [Tool 2]
- [Category]: [Tool]
**Identified Gaps:**
- [Gap 1]: Using [Old Tool], job postings show interest in [New Category] → Migration opportunity
- [Gap 2]: [Problem], not solved by current stack → Direct pain
**Integration Friction:**
- [Tool A] + [Tool B] noted as "difficult to sync" in 3 reviews → Integration selling point
### Layer 4: Pain Signals (Top 3)
[Rank by evidence strength: job postings > multiple reviews > single review > inference]
**Signal #1: [Problem statement]**
- Evidence: [2–3 job postings or review quotes]
- Frequency: Mentioned in [X] postings / [X] reviews
- Urgency: [High/Medium/Low — inferred from recency and job posting level]
- Your hook: [How your product solves this in one sentence]
**Signal #2: [Problem statement]**
- Evidence: [2–3 job postings or review quotes]
- Frequency: Mentioned in [X] postings / [X] reviews
- Urgency: [High/Medium/Low]
- Your hook: [One sentence]
**Signal #3: [Problem statement]**
- Evidence: [Job posting or review quote]
- Frequency: Mentioned in [X] postings / [X] reviews
- Urgency: [High/Medium/Low]
- Your hook: [One sentence]
### Best Personalization Hook
[One specific, credible angle to lead with. Format: "Use [Signal/Event/Person] as the hook. Example opener: '...'" ]
Example formats:
- News hook: "[CEO Name]'s post about [topic] on [date] suggests they're prioritizing X. We help companies like [similar company] solve that by..."
- Pain hook: "I noticed 5 of your recent job postings mention [skill]. That usually means..."
- Tech hook: "You're using [Tool A] but job posts show you're hiring for [new area]. We specialize in..."
- Leadership hook: "[New Hire Name] just joined as [role]. Based on her background in [area], she likely owns..."
### Recommended First Channel
[Choose one; explain why]
- **LinkedIn InMail to [Economic Buyer]?** — If active, <5 contacts in role, high trust signal
- **LinkedIn message to [Champion]?** — If they're visible, less threatening than direct to buyer, easier to warm
- **Email (warm intro)?** — If you have a mutual connection (check LinkedIn "People you know")
- **Email (cold)?** — If pain is acute enough, company is hiring (visible on LinkedIn)
- **LinkedIn outreach to [Influencer]?** — If they're highly active and thought leader (easier to get meeting)
**Why:** [Justify based on their activity level, org size, pain urgency]
### Recommended Framework
[Pick one; explain why]
- **"By the way" framework** — Best if: Pain is obvious, champion is receptive, goal is warm intro
- **MEDDIC / BANT qualification** — Best if: Enterprise deal, complex buying process, multiple decision-makers
- **ROI/efficiency hook** — Best if: Finance buyer is target, pain is cost or manual work, you have benchmarks
- **Event-triggered** — Best if: Recent funding or hire suggests receptivity; use news as proof of change appetite
- **Peer social proof** — Best if: [Competitor or similar company] is customer; drop name contextually
**Why:** [Explain fit]
### Data Quality & Confidence Scoring
[T1 only]
- **Data freshness:** Last research update [date]
- **Confidence in decision-maker accuracy:** [High/Medium/Low — based on confirmation from 2+ sources]
- **Pain signal strength:** [High/Medium/Low — based on frequency of mentions + recency]
- **Recommended next step:** [Direct outreach / Warm intro needed / Too noisy, research more / Ready to pitch]
Prompt to use when starting research:
Act as a B2B account intelligence specialist. I'm researching [COMPANY NAME] to prepare for outreach.
Depth: [Tier 1 / Tier 2 / Tier 3]
Company Info:
- Company: [COMPANY NAME]
- LinkedIn URL: [LINKEDIN_URL]
- Industry: [If known — optional]
- Company Size: [If known — optional]
- Your product: [Brief 1-sentence description of what you sell]
For Tier 1: Use all 5 layers (org structure, recent events, tech stack, pain signals, social footprint). Find 3 named decision-makers with current LinkedIn activity. Extract 3–5 pain signals from job postings and G2 reviews. Provide a complete Account Dossier using the template.
For Tier 2: Focus on layers 1–4. Find 2 key decision-makers. Extract 3–4 pain signals. Provide a 1-page abbreviated dossier.
For Tier 3: Quick snapshot only. CEO name, one recent signal, one pain signal, one tool/gap.
Research checklist:
- [ ] Company LinkedIn page reviewed (leadership, recent activity, headcount)
- [ ] CEO/VP LinkedIn activity checked (last 30 days)
- [ ] 3+ job postings analyzed (if available)
- [ ] G2/Capterra reviews mined (industry/size filter applied)
- [ ] BuiltWith tech stack verified
- [ ] Recent press/news checked (funding, hires, product launches)
Output format: Use the Account Dossier template provided. Be specific — cite sources, dates, and names. No vague claims.
Do you have a company name + LinkedIn URL?
├─ Yes
│ ├─ Is it a Tier 1 account (high-value, strategic, named deal)?
│ │ └─ Yes → Invest 20 min in full dossier (Tier 1)
│ └─ Is it Tier 2 (mid-market, account list)?
│ └─ Yes → 10-min medium brief (Tier 2)
│ └─ Is it volume prospecting or quick-qualify?
│ └─ Yes → 3-min snapshot (Tier 3)
└─ No → Ask for company name + LinkedIn URL before starting
Start with company LinkedIn page:
├─ Does it list C-suite/VP?
│ ├─ Yes → Note names, check their individual LinkedIn profiles for recent activity
│ └─ No → Company may be <50 headcount; assume CEO is economic buyer
├─ Check "People" tab on company page
│ └─ Filter by title (VP Finance, VP Sales, CTO, Chief Product Officer)
├─ Cross-check on job postings
│ └─ "Reporting to [Name]" in job posting = confirms role + name
└─ Search Google + LinkedIn for "[Company] [Role]"
└─ Use last activity date to gauge engagement
Job Postings (highest fidelity):
├─ Read 3–5 postings for your function
├─ Extract patterns: "seeking X to fix Y"
├─ Note urgency (hiring at manager/director level = high priority)
├─ Note context (hiring for new function = expansion; reqs = problems)
G2 Reviews (validation):
├─ Filter by company size + industry
├─ Read 4–6 reviews, search for keywords: "slow," "integration," "lack," "need," "expensive"
├─ Count frequency (3+ reviews mention same pain = strong signal)
└─ Prioritize recent reviews (< 6 months old)
LinkedIn Job Postings:
├─ Search "[Company Name] hiring"
├─ Sort by most recent
├─ Extract 3–5 open roles + their descriptions
└─ Note: Stack of titles reveals org priorities (e.g., 5 sales roles open = growth mode)
Tier 1 Criteria (Full Dossier — 20 min):
├─ ACV or deal size >$100k
├─ Named deal or strategic account
├─ C-suite target or enterprise buying process
└─ Can invest time for high-precision research
Tier 2 Criteria (Medium Brief — 10 min):
├─ ACV $20k–$100k
├─ Account on list of 10–50 targets
├─ Sales development (SDR) lead generation
└─ Need signal before first touchpoint
Tier 3 Criteria (Minimum Profile — 3 min):
├─ ACV <$20k or volume prospecting
├─ Account list of 100+
├─ Social selling or rapid qualification
└─ Quick decision: fit or skip
Tier 1 Breakdown (20 min):
Tier 2 Breakdown (10 min):
Tier 3 Breakdown (3 min):
Effort reduction tips:
Brief: You're an account executive for a data pipeline platform (like Fivetran, Airbyte, or dbt Cloud). Your company specializes in automating data ingestion and transformation. You've identified a mid-market e-commerce company, [TechRetail Inc.], as a target. You need a full Account Dossier before your first call with their VP of Data.
Company: TechRetail Inc. (fictitious example) LinkedIn: linkedin.com/company/techretail-inc Your product: Automated data pipeline orchestration + data quality monitoring Tier: Tier 1 (named deal, enterprise ACV)
Company LinkedIn page review:
Search results: "[TechRetail VP Data]" → Found [Alex Rodriguez], VP of Data & Analytics, LinkedIn URL [link], last post June 1, 2026 (active, 3-4 posts per week)
Search results: "[TechRetail Director Engineering]" → Found [Jamie Kim], Director of Data Engineering, LinkedIn URL [link], last post May 28, 2026 (active, replies to comments)
Cross-check on LinkedIn "People" tab:
Decision-maker map:
Company LinkedIn page:
CEO (Sarah Chen) LinkedIn:
Press/News:
Translation: Company has capital, is investing in data team (new director hire suggests urgency), CEO is actively looking at new data tools, and VP Finance (budget owner) is actively posting about finance/ops topics (responsive signal).
Recency scoring:
BuiltWith check:
LinkedIn Job Postings (last 5):
G2 Reviews (filtered by 100–1000 headcount, e-commerce):
Tech Stack Summary:
Current tools:
Gaps identified:
Integration friction:
Signal #1: Airflow Operational Overhead + Scalability Bottleneck
Evidence:
Frequency: 3 job postings mention orchestration/airflow, 3 G2 reviews mention operational pain Urgency: High — New director hire (signal company prioritizes this now), Series B capital to invest, recent job postings (hiring to fix) Your hook (Fivetran/Airbyte angle): "Your job postings show you're scaling Airflow, but the real unlock is reducing ops overhead. A managed pipeline platform lets your team focus on analytics, not infrastructure."
Signal #2: Multi-Tool Data Stack + Integration Fragmentation
Evidence:
Frequency: Mentioned in 2 job postings, 1 review, inferred from tech stack Urgency: Medium-High — They're actively hiring to solve this (Analytics Engineer role), but not yet critical Your hook (dbt Cloud / orchestration platform): "You're building a modern data stack (Snowflake + dbt), but your orchestration layer isn't built to handle it. A platform that syncs Airflow + dbt + Snowflake reduces your integration debt by 60%."
Signal #3: Data Quality + Observability (New Priority)
Evidence:
Frequency: 1 new job posting + 1 review mention Urgency: Medium — Newly prioritized, but not yet mature (hiring for it now) Your hook (dbt + data quality tools): "You just hired for data quality. The hardest part isn't monitoring—it's having a system that prevents bad data from entering your pipeline. [Your tool] catches issues before they hit Snowflake."
CEO (Sarah Chen) LinkedIn activity:
VP Data (Alex Rodriguez) LinkedIn:
CTO (Marcus Williams) LinkedIn:
## TechRetail Inc. — Account Intelligence Dossier
### Company Overview
TechRetail Inc. is a ~450-person e-commerce platform specializing in customer data and personalization, with customers across retail and CPG sectors. They just closed a $25M Series B (May 2026) to expand into EU markets and strengthen their data infrastructure—creating an active 90-day capital deployment window.
### Decision-Maker Map
**Economic Buyer:** David Park, VP Finance
- P&L owner: Data infrastructure budget + tech spend
- Last active on LinkedIn: May 30, 2026 (posts 1–2x per month on finance/ops)
- Signal: Active enough to see cold outreach; Finance controls data/infrastructure budget
**Champion:** Alex Rodriguez, VP of Data & Analytics
- Uses orchestration + data transformation tools daily; OKRs tied to data pipeline velocity + quality
- Last active on LinkedIn: June 1, 2026 (posts 3–4x per week, very engaged)
- Signal: Highly engaged in data engineering community; will likely read inbound from peers or vendors; can influence buying decision upward to Finance
**Influencer:** Marcus Williams, CTO
- Can block/accelerate: Architecture decisions, engineering practices; final say on platform integration
- Last active on LinkedIn: May 28, 2026 (lower activity, but engaged when active)
- Signal: Recent data engineering director hire (Jamie Kim) reports to him; his buy-in is required for implementation
---
### Layer 2: Recent Events (Momentum Signals)
- **May 15, 2026 (Series B Funding):** $25M Series B funding to expand EU + strengthen data infrastructure
- Implication: Capital allocated for infrastructure investment; 90-day spending window likely active; budget cycle reset
- Source: [company-linkedin-post]
- **May 22, 2026 (Director Hire):** Jamie Kim hired as Director of Data Engineering (ex-[Previous Company], 10-year data platform background)
- Implication: Company is accelerating data platform development; ops/scaling issues being directly addressed; new director will evaluate tooling
- Source: [alex-rodriguez-linkedin-post]
- **June 1, 2026 (New Data Quality Role):** Data Quality Engineer role posted; job description says "We're building new monitoring processes"
- Implication: Data quality is now a business-critical priority (likely EU expansion + data accuracy for personalization); monitoring stack being built now
- Source: [techretail-careers-page]
- **May 8, 2026 (Internal Success):** Posted case study on reducing data processing time by 40%
- Implication: Company is data-first; publicly celebrating efficiency wins; open to process improvements
- Source: [company-blog]
- **June 1, 2026 (CEO Tool Research):** Sarah Chen (CEO) reposted TechCrunch article on "Future of Customer Data Platforms" with comment: "This resonates—our roadmap is heavily data-first"
- Implication: CEO is actively researching data platform trends; data infrastructure is strategic priority
- Source: [sarah-chen-linkedin]
---
### Layer 3: Tech Stack & Gaps
**Current Stack (verified by BuiltWith + job postings + LinkedIn):**
- **Data Warehouse:** Snowflake (primary)
- **Orchestration:** Apache Airflow (primary), custom Python scripts
- **Transformation:** dbt (recently adopted; hiring for "Analytics Engineer" role)
- **Analytics/BI:** Looker
- **Customer Data:** Segment, Mixpanel
- **CRM:** Salesforce
**Identified Gaps:**
1. **Orchestration scalability + operations:** Using open-source Airflow with heavy operational overhead. Job posting for "Data Infrastructure Lead" explicitly mentions "reducing operational overhead" and "scaling Snowflake clusters." G2 reviews from similar companies note "2+ hour deployments" and "monitoring gaps." No managed orchestration solution in place (no Fivetran, Airbyte, Prefect, Dagster, or dbt Cloud observed).
- Gap implication: They're building it in-house today; Series B capital makes them a buyer now
2. **dbt integration + governance:** Recently hired for "Analytics Engineer" role, but dbt is not yet integrated with Airflow at scale. G2 review notes "fragments of Python scripts + dbt models + Airflow DAGs—hard to track dependencies."
- Gap implication: Multi-tool data stack requires integration layer; dependency tracking broken
3. **Data quality observability:** New "Data Quality Engineer" role; job posting explicitly says "building new monitoring processes." G2 review notes "lack of good monitoring."
- Gap implication: Data quality is newly prioritized; monitoring stack being built; buyer for observability tools now
**Integration friction:**
- Snowflake + Airflow: Manual integration, monitoring via Airflow logs (limited)
- Airflow + dbt: No native integration; requires custom orchestration
- dbt + Snowflake: Works, but scaling requires governance (model tracking, lineage)
---
### Layer 4: Pain Signals (Top 3)
**Pain Signal #1: Airflow Operational Overhead + Scalability**
Evidence:
- Job posting (May 20): "Senior Data Engineer required: Airflow or similar orchestration. Nice to have: dbt experience" → signals current Airflow use, interest in alternatives
- Job posting (May 10): "Data Infrastructure Lead — Owner of data platform roadmap. Must have experience scaling Snowflake clusters + reducing costs" → explicit cost + scaling pain
- G2 reviews (filtered by company size + e-commerce):
- May 2026: "Deployments take 2+ hours, debugging failed jobs is painful"
- April 2026: "Steep learning curve, lack of monitoring"
- New hire context: Jamie Kim (Director of Data Engineering, hired May 22) background in "scaling data platforms" at previous company → signals this pain was a hiring requirement
Frequency: Mentioned across 3 job postings + 2 G2 reviews = high consensus
Urgency: **High** — Newly hired director to fix; Series B capital allocated; recent job posts
Your hook: "Your Series B math doesn't work if 2 hours of each deployment day is spent on Airflow ops. Your new Director of Data Engineering (Jamie Kim, based on her background) will likely evaluate orchestration solutions that cut operational overhead by 50%+ within Q3. A managed platform lets your team focus on data strategy, not infra."
---
**Pain Signal #2: Multi-Tool Data Stack Fragmentation + Dependency Tracking**
Evidence:
- Job posting (May 28): "Analytics Engineer — Transform data using SQL, dbt, Snowflake" → signals dbt adoption but not yet mature
- Job posting (May 20): "Senior Data Engineer — Airflow or similar + nice to have dbt" → signals co-existence of two transformation approaches
- G2 review (May 2026): "We have Python scripts, dbt models, and Airflow DAGs—hard to track dependencies. Integration between tools needs improvement"
- Job posting (June 1, Data Quality Engineer): "Own data quality and testing" → signals they want to centralize quality, but current stack is fragmented
Frequency: Multiple job postings + 1 detailed review = clear pattern
Urgency: **Medium-High** — They're actively hiring to solve (Analytics Engineer role), but not yet critical path
Your hook: "You're building a modern stack (dbt + Snowflake), but your orchestration layer wasn't built for it. You have transformation logic scattered across Python scripts, dbt, and Airflow. Consolidating onto a platform that syncs all three cuts your dependency tracking burden by 70% and makes your data governance scalable."
---
**Pain Signal #3: Data Quality + Observability (New Business-Critical Priority)**
Evidence:
- Job posting (June 1): "Data Quality Engineer — We're building new monitoring processes" (new role, recent post)
- G2 review (April 2026): "We lack good monitoring. Transitioning to Airflow, but monitoring strategy not established"
- Context: Series B expansion into EU + personalization focus = data accuracy directly impacts customer experience + revenue
- CEO signal (June 1): "Our roadmap is heavily data-first" → investment in data quality is strategic
Frequency: 1 recent job posting + 1 review + strategic context = emerging priority
Urgency: **Medium** — Newly prioritized (hiring today), but not yet mature; however, will become critical within 60 days
Your hook: "You just added a Data Quality Engineer role. That means data accuracy is now on the executive agenda (probably triggered by your EU expansion + personalization roadmap). The hardest part of data quality isn't monitoring—it's preventing bad data from entering your pipeline in the first place. Most platforms add monitoring after the fact. [Your tool] prevents issues upstream."
---
### Best Personalization Hook
**Use the Series B capital + new director hire as the entry vector. Lead with Jamie Kim's background as social proof.**
**Recommended opener:**
"Alex, I noticed TechRetail just brought Jamie Kim on as Director of Data Engineering (congratulations to the team). Her background at [Previous Company] was building data platforms that scaled from Airflow to 10B+ events/day. I'm guessing that was part of why she's here—to tackle the same scaling challenges you're hitting post-Series B. We help engineering teams like yours cut Airflow operational overhead by 50%+ while keeping your dbt + Snowflake investments intact. I'd love to share how [similar company of his size] solved this in Q2. Do you have 20 minutes next week?"
**Alternative hooks (in priority order):**
1. **News hook:** "Series B expansion into EU requires data accuracy at scale. Your new Data Quality Engineer role confirms that's on your agenda. Here's how [company] handles data quality checks upstream..."
2. **Tech hook:** "Your job postings show you're hiring for dbt + Airflow. The tricky part—and the reason most teams hit scaling walls—is integrating the two without hiring a platform team. [Your tool] solves that..."
3. **Cost hook:** "Your 'Data Infrastructure Lead' role mentions reducing Snowflake costs. Most teams hit a wall: Airflow deployments get slower, dbt queries run longer, costs climb. [Your tool] is built to run both efficiently..."
---
### Recommended First Channel
**LinkedIn InMail to Alex Rodriguez, VP of Data**
**Why:**
- He's highly active (3–4 posts per week, last activity today), so likely to open + read InMail
- As VP of Data, he owns the day-to-day pain (orchestration ops, transformation governance)
- He's *not* the economic buyer (David Park, VP Finance is), but he's the Champion who can get a meeting scheduled and influence upward
- Direct to Economic Buyer (David Park) skips the champion; less warm, requires CEO-level social proof
- Email (cold) is possible, but his LinkedIn engagement suggests InMail will outperform
**Why not alternatives:**
- Email (warm intro): Faster if you have a mutual connection, but no evidence of one; LinkedIn InMail is warmer
- LinkedIn message: InMail is higher-intent from vendor perspective; shows you respect their time
- Influencer outreach (Marcus Williams, CTO): He's less active; Alex is the more receptive audience
---
### Recommended Framework
**"Event-triggered" framework with peer social proof**
**Why this framework:**
1. **Event trigger:** Series B + new director hire = proof of change appetite and budget allocation
2. **Peer social proof:** You have a customer of similar size/stage in e-commerce or data infrastructure who solved this post-Series B; that company becomes your reference
3. **Credibility:** New director (Jamie Kim) will want to evaluate solutions quickly; having case studies from her peer network accelerates deal cycle
**Execution:**
- **First touch (InMail to Alex):** News hook (Series B) + new director hire as proof they care about this problem + reference customer case study (if you have one in e-commerce/data infrastructure at similar scale)
- Message length: 40 words max + one link to case study
- Goal: Get 20-minute discovery call
- **Discovery call with Alex + Jamie Kim:** MEDDIC framework (understand budget, stakeholders, timeline tied to Series B capital window)
- Key: Jamie Kim will likely own evaluation; align on technical proof (demo dbt + Snowflake scenario)
- **Close:** ROI framework (reduce ops overhead + Snowflake costs)
- Benchmark: "Most customers see 50%+ ops overhead reduction in first 90 days + 20–30% Snowflake cost savings"
---
### Data Quality & Confidence Scoring
- **Data freshness:** Research completed June 2, 2026 (current as of today)
- **Confidence in decision-maker map:** **High**
- Alex Rodriguez confirmed via LinkedIn as VP of Data (multiple sources: company page, personal LinkedIn, job posting context)
- David Park confirmed via LinkedIn as VP Finance (company page + financial posting patterns)
- Marcus Williams confirmed as CTO (company page, Jamie Kim reports to him)
- All three confirmed active within last 3 days
- **Confidence in pain signal strength:** **High**
- Airflow pain: 3 job postings + 2 recent G2 reviews + recent director hire = very high confidence
- dbt integration pain: 2 job postings + 1 review + hiring signal = high confidence
- Data quality pain: 1 recent job posting + 1 review + strategic context (EU expansion) = medium-high confidence
- **Recommended next step:** **Ready for warm outreach**
- Series B timing + recent hires + active decision-makers = high-intent window (30–60 days before capital is deployed elsewhere)
- Priority: InMail to Alex Rodriguez within 48 hours
- Secondary: Get warm intro to Jamie Kim via mutual connection if available (de-risks the technical conversation)
Tier 1 research at enterprise scale: If company is >2,000 headcount, you may need to add a "Sponsor" layer (Director-level introducer to the Economic Buyer). At that scale, cold reaching the CFO directly is less effective than going through a trusted sponsor.
Adapt the dossier template to your product: The template above is generic. For your use case (whatever product you sell), replace the "Your hook" bullets with your specific value prop. Examples:
When to re-research: Update this dossier if (a) company raises new funding, (b) key decision-maker leaves/joins, (c) new product launch or pivot, (d) major news (acquisition, public filing). Otherwise, dossier is valid for 60 days.
Single-company vs. account list: Use Tier 1 for named deals; use Tier 2/3 for account lists. If you're working through a list of 100 accounts, run all 100 at Tier 3 first (identifies low-hanging fruit), then tier up the top 10–15 to Tier 1 for deeper research.
Research as qualification: Pain signals should inform qualification logic. If you see <3 pain signals after Layer 4 mining, the company may be poor fit (not hitting the problems your product solves). Consider deprioritizing until signals become clearer.
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