Your SDR just spent 3 hours manually scraping LinkedIn profiles, copying data into a spreadsheet, cross-referencing company info, and formatting a prospect list. She got through 40 contacts. Your competitor's team processed 400 — and already sent personalized outreach to the best 80.
Here's how outbound works at most companies: Someone identifies target accounts. Then they open LinkedIn. One by one, they visit profiles, copy names, look up emails, check company details, paste into a spreadsheet, deduplicate against the CRM, and flag the promising ones. Rinse. Repeat. Collapse.
The busywork isn't selling. It's data plumbing. And it's eating 60-70% of your team's week.
Some teams discover Captain Data and automate the extraction — scraping LinkedIn search results, enriching company profiles, pulling job postings at scale. Suddenly the 3-hour task takes 15 minutes. But then what? The extracted data still lands in a spreadsheet. Someone still has to score it, import it, deduplicate it, research each prospect, write outreach, and remember to follow up.
Extraction without execution is just faster data hoarding.
Today, that changes.
nRev now integrates directly with Captain Data. Automated web extractions don't dump into spreadsheets. They trigger complete revenue workflows — from raw LinkedIn scrape to scored prospect to personalized outreach to booked meeting. The entire path from "data exists on the web" to "meeting on the calendar" runs without manual intervention.
Why Extraction Alone Doesn't Move Pipeline
Captain Data is brilliant at getting data off the web and into your hands. LinkedIn profiles, company directories, job boards, review sites, event attendee lists — if it's on a web page, Captain Data can extract it and structure it.
But structured data in a spreadsheet is still just a spreadsheet. The gap between "I have 500 extracted profiles" and "I've booked 15 meetings from them" is where most teams stall.
The execution gap is measurable: Teams using extraction tools report 3-5x more data collected — but only 20-30% more meetings booked. The bottleneck moved from "finding data" to "acting on data." And that bottleneck is entirely human: scoring, routing, personalizing, sending, following up.
Most teams hit a ceiling because their process looks like this:
Captain Data extracts → CSV export → Manual review → CRM import → Deduplicate → Research → Write emails → Send via separate tool → Track manually → Miss follow-ups
With nRev + Captain Data, the same extraction powers an end-to-end pipeline:
Captain Data extracts → nRev ingests automatically → ICP scoring + deduplication → Enrichment and research → Personalized outreach drafted → Rep reviews top prospects → CRM synced → Follow-ups automated
Same data. Automated pipeline. No spreadsheet in sight.
What Captain Data Brings to the Table
Captain Data is a web extraction and automation platform built for sales and growth teams who need to pull structured data from anywhere on the web — without writing code.
LinkedIn Extraction at Scale: Pull profiles from LinkedIn Sales Navigator searches, extract company employees by department, scrape event attendee lists, and collect group members. The data LinkedIn won't export, Captain Data extracts — names, titles, companies, locations, and profile URLs.
Multi-Source Workflows: Captain Data doesn't just scrape one site. It chains extractions: start with a LinkedIn search, enrich with company website data, pull phone numbers from another source, and deliver the combined result. Multiple sources, one output.
Job Board and Directory Scraping: Monitor careers pages for hiring signals, scrape industry directories for prospect lists, and extract data from review sites — structured and ready for your pipeline.
API-First Architecture: Everything Captain Data does is available via API. No manual exports. No dashboard-only access. Data flows programmatically into whatever comes next — which, with this integration, is nRev.
The difference between Captain Data and manual data collection? Scale and reliability. What takes a human three hours, Captain Data does in minutes, consistently, without errors. nRev takes that consistent data flow and turns it into consistent pipeline.
What nRev Adds: From Extracted to Executed
Captain Data pulls data off the web. nRev decides what that data means for your revenue — and acts before your team touches a spreadsheet.
Real-Time Ingestion: Captain Data extractions flow directly into nRev via API. No file exports. No scheduled downloads. The moment an extraction completes, nRev has the data and starts processing. See how connections work →
Intelligent Scoring and Deduplication: Raw extractions are messy. The same person appears twice with slightly different names. A prospect is already in your CRM. A company doesn't match your ICP. nRev handles all of it:
- Deduplication against your CRM and existing nRev records
- ICP scoring based on role, company size, industry, funding stage
- Relationship matching — is this an existing customer? An open opportunity? A churned account?
- Data validation — incomplete records get flagged, not forced into your pipeline
Contextual Enrichment: Captain Data gives you the skeleton. nRev adds the muscle:
- Contact details (email, phone) via integrated enrichment providers
- Company intelligence (tech stack, funding, news, hiring velocity)
- Behavioral signals (have they visited your site? Are they active on G2?)
- Previous touchpoints with your brand
Multi-Channel Execution: Depending on the prospect's profile and fit, nRev triggers different plays:
- High-fit LinkedIn extraction → email outreach with LinkedIn-aware personalization
- Job board signals → hiring-angle messaging
- Event attendee extractions → event-specific follow-up sequences
- Directory scrapes → industry-tailored outreach campaigns
Five Playbooks That Turn Extractions into Pipeline
Playbook 1: The LinkedIn Sales Nav Activator (Extract → Enrich → Outreach in Minutes)
The Scenario: Your BDR runs a Sales Navigator search: "VP of Sales" at SaaS companies with 50-200 employees in the US. 800 results. In the old world, she'd spend two days manually processing this list. With Captain Data, the extraction takes 20 minutes. With nRev, the entire pipeline takes 25.
The nRev Workflow:
Extract: Captain Data pulls all 800 profiles from the Sales Navigator search — names, titles, companies, LinkedIn URLs
Ingest: nRev receives the extraction automatically via API
Deduplicate: Cross-reference against CRM. 120 contacts already exist (customers, open deals, previously contacted). Remove them.
Score: Of 680 net-new contacts, nRev scores by ICP: SaaS vertical confirmed, company revenue range matches, role seniority verified. Result: 290 high-fit, 240 medium-fit, 150 low-fit.
Enrich: For the 290 high-fit contacts, pull verified email addresses, company funding data, recent news, and tech stack information.
Draft: Personalized outreach using company-specific context:
- Recently funded: "Congrats on the round, [Name]. Most VP Sales I talk to at your stage are trying to build repeatable outbound before the board starts asking about pipeline metrics. Here's how [Similar Company] tackled that..."
- Scaling team (job postings detected): "Saw [Company] is adding AEs. When the team doubles, the pipeline math changes fast. We help VPs in your situation get ahead of the visibility gap before it becomes a fire."
Route: High-fit contacts assigned to reps by territory with draft outreach and full research brief
The Outcome: 800 raw LinkedIn profiles become 290 scored, enriched, ready-to-contact prospects with personalized outreach — in under 30 minutes. No spreadsheet. No manual research. Your BDR's entire week of data work now takes less time than a coffee break.
Playbook 2: The Competitor User Hunter (Find and Convert Competitor Customers)
The Scenario: You know your competitor has customers. You want them. But "who uses [Competitor]?" isn't a query your CRM can answer. Captain Data can.
The nRev Workflow:
Extract: Captain Data scrapes multiple sources for competitor signals:
- Job postings mentioning competitor tool in requirements
- G2 and Capterra reviews left by identifiable users
- LinkedIn profiles mentioning competitor in their experience section
- Case studies and testimonials on competitor's website
Aggregate: nRev combines all sources, deduplicates, and builds a unified competitor customer list
Enrich: For each identified competitor user, pull company details, contact information, and role seniority. Cross-reference with your CRM — are any of these already in your pipeline?
Score: Rank by conversion potential:
- Left a negative review + ICP fit = high priority
- Mentioned competitor in past tense on LinkedIn = may have switched, verify
- Job posting requires competitor experience = active user, displacement angle
- Competitor case study subject = happy customer, longer-term nurture
Draft: Competitive displacement messaging tailored to signal type:
- Negative reviewer: "I saw your note about [specific pain from review] with [Competitor]. We built [Feature] specifically because teams like yours were running into that wall. Worth a quick comparison?"
- Active user with hiring signal: "Most teams scaling past [headcount] outgrow [Competitor]'s [limitation]. Before you onboard 5 new reps on it, might be worth seeing the difference."
The Outcome: You build a competitor customer list that doesn't exist in any database, enriched with reasons to switch, and delivered to reps with tailored displacement messaging. This playbook alone can source 20-30% of quarterly pipeline.
Playbook 3: The Hiring Signal Machine (Job Posts → Pipeline)
The Scenario: When companies hire for specific roles, they're making budget and priority decisions in public. A company posting "Head of Revenue Operations" is building the function. A company posting "5 SDRs" is scaling outbound. Every job posting is a decoded intent signal — if you can process them fast enough.
The nRev Workflow:
Extract: Captain Data scrapes careers pages across your target account list weekly — LinkedIn job postings, company career sites, and job board aggregators
Parse: nRev categorizes job postings by signal type:
- RevOps/Sales Ops hiring → building infrastructure → tool evaluation coming
- Sales team scaling (3+ roles) → pipeline growth priority → enablement needs
- CRO/VP Sales hiring → new leadership → budget reallocation, new vendor evaluation
- Job description mentions your category or competitor → active consideration
Score: Combine hiring signals with existing ICP fit and other intent data
Route: Based on signal strength:
- New sales leader + your category mentioned → immediate high-priority outreach
- Team scaling + no tool mentioned → educational approach: "most teams at your stage hit [problem]"
- RevOps hire + competitor mentioned → competitive displacement play
Alert: Weekly digest to BDR team: "14 target accounts posted relevant roles this week. Here's the priority list with hiring context and recommended angles."
The Outcome: You see buying intent 60-90 days before traditional intent data providers flag the account. Job postings are the earliest public signal that budget, priority, and headcount are aligning — and you're using them before anyone else.
Playbook 4: The Event Attendee Pipeline (Turn Attendee Lists into Meetings)
The Scenario: You're attending a conference next month. The organizer doesn't share the attendee list. But the speakers are public. The sponsors are public. The LinkedIn event has visible attendees. The conference hashtag has active participants. Captain Data can build the list nobody else has.
The nRev Workflow:
Extract: Captain Data pulls from multiple event sources:
- LinkedIn event attendees and commenters
- Speaker profiles and their companies
- Sponsor company employee lists (decision-makers at sponsoring companies)
- Social media posts using the event hashtag
Deduplicate: nRev merges all sources, removes duplicates, and cross-references with your CRM
Score: Filter for ICP fit: right role, right company size, right industry. Of 600 extracted profiles, 85 are high-fit decision-makers worth meeting at the event.
Research: For the top 85, pull company context: what they sell, recent milestones, and any connection points with your solution
Draft: Pre-event outreach:
- "Hi [Name], I see we'll both be at [Event] next month. I've been following [Company]'s work on [specific area] — would love to grab 15 minutes to compare notes on [relevant topic]. Free for coffee on day 2?"
Deliver: Each rep gets a briefing card for their top 10 prospects at the event — who to find, what to say, and why it matters to them
The Outcome: Your team arrives at the conference with pre-scheduled meetings and a hit list of exactly who's worth their time. Every hallway conversation is targeted. Event ROI goes from "badge scanning" to "pipeline building."
Playbook 5: The Continuous Prospecting Engine (Never Run Out of Pipeline)
The Scenario: Your team runs a prospecting blitz at the beginning of each quarter, fills the pipeline, then goes into execution mode. By mid-quarter, the top of funnel is dry. New pipeline stalls. You scramble again next quarter. The boom-bust cycle repeats.
The nRev Workflow:
Schedule: Captain Data runs recurring extractions on a weekly cadence:
- LinkedIn Sales Navigator saved searches (pulls new matches weekly)
- Target account careers pages (catches new hiring signals)
- Industry directories and association member lists (surfaces new companies entering your market)
Flow: Every extraction feeds automatically into nRev
Process: Weekly batch: deduplicate → score → enrich → draft → route. Each Monday, every rep gets 15-20 new scored, researched, ready-to-contact prospects.
Balance: nRev monitors pipeline per rep. If someone's pipeline is healthy, their new prospect flow decreases. If someone's pipeline is thin, they get priority allocation. No more feast or famine.
Report: Weekly Slack digest to sales leadership: "This week: 120 new prospects extracted, 45 qualified after scoring, 45 distributed to reps. Current pipeline coverage by rep: [breakdown]."
The Outcome: Pipeline generation becomes continuous, not cyclical. Your reps never run dry. Your leadership always has visibility. The entire outbound engine runs on autopilot with human quality control at the review stage.
The Difference: Data Dump vs. Revenue Engine
Built for Who Actually Needs This
Growth Teams Without Dedicated Data Ops: You don't have a team to clean, score, and route extracted data manually. nRev automates the entire post-extraction pipeline so your small team operates like a much larger one.
BDR/SDR Leaders Running High-Volume Outbound: Your team needs fresh prospects weekly. Captain Data + nRev creates a continuous prospecting engine that never runs dry and never sends unqualified garbage.
RevOps Building Scalable Pipelines: You need extraction, enrichment, scoring, routing, and outreach to work as one system — not six disconnected tools duct-taped together.
Competitive Intelligence Teams: Competitor customer lists, hiring patterns, and market signals are all extractable. nRev turns them into actionable displacement plays.
Setup: 15 Minutes to First Workflow
What you need: Captain Data account with active workflows, nRev account, 15 minutes.
Step 1: Connect Captain Data API in nRev (view connection docs →)
Step 2: Map Captain Data extraction output fields to nRev variables (name, title, company, LinkedIn URL, email if available)
Step 3: Build your first playbook:
- Choose extraction source (LinkedIn Sales Nav, job boards, event attendees, competitor signals)
- Add scoring rules (ICP fit criteria, role filters, company size)
- Add enrichment nodes (email verification, company data, contact details)
- Define actions (Slack alert, email draft, CRM record creation)
Step 4: Run Captain Data extraction and watch it flow through nRev
Step 5: Review output quality and adjust scoring thresholds
Total time: 15 minutes to first workflow. Compare to manually processing Captain Data extractions in spreadsheets: 4-6 hours per batch.
Read full setup documentation →
The Bottom Line
Captain Data extracts data from anywhere on the web. nRev turns that data into scored, enriched, ready-to-contact prospects with personalized outreach — automatically.
Extraction without execution is just faster data hoarding. Captain Data + nRev closes the gap between "data exists on a web page" and "meeting is on the calendar."
You already have the extraction power. Now put it to work.
Ready to turn web extractions into pipeline? Connect Captain Data to nRev →
Questions about your extraction workflows or prospecting volume? Our team builds your first playbook with you — free, no sales pitch.