LinkedIn B2B Data Tool Evaluation

We've built GTM systems for 267+ B2B companies. Most were running 20+ tools. They didn't need half of them. What they needed was the right tools in the right layers, connected to each other. Here's the 12-tool, 5-layer architecture we now build every system on: 1. Signal layer This is where buying intent gets detected. PredictLeads - tracks hiring surges, tech adoption, funding rounds, and news events across 100M+ companies Common Room - merges community signals, product usage, G2 intent, and job changes into one unified view Attention - lets you query your entire sales call history with natural language prompts Without this layer, you're guessing who to reach out to. 2. Data layer This is where raw signals become actionable contacts. Openmart- 200M+ local business records with verified owner contacts Wiza - converts LinkedIn profiles into emails, phones, and firmographics (up to 2,500 contacts per batch) Prospeo - 98%+ verified email accuracy across a 200M+ contact database FullEnrich - waterfalls through 20+ providers until it finds verified data (80%+ find rates) Apify - custom data extractor for anything the other tools don't cover Without this layer, your signals have no one to send to. 3. Action layer This is where outreach actually happens. Instantly.ai - high-volume cold email with full API control over campaigns, warmup, and deliverability lemlist - multichannel sequences combining email, LinkedIn, and calls The enrichment data decides the channel. LinkedIn URL found? Route to Lemlist. Email only? Route to Instantly. Without this layer, your enriched data just sits in a spreadsheet. 4. System of record This is where every touchpoint gets tracked. Attio - full CRM with API access to deals, contacts, companies, custom objects, and pipelines Every signal, enrichment result, and outreach event writes back here automatically. Without this layer, your team has no shared source of truth. 5. Revenue layer This is where the loop closes. Hyperline - handles billing, subscriptions, usage metering, and invoicing with webhooks for every payment event Without this layer, you're automating everything except getting paid. That's the full architecture: Signal → Data → Action → CRM → Revenue. Claude Code connects every layer by reading the API docs, writing integration scripts, handling errors, and retrying until the pipeline works end-to-end. No copy-pasting between dashboards. No manual handoffs between stages. No engineering team required. Which of these 5 layers is the biggest gap in your current setup?

After analyzing 500+ B2B campaigns, I've identified the #1 attribution challenge plaguing LinkedIn advertisers. B2B marketers struggle to prove LinkedIn ads ROI, despite seeing actual pipeline impact. Your prospect sees your LinkedIn ad > searches your product on G2 > returns to your site 2 weeks later > books a demo. Credit goes to: "Organic search" or "G2 referral" But credit should go to: Your LinkedIn campaign that started it all. Traditional attribution is killing your efforts to scale LinkedIn. How we're solving this at Factors.ai: > Track the complete buyer journey, not just last-click > Measure the lift in conversion rate when accounts are exposed to LinkedIn ads > Identify how many touchpoints actually drive conversions > Compare business metrics (win rate, ACV, sales cycle length) between targeted vs. non-targeted accounts One customer discovered their "failed" LinkedIn campaign actually influenced 67% of their pipeline - it just wasn't getting credit. They doubled their LinkedIn spend and reduced overall CAC by 31%. Stop optimizing for clicks. Start optimizing for business impact. If you're struggling to prove LinkedIn ROI to your leadership team, I'd love to show you how we're solving this exact problem.

𝗠𝗼𝘀𝘁 𝗕𝟮𝗕 𝗺𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀 𝗮𝗿𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝘁𝗵𝗶𝗻𝗴. I sat down with Jae O. at Advertising Week NYC last month. Jae leads ads measurement, intent, recommendations, and experimentation at LinkedIn. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Too many B2B marketers treat measurement like B2C. Anyone can buy toothpaste. Click an ad, make a purchase, measure the conversion. But who's buying million-dollar enterprise software? Not everyone who visits your website. As Jae put it: "You should be measuring towards value." 𝗧𝗵𝗲 𝟵𝟱-𝟱 𝗥𝘂𝗹𝗲: Here's where most B2B brands fall short. They underestimate the importance of time in market. Only 5% of B2B buyers are actively in-market at any given time. That means 95% of your potential buyers aren't ready to purchase. They're researching. Building trust. Evaluating options. You need an always-on approach that builds credibility during that 95% of the time they're not buying. Patience is key. The brands that understand this long game win. 𝗛𝗼𝘄 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻'𝘀 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗧𝗵𝗶𝘀: 𝟭) 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗥𝗲𝗽𝗼𝗿𝘁𝘀 (𝗥𝗔𝗥) Track ROI at the company level with a 365-day lookback window. RAR connects employee engagement at target accounts with subsequent conversions and purchases over time. This matters because B2B buying involves multiple stakeholders. The person who clicked your Thought Leader Ad six months ago might not be the person who signs the contract today. RAR captures the full influence of creator content across the entire buying committee. 𝟮) 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻𝘀 𝗔𝗣𝗜 (𝗖𝗔𝗣𝗜) Stop chasing clicks. Start optimizing for business outcomes. CAPI integrates your first-party data directly into LinkedIn's ad systems. CRM interactions. Website behavior. Offline sales. Lead qualification status. LinkedIn uses this data to optimize campaigns for Marketing-Qualified Leads and Sales-Qualified Leads in real time. The results: 31% more conversions, 20% lower cost per action, 39% drop in cost per qualified lead. 𝟯) 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Measure the incremental reach that creator content delivers beyond conventional advertising. LinkedIn's demographic data reveals what content resonates with specific professional communities. You can track awareness lift and engagement patterns across that crucial 95% who aren't ready to buy yet. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: 70% of marketers report LinkedIn delivers positive ROI for their organization. This measurement infrastructure connects ad investment to actual revenue over extended time horizons. B2B buying committees don't move fast. Your measurement shouldn't expect them to. The smartest marketers are measuring what matters. Not what's easy. -- Enjoy this? ♻️ Repost it to your network & follow Brendan Gahan for more. Interested in LinkedIn Influencer Marketing? Reach out to us Creator Authority .

We analyzed the GTM tech stacks of the 63 fastest-growing B2B companies (Stripe, Anthropic, Databricks, Canva, Rippling, Ramp, Deel, OpenAI, and more) across 60 tools and 21 categories. 65% of the fastest-growing private B2B companies use Clay. Zero uses an off-the-shelf AI SDR today. 1/ The "default stack" that shows up in over 50% of these companies: Salesforce + HubSpot + Gong + Outreach + ZoomInfo + Clay + dbt Labs + Snowflake + Zapier That's the baseline (looks similar to what it did 5 years ago). But, there are some new tools starting to also show up in tech stacks of the fastest-growing companies (more on that below). 2/ The companies running the most sophisticated go-to-market motions are the same ones that invested early in data infrastructure: dbt Labs (84%) and Snowflake (79%) are nearly as common as Gong (84%). 3/ Intent & Signals 6sense (38%) and Demandbase (32%) still own the intent category. They’re the two that show up in almost every mature stack. But a new class of signal tools is gaining traction: Common Room (19%), Sumble (11%), and Unify (11%) are showing real adoption at these companies. They’re smaller, lighter, and more tightly integrated into sales workflows than the traditional ABM platforms. 4/ Other findings: → Salesforce: 100%. Uncle Benioff's grip is strong. → HubSpot: 95%. Most run both SFDC (for Sales) and HS (for Marketing). → Gong: 84%. The default operating system for customer conversations. → Clay: 65%. Now the # 2 data tool, ahead of Sales Navigator (ZI is # 1). → Outreach: 71%. Nearly 2x Salesloft's adoption. → 15+ GTM tools per company (on average). ______________________ Data sourced from Sumble and Clay, plus Claude (to pull customer logos from websites and review sites). If you found this Research Report useful, tag someone who geeks out on GTM tech stacks. And if you think we’re missing a tool or a company, comment below (or DM me). This dataset is a living thing that we’ll keep updating. ______________________ Complete analysis (including an interactive dataset and every company's full GTM stack) in the latest issue of The Signal: https://lnkd.in/g4MVJjFF

The $1,000 GTM Stack That Created $250k Pipeline   Most early-stage teams overthink their stack.   They buy enterprise tools, overpay for features they won't use for 12 months, and spread budget across channels that don't compound yet.   At the early stage, outbound is your fastest path to revenue. Inbound, SEO, content, ads... all of that compounds later. But right now you need pipeline. That means your stack should be built to find the right people, reach them fast, and track what's working.   Here's the exact setup and why each tool is there 👇   ✅ Data & Enrichment   → Prospeo (Basic) to source verified B2B contacts at $0.01/email with 98% accuracy. → Clay (Launch) to run waterfall enrichment across 150+ providers. New pricing just cut data costs 50-90%.   ✅ Outbound Engine   → Smartlead (Basic) for cold email with unlimited mailboxes, built-in warmup, and 6,000 sends/month. → GojiberryAI (Pro) for LinkedIn outreach powered by 30+ intent signals. Not blasting connections. Reaching people already showing buying behavior.   ✅ AI Orchestration   → Claude Team (5 seats) as the AI brain of the operation. Outbound copy, lead scoring, campaign analysis, account research. The whole team gets access.   ✅ Reporting & Diagnostics   → Outreach Magic (Core) to unify reporting across email and LinkedIn. Sender health, segment performance, full stack visibility in one dashboard.   ✅ CRM & Comms   → HubSpot (Starter) to centralize contacts, deals, tasks, and pipeline. → Slack (Pro, 5 users) for deal alerts, async coordination, and keeping the team tight.   Total: ~$700/month. Headroom: ~$300 for Clay credit top-ups, extra sending domains, or upgrading Smartlead when volume scales.   This stack is intentionally outbound-heavy. At the early stage, your job isn't to build brand awareness. It's to get in front of buyers and close. Everything here is built around that.   The tools have changed a lot since last year. Intent-based outreach replaced brute-force automation. AI orchestration replaced workflow plumbing. And the data layer got cheaper and more accurate at the same time.   If you're running a lean team and need real pipeline without enterprise pricing, this is the cleanest setup to start with.   👉 What would you add, swap, or tune for your team? Drop your thoughts below 🔽

The way most people build B2B lead lists is painfully slow.   You open LinkedIn, manually search for companies, copy names into a CSV, cross-reference with Crunchbase for funding data, check their careers page for hiring signals, then try to find the right person to contact.   That's easily 2 to 3 hours for a list of 50 leads. And half the data is already outdated by the time you're done.   That's why I've been using Coresignal's AI Data Search to build lead lists in seconds instead of hours.   Coresignal pulls company and employee data from 15+ public web sources. Their AI Data Search feature lets you query all of it in plain English instead of writing complex filters or reading API docs.   You literally describe your ideal leads like you're chatting with an assistant.   1. Let me walk you through it:     2. Type what you want in plain English. Something like "SaaS companies in the US that raised funding last year and are actively hiring."     3. Get a preview of 100 matching records instantly. Company names, locations, employee counts, all the key data points.     4. Refine by adding conditions in the same chat. Want only companies with 50+ employees? Type it and the list updates.     5. Enrich with 500+ data fields per company. Funding history, tech stack, headcount trends, website traffic. All the signals that help you actually qualify whether a lead is worth reaching out to.     6. Export up to 10,000 fully enriched records as CSV or JSONL.   Some prompts I used:   → Show all companies in LA that are working with Oracle. → Discover companies using HubSpot but not Salesforce. → List AI companies with 50k+ monthly visitors that have marketing positions open.   Each one came back with structured results in seconds. And you can see the actual query the AI generated underneath, so if you want to plug into their API later at a bigger scale, the query is already there.   Coresignal has been recognized by Datarade as a top public web data provider three years in a row. Their data is multi-source, cleaned, and AI-enriched.   Try AI Data Search for free at https://lnkd.in/efBWVTcb   Over to you: What's your current lead list building process? Still manual or have you found something that actually saves time?