Third-Party Data Utilization in Retargeting

What I learned after 100+ failed outbound campaigns (at a $400,000 MRR agency): Most flop because they're aimed at people who were not going to buy anyway. (Too) many companies still run outbound like so: - Pull a lead list from their CRM or a generic B2B database - Fire off 100 cold emails/week to “hit quota” - Hope and pray something sticks And they have no idea why prospects are on their list in the first place. If you’re not starting with the right inputs, it doesn’t matter how good your cold email is. It’s still a shot in the dark. One way to fix this is through intent data: For example, here are some signal plays we run for ColdIQ and our clients: 1. First-party intent: Who’s visiting your website Not everyone fills out a form, but that doesn’t mean they’re not interested. We use tools like Instantly.ai and Vector 👻. They track anonymous visitors and identify who’s checking out our content, landing pages, or product pages. This gives us a warm list of people who are already aware of us. Even if they haven’t raised their hand yet. First-party intent can also come from: - Product usage (e.g: Common Room, Pocus) - Social engagement (e.g: Teamfluence™, Trigify.io) 2. Second-party intent: Champion job changes Let’s say someone loved your product at their old company. They just switched jobs. Now they’re in a new buying position, possibly with budget and urgency. Tools like Common Room and Unify help us track job changes across our network and historical CRM contacts. We can re-engage with a hyper-relevant message, right when they’re getting settled in. Second-party intent can also come from: - Review sites (e.g: G2, Capterra) - Affinity signals (e.g: Crossbeam, WorkSpan) 3. Third-party intent: Research at scale Most often, you need to go outbound into entirely new territory. That’s where third-party data comes in. Pulling insights from: - hiring trends (e.g: LoneScale, Mantiks, PredictLeads) - tech stack changes (e.g: BuiltWith, Similarweb) - funding rounds (e.g: PitchBook, Crunchbase) or from custom AI agents (e.g: Relevance AI, Claygent) We use Clay to build many of these workflows: - Filter for buying signals - Enrich contacts in real-time - Score and segment dynamically - And combine multiple data sources The result? You’re increasing your odds of reaching out to the right person, with the right message, at the right time. Better targeting = better reply rates = better pipeline. Whenever your outbound is underperforming, start by reviewing your data strategy. What intent signals are you tracking in your GTM motion right now? Would love to hear what’s working for you 👇

I've watched 100+ outbound campaigns FAIL at ColdIQ. Most of the time, it wasn't the copy, timing, or offer. It was THIS... They were aimed at people who were never going to buy anyway. Here's what I mean: Too many companies still run outbound like this: → Pull a lead list from their CRM → Hope and pray something sticks → Fire off 100 cold emails/week to "hit quota" They have no idea why prospects are on their list in the first place. If you're not starting with the right inputs, it doesn't matter how good your cold email is. It's still a shot in the dark. One way to fix this is through intent data: Here are some signal plays we run for ColdIQ and our clients: 1️⃣ First-party intent: Who's visiting your website Not everyone fills out a form, but that doesn't mean they're not interested. We use tools like Instantly.ai and Vector 👻. They track anonymous visitors and identify who's checking out our content, landing pages, or product pages. This gives us a warm list of people who are already aware of us. Even if they haven't raised their hand yet. First-party intent can also come from: → Product usage (Common Room, Pocus) → Social engagement (Teamfluence™, Trigify.io) 2️⃣ Second-party intent: Champion job changes Let's say someone loved your product at their old company. They just switched jobs. Now they're in a new buying position, possibly with budget and urgency. Tools like Common Room and Unify help us track job changes across our network and historical CRM contacts. We can re-engage with a hyper-relevant message, right when they're getting settled in. Second-party intent can also come from: → Review sites (G2, Capterra) → Affinity signals (Crossbeam, WorkSpan) 3️⃣ Third-party intent: Research at scale Most often, you need to go outbound into entirely new territory. That's where third-party data comes in. Pulling insights from: → Hiring trends (LoneScale, Mantiks, PredictLeads) → Tech stack changes (BuiltWith, Similarweb) → Funding rounds (PitchBook, Crunchbase) OR from custom AI agents (Relevance AI, Claygent) We use Clay to build many of these workflows: → Filter for buying signals → Enrich contacts in real-time → Combine multiple data sources → Score and segment dynamically The result? You're increasing your odds of reaching out to the right person, with the right message, at the right time. Better targeting = better reply rates = better pipeline. Whenever your outbound is underperforming, start by reviewing your data strategy. What intent signals are you tracking in your GTM motion right now? 👇

EVERYTHING a healthcare marketer ever wanted to know about layering on 3P DATA but is afraid to ask! * What benefits do you get from more nuanced targeting? What benefits do you get from marketing to your ideal patients instead of everyone the same age and gender in your area? -Better lead quality (also means Ops doesn't have to waste time qualifying poor leads) -More efficient media spend -Improved CVR -Patient alignment (Ops gets the type of patients they actually want) * How do you turn a persona into an audience? You've got to build your personas out of attributes that show up in data so hone in on targetable signals. If you've got a persona with fuzzy attributes like "values convenience" try to break it down into data-based attributes like "high app usage" or "shops via mobile". Those are specific attributes you can use to more easily build an audience with 3p data. * Who is the best 3p data vendor? Depends what you're looking for. Lots of good options but the #1 thing to look for is a partner who can translate the data signals you need into media segments you can target. For claims data there's Purple Labs, Definitive Health, Swoop, and Komodo. Behavioral data like media consumption you can get from Meta's Advantage+ or Vi Analytics. Experian has demographic & lifestyle data. And for psychographic data you can try Vi Analytics or Claritas. Also important that you are in no way limited to using one data source> the best 3p audiences are gonna draw from multiple data source categories in order to build an ideal audience. * How do you set this all up? Build your persona and map to signals then grab all that data from appropriate sources & build a messaging strategy and target it sequentially along the full funnel. Probably easier to just walk through an example. Say your ideal patient persona is a physically active man in his 40s with a history of ACL tears (+possibly PT) who might need knee replacement in the future and you want to run ads on Meta. You'd get the age&gender data from Meta demo targeting, and then use Meta's interest graph to find activity level with active lifestyle signals (gym-goer, sports fan, etc.). Then you get claims data from PurpleLab to find clinical history of ACL tears (ICD-10 S83.5), and maybe any current claims for physical therapy (PT CPT codes: 97110, 97140, etc.). Then you layer on predictive data from Definitive Healthcare to find risk factors for osteoarthritis and chronic knee pain. Optionally could throw in some psychographic profiles from Claritas like "Weekend Warrior" or "Health-Conscious Male". * What do you do once you've built that audience? Education-forward messaging in the awareness phase to cover relevant information for that audience, dynamic retargeting in the consideration phase to show success stories from what you offer, and quiz-style lead forms that screen for need and funnel down to booking a consult. #healthcaremarketing

40% of our 100+ monthly meetings come from one thing: signals. Not ads. Not cold volume. Not luck. Signals. Because the future of GTM isn’t more outreach it’s more relevance. We’ve engineered our pipeline to move with intent signals. Not because it sounds cool. Because it works. → ABM campaigns? Filtered by warm behaviors. → Retargeting? Triggered by funnel actions. → Outbound? Fueled by visit data and LinkedIn signals. And here’s the kicker: Most teams already use signals. But only for manual research. That’s the bottleneck. We flipped the system. Now our CRM and Clay workflows trigger the outreach automatically. And even the messaging adapts based on what signal they triggered. The result? 🔹 20–30 meetings/week 🔹 5–10 come from first-party signals (abandoned booking forms, re-visits, etc.) 🔹 Zero ad spend increase 🔹 Higher reply rates, lower CAC We tested dozens of signal types and here’s what stood out: 🔵 First-Party Signals Signals inside your own GTM stack: → CRM Data: HubSpot, Pipedrive, Salesforce → Product Activity: Mixpanel, Amplitude → Meeting Forms: Default, Chili Piper → LinkedIn Events: Common Room, Trigify.io → Site Visits: Instantly.ai, RB2B, Warmly, 🟣 Second-Party Signals Signals from partners in your ecosystem: → Affinity: Crossbeam, Reveal → Shared ABM: Demandbase, 6sense → Champion Changes: Champify, UserGems 💎 → Review Intel: G2, Capterra ⚫ Third-Party Signals Signals from public or external datasets: → Funding: Crunchbase, PitchBook → Job Posts: Clay, PredictLeads → Social/Tech/Trend: Similarweb, BuiltWith, SpyFu → Person + Firmo Data: #Apollo, ZoomInfo → ICP Triggers: PandaMatch 🐼, Ocean.io We documented 57 sales triggers. These are the patterns that create real GTM leverage. Not just more noise. Want the full “Signals Playbook”? Drop a "playbook" in the comments and I’ll send it over personally. You don’t need to guess anymore. Just listen. The signals are already there. #DevCommX #SignalBasedSelling #OutboundSystems #GTMClarity #SalesEngineering #IntentSignals #RevenueArchitecture #B2BSaaS #MarketingOps #SalesOps #SpencerWritesSystems