Signal Stacking Strategies for Better Results

Many GTM teams treat signals like Clippy pop-ups. Basic and only so helpful. Here's an example and how to fix this: Clippy: "Look the company is hiring a DevOps Engineer! Quick pitch our DevOps tool, they must be investing in DevOps." Thanks, Clippy... that's a really thoughtful analysis. GTM teams see a signal and jump into a campaign. No further context. No depth. Just surface-level reaction. However, the best GTM teams don't treat signals like tips. They look at signals as breadcrumbs that lead to their strategy. They do 2 things: 1️⃣ Combine & layer signals 2️⃣ Go deeper This helps build a narrative around what the customer is trying to do, not just what they're doing. My talk yesterday at G2's AI in Action roadshow centered around Ideal Customer Signals and went over this example: 🛰️ Signal: Hiring DevOps Engineers 📎 Clippy: Quick, they're investing in DevOps. We have a DevOps tool. [Insert Pitch] But here's what layered thinking looks like: LAYER 1️⃣ : 🟢 Company is investing in DevOps since they are hiring DevOps Engineers LAYER 2️⃣ : 🟢 DevOps = faster deployment cycles 🟢 Better developer experience LAYER 3️⃣ : 🟢 Faster deployment cycles = faster innovation & time to market 🟢 Competitive urgency or pressure to scale 🟢 Tech stack modernization and deployment of new tooling Now let's combine it with another signal: 🛰️ Signal: New CTO is hired! LAYER 1️⃣ : 🟢 CTO must be making changes over the next 3-6 months LAYER 2️⃣ : 🟢 CTO's background indicates he took his last company public 🟢 Does this company have ambitions to take the company public? LAYER 3️⃣ : 🟢 What does engineering need to do to prep for an IPO? LAYER 4️⃣ : • Control the chaos: Build SOX-compliant internal controls • Lock down security: Harden infrastructure and improve audit logging • Prepare to scale: Make systems production-grade for growth • Streamline releases: CI/CD processes become mission-critical • Document everything: Create traceability for audits and diligence That’s just two signals. Now imagine layering in: 👉 Product launches 👉 Announced initiatives 👉 Company news 👉 Regulatory changes 👉 Changes in team sizes 👉 Tech stack changes 👉 Changes in competitive environment The best GTM teams don’t just react to signals. They forecast with them. They arm sales with insights and a narrative, not just data. They uncover potential challenges before customers know they will experience them. The teams who win? They layer and go deeper to find the hidden trail. Before anyone else even knows it’s there.

Signals aren’t about a single click or action. They’re about piecing together the whole story of how interested someone is. But we tend to lose this when we try to deploy fancy signal plays, especially with all the complex AI workflows we want to copy. So, like most things, it's helpful first to strip it back and understand the basics. I personally think in terms of systems and frameworks, so here's how I've broken it down for myself to get my arms around this concept. 🚨 Signals = the observable actions buyers take that might show interest. (Website visits, job changes, pricing page views, webinar attendance, etc.) 🔎 Intent = the buyer's underlying motive. (It's the difference between activity and actual interest) Types of signals: ↳ First-party: Data you capture directly (website, product usage, emails, webinars) ↳ Second-party: Data shared by a trusted partner (G2, event lists, vendor benchmarks) ↳ Third-party: Aggregated external data (intent topics, technographics, search trends) ↳ Trigger events (a subset of third-party): External business changes that create or accelerate need (job changes, fundraising, M&A, new office openings, leadership hires) Great! Now, how do you make signals work vs. become noise? Where most teams miss is in stacking signals and tailoring the response accordingly. One action in isolation rarely means much. But layered together, they show fit + intent + timing. ❤️ Single signal (weak): One webinar attendee. One pricing page visit. One product signup. ↳ On its own, it’s not enough to act. 🧡 Stacked first-party signals (moderate): Multiple stakeholders from the same account sign up, log into the product, and return to high-intent pages like pricing or security. ↳ This starts to suggest real intent, but still needs validation. 💛 Stacked multi-source signals (strong): Add in second- or third-party context like a New VP hire or funding announcement ↳ Now you’re looking at a pattern worth prioritizing. 💚 Stacked + sequence of behavior (highest conviction): Stakeholders engage across channels and time — e.g., attended a bunch of webinars, reviewed your product pages then pricing page, signed up for a trial (or engaged with an interactive product demo video), then multiple logins, and a new budget or stakeholder trigger event. ↳ This isn’t noise. This is a deal in motion. They don't have to all occur, but when you observe, you can see the intent happening in action of them being interested. The key: advanced teams don’t chase one-off actions. They wait for the story to emerge — then tailor the response (light nurture vs. AE outreach vs. exec alignment). It’s less about catching a moment, and more about reading a narrative as it unfolds. That’s how signals stop being noise — and start becoming your operating system. What did I miss or get wrong here? Always learning, especially on this topic!

Everyone else is chasing home runs but you’re over here playing Moneyball with buying signals. That’s what signal stacking is all about. Instead of waiting for the perfect lead to fill out a demo form, you start piecing together early clues — intent, hiring, tech, even who’s in their network. Alone, these signals might not say much. But when they show up together it can help you move from guesswork to confidence. Here’s what it looks like in action: 🏥 Healthcare Tech - Someone on the clinical team is Googling patient portal integrations - The hospital announces a digital transformation initiative - There’s a mutual connection to the CIO 🏗️ Construction SaaS - A GC downloads a cost control whitepaper - They hire a VP of Ops from a tech-savvy firm - They just landed a 7-figure municipal contract 🔐 Cybersecurity - They’re using Auth0 - Hiring a Product Manager for Identity & Trust - Engineers are posting in GitHub about identity workflows Signal stacking helps you get in earlier, with more context, and a way better message. One signal is interesting but multiple signals is a story worth chasing.

Outbound isn’t dead. Old-school outbound is. In B2B today, success doesn’t come from sending more emails or making more calls. It comes from knowing when to engage — and why. 👉 Signals beat sequences 👉 Timing beats volume 👉 Intent + AI beats lead lists Why? → 70% of B2B buyers do deep research before ever speaking to sales (Forrester). → Cold calls convert at just 2.3% — unless hyper-targeted, they kill ROI. → WhatsApp and SMS open rates hit 98%, but mistimed outreach burns trust instantly. Here’s what works in 2025: 1️⃣ Layering 3 types of signals: → Public: job postings, funding rounds, PR announcements. → 3rd-party intent: Bombora, 6sense, ZoomInfo. → 1st-party: pricing page visits, product downloads, high-intent behaviors. 2️⃣ AI-led prioritization: → AI filters noise → surfaces the top 5% of leads who are truly in-market. 3️⃣ Precision outreach at the moment of intent: → No mass emails. → No spammy sequences. → Thoughtful, highly personalized outreach — when the buyer is ready. A real example: Deel (global payroll / HR SaaS, fastest SaaS to $400M ARR): → Deel built an AI-driven GTM engine monitoring 3K+ signals globally: → New funding → hiring ramp → global payroll need. → Job postings in new markets → expansion signal. → PR on new market launches → likely fit for Deel. Results: → SDRs contact prospects within 24h of the trigger. → Win rate = 4x higher than traditional cold outbound. → Sales cycle 35% faster vs legacy approaches. Why this works: → You meet the buyer at the moment of need. → Your message matches what’s top of mind. → Personalization + timing builds trust fast. Bottom line: Mass outbound is no longer a competitive edge. Intent-driven, signal-based, AI-powered sales is what scales today. If I were building a GTM stack in 2025: → I’d start with intent + AI + orchestration. → I’d cut 90% of legacy outbound. Because in sales today — first to the signal wins the deal.

Marketers claim they want to scale personalization. Most still use the same old playbook. This approach misses key signals. The problem is clear. Most account prioritization models ignore crucial signals that indicate buying intent. These signals come from real-time engagement across digital channels, such as social media interactions, product usage data, and sales touchpoints, where prospects are actively making decisions. A CMO asking for vendor suggestions on a private Slack thread? That’s a high-intent signal. A RevOps leader debating solutions on LinkedIn? That’s critical buying behavior. Traditional CRMs miss these signals, but AI-powered tools like RoomieAI Capture are designed to catch and prioritize these conversations in real time. A champion explaining how they got buy-in for your product? That won’t trigger an MQL. This is why marketers miss high-intent signals. This is why they struggle to scale personalized outreach. A shift is happening. AI is making account research and personalization scalable. But it’s not what most people think. Forward-thinking teams are doing this: ✅  Mining signals from non-traditional sources like social media, job boards, and internal communications to identify in-market accounts before they visit your website. By using AI to uncover buying intent across the web and social platforms, they can reach high-intent prospects earlier in the sales cycle. ✅ Prioritizing accounts based on real engagement. They focus on prospects already in a buying motion, not just random website visitors. ✅ Using AI-generated insights for messaging. They create messages that resonate instead of sending generic sequences and hoping for a response. Here’s how to apply this today: 1️⃣ Audit where your best leads come from. Are they finding you through communities, referrals, or social conversations? If so, your data model is missing key signals. 2️⃣ Stop treating ‘MQLs’ as the only sign of readiness. Shift to engagement-based prioritization. Combine web intent with real conversations. 3️⃣ Experiment with AI-powered research to enrich your outreach. Use AI to gather insights, but keep your messaging human. Making this work at scale used to mean manual research and guesswork. Now, platforms like Common Room make it easier. They automatically surface high-intent signals across social media, web interactions, and internal data to help sales teams prioritize the right accounts and craft messaging that resonates at the right time. Personalization at scale isn’t about more manual research. It’s about building a smarter system. This system automates research while keeping outreach relevant. Think about AI’s role in your GTM strategy next year.

Most teams don’t have a “signal problem”. They have a routing problem. Signals show up, then nothing happens. So we built a simple structure for using intent signals in 2026. What signals help us do: 👉 Rank accounts by likelihood to convert (not lead volume) 👉 Launch the right motion when a change is detected (email, LinkedIn, ads) 👉 Alert the right person at the right time (Slack + CRM) 👉 Watch customers for expansion opportunities + early risk Signals usually fall into 3 buckets, based on where the data comes from: 1️⃣ First-Party Signals (owned data from your stack) 👉 CRM activity: stage movement, reopened deals, account history HubSpot, Salesforce, Attio 👉 Product usage: activation events, key feature adoption, drop-offs Mixpanel, Amplitude, PostHog 👉 Meetings + forms: booked demos, form intent iClosed, Default, Calendly 👉 Gated content: opt-ins for templates, guides, calculators Webflow, Tally, Gamma 👉 Marketing sequences: engagement across nurture and lifecycle email Customer.io, Kit, beehiiv 👉 Webinar attendance: registrants vs attendees, time watched, follow-up intent Luma, LinkedInEvents, Goldcast 👉 Website visitors: de-anonymized web traffic + page-level intent Warmly, RB2B, Clearbit 2️⃣ Second-Party Signals (shared, relationship-based data) 👉 Partner overlap: shared target accounts, co-sell timing Crossbeam, PartnerStack, WorkSpan 👉 Warm intros: mutuals and intro paths into target accounts Commsor 🦕, Swarm, LinkedIn 👉 Review sites: category evaluation + competitor comparisons G2, Capterra, TrustRadius 👉 Champion tracking: past users who changed jobs into new ICP accounts LoneScale, Champify, Clay 👉 Ad insights: engagement patterns from paid traffic and retargeting ZenABM, Fibbler, Factors.ai 👉 LinkedIn engagement: viewers, likers, commenters, profile visits LeadShark 🦈, Teamfluence, Trigify.io 3️⃣ Third-Party Signals (public market signals) 👉 Technographics: changes in tech stack BuiltWith, @HGInsights, TheirStack 👉 Job openings: hiring tied to budget, growth, or a new function Clay, PredictLeads, People Data Labs 👉 Firmographics: headcount, geo expansion, category shifts Apollo, DiscoLike, Ocean.io 👉 People data: key hires, leadership changes, org moves SalesNavigator, Clay, Apollo 👉 News: launches, partnerships, strategic announcements GoogleNews, Perplexity, GoogleTrends 👉 Advertisement activity: shifts in spend and channels Adyntel, Adbeat, Apify 👉 Web data: pricing/product/positioning changes Clay, Apify, ZenRows 👉 Social signals: engagement spikes and activity changes Clay, PhantomBuster, Trigify 👉 Search analytics: SEO demand + visibility shifts Semrush, Similarweb, Ahrefs 👉 Funding: recent raises tied to expansion Crunchbase, Owler, Pitchbook Signals won’t tell you “who will buy” with certainty. But they do show who is changing, who is paying attention, and who is worth routing into the right motion now.

You don’t need more data. You need better signals. Predictable growth isn’t luck, it’s built on leading indicators. Most teams struggle because: They track outcomes too late They react to problems instead of preventing them They rely on revenue instead of early signals They operate without weekly visibility A hard truth: You can’t scale what you can’t predict. Start here: 1. Identify Early Signals ↳ Define the actions that happen before a conversion ↳ Look for patterns, not assumptions 2. Track Behaviors, Not Outcomes ↳ Outcomes lag ↳ Behavior shows intent in real time 3. Build a Weekly Dashboard ↳ Keep signals visible ↳ Make decisions based on movement, not emotion 4. Set Thresholds & Triggers ↳ Know what “healthy” looks like ↳ Create alerts when momentum slows 5. Optimize Inputs, Not Outputs ↳ Fix what drives results, not the results themselves ↳ Small input shifts → big output gains The Core Indicators: 1. Engagement Indicators ↳ Show who’s warming up ↳ Early momentum signals 2. Content Consumption Indicators ↳ Reveal depth ↳ Who’s moving deeper into your world 3. Relationship Indicators ↳ Replies, conversations, signals of trust ↳ Show who’s leaning closer to a “yes” 4. Pipeline Velocity Indicators ↳ Track movement speed ↳ Aware → Interested → Ready 5. Product Experience Indicators ↳ Early user value ↳ Predicts retention, expansion, and referrals 6. Conversion Readiness Indicators ↳ Direct buying intent ↳ Clear green lights for sales Remember: Lagging indicators react. Leading indicators predict. The teams that win aren’t faster, They’re earlier. Build your leading indicator stack now. It compounds every week. Revenue problems rarely live in one function. That’s why our audits combine CRO + CFO perspectives. 👉 Start with the Growth & Profitability Scorecard https://lnkd.in/ekcgYfGe

Sellers are stuck in outdated methods. Noticeably poor results are all over the market 👎. Those actually generating $ are selling differently: For 10+ years sellers were neither trained nor equipped to focus on ACTUAL buyer value + intent. It was cold call this, cold email that, and an over-optimization of inputs and outputs. Great for investors. Not great for the buying experience. The better way: Showing sellers which signals actually matter, what they mean, & consolidating them into a digestible way for them to use. 💡 Examples of High-Intent Signals (Data from Common Room): Social level: - Prospect mentions competitor on LinkedIn - Prospect comments on an industry leader’s post Product level: - Aha! Moment reached - Upgrade screen activity - Multiple workspaces created Community level: - Activity spikes from power users - Community/forum question asked Website level: - Pricing page visits - Problem-specific pages - Resource pages (templates, etc.) Think your sellers know how to prioritize these? No. Sellers have enough on their plate. So, a new era of tools (like Clay & Common Room) are coming out to play. Give a rep a Common Room and you immediately remove the headache of navigating all these signals. 📈The right context at the right time, so sellers spend energy with prospects that will actually become pipeline. As Adam Robinson said about attention: “We are in an attention war. The prize is that you get a signal. The reason that’s important is that’s the only outbound that’s working these days.” Today’s highly accessible options for using signal-based sales vs the old model - where sellers were blasting their (likely poorly defined) ICP with communications - has buyers showing their preference… Buyers know how they want to spend their time & attention: With sellers who understand their pain & the context of their needs, at the right time. My favourite stack of signals are: Social Signals - Competitor social engagement (especially questions) - Partner/ecosystem social post engagement (non-competitive, similar tech) Website Signals - Activity on pricing & problem pages - Who has recently engaged on your website Partner Ecosystem Signals - Have your partners experienced upgrades/downgrades? - What is the prospect's existing tech stack? Do you work with THOSE companies today? (See tools like Crossbeam + EULER) Consolidating signals with Common Room to properly give a view into who actually cares + is showing intent… is like giving a flashlight to a hunter in the dead of night. THIS is how you reach buyers that care. If they care, they’re more likely to convert 🤑. There’s even a Chrome extension to pull relevant/popular LinkedIn posts directly into your Common Room to prioritize those interested in your problem space. As Florin Tatulea wrote a week ago, never reach out with “Hey I saw you liked…” What signals are you using? I’ve been researching Common Room’s resource on 100s of signals their clients use. 👇

The days of "I noticed you like Guinness!" personalization are dead. Here's how top sales teams are identifying specific pain points their solution can uniquely solve. After building Apollo's AI research agent from zero to thousands of users, I've seen what separates good prospecting from great - and it's not what most people think. The Old Way: Generic Filters & Surface-Level Personalization - Start with basic filters: job titles, industry, company size - Download list, manually research each prospect - Find something generic to mention ("saw you went to Stanford!") - Send bland outreach with superficial personalization - Hope for 1-2% response rates while burning through your TAM The New Way: AI-Powered Pain Point Identification 1. Multi-source data enrichment: Configure multiple data sources in one place to maximize contact accuracy. For example, set up a waterfall enrichment that tries several email and phone providers in sequence to find valid contact information before your first touchpoint. This significantly increases your reach rate without wasting time on bounces. 2. AI qualification for genuine pain points: Create natural language prompts that identify prospects with problems you can solve. For a localization company, this means scanning websites for translation gaps. One prospect had a product supporting 30+ languages but maintained an English-only website – a perfect opportunity to start a value-driven conversation about expanding their web presence. 3. Signal stacking for personalized outreach: Combine multiple signals in priority order to craft messages that address specific pain points. Look for companies showing international expansion signals alongside their existing language limitations. One prospect was expanding overseas but only offered English language support – a clear opportunity to help them scale localization for customer acquisition in new markets. The result? Instead of "Hope this email finds you well," you can send: "We noticed you support 14+ languages in your product, but your website is only in English. Are you looking to expand your website to better service your international users?" The best part? You can do all of this inside Apollo.io - from initial prospect search to multi-source enrichment to AI custom research to signal stacking to AI messaging - in just a few clicks. Check out the demo to see how easily you can transform your outreach from generic to genuinely valuable.

A $2B+ ARR customer just concluded an intent signal A/B test, and UserGems 💎 signals converted to deals 6-8x higher than their existing providers That's because of our contact-level intent. If you're still using account-level signals alone, you're missing out on a lot of insights. Your account scoring model is probably off. And your ABM motion likely sees low rep adoption. Examples of Account-level signals? Funding, hiring, M&A, tech stack, intent, partners, etc. Contact-level signals? Job changes, contact-level intent, website de-anonymization, etc. This $2B+ customer's been using account-level intent. 'Someone from Montreal Metros is researching your [topic]' is weak signal. It leads to a lot of noise, wasted ad budgets, and reps' time (resulting in reps ignoring these 'intent' signals altogether) 'Shane Hollander, CEO of Montreal Metros, was researching your [topic] 3 times the last 7 days & visited your homepage' -> Now, that's a c̶o̶t̶t̶a̶g̶e̶ home run 🏡 ❗But even contact-level signals alone aren't enough. If you still treat signals as an individual event, you won't be able to cut through the noise. This is why we see 1-2% reply rates on sales outreach. 'I saw you on our website' alone isn't gonna cut it. For signal-based GTM (including ABM & AI outbound) to be effective, you need to COMBINE the Account- & Contact-level signals with... 🎯 Your own first-party data (Closed lost, call transcripts, CRM data, event regs, content downloads, product usage, etc) If a target account has: - new executive just joined - they're hiring for sales & marketing roles - their tech stack is your ICP - their team's been researching & visiting your website - they came to your events a few times L12 months - they evaluated you last year but it got deprioritized -> That's an A account to target. right. now. This is how a $235M+ ARR customer gets 11-16% reply rates on their AI-assisted outbound. It's also how our ABX program consistently converts 10%+ to demo requests. This is the shift I'm seeing in many high-performing teams. They're building a signal system - some call it a 'signal hub', others call it a 'GTM cockpit' or 'AI brain'. At UserGems, we call it the AI Command Center 🧠 Once signals are unified and trustworthy, you can do a lot of fun stuff like: - Reps start their day with the hottest 30 prospects already queued up in their Outreach/Salesloft/Gong - ABM managers know which accounts to focus on - CMOs see the impact of brand programs in moving accounts through buying stages - fewer alignment & herding cat meetings This is where the true unlock of AI for GTM happens. I honestly think this will be a core requirement for every modern B2B stack and process by EOY.