Best Practices for Using Signals

Most teams track 15+ intent signals. They still can't tell you which ones book meetings. We ran outbound for 40+ B2B clients this year. Here's what we learned about signals that actually convert vs. signals that just look good in a dashboard. 𝗧𝗶𝗲𝗿 𝟭: 𝗛𝗶𝗴𝗵-𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 (act within 24 hours) These are the signals where timing is everything. Wait 48 hours and someone else books the meeting. ↳ Pricing page visits - warmest signal you'll ever get. Most teams don't even track it. ↳ Champion job changes - previous buyer, new company, new budget. 3x reply rates vs. cold. ↳ Outreach replies (even negative) - a "not now" is a timing signal, not a rejection. ↳ Demo form drop-offs - they started booking. Something stopped them. Follow up. Tools we use here: RB2B, LoneScale, OutboundSync, Default 𝗧𝗶𝗲𝗿 𝟮: 𝗧𝗶𝗺𝗶𝗻𝗴 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 (act within 1 week) Good for warm outbound. These tell you a company is in motion. But they don't mean someone is ready to buy today. ↳ New hires in your buyer's department - budget is being spent, priorities are shifting. ↳ Competitor tech changes - they're evaluating alternatives. You want to be in that conversation. ↳ G2/Capterra activity - actively comparing solutions in your category. ↳ LinkedIn engagement with your content - they already know who you are. Tools we use here: TheirStack, BuiltWith, G2, Trigify.io, Teamfluence 𝗧𝗶𝗲𝗿 𝟯: 𝗧𝗮𝗿𝗴𝗲𝘁𝗶𝗻𝗴 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 (use for list building, not outreach triggers) This is where most people start. It's also where most people stop. ↳ Funding announcements - everyone and their dog emails these companies. Low reply rates unless your angle is specific. ↳ Firmographic matches - "Series B, 50-200 employees, SaaS" describes 12,000 companies. That's not a signal. That's a filter. ↳ Hiring volume - tells you a company is growing. Doesn't tell you they need your product. These aren't bad data points. But they're list-building criteria, not intent. Stacking a Tier 3 signal with a Tier 1 signal is where the magic happens. The mistake we see most often: Teams treat all signals equally. They dump everything into one score and wonder why reply rates are 2%. Instead of scoring, 𝘁𝗶𝗲𝗿 your signals. Tier 1 gets same-day manual outreach. Tier 2 gets multichannel sequences within the week. Tier 3 feeds your automated campaigns. Different signals deserve different speed and effort.

If your go-to-market strategy relies heavily on targeting ‘Series B startups with 50-100 employees that just raised a funding round’, guess what… You’re fishing where 1,000 more competitors are fishing. That’s the reality for most marketing teams… 🐟 Fishing in the same pond with the same bait. And with generic targeting, the message also becomes generic and gets drowned in the sea of sameness. If strategy = knowing where to play and how to win… 🎯 The goal of signals is to help you understand where (& when) to play. 📓 But more importantly, they help with ‘how to win’. They help you craft a smarter way to win. While your competitors are still using surface-level signals (company size, recent funding), the best marketers are digging deeper: - Technology stack changes and migrations - Engineering team hiring velocity - Executive speaking engagements - Social media comments on key topics - Pricing page updates These nuanced signals reveal CONTEXT. The why behind the what. When a company just raised Series B, it's just noise. When they're simultaneously hiring 3 DevOps engineers, migrating from legacy infrastructure, and their CTO is speaking about scaling challenges – that's a signal. Then, your message isn't the same one they heard 47 times this week: ‘Congrats on your funding round’ Instead, your message is: "I noticed you're building out your DevOps team while transitioning infrastructure. Have you thought about how you can [specific outcome] in 60 days without disrupting your current roadmap?" Your differentiation doesn’t just lie in what you’re selling. It lies in how deeply you understand THEIR MOMENT. Everyone has access to the same basic data, but the competitive advantage lies in finding and blending new signals, interpreting them well, and landing a message that resonates given that context. Clay has launched custom signals recently, and it will lead to even more interesting experiments in this area. At Paddle, we watch for a whole range of moments that matter… - When more than 30% of a digital product company's web traffic comes from outside their home country (a signal that they are likely selling to a variety of markets, and will need sales tax compliance and local payment methods) - When a mobile app builds out a web property (a signal that they are likely to invest in a new web channel, and at some point, monetise there!) - When a large enterprise hires for or announces a new product-led offering (a signal that are investing in a new motion that will need to be flexible, fit for global scale, and fully compliant from day 1) Instead of 'fishing' where and how all your competitors are fishing... stop, think, and start to understand the companies you want to serve. Their context, the signals that indicate this, and the moments these lead to.

Most sellers focus on top-of-funnel signals. But there is serious power in also using signals that surface after the demo – when buyers go quiet or deals stall. I was chatting with my friend Saad Khan at Aligned this week, and he broke down how they use Digital Sales Rooms (DSRs) to track buying signals deeper in the funnel. Most people use DSRs as a content dumping ground. But here’s how to actually turn it into a bottom-of-funnel signal engine: 1. Map the real buying committee Every org is different. Use your DSR to track who’s engaging – not just your known champion. → Cross-check with your account map → Talk with your champion about these new players → Tailor content for the real decision-makers 2. Use engagement (or silence) as a signal No activity = no deal. If your room’s been dead for 2 weeks, that’s a sign. Time to re-engage, reposition, or de-prioritize. 3. Stack signals from other sources Combine DSR data with: → Former users re-engaging → Trial activations → Job listings tied to your initiative → Competitor activity Example: The procurement team is deep in your DSR looking at competitive content while your competitor’s AE is liking their exec’s posts. That’s not random. That's a signal. The best sellers today don’t just read signals in isolation. They connect the dots.

Where do your prospects like to be touched? Not in the creepy way. In the marketing way. Most teams mistake “touches” for impact. They blast impressions across channels, recycle the same content everywhere, and hope something sticks. That’s not strategy. That’s guessing. The reality is… your prospects are already telling you exactly where and how they want to engage. Every digital move they make leaves behind a signal: A pricing page visit that screams bottom-of-funnel interest A repeat ad click from a Director who’s clearly warming up A LinkedIn post comment where they reveal pain points in their own words A webinar signup that shows topic-level intent CRM data on accounts that suddenly light up The teams that win don’t force-feed campaigns into one channel — they align activation with those signals. So what does that look like in practice? 👉 If they’re consuming your thought-leadership posts on LinkedIn, retarget them with a case study ad instead of hammering them with a cold ebook. 👉 If they’ve visited your pricing page twice, don’t waste time showing them generic top-of-funnel ads. Hit them with a direct offer — demo, calculator, or comparison guide. 👉 If CRM shows dormant accounts are engaging again, sync LinkedIn + Google campaigns to surround them with contextually relevant messages while SDRs follow up. 👉 If multiple stakeholders from one company are poking around your content, that’s not a coincidence — it’s an account-level buying committee heating up. Saturate them with middle-of-funnel creative. This is signal-led marketing. It’s about listening before you touch. The payoff? Less wasted budget on noise More meaningful touches that actually move pipeline A cross-channel motion that compounds impact instead of scattering it That’s why we built DemandSense — to surface the right signals and trigger the right activation, whether that’s LinkedIn, Google, nurture, or retargeting. Stop guessing. Start listening. Your prospects are literally telling you how to market to them. Website LinkedIn Ads Agency: https://lnkd.in/guEafPKk B2B Strategies and Guides: https://lnkd.in/gB-WQ82f Impactable YouTube Channel: https://lnkd.in/emYVDn_T

Without data structure, intent signals are just noise. After months of refining our account-based program, I've come to a simple realization: without proper data structure, intent signals are drowned and unusable. The team is tackling this challenge using this 3-step process: 1️⃣ Capturing the RIGHT signals With AI, Clay, and countless data providers, intent signals have become more accessible and commoditized (company announcements, reviews, technographics, hiring and more). It's incredibly tempting to buy it all and see what surfaces — I've been there! But I've learned the hard way that collecting signals without strategy creates more noise than insight. Beyond the hype, we took a deep dive into our customer journey (specifically won deals) to identify common patterns in buyer attributes and behaviors. Yes, we scrap and ingest external signals, but we've placed special emphasis on our 1st party data (CRM infos, website/product tracked events, webinar viewers, ad engagement). This gives us an edge that competitors simply can't replicate. 2️⃣ Building a UNIQUE data set Playing around with new intent signals in Clay is fun — and we do it! But the game-changer was figuring out how to structure and process these signals within the CRM. We've customized HubSpot to store them in custom objects. Every signal, regardless of source, follows the same structure: name, desc, source, URL, and timestamp. This standardization has transformed our ability to combine signals, refine scoring models, and surface insights that truly resonate with our team. In the end, better iteration and more educated guesses. 3️⃣ Routing signals for HUMAN engagement The final and hardest part (in my opinion): getting these signals into the hands of our sales team for meaningful action. While we've automated the routing mechanics, we've discovered that enablement and discipline are equally crucial. We’ve set up regular team meetings to go over disqualification reasons, celebrate wins, and come up with new signal ideas. There’s nothing better than seeing our team turn these intent signals into conversations. Technology enables, but the human connection converts. Open questions to the #Growth and #RevOps in my network: what signals are you prioritizing in your growth strategy right now? What sources are delivering the best results? Any tips on improving signal routing and sales enablement? —— Follow me if you found value in this post 🙇♂️ I used to share stuff about growth, marketing and SaaS.

Most teams treat signals as one big bucket. But in outbound, every signal has a different job: fit, relevance, readiness, or real intent. If you don’t separate them, you over-enrich, mistime outreach, and burn volume on noise. Here’s the simple structure we use to read signals cleanly in 2025 👇   1. Fit Signals These show who you are speaking to. They shape routing, persona logic, and segmentation. Not intent. 👉 Job title and seniority to decide the angle 👉 Company size, firmographics, and industry to set segment-based messaging 👉 Geo to handle region-based timing and windows 2. Relevance Signals These show why your product matters to the account. They help personalize the narrative. 👉 Tech stack to anchor integration or replacement value 👉 Account hierarchy for ABM routing 👉 Website traffic to read digital maturity 👉 Hiring velocity inside the relevant department 👉 New funding as a sign of expansion or priority shifts 3. Intent Signals These are the closest to real buying behavior. But they only matter when scored by recency, frequency, and depth. 👉 Third-party topic spikes from platforms like G2 or Bombora 👉 First-party pricing or product page activity from verified ICP users 🧩 When you read signals this way, you prioritize accurately without increasing send volume. You get cleaner routing, sharper angles, and far fewer false positives. 👉 Curious which signals your team uses most today. Drop your top 3 below ⬇️

When Sales Leaders ask me: "What signals should I use?" or "What are the best signals right now?" My answer is: the best signal for you is IRRELEVANT for 99% of other companies. Meaning, the signal(s) your GTM team monitors should be HIGHLY specific to your ICP. If 99% of other companies would not find this signal interesting (or maybe they don't have access to this signal at all! eg: first-party data/signals), then you know you've found a good signal. Generic signals: - Company raised funding - Person got a new job - Company is currently hiring 3 {role}s - Person posted on LinkedIn about {topic} Specific signals: - Company updated their privacy policy page last week - Person started new job, and the JD specifically called out an OKR for them that your software perfectly solves (+ this person was champion at last company) - New HQ was opened up and their first office manager was hired <30 ago - 20+ people in one dept signed up for the free version of your software in the last 30 days - A new technology (that your product natively integrates with) was mentioned in a JD for the first time ever at a company (indicating they only recently onboarded to it) ••• The best sellers know what these signals are. They learn them in the first discovery call, or in their 20 minutes of research prepping for a call. These are the signals to use to stand out. And to add value. If you're selling right now, and you don't know what the best signal(s) for you, that's a problem. You're flying blind. You have no control over your book of business. ••• I talked about this, and a few other things I'm seeing, on 30 Minutes to President's Club. They published it today. Go give it a listen: "How to Use AI to Identify the Perfect Time to Prospect" https://lnkd.in/ee-TXsdz Thanks for having me on Nick Cegelski and Alex Murphy. Love what y'all are building!

Is signal-based selling just spray-and-pray 2.0? It might be if you don’t know how to pick the right signals for your GTM motion. A question I often hear about signal-based selling is “how do you know which signals to use?” There are so many potential signals to choose from — which ones will move the needle? The answer to this question might be different for each company. PLG companies have very different needs & sales motions than Enterprise orgs. I recommend working backward from the objective using your own historical data. Find the commonalities that lead to the achieved objective. Anecdotes from the sales team should drive the data collection to verify. EXAMPLE Objective = increase conversion to opportunity The data in your CRM shows that the majority of opportunities in the past 6 months came from: - accounts where there was a recent funding raise (company level) - accounts that have had a hiring push (company level) - accounts that a past user or customer has recently joined (person level) These 3 signals contribute to successfully converting those target accounts to opportunities. This is your starting point. Now that you have 3 signals proven to help achieve your objective, you need to figure out how you want to use them. This is where you will prioritize signals to decide which ones should get actioned first. Most often, I believe you will want to build your signal plays on person-level signals first because they provide both WHO + WHY to reach out. Now you can begin building signal plays around the signals you have uncovered. Preferably a combination of one of the signals you identified. Start small by running a play for 2-3 weeks as a test. Learn, optimize, and iterate. Once you begin seeing results from one signal play, then you should add another into your stack. This is how smart leaders will get ahead with signal-based selling this year. Not because they use the most signals, but because they know how to pinpoint the signals that work best for their company and objectives.

Imagine only cold emailing leads who WANT to buy from you… The closest way to do so? Leveraging buying signals: The idea is that these signals help you: - find relevant reasons to initiate contact - re-activate existing prospects at the perfect moment - surface new challenges to address in your messaging … and much more. There are several categories of signals: 1️⃣ First-Party Signals ↳ = Intent data gathered from your own business ecosystem. These are prospects who already know you, actively taking steps like: - utilizing your product - browsing your website - subscribing to your email list - interacting with your brand on social platforms Platforms that help you capture these signals include: 1. LinkedIn Signals: Clay, Expandi.io, Trigify.io, Jungler 2. Website Visitors: Instantly.ai, Clay, Midbound, Vector 👻 3. Product Usage: Common Room, Mixpanel, Pocus, PostHog 4. Call Transcripts: Attention, Fireflies, Claap 5. Gated Content: Distribute, Gamma 2️⃣ Second-Party Signals ↳ = Intent data sourced from your ecosystem, shared by partners. Generally, prospects who have engaged with: - your brand on a partner platform (e.g: checking out your listing on G2) - your company, while employed at a different organization - a partner of yours with an overlapping customer base Examples include: 6. Champion Tracking: Clay, Common Room, Unify, UserGems 7. Affinity Signals: Crossbeam, Reveal, The Swarm 🔆, PartnerStack 8. Ad Engagement: Fibbler, ZenABM, Factors AI 9. Software Marketplaces: G2, Capterra, ColdIQ 3️⃣ Third-Party Signals ↳ = Intent data sourced from external providers. Thus, public signals indicating companies might benefit from your solution. Examples include: 10. Technographic Data: Clay, PredictLeads, HG Insights, BuiltWith, Similarweb 11. Funding Announcements: PredictLeads, lemlist, Clay, Crunchbase, Owler, PitchBook, 12. Web Data Agent: Claygent, Parallel Web Systems, Tavily, Common Room, Unify, Linkup, Perplexity, Manus AI 13. Job Openings: Common Room, PredictLeads, Clay, LoneScale, Mantiks, TheirStack, Lemlist 14. Custom Scraping: Apify, Firecrawl, Claygent, Instant Data Scraper 15. News Monitoring: PredictLeads, Google News, Exa 16. Ads Activity: Apify, Adyntel, Ahrefs 17. Firmographic Data: Prospeo, Wiza, Exa, DiscoLike 18. Lookalike Search: PredictLeads, DiscoLike P.S: What’s your preferred tool for monitoring buying signals?

12 Signals Every GTM Team Should Understand Clearly   Most teams treat signals as one big bucket.   But in outbound, every signal has a different job: fit, relevance, readiness, or real intent.   If you don’t separate them, you over-enrich, mistime outreach, and burn volume on noise.   Here’s the simple structure we use to read signals cleanly in 2025 👇 1. Fit Signals These show who you are speaking to. They shape routing, persona logic, and segmentation. Not intent. → Job title and seniority to decide the angle → Company size, firmographics, and industry to set segment-based messaging → Geo to handle region-based timing and windows   2. Relevance Signals These show why your product matters to the account. They help personalize the narrative. → Tech stack to anchor integration or replacement value → Account hierarchy for ABM routing → Website traffic to read digital maturity → Hiring velocity inside the relevant department → New funding as a sign of expansion or priority shifts   3. Intent Signals These are the closest to real buying behavior. But they only matter when scored by recency, frequency, and depth. → Third-party topic spikes from platforms like G2 or Bombora → First-party pricing or product page activity from verified ICP users   🧩 When you read signals this way, you prioritize accurately without increasing send volume.   You get cleaner routing, sharper angles, and far fewer false positives.   Curious which signals your team uses most today. Drop your top 3 below ⬇️