How to Identify High-Intent Buyer Signals

About 2-3 months back, I found out that one of my client’s page had around 570 people visiting the pricing page, but barely 45 booked a demo. Not necessarily a bad stat but that means more than 500 high-intent prospects just 'vanished' 🫤 . That didn’t make sense to me because people don’t randomly stumble on pricing pages. So in a few back-and-forth with the team, I finally traced the issue to their current lead scoring model: ❌ The system treated all engagement as equal, and couldn’t distinguish explorers from buyers. ➡️ To give you an idea: A prospect who hit the pricing page five times in one week had the same score as someone who opened a webinar email two months ago. It’s like giving the same grade to someone who Googled “how to buy a house” and someone who showed up to tour the same property three times. 😏 While the RevOps team worked to fix the scoring system, I went back to work with sales and CS to track patterns from their closed-won deals. 💡The goal here was to understand what high-intent behavior looked like right before conversion. Here’s what we uncovered: 🚨 Tier 1 Buying Signals These were signals from buyers who were actively in decision-making mode: ‣ 3+ pricing page visits in 10–14 days ‣ Clicked into “Compare us vs. Competitor” pages ‣ Spent >5 mins on implementation/onboarding content 🧠 Tier 2 Signals These weren’t as hot, but showed growing interest: ‣ Multiple team members from the same domain viewing pages ‣ Return visits to demo replays ‣ Reading case studies specific to their industry ‣ Checking out integration documentation (esp. Salesforce, Okta, HubSpot) Took that and built content triggers that matched those behaviors. Here’s what that looks like: 1️⃣ Pricing Page Repeat Visitors → Triggered content: ”Hidden Costs to Watch Out for When Buying [Category] Software” ‣ We offered insight they could use to build a business case. So we broke down implementation costs, estimated onboarding time, required internal resources, timeline to ROI. 📌 This helped our champion sell internally, and framed the pricing conversation around value, not cost. 2️⃣ Competitor Comparison Viewers → Triggered: “Why [Customer] Switched from [Competitor] After 18 Months” ‣ We didn’t downplay the competitor’s product or try to push hard on ours. We simply shared what didn’t work for that customer, why the switch made sense for them, and what changed after they moved over. 📌 It gave buyers a quick to view their own struggles, and a story they could relate to. And our whole shebang worked. Demo conversions from high-intent behaviors are up 3x and the average deal value from these flows is 41% higher than our baseline. One thing to note is, we didn’t put these content pieces into a nurture sequence. Instead, they were triggered within 1–2 hours of the signal. I’m big on timing 🙃. I’ll be replicating this approach across the board, and see if anything changes. You can try it and let me know what you think.

Your highest-intent prospects aren't all the same person. I was reviewing several of our recent BOF campaigns and I was reminded of the fact that: The closer someone gets to conversion, the more your messaging matters. But most marketers treat high-intent audiences like they're all the same person. They're not. Someone who abandoned cart yesterday needs different messaging than someone who's been browsing for three weeks. Someone on mobile at 2pm needs different creative than someone on desktop at 9pm. Here’s what you should do: 1️⃣ Understand intent decay patterns. We've tracked this across client accounts - purchase intent has a half-life. After someone shows buying signals, you have roughly 72 hours of peak conversion opportunity. Day 4-7, intent drops 60%. By week two, you're basically starting over. Many advertisers waste this window with generic "complete your purchase" messaging. 2️⃣ Segment your BOF audiences by recency, not just behavior. Recent cart abandoners get urgency-focused creative. Week-old browsers get social proof and reviews. Month-old prospects need fresh product education. Same goal, different psychology. We've seen 40%+ ROAS improvements just from this basic segmentation. 3️⃣ Rotate creative elements based on engagement, not calendar. Most teams mess up by refreshing on schedule instead of performance. Monitor micro-signals: when CTR drops 15% from peak, when frequency hits 2.5x without converting, when engagement falls while impressions climb. Don't wait for Meta to flag fatigue. 4️⃣ Test messaging depth, not just messaging type. Generic "20% off" performs worse than "still thinking about those running shoes?" for cart abandoners. Specific beats generic at every intent level. We use AI to personalize hooks based on browsing behavior, and it consistently outperforms broad creative by 25-35%. Most BOF campaigns fail because they treat high-intent traffic like low-intent traffic. You've already done the hard work of getting someone interested. Don't waste it with lazy messaging.

The exact moment prospects start mentally buying (listen for this word) Your prospect just switched from saying "if we move forward" to "when we implement" and you missed it completely. That tiny language shift is the biggest buying signal in sales, and 90% of reps never notice it. After analyzing thousands of sales calls, I've found one pattern that predicts closed deals better than anything else: Prospects who start using "we" language. When someone says "Will WE be able to do this?" or "When WE implement this..." they've mentally crossed the ownership threshold. They're no longer evaluating. They're PLANNING. But most sellers don't pick up on this subtle shift, or worse, they keep using hypothetical language themselves: "If you decide to move forward..." "Should you choose our solution..." "You could potentially see..." Start mirroring their ownership language immediately. Use "we" early and often: "When we implement this..." "Here's what our timeline will look like..." "The team we'll assign to you..." This one language shift has helped my clients close 25% more deals without changing anything else in their process. Watch the full episode to see all 6 buying signals you're probably missing 👇 Check the comments for the link

"Intent" is great in theory. “Know exactly which of your target accounts are in market right now" is the grand promise. But in practice, we never have pure, uncut buyer intent. Instead we have various types of “intent signals.” These can be useful, but hard to understand when they all get grouped into a single buzzword. The distinctions between each are important when deciding where to focus. Here's my *rough draft* framework for understanding the 6 types of intent signals (through the buyer's eyes): 1.) Declarative Intent (Zero Party) 🗣️ -- The buyer *explicitly* states a need, budget, or timeline in their own words. -- Example: Buyer says "I'm launching a pilot by EOQ." -- Found with Gong, Fathom, Live Chat, Forms. 2.) Direct Brand Engagement (1st Party) 🔍 -- The buyer consumes relevant information on properties you own. -- Example: Pricing page visits, free trial started. -- Found with Common Room, RB2B, Vector 👻, Koala, Warmly, etc. 3.) Off-Property Brand Engagement (2nd Party) 🌐 -- The buyer interacts with content about your product on someone else's property. -- Example: LinkedIn Ad likes, G2 profile views. -- Found with Fibbler, Sales Nav, G2. 4.) Category Engagement (2nd Party) 🥊 -- The buyer researches rivals or seeks information on your category. -- Example: Engaged with competitor on social, G2 category views. -- Found with PhantomBuster, Trigify.io, G2. 5.) Category Research Activity (3rd Party) 📚 -- Someone at the account consumes content related to your product or problem. -- Example: Reading many "AI for video" articles. -- Found with Bombora, TechTarget, Foundry. 6.) Company Investment Activity (3rd Party)💰 -- The company indicates (or implies) an investment in your space. -- Example: Announces AI team, opens US warehouse.  -- Found with Keyplay, Clay, UserGems 💎. What's your take? Do you see intent differently? Is there a category I've missed or one you'd frame differently? I get asked about intent all the time. I'd love to get more clear on the details. Any comments or feedback I'll roll into PeerSignal.org's research on this topic.

If 2024 taught us anything about Cold Email, it’s this: 👇 General ICP Outreach isn’t enough to drive results anymore. With deliverability getting tougher every day, there’s only one way to make outbound work: → Intent-Based Targeting Here’s how we do it at SalesCaptain to book 3x more demos ⬇️ Step 1️⃣ Identify High-Intent Triggers The goal? Find prospects showing buying signals. ✅ Website visits – Someone browsing pricing or case studies? (We use tools like RB2B, Leadfeeder, and Maximise.ai). ✅ Competitor research – Tools like Trigify.io reveal when prospects engage with competitor content. ✅ Event attendance – Webinar attendees or industry event participants often explore new solutions. (DM me for a Clay template on this) ✅ Job changes – Platforms like UserGems 💎 notify us when decision-makers start new roles (a prime buying window). ⚡️ Pro Tip: Categorize triggers: → High intent: Pricing page visits → Medium intent: Engaging with case studies This helps prioritize outreach for faster conversions. Step 2️⃣ Layer Intent Data with an ICP Filter Intent data alone isn't enough, you need to ensure the right audience fit. Tools like Clay and Clearbit help us: ✅ Confirm ICP fit using firmographics ✅ Identify the right decision-makers ✅ Validate work emails ✅ Enrich data for personalized messaging ⚡️ Key Insight: Not everyone showing intent fits your ICP. Filter carefully to avoid wasted resources. Step 3️⃣ Hyper-Personalized Outreach Golden Rule: Intent without context is meaningless. Here’s our outreach formula: 👀 Observation: Reference the trigger (e.g., webinar attended, pricing page visit) 📈 Insight: Address a potential pain point tied to that trigger 💡 Solution: Share how you’ve helped similar companies solve this pain 📞 CTA: Suggest an exploratory call or share a free resource ⚡️ Pro Tip: Use tools like Twain to personalize at scale without landing in spam folders. 📊 The Results? Since focusing on intent-based outreach, we’ve seen: ✅ 3x Higher Demo Booking Rates 📈 ✅ 40% Reduction in CPL (focusing on quality over quantity) ✅ Larger Deals in the Pipeline with higher-quality prospects It’s 2025. Let’s build smarter, more profitable campaigns. 💡 Do you use intent signals in your outreach? Drop me a comment below! 👇

Intent signals are not ZoomInfo intent topics, Bombora, scoops, or G2 intent. These are the types of "signals" we've relied on for the past decade, but the truth is that none of them have truly performed exceptionally well. To be fair, they've delivered some decent leads, but nothing truly outstanding IMO. Also they are signals that every outbound SDR truly despises. Intent signals, in today’s context, are highly unique to each business and are custom-built based on your specific needs and criteria. Let's pretend you're selling a B2B solution that solves a Customer Success problem. Old Intent Signals tools would tell you: "This company is researching customer success topics, so you should reach out!" Today's Intent Signals would look more like this: "This company is using Zendesk (your competitor). Zendesk has announced they're raising prices next month (good opportunity since you're cheaper). This company also received funding a month ago. They are also actively hiring Customer Success Managers with a good grasp of CS technologies. Additionally, their CEO posted on LinkedIn that their biggest bet this year is improving customer support and experience. Oh, and they just hired a Head of Customer Success." Who would you rather reach out to? Again, we're building all of this on Clay + Ai solutions.

The Pipeline Problem: A First-Principles Look at Real GTM Solutions “We don’t have enough pipeline” remains the top GTM challenge in my conversations. My question - What are we going to do about it? Previously, I used First Principles thinking to highlight flawed pipeline assumptions. Today, I would like to share how a First Principles mindset can provide a solution to those problems. Spoiler alert - the content here represents the building blocks of a First Principles Pipeline Generation framework that I’ll be sharing soon. First Principles means stripping away inherited beliefs - “that’s the way it has always been done” - and focusing on the core truths about buyers, their pain points, and how they want to buy. Instead of more low-quality MQLs, think about creating a GTM machine that consistently produces revenue‐ready mid-stage pipeline. I would love to hear your thoughts on the five First Principles solutions below to common pipeline problems: 1) Discard Volume‐Only Thinking -Fundamental Truth: Not all leads are created equal. -Solution: Shift from raw lead volume to conversion efficiency and revenue impact. Track pipeline velocity, win rates, and ICP fit, rather than fixating on MQL count. 2) Identify True Buying Signals -Fundamental Truth: Buyers who feel urgent pain will actively seek solutions. -Solution: Replace MQL‐centric scoring with Problem‐Qualified Leads (PQLs)—prospects who exhibit strong intent signals. 3) Design Around the Buyer -Fundamental Truth: Buyers move nonlinearly, self‐educate, and engage on their schedule. -Solution: Map your pipeline stages to actual buyer actions, not just internal sales steps. Track signals like demo requests, consultation requests, or event triggers. 4) Make Pipeline a Team Sport -Fundamental Truth: Silos lead to “pipeline leakage.” -Solution: Align marketing, sales, and Revenue Operations on the same ICP, the same metrics (pipeline velocity, CAC, conversion rates, and unit economics), and continuous feedback loops. 5) Adopt a Buyer‐First Mindset -Fundamental Truth: A genuinely customer‐centric approach drives better conversion and loyalty. -Solution: Prioritize trust‐building, value‐focused content, and ongoing engagement with your ICP over quick‐hit lead generation. Thanks for reading! My question to you. Are these five First Principles “mom and apple pie” or do they form the foundation for a future GTM machine that can manufacture qualified, revenue‐ready opportunities? I’ve also included an updated version of Winning by Design’s Data Model, which helps visualize where to optimize and invest for improved pipeline performance #firstprinciples #GTMexecution #WinningbyDesign

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.

What it means to shift from 'spray-and-pray' marketing to a unified, account-based GTM aimed at your exact ICP ⤵ Last September I featured a story about how Parabola did all this (a) in under 30 days, (b) without a RevOps team, and (c) with a TON of 🔥 automation. Here’s the update: pipeline has grown by 717% since September. All with the same size marketing team 🤯 How they rapidly built a scaled ABX motion from scratch: 1️⃣ Identified - ICP-fit accounts & contacts are sourced using Clay, Sales Nav, Cargo 🧱 and ChatGPT - All data gets pushed to Salesforce 2️⃣ Aware - Accounts get warmed up with LinkedIn paid ads, scaled email, LinkedIn connections and referrals – in addition to other bespoke tactics and campaigns - They are flagged as aware if buyers start engaging via email (2+ contacts with 2+ email opens), accept LinkedIn connections, visit the website, or engage in the community - Tools: website tracking (Clearbit, HubSpot), email & LinkedIn automation (Apollo, Outreach, La Growth Machine), community (Slack) 3️⃣ Interested - Accounts progress as they demonstrate more meaningful engagement & intent - Key signals include visiting high-intent website pages (ex: pricing page), starting a free trial, attending an event, etc. - Tools: webinar (Sequel.io), trial (Redshift), website tracking, product usage data, ETL 4️⃣ Evaluating - All of the above is meant to generate high intent, ICP pipeline for sales - Accounts progress here by booking a meeting with sales or requesting a demo - This is where more manual, high-touch outreach comes into play (aimed at warm accounts) - Tools: meeting routing & booking (Calendly), marketing automation (HubSpot) The big learnings over the past 8 months:  - Content & offers are everything for getting a response  - Not all triggers are created equal when scoring account stages  - The data won’t be perfect, don’t let that stop you  - The best GTM plays involve both marketing & BDRs This is what a focused GTM looks like. Marketing, sales, CS, product and even ops all play a role in building pipeline with the right accounts. Huge shoutout to the Parabola team (Alex Yaseen, Ben Pollack, Adam Reisfield) for taking folks behind-the-scenes! PS, Parabola just launched a game-changer for anyone interested in automating workflows like ABX stages (think Cursor for Ops teams). You can check it out here: https://parabola.io/ #abx #marketing #automation

Signals are EVERYTHING in outbound Lets be real these days just scraping data or dropping a “saw you’re hiring” won’t cut it Your prospects deserve more In a recent article I contributed to with Leslie Venetz and 🦾Eric Nowoslawski ZoomInfo showed exactly how sales teams can (and should) use buyer intent signals to go from surface level outreach to meaningful conversations Why should you care? Signals help you find the 𝗿𝗶𝗴𝗵𝘁 𝗽𝗲𝗿𝘀𝗼𝗻, 𝘁𝗶𝗺𝗶𝗻𝗴 and 𝗺𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 all in one Its not about throwing data around Its about understanding your prospect’s challenges and connecting with their priorities in real time ------- 𝙀𝙭𝙖𝙢𝙥𝙡𝙚 Instead of "Hey saw you’re looking to enhance your customer support platform!" Try something like “Hey Sarah I noticed your team is growing and you’re looking to scale your customer support operations. One thing I’ve seen with other companies in your space is that as they grow maintaining personalized customer experiences can get tricky without the right automation in place. Have you considered streamlining your workflows to keep the experience seamless for both customers and agents?" 𝙋.𝙎. As you scale, integrating AI driven automation into your support workflow can reduce response times and improve agent efficiency without sacrificing that personal touch your customers love -------- This approach allows you to go deeper than just surface level data It’s about staying ahead of your prospect’s needs and leading with value every step of the way Plus you’ve got signals doing the heavy lifting Thats how you keep conversations moving forward and relevant Check out the full ZoomInfo article for more insights👇 [link to article] P.S. You might wanna grab a coffee before diving in—this ones packed with value 😎