How to Use Intent Data

I’ve been using Trigify.io for 1.5+ years now. Now, I’ve been tracking 25+ LinkedIn profiles on it, mostly for my clients. And one thing is very clear to me: Posting on LinkedIn isn’t the hard part.  Tracking what happens after is. Most people stop at “posting consistently”. That’s where the real problem starts. What usually goes wrong... 1. Everyone tries to create content for their ICP – very few do it consistently – even fewer attract the right people 2. Some posts get engagement – likes – comments – profile visits 3. And then… nothing – no tracking – no follow-up – no system That’s wasted INTENT. What actually works (when you track it): 1. Lead magnet → warm outbound → Post a lead magnet → Track who engages with it → Enrich those profiles → Use that list for outbound You’re no longer cold. You’re starting with context. 2. Competitor & creator engagement → Track engagement on competitor posts → Or people consistently posting in your niche → Scrape those interactions → Enrich the data Now you’re reaching out to people who are already active in the problem space. 3. Keyword-based intent signals (underrated) → Track engagement around keywords like:    ~ “intent signals”    ~ “GTM Engineer”    ~ “RevOps” → Build lead lists from those conversations → Reuse them for:    ~ outbound    ~ content ideas    ~ account research Same signal. Multiple plays. Why I’ve stuck with Trigify.io, it’s not because it’s flashy. But how it turns LinkedIn activity into usable intent. Post-level tracking. Profile-level monitoring. Keyword-level listening. And now, workflows make this operational. Instead of exporting data, stitching tools, and manually following up, you can: - track engagement - enrich profiles - route data where it needs to go - trigger next steps automatically They’ve also been adding integrations around enrichment, sequencing, and data sync, which makes these workflows practical in real GTM setups. But the core idea stays the same: If you’re already creating content and doing outbound, you should be tracking who’s raising their hand. Otherwise, you’re guessing. And guessing doesn’t scale. This is the difference between posting content and building a system. Do you actually know who’s engaging with your content?

Sales and marketing leaders’ obsession with “intent” is undermining their Account-Based GTM strategy. Here are the 3 biggest mistakes GTM teams are making on intent (and how to use intent effectively): 1. Intent is not magic Unfortunately, intent has been marketed as if it’s magic. As if it can 100% accurately identify ALL companies that have a qualified opportunity. It cannot. Intent is simply an indication of interest and engagement on a *topic* related to the product you sell. At its best, vendors should utilize good sources and strong algorithms so that the level of confidence in the signal is clear. But often, in the interest of showing huge volumes of intent, vendors end up stretching the signal to cast as wide a net as possible and generate a large amount of false positives. 2. Intent is not your Ideal Customer Profile (ICP) I see this every day. Sales and Marketing teams get a list of high intent accounts and then “go after them.” This is counterproductive and wasteful because not all high intent accounts are in your ICP. The whole purpose of an account-based GTM is to align Sales and Marketing resources to accounts that have the highest LTV and thus generate the greatest enterprise value. This means being ultra clear on your ICP and avoiding the “intent temptation” of going after accounts that are interested in your solutions but are not in your ICP. Just because someone WANTS something doesn't mean they can or should buy it. 3. Intent should not be used in isolation from other data sets Intent only becomes powerful when it’s focused on your ICP and combined with other important data sets. Used in isolation, without other signals, you will never maximize your investment in intent. If tech companies want to increase the power and benefit of intent, they first need to combine intent with technographic data. Overlay the list of high-intent accounts with a list of companies that have the technologies your customers need to have and your hit rate on demand gen will improve significantly. The more robust solution to integrating intent into your broader GTM is to model it, with all other relevant data (firmographics, technographics, website engagement, Sales and Marketing engagement, etc) against closed won opportunities over the last 2 years. This will give a relative weighting for each data feature and intent keyword such that intent can be integrated into a more accurate score to represent propensity to buy soon. TAKEAWAY: Addressing the above issues are intended to arm you against what we’ve all heard many times, “This intent is BS, I called an account and they’re not ready to buy!” Don’t expect magic. Intent can't make a bad account great. But if you understand how intent relates to your ICP, and then use it in conjunction with other data sets, it becomes a powerful part of your account-based go-to-market strategy.

The difference between 2% and 15% reply rates isn't your messaging. It's your targeting. Let me explain: Random outreach looks like this: You search for: "VP of Sales at Series B companies" You get 1,000 names. You send the same message to everyone. You hope someone responds. Response rate: 2-3% if you're lucky. While signal-based outreach looks like this: You search for: "VP of Sales at Series B companies WHO are also..." → Hiring for their sales team right now  → Just raised funding in the last 90 days  → Recently added relevant tech to their stack You get 150 names. You send the same message (with context). Response rate: 12-15% because you reached out at the right moment. Same ICP. Same message structure. Completely different results. Here's why intent signals matter: Hiring = Active pain point exists  Funding = Budget just unlocked  Tech changes = They're in buying mode These aren't "nice to know" signals. They're "ready to buy" signals. And most outbound teams ignore them completely. The problem? Most databases only give you: ↳ Name ↳ Title ↳ Company ↳ Email They don't tell you WHEN to reach out. This is changing. Prospeo.io V2 launched this month What makes it different is the intent layer You can search for prospects based on: 1. Standard filters (title, company size, industry) 2. Intent signals (hiring, funding, tech stack, department growth) 3. Get verified contacts (emails + mobiles) in one search So instead of blasting 1,000 random prospects... You reach out to 150 prospects who are showing real buying signals. With triple-verified emails (98% deliverability) and direct mobiles (25-30% pickup rates). The shift: Stop asking: "Who should I reach out to?" Start asking: "Who's ready to buy right now?" Intent signals answer that question. ___________________________ P.S. What intent signals do you track before reaching out? Drop them below. Let's learn from each other.

Fixing your email copy won't save your outbound campaign if you're emailing the wrong people. I've watched 100+ outbound campaigns fail at ColdIQ (and the problem is almost never the messaging). Here's what I mean: Most companies just grab some list from Apollo, send out like a hundred cold emails, and hope they get a reply. They have no idea if these people actually need their solution. The fix? Intent data. You'll never be 100% sure, but these signals help you: - Target people who are actually in-market - Re-engage old lists when timing makes sense - Stop wasting time on prospects who'll never convert There are several categories of intent signals: 1/ First-Party Intent ↳ Data collected from YOUR business ecosystem. These are people who already know you exist. They're: - Using your product - Visiting your website - Engaging with your content - Subscribing to your newsletter Tools to track this: Website Visitors: Instantly.ai, Clay, Midbound, Vector 👻 Product Usage: Common Room, Pocus, Mixpanel, Amplitude Social Engagement: Teamfluence™, Trigify.io, Clay 2/ Second-Party Intent ↳ Data collected about your company, via partners. That would be prospects that interacted with: - One of your partners who has overlapping customers - Your brand through a partner site - like your G2 page if you're a SaaS Examples of platforms to uncover these signals include: - Champion Tracking: Clay, Common Room - Affinity Signals: Crossbeam, WorkSpan - Review Sites: G2, Capterra 3/ Third-Party Intent ↳ Data collection via external providers. This is public data that shows they might actually need what you're selling. Examples include: Hiring Trends: LoneScale, Mantiks, PredictLeads, Clay Tech Stack Changes: BuiltWith, Similarweb, HG Insights, Clay Funding Events: Crunchbase, PitchBook, Owler - A Meltwater Offering, Clay Custom AI Agents: Relevance AI, Claygent, Apify We use Clay to build most of these workflows: → Filter for buying signals → Enrich contacts in real-time → Combine multiple data sources → Score and segment dynamically Better targeting = better reply rates = better pipeline. P.S: What intent signals are you tracking in your GTM motion right now?

"Oh wow… you saw I follow [competitor] You must be the only rep using that as intent, right?" Wrong. I get that same email every week. It’s not creative. It’s not relevant. And it’s definitely not intent. Here’s the uncomfortable truth: Most intent signals used in outbound today aren’t signals. They’re just triggers that are easy to scrape. Examples? → “Saw you follow our page” → “Congrats on the funding” → “Noticed you’re hiring SDRs” → “Saw your job change” All of these have two things in common: They’re available in every sales tool They get saturated in weeks And once they do, they lose all effectiveness. Because everyone sends the same message at the same time. If you want replies, you have to dig deeper. Here’s what I believe actually works in 2025: 1. Intent = psychology, not data Real buying signals come from pressure, frustration, or urgency. But those don’t live in Sales Navigator. They show up in behavior. Ask yourself: → What do your buyers complain about? → What makes them feel exposed or behind? → What’s the task they keep putting off? That’s intent. Because emotion drives action. 2. Non-scalable always beats obvious Most reps try to find signals that scale. But by the time you found it, so did 300 others. The best campaigns I’ve run are the ones that can’t be automated: → I looked up their stack manually and noticed a missing piece → I saw their outbound team scaling with no RevOps → I found their SEO traffic dropped 40% in 3 months Those signals don’t show up in your CRM. You have to go get them. Which is why they work. 3. Niche intent > generic plays Intent only works when it maps to your exact ICP. In SaaS, I look for: → Tool removals = something broke → Multiple outbound hires = process chaos → VP of Sales change = new playbook window In ecom, you might watch: → New shipping provider = ops transition → Bad product reviews = CS friction → Ad spend spike = budget shift The more specific the signal, the more relevant the message. And relevance is the only thing that works now. Most cold emails today are based on the same 5 signals. The ones that convert? Built on insight, emotion, and real research. Yes, it’s slower. Yes, it’s unscalable. But that’s why it works. What’s one signal you’ve used that actually led to meetings?

🛒 You can’t track purchase intent by tracking ATCs. 𝟭. “𝗔𝗧𝗖” 𝗷𝘂𝘀𝘁 𝗺𝗲𝗮𝗻𝘀 “𝘀𝗮𝘃𝗲 𝗳𝗼𝗿 𝗹𝗮𝘁𝗲𝗿”. It’s a placeholder, not a promise. 𝟮. 𝗣𝗲𝗼𝗽𝗹𝗲 𝘂𝘀𝗲 𝘁𝗵𝗲 𝗰𝗮𝗿𝘁 𝗹𝗶𝗸𝗲 𝗣𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁. It’s a tool for collecting, not committing. 𝟯. 𝗧𝗵𝗲 𝗰𝗮𝗿𝘁 𝗵𝗲𝗹𝗽𝘀 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗲, 𝗻𝗼𝘁 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲. It helps them compare…not decide. 𝟰. 𝗡𝗼 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻 = 𝗻𝗼 𝗰𝗼𝗺𝗺𝗶𝘁𝗺𝗲𝗻𝘁. Clicking isn’t buying. It costs nothing to put something in an online cart. 𝟱. 𝗔𝗧𝗖𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗰𝘂𝗿𝗶𝗼𝘀𝗶𝘁𝘆 𝗼𝗻𝗹𝘆. Interest? Yes. Intent? Not even close. If you really want to track intent, do this instead: ✅ 1. Track high-friction actions Not all clicks are equal. Look for: • Initiate Checkout • Payment Info Entered • Return Visitor → PDP → Checkout • Product added after reading reviews These behaviors show someone is moving past curiosity into commitment. ✅ 2. Analyze sequence, not single actions One ATC means nothing. But: 𝘈𝘛𝘊 → 𝘝𝘪𝘦𝘸 𝘴𝘩𝘪𝘱𝘱𝘪𝘯𝘨 𝘱𝘰𝘭𝘪𝘤𝘺 → 𝘈𝘥𝘥 𝘢𝘥𝘥𝘳𝘦𝘴𝘴? Now we’re talkin’ intent. Watch the flow, not the isolated click. ✅ 3. Measure time spent on key friction points If someone lingers on: • Product comparisons • Return policy pages • Size charts or FAQs They’re mentally preparing to convert. They’re not just browsing at that point, they’re weighing the trade-offs. ✅ 4. Look for repeat product interactions If someone revisits the same PDP 2–3 times in a week, that’s real consideration. Bonus points if they come back from an email or ad reminder. ✅ 5. Use survey overlays or post-exit polls Ask simple, direct questions like: “Are you planning to buy today?” “What’s stopping you from checking out?” Self-reported “logic” + behavioral data = gold. 𝘛𝘓𝘋𝘙: 𝘈𝘛𝘊 𝘪𝘴 𝘪𝘯𝘵𝘦𝘳𝘦𝘴𝘵-𝘭𝘦𝘷𝘦𝘭 𝘣𝘦𝘩𝘢𝘷𝘪𝘰𝘳 𝘰𝘯𝘭𝘺. 𝘐𝘵 𝘸𝘰𝘯’𝘵 𝘵𝘦𝘭𝘭 𝘺𝘰𝘶 𝘪𝘧 𝘺𝘰𝘶𝘳 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘢𝘳𝘦 𝘵𝘳𝘶𝘭𝘺 𝘳𝘦𝘢𝘥𝘺 𝘵𝘰 𝘣𝘶𝘺. 𝘛𝘰 𝘵𝘳𝘶𝘭𝘺 𝘵𝘳𝘢𝘤𝘬 𝘪𝘯𝘵𝘦𝘯𝘵, 𝘮𝘰𝘯𝘪𝘵𝘰𝘳 𝘤𝘩𝘦𝘤𝘬𝘰𝘶𝘵 𝘮𝘰𝘮𝘦𝘯𝘵𝘶𝘮.

Too many digital marketers hide behind analytics tools instead of talking to real customers. To uncover your customers’ search intents, do customer research beyond just keyword research. This might include face-to-face conversations, customer surveys, listening to sales calls, or using AI to analyze customer interactions. Your goal is to uncover the actual problems your customers are trying to solve, the specific language they use to describe their situation, and the questions they ask while making a purchase decision. Then, combine these qualitative insights with quantitative data—e.g. analyze your GA4, Search Console, Google Ads Search Terms report, page-by-page content performance, etc. You’ll discover pages on your website with intent mismatches. These are pages that generate traffic but for the WRONG intents. For example, we worked with a brand that runs team-building events for corporations. They offered a team-building activity titled “Write a Country Song Like Taylor Swift“. The page for this activity received most of its traffic from people who were looking for a Taylor Swift-themed drink at Starbucks. These visitors were looking to purchase coffee, not a corporate team-building event. Conversely, you’ll find pages on your website with low traffic but high conversions – these often reveal highly specific, valuable intents to target. This combined qualitative and quantitative analysis helps you identify search intents worth targeting and pinpoint content that may need updating or even pruning. Tomorrow: Using these insights to create content truly aligned with user intent.

🎯 Here's what nobody's talking about: Companies are spending millions on intent data, but most sales teams can't consistently act on it. After hundreds of conversations with revenue leaders, I'm seeing a fascinating pattern emerge. The excitement isn't just about getting intent data - it's about actually doing something with it. Here's what typically happens: - Marketing gets intent data → easily targets accounts programmatically - Sales get the same account-level intent data → need to find people in those accounts & reach out one by one - 3 days later → back to business as usual - Result? Massive missed opportunity But here's what's getting our customers excited: We're not just identifying the right people in those high-intent accounts, we're guaranteeing they get contacted. Here’s a real example: One customer went from actionizing 30% of their intent signals to over 90% in weeks. How? The secret isn't more tools or more data. It's orchestration: - Auto-identify & prioritize perfect-fit contacts in intent accounts - Multi-thread intelligently to avoid account burnout - Auto-enroll them into the right sequences for reps to review before sending - Automate fallback to an autopilot sequence if reps don't action in 7 days The math is simple: 2X more intent accounts properly worked = 2X more opportunities (And yes, we're seeing exactly this with our early adopters) We're at an inflection point: The winners won't be those with the most intent data, but those who can consistently turn it into conversations. What's your experience with intent data activation? Are your teams consistently acting on the signals you're paying for?

Intent detection enables search and chat systems to understand and respond to user queries accurately. E.g. in e-commerce, a query like 𝗜 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗿𝗲𝘁𝘂𝗿𝗻 𝗮 𝗧𝗩 𝗜 𝗯𝗼𝘂𝗴𝗵𝘁 𝗹𝗮𝘀𝘁 𝘄𝗲𝗲𝗸 would be a 𝗥𝗲𝘁𝘂𝗿𝗻 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 intent. Correct intent detection can guide users with specific instructions on processing returns. Intent systems often use supervised classification or similarity-based models, such as sentence transformers (SetFit). The paper 𝗜𝗻𝘁𝗲𝗻𝘁 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗟𝗟𝗠𝘀 explores using large language models (LLMs) for intent detection, employing 𝗶𝗻-𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗜𝗖𝗟) and 𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁 (𝗖𝗢𝗧) 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴. In ICL a pre-trained LLM can be bootstrapped to solve specific tasks by observing some examples. E.g., ask an LLM to classify some text + provide examples of categories for the LLM to learn on the fly: "𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝘆 𝘁𝗵𝗶𝘀 𝘁𝗲𝘅𝘁: Patient has sore throat and difficulty breathing. 𝗜𝘀 𝗶𝘁 (𝗮) respiratory, (𝗯) cardiovascular, 𝗼𝗿 (𝗰) gastrointestinal? Examples: Respiratory: "The patient has a persistent cough and shortness of breath." (..etc)." In COT prompting, LLMs are provided with step-by-step reasoning to enhance their reasoning capabilities. E.g., 𝗣𝗿𝗼𝗺𝗽𝘁: The customer wants a book that’s a mystery and good review. Let’s think step by step: Step 1: Book should be in the mystery genre. Step 2: Book should have high ratings and positive reviews. 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻: Based on these criteria I recommend “The Silent Patient” which is a popular mystery novel with excellent reviews. 𝗧𝗵𝗲 𝗟𝗟𝗠 𝗶𝗻𝘁𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺: 1.      𝗢𝗳𝗳𝗹𝗶𝗻𝗲 : from the training data (intent, query 1..n), store SetFit embeddings of queries in a vector DB, and create intent descriptions using an LLM. 2.      𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: for a query, retrieve “k” most similar queries, and create a COT prompt using the queries and intent descriptions. Experiments with the Claude and Mistral LLM families show better performance than the SetFit models. However, the high compute and latency costs of LLMs make them challenging to use at scale. Hence the approach uses a hybrid system that only routes queries to the LLM intent system if the SetFit model is uncertain about a query. This hybrid architecture balances performance and cost. Link: https://lnkd.in/e7KgPZjU

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! 👇