Developing Lead Scoring Models

Your lead scoring is broken. Here's the model that predicts revenue with 87% accuracy. Most B2B companies score leads like it's 2015. ┣ Downloaded whitepaper: +10 points ┣ Attended webinar: +15 points ┗ Opened email: +5 points Meanwhile, 73% of these "hot" leads never convert. Here's what we discovered after analyzing 10,000+ B2B leads: The leads scoring highest in traditional systems aren't buyers. They're information collectors. They download everything. Open every email. Click every link. But when sales calls? ↳ "Just doing research." ↳ "Not ready yet." ↳ "Send me more info." The leads that DO convert show completely different signals: They don't just visit your pricing page. They spend 8 minutes there, come back twice more that week, then search "[competitor] vs [your company]." They're not reading blog posts. They're calculating ROI and researching implementation. Activity doesn't equal intent. And that's where most scoring models fall apart. We rebuilt lead scoring from the ground up. Instead of rewarding every action equally, we weighted four factors based on what actually predicts revenue: ┣ Intent signals (40%) - someone searching "implementation" is closer to buying than someone downloading an ebook ┣ Behavioral depth (30%) - how someone engages tells you more than what they engage with ┣ Firmographic fit (20%) - perfect ICP match or bust ┗ Engagement quality (10%) - quality of interaction matters The framework is simple. The impact isn't. We map every lead to one of four tiers: ┣ 90-100 points → Sales gets them same-day ┣ 70-89 points → Automated nurture + retargeting ┣ 50-69 points → Educational content track ┗ Below 50 → Long-term relationship building No more dumping mediocre leads on sales and wondering why they don't follow up. Results after 6 months: ┣ Sales acceptance rate: +156% ┣ Sales cycle length: -41% ┗ Lead-to-customer rate: +73% The biggest shift wasn't the scoring model. It was the mindset. 🛑 Stop measuring marketing by MQL volume. ✔️ Start measuring it by how many MQLs sales actually wants to talk to. Your automation platform will happily score 500 leads as "hot" this month. But if sales only accepts 50, you don't have a volume problem. You have a scoring problem. Traditional scoring optimizes for activity. And fills your pipeline with noise. Revenue-predictive scoring optimizes for intent and fills it with buyers. If you'd like help with assessing your current lead scoring logic, comment "SCORING" and I'll get in touch to schedule a FREE consultation.

I replaced my client's 3-person SDR team and saved 100+ hours monthly by automating lead research and scoring with Clay. We created a process that automatically researches, enriches, and scores leads based on 6 key data points. In this post, I'll show you exactly how we built this system that anyone can implement. 1. Industry targeting: Instead of settling for broad categories like "Software" or "Technology," given by LinkedIn or major data providers, we set up an AI enrichment in Clay that reads websites and LinkedIn data to output specific niches like "HealthTech," "Martech," etc., making targeting much more precise. 2. Seniority filtering: We went beyond basic titles like Director or VP. Using Clay's AI enrichment, we analyze complete LinkedIn profiles to categorize prospects into Tier 1, 2, or 3 based on actual decision-making authority. You could feed the AI model their complete LinkedIn profile like their work experience, summary, or any other data available. 3. Persona identification: For complex segmentation, we set up Clay to identify hyper-specific personas. For example, we could identify "sales leaders managing 10+ SDRs in cybersecurity companies,". 4. Headcount qualification: Clay provides accurate headcount data from company LinkedIn profiles. We use this in the lead-scoring process to prioritize accounts within the client's sweet spot. 5. Intent signals tracking: Clay's AI Agent or native integrations can get critical signals like: - Job changes/Champion movements - Recent relevant posts - Hiring activity - Expansion/funding events - Tech stack changes - Event/conference participation 6. Lead scoring: To score leads with 100% accuracy, we use all the data points above and assign scores: - We pick scoring criteria based on the client's ICP (industry, headcount, seniority) - Set up simple comparisons (ranges for company size, exact matches for industries) - Assign points based on importance (right industry = 10 points, Tier 1 decision-maker = 10 points) - Clay adds everything up automatically This gives instant clarity on which leads deserve attention first. 7. CRM integration & data enrichment: Clay pushes everything directly to the CRM: - All enriched data flows straight to HubSpot or Salesforce - Custom variables map additional research findings to correct fields - Leads get tagged by priority score - The sales team only works on qualified, high-scoring prospects - Everything stays updated automatically with scheduled runs We also set up Clay to pull existing contacts from their CRM: - Dedupe them automatically - Re-enrich and score them based on fresh data - Push back with updated priorities - Let the team focus only on prospects most likely to convert This system now handles the same workload that previously took 3 people, while also delivering higher quality leads that convert better.

🚀 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐧𝐠 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐝 𝐒𝐜𝐨𝐫𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐆𝐀𝟒 𝐃𝐚𝐭𝐚 𝐢𝐧 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲: 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐞𝐬 Aligning marketing and sales teams is key to growth. Predictive lead scoring with BigQuery ML and GA4 helps prioritize high-value leads, ensuring the sales team focuses on top conversion prospects. 🤔 What is Predictive Lead Scoring? Why Does It Matter? Predictive lead scoring leverages machine learning, historical data, and behavioral signals to assess conversion likelihood. Using GA4 BigQuery ML, you can create a tailored model that helps sales teams to: ✔️ Prioritize effectively by focusing on high-probability leads. ✔️ Save time by minimizing effort on unqualified leads. ✔️ Improve collaboration between marketing and sales, with clear data-backed insights. ⚙️ Step-by-Step Guide to Building a Predictive Lead Scoring Model: 1. Extract Lead Data from GA4: Start by querying GA4 data to identify meaningful user interactions such as form submissions, page views, and engagement metrics. Combine these signals with CRM data (if available) for a holistic view. 2. Prepare Data for Machine Learning: Clean and preprocess the data to include features like ✔️ Engagement signals (page views, session duration). ✔️ Conversion-related events (e.g., form submissions, purchases). ✔️ Demographics and geography (from geo parameters). 3. Train the Predictive Model with BigQuery ML: Use a binary classification model (e.g., logistic regression or boosted trees) to predict the likelihood of conversion. 4. Score New Leads in Real-Time: Once trained, use the model to assign predictive scores to incoming leads. 5. Visualize and Share Insights: Use tools like Google Looker Studio to create dashboards showing lead scores, enabling sales teams to focus on high-value leads. 📈 Business Applications of Predictive Lead Scoring 💡 Prioritize High-Value Leads 💡 Optimize Marketing Strategies 💡 Improve Sales and Marketing Alignment 🚀 Pro Tip: Continuously Update the Model - Predictive lead scoring models improve with time and data. Regularly retrain the model using updated GA4 and CRM data to reflect changing user behavior, market conditions, and campaign strategies. 🔍 Real-World Example: For a SaaS business, implementing predictive lead scoring using BigQuery ML led to: 💡 A 25% increase in conversion rates by focusing on high-value leads. 💡 A 15% reduction in sales cycle time, allowing teams to close deals faster. 💡 Better marketing ROI by identifying and amplifying successful lead acquisition channels. 🚀 Final Thoughts: Predictive lead scoring with GA4 and BigQuery ML enhances lead prioritization and fosters collaboration between marketing and sales. Embrace data-driven insights to align priorities, boost efficiency, and drive growth. #DigitalAnalytics #BigQuery #GA4 #LeadScoring #PredictiveAnalytics #MachineLearning #SQLForMarketing #MarketingOptimization

Your ICP is not a persona slide. It's a lot of things. But the first thing it is? A scoring system. Can't score a company 0 to 100 on fit? Then you don't have an ICP. You have an opinion. Here's how to build one today. Step 1. Score your best customers. Open your CRM. Top 20 accounts. Not biggest logos. Best behavior. Rate each one, 1 to 5: Revenue. Velocity. Time to impact. Feature depth. How easy they are to work with. Multiply. Sort. Your top 20% just showed you what ideal looks like. Step 2. Find the pattern. What do those top accounts have in common? Firmographics. Industry, size, geo. Technographics. What tools they run. Signals. What happened before they bought. 5 to 8 attributes that keep repeating. That's your scoring criteria. Step 3. Weight it. Not everything matters equally. Industry match might be 25 points. Revenue range 20. Tech stack 15. Signals 15. Here's what most people miss. Different customer types need different weights. A TripAdvisor rating predicts buying behavior for a small restaurant. Means nothing for a PE-backed chain. Multiple segments? Multiple weight models. Score out of 100. Step 4. Tier your list. Tier 1 (80+): Looks like your best customers. Tier 2 (50 to 79): Good fit. Some gaps. Tier 3 (below 50): Not now. What you do with each tier is a different post. This one is about the score. Now the hard part. The smaller you are, the narrower tier 1 should be. At €1M ARR you don't need 5.000 tier 1 accounts. You need 50. But at that stage you have less data. Maybe 15 customers, not 500. Your model is more hypothesis than proof. That's fine. Start with 10. Iterate every quarter. Step 5. Validate across the whole journey. Your scoring model is a hypothesis. Here's how you prove it. Map these cycles per tier: MQL to SQL time. SQL to Win time. Win to Onboard time. Time to first impact. Time to full impact. Those are your actual validation cycles. If tier 1 accounts move faster, onboard smoother, and reach full impact sooner, your model works. If not, adjust the weights. Check every quarter. Homework: pull your top 10 customers. Score them. What do the top 5 have in common that the bottom 5 don't? That's your scoring model v1. ← Previous: https://lnkd.in/e49kzxXS Next → https://lnkd.in/eHXJunHT

More leads don't always mean more growth. Sometimes, they just mean more wasted budget. I recently worked with a fast-growing gifting and floral commerce brand that had a common scaling challenge: High traffic. More leads. But declining conversions and rising CAC. The problem wasn't a lack of marketing efforts. It was a lack of data-driven decisions. Here's what we discovered: ❌ Lead qualification was based only on form submissions ❌ Multiple campaigns were running without clear attribution ❌ Every lead received the same nurturing journey ❌ Mobile users were bringing traffic but not converting The solution? We stopped treating every lead equally. Using behavioral data, we built a smarter lead scoring system based on intent signals like: → Pages visited → Time spent on the website → Category interest → Repeat visits Then we: ✅ Shifted budget toward high-performing channels ✅ Created personalized nurture journeys ✅ Optimized the mobile experience using real user behavior The outcome after 6 months: 📈 52% improvement in lead quality 📉 41% reduction in CAC 🚀 67% increase in revenue per lead 📱 Mobile conversion improved significantly The biggest lesson? Growth is not about generating more leads. It's about understanding the right leads. How are you using data to improve your growth strategy? #DataAnalytics #GrowthStrategy #LeadGeneration #MarketingAnalytics #DigitalMarketing #CRO

Lead Routing Nightmares: The Event Marketer’s Version of a Horror Story 🔥 You just wrapped a killer event. The booth was buzzing, your sessions were standing-room only, and your reps are already talking about deals in motion. Then the questions roll in… “Hey… where did my leads go?” “Why is my best prospect marked as ‘Cold’?” “Wait - why did Sales never follow up?” Cue: The Black Hole of Event Leads™️ If you’ve ever lost sleep over MQLs vanishing into thin air, you’re not alone. But it doesn’t have to be this way. Here’s how field marketers and ops teams can team up to close the loop and stop wasting pipeline: 🧠 1. Align on Lead Nomenclature - Before the Event Set up your event in Salesforce/Marketo with clear, agreed-upon campaign tags. Here's a few suggested ideas: Campaign Type = [SPON-EVT] / [HOST-EVT] Source = “Field Event - [Name]” Lead Status = “Qualified - Needs Review” Custom field = “Event Name” for easy attribution Avoid freeform chaos by using picklists. Bonus points if you templatize campaign setup and share it with reps beforehand. 📊 2. Define Lead Scoring Criteria Based on Event Type Not all event leads are created equal. Scoring helps route leads intelligently based on real engagement: 👉 Sponsored Events Hot = Scanned + had meeting or high intent = Route to AE Warm = Scanned, no meeting = Route to SDR Cold = Badge swipe or booth fly-by = Nurture 👉 Hosted Events Attended = High priority Registered, No-Show = Lower score Feed these signals into Marketo or HubSpot scoring models to influence routing logic in Salesforce. 🔁 3. Build Real-Time Routing Rules That Don’t Suck Marketo or HubSpot should automatically route hot leads (e.g. attended, scheduled meeting, high intent) directly to assigned reps or territories. → For example: “If [Lead Scanned at X Event] AND [Title includes VP/Director] → Assign to AE within Account Owner’s region in Salesforce” Not sure how to build this? Partner with MOPs before your event and test the flow with dummy records. 📬 4. Make the First Follow-Up Effortless No rep should have to dig through Salesforce reports post-event. Give them a filtered lead view in Salesforce or a dedicated HubSpot List. Preload a follow-up sequence into Outreach/Salesloft with messaging tailored to the event theme. Bonus: Add a “Last Event Touched” field so Sales can reference context without playing detective. ✅ 5. Pressure-Test the Whole Flow Create a test lead pre-event and walk it through your campaign flow: → Marketo/HubSpot Form → MQL Score → Salesforce Assignment → Rep Notification No skipped steps. No surprises. TL;DR: Field events should drive revenue, not reporting nightmares. Talk to your MOPs teammates early, test everything twice, and don’t let good leads die in a broken workflow. 🧡 Have your own horror story or workaround? Let’s trade notes👇 #FieldMarketing #B2BMarketing #EventMarketing

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.

Is your lead scoring still stuck in the pre-AI era? Traditional lead scoring gives you a number: "This lead is a 7 out of 10." or a "Medium Fit". Clean. Deterministic. Easy to route and prioritize. But here's what I keep running into with clients: SDRs look at that "7" and have no idea what it actually means. The score works for sorting, but it fails at decision-making. -- The observation: Most scoring models combine database filters (headcount, industry) with some AI-generated attributes (intent signals, "strength of social media presence," engagement propensity). You get a weighted score. But the rationale for the score is abstracted away. Your SDR sees a 4 and a 7, knows they should call the 7 first, but has zero context for how to approach either conversation. What if lead scoring needs two layers instead of one? ↳ Quantitative score (the "7/10") - for routing and prioritization ↳ Qualitative context (the "why") - for understanding and action Keep the first layer mostly deterministic - company size, technographics, behavioral signals, AI-generated attributes, whatever your model weights. The second layer is where AI actually helps. Not by making the score "better," but by explaining it with real data: Example context block: Score: 7/10 Recent activity: - CRO posted on LinkedIn yesterday about "evaluating new sales tools" - Engineering lead attended our webinar 2 weeks ago Company signals: - Series B raised 6 months ago - Hiring 3 SDR roles in past 30 days Timing context: - Q4 budget cycle likely starts in 2 weeks - No demo requests but high research activity Override signals: - Engagement spike suggests urgency despite mid-tier score - Multi-department interest (sales + eng) suggests internal testing -- The shift this enables: 1. Agency - SDRs and agents can override when context reveals the score misses something 2. Transparency - Everyone sees the same reasoning 3. Better judgment calls - That 6-score lead who just posted about their pain point might be more valuable than the 7 who downloaded something 3 months ago -- Future state thinking: This context layer doesn't have to be static. Imagine the context is updated periodically and by real-time events. And then you give an agent decision rights based on context thresholds: "If a lead's engagement score spikes in a short period of time and they exhibit key buying signals, send personalized outreach." The agent isn't making the scoring decision. It's acting on the combination of deterministic score + contextual signals that suggest the timing is right. -- As we move to an era of abundant intelligence, we don't have to abstract away all the details and tokens. We have AI for that now. Ironically, we can now architect flows that feel less rigid and more human by removing humans from the process. Anyone else experimenting with this? What am I missing?

Your website gets 10,000 visitors/mo. You're chasing cold prospects. That's backwards. When I built the Outbound Sales Engine for Wingify, I started with Warm Outbound (today it generates $1mn/mo in pipe). Here's the playbook: 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗪𝗮𝗿𝗺 𝗢𝘂𝘁𝗯𝗼𝘂𝗻𝗱; 𝗡𝗼𝘁 𝗖𝗼𝗹𝗱 Your website gets 10,000+ visitors monthly. 99% leave anonymously. That's 9,900 prospects you're ignoring to chase cold leads instead. These visitors didn't request demos, but they're 3x more likely to convert than random cold prospects. Start here. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗜𝗖𝗣 𝗦𝗰𝗼𝗿𝗲𝗰𝗮𝗿𝗱, 𝗡𝗼𝘁 𝗬𝗼𝘂𝗿 𝗜𝗖𝗣 𝗪𝗶𝘀𝗵𝗹𝗶𝘀𝘁: Export your top 20% customers by ACV. Find the pattern. Create a 0-40 point scorecard based on real data, not assumptions. At Wingify it was: B2B SaaS, 50-500 employees, Optimizely users were 3x more likely to buy. Score <10? Don't waste time. This saved 60% of SDR effort. 𝟯. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝗟𝗲𝗮𝗱 𝗦𝗰𝗼𝗿𝗶𝗻𝗴 𝗙𝗼𝗿𝗺𝘂𝗹𝗮 (that SDRs Can't Override): Fit (40) + Intent (40) + Recency (20) = Priority. - Hot leads (75-100): Called within 2 hours - Medium (40-74): Automated sequences - Cold (<40): Quarterly nurture No debates. No cherry-picking. No exceptions. 𝟰. 𝗪𝗿𝗶𝘁𝗲 𝟯 𝗘𝗺𝗮𝗶𝗹 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀, 𝗡𝗼𝘁 𝟯𝟬: You need exactly 3 core templates: Intent-based , Persona-based and Trigger-based. Each under 75 words. No jargon. One clear CTA. One Link to track. Our best performing email was 47 words and converted at 12% meeting rate. 𝟱. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗬𝗼𝘂𝗿 𝗢𝘂𝘁𝗿𝗲𝗮𝗰𝗵 𝗟𝗶𝗸𝗲 𝗮 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆, 𝗡𝗼𝘁 𝗮 𝗛𝗮𝗿𝗮𝘀𝘀𝗺𝗲𝗻𝘁 𝗖𝗮𝗺𝗽𝗮𝗶𝗴𝗻:  8 touches maximum. After that, they go to quarterly nurture. At Wingify, 40% of our pipeline came from leads we "gave up on" but nurtured for 6+ months. 𝟲. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗔𝗰𝘁𝗶𝘃𝗶𝘁𝗶𝗲𝘀 𝗔𝗡𝗗 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗕𝘂𝘁 𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗲 𝗼𝗻 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Track everything: emails, replies, meetings, SQLs. Pay SDRs on qualified pipeline generated. One SDR sent 50 emails/day (1% reply). Another sent 20 (5% reply). Quality wins. 𝟳. 𝗨𝘀𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗪𝗿𝗶𝘁𝗶𝗻𝗴, 𝗛𝘂𝗺𝗮𝗻𝘀 𝗳𝗼𝗿 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗮𝗹𝗹𝗶𝗻𝗴: AI writes drafts → Humans edit for relevance → AI tests subject lines → Humans handle calls. 70% can be automated, but the crucial 30% (strategy, personalization, objections) needs humans. 𝟴. 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝘁𝗼 𝗥𝘂𝗻 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗬𝗼𝘂: Document everything. Create Looms. Build Slack alerts for hot leads. Set up weekly pipeline reviews. If you can't take a 2-week vacation without the machine stopping, you built a job, not an engine. When I left, Wingify’s OB engine was running with 1 hour of my weekly input. I'm slightly jealous of sales leaders starting their outbound engines today. Because with AI, you can build the playbook in 3 months. The formula is still the same: Right Message + Right Prospect + Right Time + Right Channel. And it still works.

The days of MQLs and SQLs are over. Say hello to PQLs. In Product-Led Growth (PLG) strategies, the good old traditional metrics like MQLs (Marketing-Qualified Leads) and SQLs (Sales-Qualified Leads) don’t cut it anymore. For PLG SaaS companies, Product-Qualified Leads (PQLs) are way more effective, especially if you add a sales motion to your self-serve funnel. Why? Because PQLs are users who: ✅ Fit your ICP ✅ Have experienced product value ✅ Show buying intent Unlike MQLs/SQLs, PQLs don’t need to be convinced. They’ve already experienced your product’s value. Your job? Help them take the next step. The key to a successful sales motion for a PLG company is scoring these leads to focus your sales efforts on the most promising ones. To do so, there are 3 types of criteria you can focus on: 1️⃣ 𝗗𝗲𝗺𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰/𝗙𝗶𝗿𝗺𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 (𝗪𝗵𝗼 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲) - Job title → Within your ICP? - Team size → Bigger teams = bigger revenue potential. - Email type → Business email = higher intent. 2️⃣ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗨𝘀𝗮𝗴𝗲 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 (𝗛𝗼𝘄 𝘁𝗵𝗲𝘆 𝘂𝘀𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁) - Have they reached an activation milestone? - Do they use key features regularly? - Are they inviting colleagues to collaborate? 3️⃣ 𝗕𝘂𝘆𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗻𝘁 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 (𝗔𝗿𝗲 𝘁𝗵𝗲𝘆 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝗯𝘂𝘆?) - Viewed pricing page - Asked pricing questions in support - Booked a demo (strong intent) To target your PQLs, score each signal based on its impact. The higher the score, the hotter the lead. Sales can then prioritize the right outreach, targeting people who are already convinced of the value of your product but need a human touch to fully upgrade. 🛠 𝗧𝗼𝗼𝗹𝘀: CRMs like Hubspot, ActiveCampaign, or Customer.io allow you to create a custom scoring system. Just make sure your product data is properly synced, as it’s the cornerstone of a good PQL scoring. How are you identifying and scoring your PQLs? Let’s chat below! 👇