Lead Scoring Models for B2B Marketing

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.

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.

Account scoring can be notoriously difficult to build. RFM scoring is one of the most useful frameworks in RevOps and in many motions it can outperform ML models. But... it completely breaks down in enterprise selling Traditional RFM measures Recency, Frequency, and Monetary value of purchases. Works great in transactional B2B where customers buy often In enterprise? Customers purchase once every few years. Frequency is meaningless. Recency is a lagging indicator. By the time those metrics drop, you've already lost the renewal window Here's how to adapt it 𝗥 = 𝗥𝗲𝗰𝗲𝗻𝗰𝘆 𝗼𝗳 𝗠𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Stop measuring last purchase date. Measure the last time a qualified stakeholder took a high-intent action Your VP of Finance logging into the platform last week matters. An intern opening a marketing email does not reset the recency clock 𝗙 = 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 𝗼𝗳 𝗠𝘂𝗹𝘁𝗶-𝗧𝗵𝗿𝗲𝗮𝗱𝗲𝗱 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Don't count total activities. Count breadth and depth across the account A single power user logging in daily is a frequency of one. Five people across three departments engaging monthly is far healthier Track the trend. An account going from 2 active contacts to 6 over a quarter is accelerating. Going from 6 to 2 is a churn signal no matter how active those remaining 2 are 𝗠 = 𝗠𝗼𝗻𝗲𝘁𝗮𝗿𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗦𝗽𝗲𝗻𝗱 Current ARR matters but it's incomplete. Score current spend relative to total addressable wallet An account paying you $200K when they could spend $2M is a very different score than one paying $200K at full penetration The segments that matter most: → High R, High F, Low M = engaged but underleveraged. This is your expansion pipeline → Low R, Any F, High M = big accounts going quiet. Most dangerous segment in your book. Every CS team needs automated alerts here Traditional RFM asks "what has this customer done for us" Enterprise RFM asks "how healthy is this relationship and where is it heading" That directional shift is what makes scoring predictive instead of descriptive Good luck out there scoring accounts + see previous wallet share post (TAW) Go forth and operate 👋 (more to come in this weekend's Substack) P.S. the Substack is thriving and growing. Thank you for your support 

We're still arguing about MQLs vs SQLs while AI is identifying revenue opportunities we didn't know existed. The gap between manual lead scoring and AI-powered prioritization? About 40% higher conversion rates. 𝗧𝗵𝗲 𝗟𝗲𝗮𝗱 𝗦𝗰𝗼𝗿𝗶𝗻𝗴 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗡𝗼𝗯𝗼𝗱𝘆'𝘀 𝗧𝗮𝗹𝗸𝗶𝗻𝗴 𝗔𝗯𝗼𝘂𝘁: 𝟭. 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 The agent ingests every CRM record. Every won deal. Every lost opportunity. Learns what actually predicts success in YOUR sales cycle. Not generic industry benchmarks. Your actual conversion patterns. 𝟮. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝗰𝗼𝗿𝗶𝗻𝗴 𝗧𝗵𝗮𝘁 𝗔𝗱𝗮𝗽𝘁𝘀 Lead downloads whitepaper? Score updates. Opens three emails? Score adjusts. Visits pricing page twice? Score jumps. Ghost for two weeks? Score drops. Every interaction recalculates priority instantly. 𝟯. 𝗠𝘂𝗹𝘁𝗶-𝗦𝗼𝘂𝗿𝗰𝗲 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 CRM data? Check. Email engagement? Tracked. Website behavior? Monitored. External research? Pulled from ChatGPT and Perplexity. Industry news? Factored in. Your lead score isn't just internal data anymore. It's everything that matters. 𝟰. 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗠𝗼𝗱𝗲𝗹 𝗨𝗽𝗱𝗮𝘁𝗶𝗻𝗴 Last quarter's scoring model? Already outdated. The agent learns continuously. Market shifts? Model adapts. New competitor enters? Scoring adjusts. Buyer behavior changes? Algorithm evolves. 𝟱. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 High-scoring leads → Senior reps immediately Medium scores → Nurture campaigns Low scores → Long-term drip Rising scores → Alert for re-engagement 𝗬𝗼𝘂𝗿 𝗟𝗲𝗮𝗱 𝗦𝗰𝗼𝗿𝗶𝗻𝗴 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸: 𝟭. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗪𝗲𝗶𝗴𝗵𝘁𝗲𝗱 𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 Industry fit: 30 points Title match: 25 points Engagement level: 20 points Company size: 15 points Intent signals: 10 points 𝟮. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 Don't just score likelihood to engage. Score likelihood to generate revenue. Big difference. 𝟯. 𝗦𝗲𝘁 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗥𝗲-𝗥𝗮𝗻𝗸𝗶𝗻𝗴 Scores aren't static. Priority lists update hourly. If you found value from this post, please ♻️ Repost. We are all learning together.

Old way: Subscriber → Lead → MQL → SQL → Opportunity New Way: Identified → Aware → Interested → Evaluating → Selecting Don't get me wrong, the "Old way" still has it's place for attribution/reporting. But for sales reps, these stages are often ignored. I believe default "lead stages" are one of the many factors contributing to marketing being siloed from sales. And I've literally seen this across 100s of cases. Why doesn't this work? - Marketing looks to maximize MQLs to boost their numbers - Sales looks to minimize MQL → SQLs conversion to boost their close rates - There is SO much signal left out between a cold account and "Subscriber" - The journey to becoming a MQL is often a black box So what's the solution? Modern GTM teams are leveraging "Awareness stages" derived from ABM best practices. Where you capture all the nuance between a cold ICP account and a prospect submitting their info via a form (e.g. demo or content). Awareness scores aggregate signals into actionable stages: - Identified - Aware - Interested - Evaluating - Selecting This allows reps to prioritize their activity: - First, touches to move accounts from aware → selecting. - Then, activity to move accounts from identified → aware. But it has to be paired with a solid account fit scoring model. So both marketing + sales are optimizing around the right accounts. This also simplifies reporting: ↳ ICP pipeline progression becomes the north star. We set up awareness scoring for all our ABM projects. To help come up with the models, I've studied how the best do it: 1️⃣ Clay Primary GTM Motions: Content, Community, PLG Aware: ↳ Website Visit (L30) or ↳ 2+ contacts connected with founders or ↳ 1+ engagement on social or ↳ Attended "low intent" event or ↳ Intro call form investors Interested: ↳ Created a workspace or ↳ High-intent web visit (e.g. pricing) or ↳ Positive outbound reply or ↳ Attended "high-intent" event Evaluating: ↳ Contact sales form or ↳ Discovery meeting held 2️⃣ Parabola Primary GTM Motions: Outbound, Community, PLG Aware: ↳ Website Visit (L30) or ↳ 2+ contacts connected with CEO or ↳ 2+ contacts with 2+ email opens (L30) or ↳ 1+ contact with 1+ email clicks (L30) or ↳ Is a member of community Interested: ↳ Trial start or ↳ High-intent web visit (e.g pricing) or ↳ Event attendance or ↳ Dinner attendance or ↳ Webinar attendance Evaluating: ↳ Demo requested or ↳ Demo booked 3️⃣ Userpilot Primary GTM Motions: Content, LinkedIn Ads, SEO Aware: ↳ 50+ LinkedIn ad impressions Interested: ↳ 3+ ad clicks or ↳ 5+ ad engagements or ↳ contact with 3+ webinar attendances or ↳ contact with UX onboarding Evaluating: ↳ trial sign-up ↳ demo scheduled How to set this up yourself: - Track every relevant event in your CRM. - Create dynamic lists when accounts + associated contacts reach thresholds. - Setup workflows to update a single-select property when new accounts are enrolled in the lists. Easily one of my favorite workflows to setup.