Behavioral Data Utilization in Lead Scoring

my competitor and i launched identical linkedin campaigns. same budget, same audience, same product category. i crushed him 8:1 on deal conversion. he was confident going into the test. better product. stronger brand recognition. more funding. bigger team. we both targeted VPs of sales at 500+ person companies. same demographic criteria. same ad creative quality. $10K budget each. month one results: me: 47 deals closed. him: 6 deals closed. he was convinced i got lucky with better prospects. "let me see your targeting strategy," he asked. i pulled up my dashboard. "i don't target demographics at all." "what do you mean? you're running linkedin ads." "i target behaviors." i showed him my approach: instead of job titles, i track content consumption. instead of company size, i monitor website journeys. instead of industry filters, i watch engagement patterns. "i built an audience of people who've consumed competitor content in the last 30 days. downloaded sales automation guides. attended webinars about pipeline management. visited pricing pages of tools like ours." my "audience" wasn't demographic. it was behavioral. "linkedin lets you upload custom audiences," i explained. "i upload lists of people who've shown buying behavior. then i target those lists with ads." he was targeting people who might need our product. i was targeting people actively shopping for our product. "how do you identify buying behavior?" he asked. "third-party intent data. website pixel tracking. content engagement scoring. competitor analysis tools." i showed him my process: week 1: identify companies researching sales tools. week 2: find individuals at those companies consuming content. week 3: build custom audiences from behavioral data. week 4: launch ads to pre-qualified prospects. "demographics tell you who someone is," i said. "behavior tells you what they're doing." he was advertising to VPs of sales. i was advertising to VPs of sales currently shopping for solutions. same title, completely different mindset. my prospects were already in buying mode. his were just scrolling linkedin. the conversion difference made perfect sense. he rebuilt his entire approach: behavioral targeting instead of demographic filtering. intent data instead of job title assumptions. shopping behavior instead of profile characteristics. next month's results for him: 52 deals closed. 9x improvement over his original campaign. the lesson was clear: demographics describe who people are. behavior reveals what people need. target the behavior.

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.

How I Built a 24/7 Lead Qualification Machine That Never Sleeps: I used to spend 15+ hours weekly qualifying leads manually. I'd review form submissions, score leads based on behaviour, and route them to the right sales reps. It was mind-numbing work that kept me from strategic tasks. Then I built an automatic lead qualification system using n8n that transformed our pipeline. Here's exactly what I did: First, I connected our webforms, CRM, and analytics using n8n's visual workflow builder. When a prospect submits a form, it triggers an automated qualification sequence. The system pulls their company data from Clearbit, website behaviour from our analytics, and past interactions from our CRM. The key breakthrough was implementing a dynamic scoring algorithm. Created a weighted scoring system based on factors like company size, engagement level, pages visited, and prior touch points. The workflow automatically calculates a lead score and assigns a qualification category, from "sales-ready" to "needs nurturing." For high-scoring leads, the system immediately routes them to the appropriate sales rep based on territory, industry, and current workload. The rep receives a Slack notification with the complete lead profile and engagement history. For mid-tier leads, the system triggers a personalized email sequence with qualifying questions. Low-scoring leads enter automated nurture campaigns. What makes this powerful is the continuous learning component. Every sales outcome (win, loss, disqualification) feeds back into our scoring algorithm. When a sales rep marks a lead as unqualified, the workflow prompts them for a reason code, then adjusts future scoring weights accordingly. Our qualification accuracy improves weekly. The results were immediate and significant: - Lead response time dropped from 4+ hours to under 3 minutes - Sales productivity increased 37% with reps focusing solely on qualified opportunities - Lead-to-opportunity conversion rate improved 42% - My time spent on lead management decreased from 15 hours to 2 hours weekly Future of sales is about automating the repetitive analysis and routing tasks that consume hours of marketer time. The beauty is that I built this entire system without writing code. n8n's visual workflow builder made it possible for me to create a sophisticated lead qualification machine. What manual qualification processes are stealing your team's time? That's where your automation opportunity lies.

Your sales team can’t manually score 100+ B2B leads. Nor your 5-people marketing team can create tailored content for all of them. Let’s talk about the problem no one likes to admit: It’s not the lack of leads holding businesses back: it’s the lack of clarity about what to do with them. CRMs packed with contacts. Some opened last week’s email three times - without a follow up. Some booked a demo and then got ghosted. Manual lead scoring isn’t scalable. Random follow-ups don’t convert. And sending the same content to everyone? That’s a fast track to getting ignored. If this sounds familiar, you’re not alone. Here's how you solve it: Step 1: Define What “Hot” Actually Means The first step is to sit down (with sales and marketing) and map out the behaviors that signal a lead is ready to move. It’s not always filling out a contact form. It might be: ✅ Visiting the pricing page three times in one week ✅ Attending a webinar and asking a question ✅ Downloading two high-intent resources back-to-back Every one of these actions should have a score attached to it. That score? It’s your lead’s readiness, quantified. Step 2: Build an Automated Lead Scoring System Now that you know what matters, you can use platforms like Make to pull in data from your CRM. You’re working with real-time data, so you know exactly when someone crosses the threshold from “just browsing” to “ready for a conversation.” Step 3: Tailor Follow-Ups Based on Where They Are Hot leads and cold leads aren’t the same. But they still get the same generic emails, signed by someone in your sales team to make it sound more "personal" Once you have scoring in place, you can trigger different follow-ups based on their readiness: ✅ High-score leads get a direct invite to book a call or demo ✅ Mid-score leads get case studies or proof points to build trust ✅ Lower-score leads get nurtured over time with educational content Automation sends the right message at the right time-without sounding like a bot. (If you train it right.) Step 4: Surface the Right Leads to Your Sales Team With a clean system in place, your team gets notified immediately when a lead is warm. Step 5: Let the Data Drive Smarter Decisions The more the system runs, the better your insights get. Then you can refine the scores, adjust the workflows, and keep improving without adding more manual work. This is exactly the kind of system we’ve implemented inside Lumina Studio Marketing and for our clients. It’s simple, scalable, and works even for small teams who don’t have time to babysit their CRM. If you’re sitting on a list of leads and you’re not sure where to focus-this is where I’d start. Curious what a system like this could look like for your business? I’m Anna Valenti, founder of Lumina Studio Marketing, where we build AI-powered systems that help you automate smarter, without losing your voice. 📩 anna@luminastudiomarketing.com ❤️ Lumina Studio 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.

𝐅𝐨𝐫 𝐲𝐞𝐚𝐫𝐬, 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐫𝐚𝐧 𝐨𝐧 𝐡𝐢𝐧𝐝𝐬𝐢𝐠𝐡𝐭. Dashboards told us what already happened—open rates, MQLs, churn numbers. By the time we saw the problem, it was too late. 𝐋𝐞𝐚𝐝𝐬? 𝐃𝐞𝐚𝐝. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬? 𝐆𝐨𝐧𝐞. 𝐁𝐮𝐝𝐠𝐞𝐭? 𝐁𝐮𝐫𝐧𝐞𝐝. But AI and predictive analytics are flipping the game. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐲𝐦𝐨𝐫𝐞. 𝐈𝐭’𝐬 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞. 🔹 𝐋𝐞𝐚𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 Traditional lead scoring is broken. A whitepaper download? That’s not intent—it’s noise. When we actually analyzed behavioral data using platforms like HubSpot, we found that multiple pricing page visits and engagement with onboarding content predicted conversions 3x better than generic lead scores. 𝐖𝐢𝐭𝐡 𝐦𝐮𝐥𝐭𝐢-𝐭𝐨𝐮𝐜𝐡 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 and 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐜𝐨𝐡𝐨𝐫𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ✔ Leads with 𝐫𝐞𝐩𝐞𝐚𝐭 𝐯𝐢𝐬𝐢𝐭𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐩𝐚𝐠𝐞 had a 𝟑𝐱 𝐡𝐢𝐠𝐡𝐞𝐫 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 ✔ Prospects engaging with 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐝𝐞𝐦𝐨𝐬 moved through the funnel 𝟒𝟐% 𝐟𝐚𝐬𝐭𝐞𝐫 ✔ Combining 𝐢𝐧𝐭𝐞𝐧𝐭 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐰𝐢𝐭𝐡 𝐟𝐢𝐫𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐬 increased lead quality 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐧𝐟𝐥𝐚𝐭𝐢𝐧𝐠 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐜𝐨𝐬𝐭𝐬 We stopped chasing the wrong leads. And our pipeline? Tighter than ever. 🔹 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 A churn report tells you what you lost. But by then, it’s a post-mortem. Advanced platforms flag disengagement before it happens. A simple tweak—triggering check-ins for inactive accounts—cut churn by 15% in six months. A simple intervention—𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐢𝐧𝐠 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐫𝐞-𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 when customers showed 𝟑+ 𝐝𝐢𝐬𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬—led to a 𝟏𝟓% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐜𝐡𝐮𝐫𝐧 𝐢𝐧 𝐬𝐢𝐱 𝐦𝐨𝐧𝐭𝐡𝐬. 🔹 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐅𝐢𝐭 Guessing what users want is a waste of time. Predictive analytics showed us which features had a 𝟒𝟎% 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 before launch. The result? No wasted dev cycles, no misfires—just 𝐝𝐚𝐭𝐚-𝐛𝐚𝐜𝐤𝐞𝐝 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. If you’re still relying on past data to drive strategy, 𝐲𝐨𝐮’𝐫𝐞 𝐩𝐥𝐚𝐲𝐢𝐧𝐠 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲’𝐬 𝐠𝐚𝐦𝐞. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐛𝐚𝐜𝐤. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐰𝐡𝐚𝐭’𝐬 𝐧𝐞𝐱𝐭. #PredictiveAnalytics #MarketingStrategy #DataDriven #Growth

Managing $20M+ in media buying taught us that bad leads kill ROAS faster than bad creative. The old way was guesswork: → Basic CRM rules ("opened 3 emails = qualified") → Manual scoring that never updated → Sales chasing leads that never close For high-ticket verticals one garbage lead can wreck your month. Here's what we rebuilt: Dynamic scoring that learns daily: Our AI model ingests conversion data, campaign performance, and intent signals. No more static if/then rules. Full-funnel visibility: It tracks from first click to closed deal across ad platforms, CRM, and analytics. Real journey scoring, not single-touch guesses. Predictive weighting. The system discovers which behaviors actually predict revenue, scroll depth, session time, creative engagement, not just form completions. The impact: → Lower CAC (we're not bidding on junk traffic) → Sharper lookalike audiences → Sales teams chase only 80%+ close probability leads AI lead scoring became our quality gate between ad spend and wasted budget. If you're running serious paid media with static lead rules, you're leaving money on the table. Are you tracking which scored leads actually convert to revenue? #ads #metaads #marketing #marketingagency

🚀 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

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?

How a startup drove 3,000% lift in sales conversions for enterprise bank customers. I met with the Viktoria Izdebska, CEO of Octrace, a startup that finds and prioritizes lead through real trigger events that actually drive sales conversions. Most GTM teams are drowning in correlated signals that feel meaningful but don’t actually cause conversions. Octrace did something a bit different. Viktoria came from the hedge fund industry so she knew that correlation did not indicate causation and was in search for causal triggers. She applied that same learning to lead scoring in B2B. Octrace built a system to identify causal trigger events — the kind of things with enough explanatory power that a human seller would say: “Yeah… if that happened, I’d absolutely call this lead today because it means they have a real pain.” Their identification pipeline was: 1. Identify the right signals Viktoria worked with the bank’s head of sales to determine the exact real-world events that actually matter. She also used an LLM and data from previous customers to assist in the discovery and creation of which events to track Not just “job postings” or “web visits” but things like: - A CEO turning 60 (succession triggers) - Keywords in financial statements that imply asset liquidation - A company opening a new manufacturing plant Signals grounded in reality 2. Collect those signals at scale Public, semi-public, and scraped sources across structured + unstructured data 3. Run each signal through an LLM agent to determine if it’s a “hit” Each incoming data point was evaluated in real time: “Is this the thing we care about? Does it match the trigger condition?” 4. Let another LLM score the combination of signals Not classical ML. Not random forest. Not feature engineering. Just a smart, explainable LLM evaluating causation. 5. Process the signals in real-time for the model to compute 6. Compare outcomes vs a control list Because they had access to CRM conversion data, they could backtest and refine signal selection and weighting. The result was a lead list that was explainable and outperformed their own lead list to reps by 3,000%. Customers loved them. Viktoria and I both came from a finance background. She was at a hedge fund prior to her company and I was on the trading floor. We both realized that models can, over time and with human guidance, discover and weight signals better than humans, and outperform intuition through backtesting - a concept finance traders have been using since the 1990s.