Lead Scoring Systems

The Power of Lead Scoring: A Case Study One year ago, I worked with a tech startup with a big problem at hand... They reached out to me because their lead conversion was extremely low. Here's their story: This client faced a common struggle: Turning leads into customers. Despite their efforts, they couldn't crack the code. And there was one main reason for this—they had ZERO lead scoring in place. Now, I know what you might be saying “Jordan, what’s lead scoring?” Okay, so here's the deal with lead scoring: It's like having your own personal radar system for your sales and marketing efforts. You're basically assigning points to leads based on how interested they are in what you’re offering and how qualified they are—by a strict set of standards you create. So, instead of wasting time chasing after every lead out there, you can focus on the ones that are most likely to buy. It's all about working smarter, not harder. That's how you close more deals with less effort. Now, here’s the 5 part lead scoring system we put in place for this tech startup: Demographics: We looked at the industry, company size, job title(s), and location of their prospects. Behavioral Data: We monitored website visits, content downloads, and social media engagement. Engagement Level: The frequency that leads interacted with their content. By looking at this we were able to identify the most engaged prospects. Purchase Intent: Signals like demo requests or inquiries about pricing helped us to prioritize leads that were ready to make a decision. Lead Source: Understanding where leads came from provided insights into their level of interest and intent. Together, we introduced a cohesive lead scoring system—a smart move that changed the game for this startup. By implementing these five key criteria, they could finally stop wasting time and pinpoint which leads were worth pursuing. With this system in place, they saw incredible results. Leads weren’t just numbers anymore—they were real people with real needs. By focusing on the most promising leads, our client saw their conversion rates soar. In the end, it all came down to simplicity. By streamlining their approach and zeroing in on what mattered most, they saw record high sales numbers that year. P.s. - Does your company use lead scoring? If so, what’s the biggest challenge you’re facing right now? Thanks for reading. Enjoyed this post? Follow Jordan Nelson Share with your network to help others increase their sales with 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.

🔥 SaaS GTM Strategy Hot Take – Do ABM buying stage models using intent data work? The short answer is yes. 🧐 Consider This Statistician George Box famously quipped, “All models are wrong, but some are useful.” This seems to be true with ML-based buying stage models. They are not perfect, but they can be quite useful. In simple terms, a buying stage model is designed to predict the likelihood of an outcome occurring, in this case, a qualified opportunity being opened. It’s just a more sophisticated version of the old-school MQL scoring model made popular by the MAP platforms. Those were rule-based models using limited data and based on intuition and opinion. Today’s models leverage thousands of intent signals from third-party sources and your marketing programs, website, and CRM programs. They weigh the frequency, recency, and volume of the intent signals. And they’re dynamic: The model adjusts as more input data is provided and outcomes occur. 💡 Rethinking Your Strategy Of course, your marketing and sales teams should prioritize an ICP account that shows surging engagement on your website. If you have a strong brand in the category, at any point in time, that may be about 5% of the total Serviceable Addressable Market (SAM). What should you do with the other 95%? Especially if, according to @6sense research, 84% of them will have a favorite in mind when they contact your sales team. 🚀 Creating Pre-Purchase Preference 🚀 By leveraging a combination of ICP and Buying Stage segmentation, you can run campaigns to help your potential customers understand the “why change” and “why now” aspects of your solution. You can provide them with clear criteria for solving their problem and arm them with the value drivers for the business case. And, maybe give them some FOMO by showing name brands who’ve been successful in doing it. 🤔 Still Skeptical? 🤔 The great thing about models is that we can backtest them. That is, we can run the model on a known set of inputs and outcomes and see how effective it was at predicting the results. It’s not uncommon for good buying stage models to be 3-5 times more predictive than ICP targeting alone. The image below shows one such ABM program, from initial account targeting to increasing intent and a closed-won deal. Total time: less than six months for a $100K ARR deal. 💰💰💰 💬 Let's Discuss ❓What programs are you running for ICP accounts that are not yet “in-market”? ❓What intent data sources are proving to be more valuable than others? ❓Are your sales teams leveraging the intent data signals to inform their outreach messaging and strategies? ——— 👍 Like this? 🤔 Comment to share your thoughts ♻  Repost to share with your network #abm #b2bmarketing #saasgtm #gtmstrategy #b2b #tam #gtmalignment #pointbreakgtm

Dear Marketing - You can't give everyone the VIP treatment. It doesn't matter how "scalable" or "AI-first" your strategy has become. It's a trap many marketers fall into (or dive into depending on your mood), and it greatly affects ABM strategy. "Let's give everyone a highly-personalized marketing HUG." It's so important to structure all marketing efforts (ABM mainly) on the right accounts. Priorization is everything. I'll type it again... prioritization is everything! HockeyStack has been one of my main resources recently to up-level my understanding of how to THINK about prioritization in a meaningful way. Because it may seem easy, but it is damn hard. Spewing “let’s focus on the best-fit accounts” is one thing, but actually aligning sales and marketing on which accounts matter, why they matter, and how to engage them is where things get damn messy. The best teams I've come across have built a scoring framework to help build a foundation around the gut feel. They assign scores based on: Fit – How well does the account match your ICP? Intent – Are they actively researching solutions like yours? Engagement – Are they interacting with your content, attending events, or engaging with sales? "Kyle, c'mon. We were talking about this in 2018." "Well, fictional marketing leader reading this post. We are still lost and haven't advanced from spray and pray. I'm sorry." This is OLD NEWS but it still amazes me how many marketers do not take this approach to building clear account tiers: High-score accounts get custom content, executive involvement, and deep sales engagement. Mid-score accounts get automated nurtures and light-touch outreach. Low-score accounts? They wait until they show stronger intent. Scoring models and frameworks are important, but the alignment between GTM leadership is even more important. If sales is chasing one set of accounts while marketing invests in anything, y'all are screwed. And even MORE importantly, the best teams constantly adjust because if you do it correctly, the tiers will change based on actions. ABM isn’t about reaching more accounts. It’s about reaching the right ones at the right time.

The Playbook of Signals to Help Prioritize Leads I keep repeating this - Stop doing blind outbound! Signals are how you do outbound effectively. But, how do you use Signals effectively in your pipeline? Here's a breakdown with 10 different types of signals you can use: 1. Score leads using AI: Evaluate each lead based on fit and intent with automatic scoring. Consider company size, revenue, job title relevance, historical engagement, and conversion likelihood based on past deals. 2. Use intent data: Combine third-party intent data (G2, Clearbit, etc.) with self-determined intent signals to identify executives actively seeking your solution. 3. Monitor engagement with outreach: Track open rates, response rates, and call connect rates. Prioritize leads who open multiple emails, reply promptly, or consistently answer calls. 4. Track digital activity: Prioritize leads engaging on LinkedIn, visiting your pricing page, or consuming your content - these actions signal genuine interest. 5. Match with ICP: Essential, but don't let it be your only filter! 6. Monitor pipeline velocity: Momentum matters. Prioritize leads rapidly moving through multiple stages. Also focus on personas with historically faster close rates (e.g., Directors of RevOps vs. VPs of Finance). 7. Note multiple stakeholders: When several people from one company engage with your outreach, it signals higher organizational buying interest. 8. Identify competitor dissatisfaction: Prioritize leads showing dissatisfaction with competitors (job postings for replacement tools, negative comments). Strike while it's hot! 9. Avoid high-churn profiles: Deprioritize leads matching patterns of customers who churned quickly in the past. 10. Check data quality: Leads with incomplete information (missing company size, outdated job titles) waste valuable SDR time. There could be more signals - that's the beauty of this approach. There's a wealth of information to triangulate with. However, tracking all these signals can be intimidating. - P.S. This is precisely the problem we're solving at Highperformr - a signals-based platform that does the work for you. Message me to know more! #PipelineManagement #AISDR #Signals #PrecisionOutbound

Predict, Personalize & Perform : From Leads to Loyalty Let’s be honest—customer lifecycle marketing (CLM) in B2B used to be a fancy word for “email nurture” and “CRM segmentation. But today, with AI, machine learning, and predictive data models, CLM is becoming something much more powerful: ➡️ A living, learning ecosystem that adapts to each buyer journey in real time. Here’s how we’re seeing AI and ML revolutionize CLM in B2B: 🔍 1. Predictive Journey Mapping Machine learning algorithms are helping identify where an account or contact actually is in the funnel—not just where your CRM says they are. ✅ No more generic MQL > SQL flows ✅ Dynamic scoring based on behavior, content engagement, and intent signals ✅ Real-time stage shifts based on predictive fit and readiness — 📈 2. Hyper-Personalized Nurturing (at Scale) AI models now create content clusters matched to personas, industries, and even buying committee behavior. 🎯 Email sequences, LinkedIn ads, and landing pages are personalized based on: Buyer role Past touchpoints Predicted product interest ICP match + firmographic data It’s not just segmentation—it’s micro-personalization powered by behavioral AI. — 🔁 3. Intelligent Retargeting & Re-Engagement Using ML-powered intent data and anomaly detection, you can now: Spot churn risks before they happen Trigger re-engagement sequences based on drop-off patterns Retarget accounts that show subtle buying signals across web, search, and social Retention is no longer reactive. It's predictive. — 📊 4. Revenue Forecasting + Attribution Modeling Thanks to data science, we can model: Which touchpoints actually move pipeline Which leads are likely to convert within a time window How to attribute revenue across full-funnel programs—not just the last touch This gives marketing the credibility and confidence we’ve needed for years. — 💡 The CLM Stack of a Modern B2B Org Should Include: ✔️ Customer Data Platform (CDP) ✔️ AI-powered segmentation + scoring ✔️ Predictive content engines (LLMs + RAG) ✔️ Lifecycle orchestration tools (e.g. Ortto, HubSpot, Marketo w/ ML layers) ✔️ Analytics + BI layer for optimization 🧠 Final Thought: In 2025, CLM isn’t just “marketing automation” with better templates. It’s about building an AI-powered engine that understands, anticipates, and activates each step of the buyer journey. You don’t need more content. You need smarter orchestration. 💬 Curious to hear from other B2B leaders: How are you bringing AI into your lifecycle marketing stack?

I spent the last 10 weeks grilling & quizzing Marketing Pro's trying to understand how I could help them with data enrichment, scoring & research. One thing was clear, they are very tired of spending crazy amounts of cash on legacy products like ZoomInfo, Cognism etc. Days of spending +$20-100k for data is gone. Data is available now at cheaper costs per record than it ever has been, so think about that when your next renewal comes up. 😉 Here's a flow I've noticed our Marketing users start running: → MQL comes in via an Inbound or maybe a form downloaded etc → Lead gets added to HubSpot → Which due to the continuous sync get's added to Trigify.io → In Trigify they are then tracking the new MQL's come in where they run the following: 1️⃣ Custom scoring based on Signals they are spotting 2️⃣ Using an AI Agent to run research on the individual that inbounded 3️⃣ Analysing the company & finding potential angles on why they might have inbounded in the first place 4️⃣ Data is then validated via our 18 different contact providers 5️⃣ Seamlessly pushed back into Hubspot without you touching anything Some have taken this a step further, and have used this information to customize the follow-up emails being sent via tools like Calendly. So you don't need to enter everyone into the generic flow on Pardot, everything can be 100% customized even within your marketing campaigns.

Why Your Sales Metrics Might Be Missing the Mark Too many sales leaders focus solely on revenue numbers - but that's like driving by looking in the rear-view mirror! As a sales leadership team coach, I've learned the real crux lies in tracking leading indicators. Here are the metrics that predict sales success: ✅ Quality Customer Visits * Not just any meetings, but targeted visits with qualified prospects * Track who your team is meeting (decision-makers vs. gatekeepers) 🎯 Customer Intelligence * Deep understanding of prospect needs * Clear identification of desired outcomes * Alignment between customer goals and your solutions Revenue is the result, not the strategy. By focusing on these leading indicators, you can course-correct before it impacts your bottom line. 💡 Quick Tip: Create a simple scorecard tracking these metrics for your team. Review weekly, not just monthly. What leading indicators do you track in your sales organization? Share your insights below! 👇 #SalesLeadership #B2BSales #SalesMetrics #LeadingIndicators DM me for a free 1-to-1 session on how you can develop great sales teams!

Struggling to hit sales targets with a lean ops team and tighter budgets? There's a smarter way to drive conversions. For lean sales ops teams, every dollar and every minute count. Scaling sales with constrained resources demands strategic focus.  Relying solely on manual processes or guesswork leaves significant revenue untapped, especially when competing with larger teams. This is where AI becomes the ultimate force multiplier. Modern AI tools are transforming how sales ops maximize efficiency and conversion without needing massive headcount. AI empowers focused efforts through three key areas. ✔ Predictive analytics for lead scoring ensures teams target the highest-potential prospects. ✔ Personalized outreach automation enables hyper-relevant communication at scale. ✔ Workflow optimization automates administrative tasks, freeing sales reps to sell. At Software Finder, our own sales ops embodies this approach. We leverage an intelligent lead scoring model that processes historical conversion data and engagement signals. This ensures our team prioritizes the warmest leads with surgical precision, leading to significantly higher conversion rates and a more efficient sales cycle. This demonstrates how smart technology consistently outperforms sheer size. For leaders, this approach unlocks a pathway to consistent revenue growth, even with slow resource scaling. It elevates the sales focus from manual effort to strategic intelligence, ensuring every action contributes directly to conversion. This is precisely how lean teams outmaneuver competitors in today's market. What AI strategies are you deploying to maximize your sales ops conversions with a limited budget? Share your insights.

Decision trees are a fundamental tool for every Data Scientist. But for 3 years, I was hesitant to use them. In 3 minutes, I'll share what they are (and how they became a key part of a $15,000,000 lead scoring model). Let's go: 1. Decision Tree: A decision tree is a graphical representation used for decision-making and data analysis. It resembles a tree structure and is commonly used in machine learning, specifically in classification and regression tasks. 2. Structure: A decision tree consists of nodes and branches. The top node is known as the root node, and it represents the entire dataset. Decision Nodes: These are where the splits happen, based on a certain condition or attribute. Leaf/Terminal Nodes: These nodes represent the outcome of the decision process. 3. Splitting Criteria: This is the method of choosing the attribute for splitting the data at each node. Common criteria include Gini impurity, Entropy (information gain), and variance reduction. 4. Why I was hesitant to use them: I stuck to linear models for the longest time. I understood Linear Models, and I didn't trust Tree-Based models. I knew they were prone to overfitting which had bitten me in the past. And I also knew they couldn't predict over the max/min of the data so extrapolation was a weakness. So why did I start? 5. The Random Forest (Decision Trees on Steroids 💪 ): In 2016, I'd been monitoring several DS competitions on a website called Kaggle (it was new to me back then). And I stumbled upon a challenge where Random Forests were being used. Come to find out, a Random Forest was an ensemble of decision trees that boasted much higher stability and better performance. So I gave it a whirl. 6. Improving my $15,000,000 Lead Scoring Model: Up until now I'd made my company a ton of money, scaling from $3M to $15M in 2 short years. All with a simple Logistic Regression Model! But what made it even better was using Random Forest, which was all based upon Decision Trees. ✅ Performance increased! ❌ But explainability suffered. 7. Explainability (Random Forest Black Box): A problem surfaced, Random Forest was a Black Box. The solution: Use Decision Trees to help simplify my Random Forest so I could trust the model and explain it to management. Combining Decision Trees and Random Forest created a win-win that further improved my $15,000,000 Lead Scoring model. === Want help improving your data science skills? 👉 Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f 👉 ChatGPT for 10X Faster DS Projects: I have a live workshop where I'll share how to use ChatGPT for Data Science (so you can complete projects 10X faster): https://lnkd.in/gCvh6UAy If you like this post, please reshare ♻️ it so others can get value.