Sales Analytics for Lead Scoring

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.

Demand Capture 101. This is actual data from a $60MM ARR SaaS company. Let’s break it down 👇   How a lead/account enters your pipeline is the biggest predictor of sales velocity metrics - win rates, sales cycle lengths, even ACVs.    Because how they enter your pipeline is a surrogate for buying intent & indicator of how far they are complete in the buying process.    Here’s how to measure it & use it to drive your revenue strategy:   1. Measure the Opportunity Source in Salesforce on the opportunity record.    Campaign Source = What campaign type did they convert on to move this opportunity into pipeline? (e.g. demo request, e-book download, cold call, trade show, etc.)   Source / Channel = What source or channel did they come from in order to convert? (e.g. LinkedIn ad, organic search, account intent data, ZoomInfo, etc.)    Using both of these data points combined will literally guide your strategy.    This shows you the optimal paths to *capture demand* and is easily measurable using software-based attribution.   2. Separate conversion sources between *Declared Intent* and *Low Intent*.    Declared Intent = The buyer declares intent to buy from you (e.g. Demo Request, Contact Sales) Low Intent = You assume the buyer has intent based on their digital behavior (e.g. ebook download, webinar attendee, trade show badge scan, intent data, etc.)    3. Calculate core sales analytics between the two sources.    Calculate conversion rates, lead-to-win rate, net new ARR, sales velocity, and more.    4. Visualize how much conversion intent matters to sales velocity and sales productivity.    149X higher lead-to-win rates for declared intent conversions   Declared intent = 26 “leads” to win 1 deal for $54k ARR Low Intent = 3,868 “leads” to win 1 deal for $130k ARR   18X greater sales velocity for declared intent conversions   Declared intent = $14.2MM annual sales velocity Low intent = $781k annual sales velocity 5. Recognize not all MQLs are created equal Measuring on MQLs incentivizes teams to get the most volume of MQLs for the lowest cost (low intent conversions), which is entirely misaligned with sales productivity and sales goals. Separate these into two Pipeline Sources (Declared Intent, Low Intent). Plan and build your goals for these two sources separately.   __   Now you know exactly HOW you want buyers to enter pipeline (capture demand) for maximum sales velocity & sales team efficiency. You also know exactly WHY buyers choose to take those paths to enter pipeline & WHAT triggers / channels / tactics move them to conversion. And with all of these insights, you can re-architect your strategy that optimizes for REVENUE. #revenue #sales #marketing #b2b #gtm p.s. Every SaaS company’s data looks like this, because it’s universal to how buyers buy. Most just don’t take the 3 hours of time to analyze their own data and see it for themselves.

During my time at CloudKitchens we 8x'd sales efficiency by building the most badass lead scoring system I've ever seen. Here's how we did it: For context, CloudKitchens sells delivery-only, otherwise known as "ghost" kitchens. In the early days, we were reaching out to every restaurant in existence asking them if they wanted to open a delivery-only kitchen. This was dumb, but we were under pressure to move quickly. After seeing low reply rates & conversion rates, we took a big step back and asked ourselves two questions: 1. Which restaurants are best positioned to open a delivery-only kitchen? 2. How do we find them? The answer to the first question was simple: we need to reach out to restaurants that are already doing high delivery volume. The answer to the second question wasn't so simple, because there aren't any ready-to-use data platforms that tell you how much delivery individual restaurant locations are doing. Here comes the badass part: I worked with our data science team to build an algorithm that approximated delivery volume by looking at "review velocity" AKA how many net new reviews each restaurant was getting every day. We created a correlation using data from our own kitchens that told us approximately how many orders = one review. Based on this approach we were able to approximate with high confidence how much daily delivery volume every restaurant was doing across all major delivery apps. This was key for lead scoring, but even more key for outreach. Now, instead of using generic messaging like, "Hey, want to expand with CloudKitchens?" we could say, "Hey, we see you're doing X daily delivery volume and therefore if you open a CloudKitchen you will be profitable on day one." Taking this type of approach is more important than ever in 2024. Shoutout to Tarek Rabbani working with me on this, and being the best data scientist I've ever known :)

AI-Powered lead scoring is one area of sales where AI gets put to ACTUAL good use. And it works like a charm. 𝟭 - 𝗜𝘁 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲𝘀 𝘁𝗵𝗲 𝗴𝘂𝗲𝘀𝘀𝘄𝗼𝗿𝗸 Relying on manual action from creative revenue people is a losing game. The dream was always AI algorithms processing vast amounts of data to determine what actually matters, and now it's here. Knowing > Guessing 𝟮 - 𝗜𝘁 𝘁𝘂𝗿𝗻𝘀 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 If you want to keep a sane mind, you can’t track every single source. • Salesforce CRM data • HubSpot marketing campaign results • Sales engagement platform interactions • Email opens and clicks • Website visits AI collects, processes, and finds the right patterns. 𝟯 - 𝗜𝘁 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 This isn't about looking at variables in isolation. AI considers: • Temporal data (when did they interact?) • Categorical data (what industry are they in?) • Numerical data (how many Twitter followers do they have?) • Behavioural data (did they just visit the pricing page?) It's all interconnected, and AI sees the full picture. 𝟰 - 𝗜𝘁 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 Here’s what we found when we implemented this: • Only 4% of leads scored above 85 • These high-scoring leads had a 40% historic close rate Immediately we have a data-backed, new north star ICP to focus our sales team on. Sales teams don’t need more leads, they need fewer leads that convert, and they need priority updates in real time. 𝟱 - 𝗜𝘁 𝗱𝗲𝗺𝘆𝘀𝘁𝗶𝗳𝗶𝗲𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 It shows you: • Which features have the highest impact on the score • How different variables are weighted • Why a lead received its specific score The hardest part of any sales team's pivot is buy-in. Now you have the data to back your claims, and your team is excited to make the switch. so. The question isn't whether AI-powered lead scoring is better. The question is: How much revenue are you leaving on the table by not using it? What's your current approach to lead scoring?

67% of sales time goes to dead-end leads. That’s not a typo. It's a huge problem for marketing. Why? The sales team burns out. You lose revenue. AI can fix this bottleneck. AI goes beyond simple scoring, offering detailed insights that human analysis can't match (all in real-time). Here are 9 proven tactics to leverage AI-driven lead qualification: 1/ Use Predictive Scoring → Leverage historical data to predict conversion likelihood → 43% improvement in qualification accuracy → Automatically flag high-potential prospects 💡 Pro tip: Start with your last 12 months of closed deals to train your AI model. 2/ Real-time Behavior Analysis → Track digital footprints across platforms → Identify purchase intent signals instantly → Generate real-time engagement scores 💡 Pro tip: Focus on high-intent actions like pricing page visits and demo requests. 3/ Natural Language Processing → Analyze communication patterns → Understand sentiment and urgency levels → 3x faster response to high-intent leads 💡 Pro tip: Include email subject lines in your analysis - they often reveal true intent. 4/ Automated Engagement Tracking → Monitor interaction frequency → Score based on meaningful touchpoints → 56% reduction in qualification time 💡 Pro tip: Weight recent interactions higher than historical ones. 5/ Dynamic Profile Enrichment → Automatically update lead information → Create comprehensive buyer personas → 78% more accurate ideal customer profiles 💡 Pro tip: Verify enriched data quarterly to maintain accuracy. 6/ Multi-channel Attribution → Track leads across all platforms → Identify most effective conversion paths → 40% better resource allocation 💡 Pro tip: Set up unique tracking parameters for each channel. 7/ Smart Segmentation → Auto-categorize leads by potential value → Prioritize high-ROI opportunities → 2.5x increase in conversion rates 💡 Pro tip: Create no more than 5 segments to keep it actionable. 8/ Intent Data Analysis → Monitor research patterns → Predict purchase readiness → 65% faster sales cycles 💡 Pro tip: Look for competitors' branded searches as buying signals. 9/ Automated Lead Routing → Match leads to best-fit sales reps → Reduce response time by 91% → 34% higher close rates 💡 Pro tip: Route based on industry expertise, not just rep availability. Companies that adapt now will have a distinct advantage over those still relying on manual processes. The question isn't if you should implement AI-driven qualification, but how quickly you can get started. _________ ♻️ Repost if your network needs to see this. Follow Carolyn Healey for more AI-related content.