The Real Problem: You’re Scoring Activities, Not Intent
Traditional lead scoring treats every action like it means the same thing for every lead. But here’s what we’ve learned after years of this: context matters way more than we thought.
A VP downloading your enterprise buyer’s guide hits differently than an intern doing the same thing. Someone binge-reading your blog at 2 am on a Tuesday shows different intent than someone casually clicking through from a newsletter. And a lead who’s been silent for eight months shouldn’t have the same score as someone who engaged with you yesterday, but most scoring models don’t account for that.
The old playbook was built on assumptions and gut feelings about what “good” behavior looks like. That worked okay when we had 50 leads a month. But now? When you’re processing hundreds or thousands of inbound leads, you need something smarter.
Start With the Basics (But Actually Do Them Right)
Before we get into the AI stuff, let’s talk about the fundamentals that most teams skip over.
Your lead score needs a job description. What is it actually supposed to tell you? Too many companies use lead scoring as a catch-all for “Is this person important?” But that’s not specific enough. Your score should answer one question: Is this person ready for a sales conversation right now?
That means combining two things: fit (do they match your ideal customer profile?) and intent (are they showing signs they’re actively looking?). If you’re only measuring one of these, you’re missing half the picture.
Pick criteria that actually correlate with buying. Here’s what tends to work:
Email clicks matter more than opens, anyone can accidentally trigger an open, but clicking shows real interest. Form fills are gold, especially for high-value content like ROI calculators or demo requests. Page views on specific pages (pricing, features, case studies) tell you what someone’s researching. And webinar attendance? That’s someone investing 30-60 minutes of their time to learn from you. That’s serious intent.
On the flip side, stop using job title or company size as scoring criteria. Those should be gates, either they meet the minimum bar or they don’t. You don’t need to add points for someone being a VP; you need to require it.
Let scores decay over time. This is the part everyone forgets. If someone was hot six months ago but has gone completely dark, their score needs to reflect that. Subtract points for inactivity. Otherwise, you’re calling leads who’ve already moved on, and your sales team starts ignoring your scores altogether.
Why Predictive Scoring Changes Everything
Here’s where AI enters the chat and actually earns its keep.
Manual scoring is like trying to predict the weather by looking out your window. Predictive scoring is like having a supercomputer analyze decades of weather patterns, atmospheric conditions, and real-time satellite data. Both are trying to answer the same question, but one has way more information to work with.
AI-powered predictive models analyze every lead that’s ever come through your system, the ones who converted and the ones who didn’t,and find patterns you’d never spot manually. Maybe it turns out that leads from Series B companies convert 3x better than Series A, but only if they visited your integration page. Or that CTOs who attend your webinar and then go silent for two weeks before re-engaging have the highest close rates.
You wouldn’t find these patterns in a spreadsheet. AI does it automatically.
The catch? Your data needs to be clean. Garbage in, garbage out. If your CRM is full of outdated job titles, duplicate records, and incomplete contact info, your AI model is going to learn the wrong lessons. This is where most companies stumble, they get excited about AI and skip the unglamorous work of cleaning their data first.
The best approach is using a scoring tool that lives inside your CRM and continuously syncs with your most recent data. It should learn from both your wins and your losses. Understanding why deals don’t close is just as valuable as understanding why they do.
Enrichment: The Secret Weapon Nobody Talks About
Lead scoring tells you who to call. Lead enrichment tells you what to say when you call them.
Most enrichment tools will give you the basics: firmographics, technographics, maybe some intent data from third-party sources. That’s helpful, but it’s table stakes now. Everyone has access to that data.
What actually moves the needle is understanding the specific context around each lead. What problems are they trying to solve right now? What initiatives is their company focused on? Who else on their team should you be talking to? What recent company news or funding rounds might create urgency?
This is where modern AI enrichment gets interesting. Instead of just appending data fields, you can deploy AI to actively research each prospect and synthesize what actually matters. It’s the difference between knowing someone is a “Director of Marketing” and knowing they just posted on LinkedIn about struggling with lead quality and their company just raised a Series B to expand into enterprise.
That context transforms your outreach from generic to genuinely relevant. And relevance is what gets responses.
Building a Model That Actually Works
Let’s get tactical about setting this up.
Start by defining your ICP for real. Not the aspirational ICP in your marketing deck, the actual ICP based on who’s buying from you. Pull a list of your last 50 closed-won deals. What do they have in common? Industry, size, role, tech stack, buying triggers? Those patterns become your quality score foundation.
Assign points based on actual conversion rates, not vibes. This is where most scoring models go wrong. Someone decided that a pricing page visit is worth 15 points because it “felt right.” Instead, do the math. If your baseline conversion rate is 2% but leads who visit the pricing page convert at 6%, that behavior is 3x more valuable than average. Weight your scoring accordingly.
Here’s a simple framework:
- Pull your conversion data by attribute and behavior
- Calculate close rate for each
- Compare to your baseline
- Assign points proportionally
A demo request that converts at 15% should be worth way more points than a whitepaper download that converts at 3%.
Treat your model like a product, not a project. This isn’t something you build once and forget about. Buyer behavior shifts, your product evolves, your market changes. If your lead-to-customer conversion rate starts dropping, your scoring model is probably out of date. Set a calendar reminder to review and adjust quarterly at minimum.
Making It Actually Work in Your GTM Motion
A perfect lead score means nothing if you can’t act on it effectively.
Once you’ve identified your high-intent, high-fit leads, you need to engage them with messaging that matches their specific context. This is where most companies drop the ball—they’ve done all this work to score and prioritize leads, then they send the same generic outreach to everyone.
Your best leads deserve personalized messaging based on their role, their company’s situation, and the specific behaviors that triggered their high score. If someone just attended your webinar on API security and works at a fintech company, your follow-up should reference both of those things, not be some bland “touching base” email.
The teams winning right now are the ones who’ve connected their scoring and enrichment data directly to their outreach sequences, so personalization happens automatically at scale. They’re not choosing between personalized and scalable, they’re doing both.
The Bottom Line
Bad lead scoring is worse than no lead scoring. It wastes your sales team’s time, burns through your best leads, and eventually gets ignored completely.
Good lead scoring, the kind that combines solid fundamentals with AI-powered predictions and rich enrichment data, changes your entire GTM motion. Your sales team stops wasting time on junk leads. Your best prospects get engaged while they’re actually hot. Your conversion rates go up because you’re having the right conversations with the right people at the right time.
The difference between companies crushing their growth targets and companies struggling to hit quota often comes down to this: are you guessing which leads matter, or do you actually know?
AI gives you a way to actually know. But only if you put in the work to do it right.
Want to see how the best GTM teams are operationalizing AI-powered lead scoring and enrichment?
Check out Octave, a platform that helps you qualify and prioritize the right buyers with AI agents that collect deep prospect intelligence and activate leads with personalized messaging at scale.
