If you've ever felt like your sales team is spinning its wheels chasing leads that go nowhere, you're not alone. Lead scoring can help—but only if it's done right. This guide is for hands-on marketers, sales ops, or founders who want to set up lead scoring models in Quantified that actually lift conversion rates, not just create more dashboards. We'll walk through the real steps, call out common traps, and get you running with a system that tells you which leads are worth your time.
Why Lead Scoring Matters (and When It’s Overkill)
Lead scoring sounds sexy—prioritize your best prospects, close more deals, and let automation do the grunt work. The reality: if you’re just starting out or have a tiny lead pool, you probably don’t need it. But if you’re swimming in a big pile of leads and your team can’t follow up on everything, scoring helps you focus on the ones that actually buy.
When lead scoring helps: - You’re getting more leads than your team can realistically handle - Sales reps complain about “bad leads” or waste time on tire kickers - You have clear data on what makes a good customer (company size, industry, key actions, etc.)
When to skip it (for now): - You have less than 50-100 leads per month - You don’t have enough closed-won/closed-lost data to learn from - Your sales process is mostly manual or highly personalized
If you’re ready to move beyond gut feeling, Quantified makes it pretty simple. Let’s get into it.
Step 1: Define What a “Good Lead” Actually Looks Like
Don’t let software tell you what matters—start with what you know about your best customers. Before you touch Quantified, grab a notepad (yes, really) and jot down:
- What do our top 10 customers have in common?
- How did they find us?
- What actions did they take before buying? (e.g., signed up for a webinar, downloaded a whitepaper)
- What disqualified past leads? (e.g., wrong industry, no budget, student emails)
You’re looking for traits and behaviors that separate real prospects from window shoppers.
Pro tip: Avoid “vanity” signals like email opens or social follows. They don’t mean much on their own.
Step 2: Gather Your Data
To build a lead scoring model in Quantified, you need decent data on your leads. That means:
- Contact details: Name, email, company, etc.
- Firmographics: Industry, company size, job title.
- Behavioral data: What pages did they visit? Did they attend a webinar? Book a demo?
- Deal outcomes: Did they buy? Did they ghost you?
If your data is a mess, spend some time cleaning it up. Garbage in, garbage out.
What to ignore: Don’t get obsessed with tracking every micro-movement (like “clicked image #4 on landing page”). Stick to actions that actually correlate with sales.
Step 3: Connect Your Data Sources to Quantified
Quantified is flexible, but you still need to wire up your core tools. Usually, this means:
- CRM (Salesforce, HubSpot, etc.)
- Marketing automation (Marketo, Mailchimp, etc.)
- Website analytics (Google Analytics or similar)
- Webinar/event platforms (Zoom, GoToWebinar, etc.)
You can use Quantified’s built-in connectors, APIs, or even CSV uploads if you’re old-school. The key is to get all your lead and activity data under one roof.
Heads up: If you have data silos (like sales and marketing each using their own lists), fix that first. Otherwise, your scoring will be off.
Step 4: Build Your Scoring Criteria
Here’s where most people mess up: they try to score everything and end up with a Frankenstein model that nobody trusts.
Instead, start simple. Pick 3-5 criteria that actually matter. Quantified lets you assign points to each:
- Demographics (“Fit”):
- Right industry? +10 points
- Right company size? +8 points
- Job title matches your buyer persona? +5 points
- Behavior (“Intent”):
- Requested a demo? +15 points
- Visited pricing page? +10 points
- Attended a webinar? +8 points
- Opened 5+ emails? +3 points
What not to do: - Don’t add points for every little thing. More complexity doesn’t mean better accuracy. - Don’t use “gut feel” weights unless you have data to back it up. (It’s fine to start with your best guess, but revisit it later.) - Avoid negative scores for “bad” behaviors unless you have proof they matter (e.g., unsubscribing from emails).
Quantified tip: Use their “historical analysis” tool to see which actions actually correlate with closed deals. Let the data guide you.
Step 5: Set Up Your Lead Scoring Model in Quantified
This part’s pretty straightforward:
- Create a new scoring model: In Quantified, go to the Lead Scoring section and start a New Model.
- Map your criteria: Add the attributes you picked (industry, actions, etc.) and assign your points.
- Set your scoring ranges: Decide what score counts as “hot,” “warm,” or “cold.” (Quantified’s default ranges work fine to start—don’t overthink it.)
- Test on historical leads: Run your model against old data. Are your “hot” leads mostly the ones who bought? If not, tweak the points or criteria.
- Deploy and automate: Turn on the model. Set up alerts or workflows for when a lead hits “hot” status, so sales gets notified right away.
What to ignore: Don’t stress about getting the perfect model on Day 1. You’ll adjust as you go.
Step 6: Train Your Team to Use the Scores
A scoring model only works if your team trusts it—and actually uses it. Get everyone on the same page:
- Show them what the scores mean.
- Explain why certain actions or traits matter.
- Set clear rules (e.g., “Hot leads get a call within 2 hours”).
- Encourage reps to give feedback when the model misses the mark.
Reality check: Some salespeople will still chase “cold” leads. That’s fine, as long as you’re focusing most effort on those with real buying signals.
Step 7: Review, Tweak, and Keep It Simple
No model is perfect. Block time every month (or at least quarterly) to review your scoring:
- Are your “hot” leads actually converting?
- Are you missing good leads because of bad data or overly strict criteria?
- Did you add too many “nice-to-have” signals that don’t really matter?
Quantified gives you reporting tools to track this. Use them, but don’t get lost in the weeds. Scrap what’s not working. Simplify where you can.
Pro tip: Resist the urge to add more and more criteria. The simpler your model, the more likely your team will use it—and the easier it’ll be to spot what’s broken.
What Works, What Doesn’t, and What to Ignore
- Works: Scoring based on actual closed-won data, focusing on a handful of strong buying signals, getting sales and marketing buy-in.
- Doesn’t: Overengineering, using vanity metrics, or ignoring team feedback.
- Ignore: Shiny features that promise “AI-powered scoring” unless you already have thousands of leads and a lot of clean data. For most teams, simple rules beat black-box algorithms every time.
Wrapping Up: Keep It Simple and Iterate
You don’t need a PhD in data science to set up lead scoring in Quantified. Start with what you know, use real data, and keep it dead simple. The best models are the ones your team actually trusts and uses. Don’t chase perfection—just get started, see what works, and improve as you go. The goal isn’t a fancy spreadsheet. It’s more deals, less wasted time, and a sales team that knows where to focus.