If you’re drowning in B2B leads, but only a handful are actually worth your sales team’s time, you need to get serious about lead scoring. This guide is for anyone using Bricks to wrangle their inbound leads—especially if you’re tired of chasing ghosts or explaining to sales why “marketing qualified” doesn’t mean “actually qualified.”
We’ll walk through setting up scoring models in Bricks, what matters, what’s just noise, and how to keep your model from turning into a complicated mess. No jargon, no hype—just straight talk and actionable steps.
Step 1: Get Clear on Why You’re Scoring Leads (and What You Want)
Before you click anything in Bricks, pause for a reality check. What’s the real point of your lead scoring model? Hint: It’s not to make a pretty dashboard. It’s to help your sales team focus on the right leads, faster, and ignore the tire-kickers.
Ask yourself: - What does a “good” lead actually look like? (Think: company size, job title, website activity, etc.) - What’s the main use case? (Routing leads, prioritizing outreach, qualifying for nurture vs. sales, etc.) - How will you know if your scoring model is working? (Look for: increased sales productivity, faster response times, better conversion rates.)
Pro tip: Talk to your sales team. They know which leads waste their time and which ones close fast. Don’t build your scoring model in a vacuum.
Step 2: Gather and Clean Up Your Data
A scoring model is only as good as the data you feed it. Garbage in, garbage out.
What data should you use?
- Firmographics: Company size, industry, revenue, location.
- Demographics: Role, seniority, department.
- Behavioral: Website visits, downloads, event attendance, email opens/clicks.
- Source: How did they get to you? (Referral, ad, organic, etc.)
What to ignore:
Don’t get lost in obscure data points like “last login time” for a B2B purchase, or obsess over vanity metrics like social follows. Focus on the stuff that actually signals intent and fit.
Check your data quality
- Are you missing a ton of key fields?
- Are there duplicates or old leads clogging things up?
- Is your data up to date?
Bricks tip: Use Bricks’ built-in deduplication and data enrichment tools before you start scoring. If you skip this, your model’s going to be off from day one.
Step 3: Map Out Your Scoring Criteria
Now’s the time to decide what gets points—and how much.
Start simple
Don’t try to build the perfect model right away. Start with a few “must-have” signals and a couple of easy behavioral triggers.
Typical starting points: - +25 points if company matches your ICP (ideal customer profile) - +10 points if contact is a decision-maker - +15 points for requesting a demo or pricing - +5 points per meaningful website visit (ignore the “About Us” page) - -10 points for students or non-business emails (Gmail, Yahoo, etc.) - -20 points for companies outside your target region
Avoid these traps: - Overweighting email opens/clicks. (Plenty of bots and accidental clicks.) - Adding 20 different scoring rules before you’ve validated the basics.
Pro tip: If you’re not sure how much to weight something, start small. It’s easier to tweak scores up than explain why your pipeline just filled up with junk.
Step 4: Build Your Scoring Model in Bricks
Here’s where you get hands-on in Bricks. Their scoring engine is pretty straightforward, but don’t get lost in the bells and whistles.
How to do it:
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Go to the Scoring Models Section:
In Bricks, find the “Lead Scoring” or “Scoring Models” tab. (Naming may shift with updates, but it’s always easy to spot.) -
Create a New Model:
Click “Create New Model.” Give it a clear name—no one wants to guess what “V2-Test-Final-Final” means three months from now. -
Add Criteria:
- Select your fields (like company size, industry, etc.).
- Set the point value for each.
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Choose positive or negative scores as needed.
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Define Behavioral Triggers:
- Use Bricks’ pre-built triggers for things like “Visited Pricing Page” or “Requested Demo.”
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Adjust point values to match your earlier plan.
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Set Score Thresholds:
- Decide what score qualifies as “hot,” “warm,” or “cold.”
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Set up routing rules if you want leads to go to specific reps or nurture tracks based on their score.
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Test with Sample Data:
- Bricks lets you run the model on existing leads to see how they score. Do this before turning it on for new leads.
What not to do:
Don’t turn on auto-routing or notifications until you’ve seen the scores in practice. Watch out for random leads getting “high” scores because of a weird data quirk.
Step 5: Test, Review, and Get Feedback
Even the best model is a guess until you see it in action. Here’s how to avoid embarrassing yourself:
- Run the model on historical leads.
See who would’ve been marked as “hot” or “cold.” Did it match reality? - Share with sales.
Get honest feedback. If reps say, “That lead is junk—why is it scored so high?” revisit your criteria. - Track what happens next.
Are “high score” leads moving to opportunities faster? Are low-scoring leads actually poor fits? - Watch for false positives/negatives.
If your model says every lead is amazing (or terrible), your weights are probably off.
Pro tip: Don’t aim for perfection. Your first version should be “good enough to learn from,” not “perfect and forever.”
Step 6: Adjust, Simplify, and Repeat
You will get things wrong. That’s normal. The real trick is to keep tweaking.
How to improve:
- Drop useless criteria:
If a signal isn’t helping, get rid of it—or at least set its score to zero. - Re-weight based on outcomes:
If demo requests are a better indicator than job title, give them more points. - Limit the number of rules:
More rules doesn’t mean better results. Most solid models have fewer than 10 key criteria.
What to ignore:
Don’t add rules just because you can. If you can’t explain why a criterion matters to a skeptical sales rep, it probably doesn’t.
Step 7: Automate (Carefully)
Once you’re confident your model isn’t spitting out nonsense, you can start automating actions in Bricks:
- Auto-assign hot leads to sales reps
- Send instant alerts for high-scoring leads
- Trigger nurture campaigns for low-scoring leads
Caution:
Automation amplifies mistakes. If your scoring is off, you’ll annoy sales (or worse, your prospects) at scale. Always double-check before flipping the switch.
Final Thoughts: Keep It Simple, Iterate Often
Don’t let scoring models turn into a science project. The best ones are simple, transparent, and evolve as you learn. Start with what you know, validate with real results, and don’t be afraid to scrap what isn’t working. Bricks makes it easy to adjust your model—use that flexibility to your advantage.
Remember, the goal isn’t a perfect score, it’s helping your team focus on the right leads. Keep it practical, keep it honest, and revisit your model every quarter. That’s how you turn lead scoring from a “nice-to-have” into a genuine sales advantage.