If you’re drowning in leads but only a few ever go anywhere, you’re not alone. Most sales teams spend too much time guessing which prospects to chase—and wind up wasting hours on dead ends. Custom lead scoring can change that, but let’s be real: the default settings in most CRMs are useless, and “AI-powered” solutions are usually just fancy coin flips. If you want to actually improve your pipeline, you need a scoring model built for your business—not someone else’s template.
This guide is for sales managers, ops folks, and anyone responsible for pipeline quality who wants to build a scoring system in Grow that actually works in the real world. We’ll skip the fluff, focus on what matters, and call out the pitfalls to avoid.
Why Bother With Custom Lead Scoring?
You probably already know the basics: lead scoring is just a way to rank prospects, so you spend time on the ones most likely to buy. The problem? Out-of-the-box scoring rules (or whatever a vendor “recommends”) rarely fit your sales motion. Every business is different.
Custom lead scoring in Grow lets you: - Prioritize leads with your criteria, not canned rules. - Spot which sources and behaviors actually matter. - Cut through noise and focus your team’s time.
But don’t expect a silver bullet—the model’s only as good as the logic you put in.
Step 1: Get Clear on What a Good Lead Looks Like
Before you mess with Grow’s settings, figure out what actually predicts a sale for your team. Ignore the blog posts about “lead scoring best practices”—start with your real data and experience.
Ask yourself: - Who’s actually buying? Look at the last 6-12 months. What do your closed-won deals have in common? - Which leads waste your time? Be honest. Which prospects never buy, even after 10 calls? - What actions or traits matter? - Industry, company size, job title? - Opened an email, booked a demo, downloaded a whitepaper? - Came from referral, LinkedIn, or cold outreach?
Pro tip: Don’t try to score everything. Pick 4-6 signals that really matter. More variables = more noise.
Step 2: Map Out Your Lead Scoring Criteria
Now, write down the scoring rules you want—plain English first. Don’t jump into the tool yet. You want something like:
- +10 points if company is in target industry
- +5 points for Director-level or above
- +3 points if they open 2+ emails
- +7 points for booking a demo
- -5 points if company size < 10 employees
Score both fit (who they are) and behavior (what they do).
What works:
Simple, transparent rules. You want your team to understand why a lead scores high or low.
What to skip:
Don’t get sucked into “AI” scoring unless you have huge datasets and a data scientist on staff. Most small/medium teams do better with clear, manual rules.
Step 3: Set Up Custom Fields in Grow
Open Grow and make sure you have the data you need. If you can’t score on a field, you’ll need to add it.
- Go to your lead or contact object.
- Add custom fields for anything you plan to use (e.g., “Job Title Seniority,” “Industry Fit,” “Demo Booked”).
- Make sure these fields are getting populated—ideally through forms, integrations, or sales reps.
Don’t skip this:
If your data’s a mess, your scoring will be too. Garbage in, garbage out.
Step 4: Build Your Scoring Model in Grow
Now, let’s get into Grow’s scoring setup:
- Head to the lead scoring section.
- Usually under Settings > Lead Scoring or similar.
- Choose “Custom Model.”
- Skip default or “AI” if you want control.
- Add your criteria.
- For each rule, select the field, set the value/condition, and assign points.
- E.g., “If Industry equals Manufacturing, add 10 points.”
- Set negative scores for red flags.
- E.g., “If Company Size < 10, subtract 5 points.”
- Test your rules on recent leads.
- See if top scorers actually match what you expect.
What to ignore:
Don’t overcomplicate with 20+ rules or micro-points (like +0.5 for trivial actions). You want signal, not noise.
Step 5: Define Score Thresholds
Scoring is pointless unless you act on it. Decide what score means “hot,” “warm,” or “cold.”
- Example:
- 20+ points = Hot (send to sales)
- 10-19 = Warm (nurture)
- <10 = Cold (ignore or automated drip)
Set up views or automations in Grow to route leads based on these buckets.
Pro tip:
Start with rough thresholds. Adjust as you learn what works.
Step 6: Train the Team—And Get Feedback
A scoring model nobody understands will get ignored. Make sure everyone knows:
- What the scores mean
- How to use them in their workflow
- When to override (sometimes gut instinct trumps the model)
Ask reps if the scores feel right. Are good leads getting missed? Are duds sneaking through? Tweak as needed.
Step 7: Review and Improve—Ruthlessly
No model is perfect out of the gate. Every quarter (or even monthly at first), pull a list of:
- Leads that scored high but went nowhere
- Low-scoring leads that closed
Ask: - Did we overvalue certain behaviors? - Are we missing key signals? - Is the data getting entered correctly?
Tweak your rules. Cut what doesn’t help. Add only what’s proven.
What works:
Continuous, small tweaks—not massive overhauls every few months.
What doesn’t:
Set-and-forget. The market changes, and so does your ideal customer.
Common Pitfalls (and How to Dodge Them)
- Scoring on vanity signals:
Just because someone opens an email doesn’t mean they’ll buy. - Overweighting one field:
Don’t let a single action (like downloading a PDF) outweigh everything else. - Not updating as you learn:
The best scoring models are always evolving. - Letting the model replace human judgment:
It’s a tool, not gospel. Encourage reps to flag exceptions.
Wrapping Up: Keep It Simple, Iterate Fast
Building a custom lead scoring model in Grow isn’t rocket science, but it does take some focus. Start simple, use signals that actually predict sales (not what looks good on a dashboard), and keep talking to your team about what’s working. The goal isn’t a perfect score—it’s a better pipeline.
Don’t chase the latest trend or get buried in complexity. The best models are the ones your team actually uses—and trusts. Start with what you know, watch the results, and keep refining. You’ll get there.