If you’ve ever rolled your eyes at “AI-powered lead scoring” that spits out random numbers, you’re not alone. Most sales and marketing pros want something useful, not magic beans. This guide is for people who actually work leads and want to use their definition of what a good lead looks like—not just what the software vendor thinks.
We’ll walk through how to build custom lead scoring models using Leadsotters. I’ll help you avoid the hype, show you where the features actually help, and flag what’s not worth your time. If you want a lead scoring setup that your team actually trusts, keep reading.
Step 1: Get Clear on Why You’re Scoring Leads
Before you even log into Leadsotters, take five minutes to write down what you actually want out of lead scoring. Don’t just say “more sales.” Think about:
- Who needs to use these scores? Is it the SDRs, the marketing team, or someone else?
- What are you trying to improve? Faster follow-up, better conversion, less time wasted on tire-kickers?
- What does a “good lead” actually mean to your business? This is always more specific than you think.
Honest take: If you skip this step, your scores end up as noise—nobody trusts them, and your sales team ignores them. Don’t chase “best practices” that don’t fit your business.
Step 2: Define Your Scoring Criteria
This is where most teams either overcomplicate things or go too vague. Leadsotters gives you a bunch of fields and data points, but you need to pick what matters for your sales process.
Common criteria to consider:
- Demographics (job title, company size, industry)
- Engagement (opened emails, visited your site, requested a demo)
- Source (came from a webinar, a cold list, referral, etc.)
- Fit (do they match your ideal customer profile?)
What to ignore: Don’t score on everything you can track. For example, “clicked an email” is not the same as “requested a demo.” Pick signals that actually predict a sale.
Pro tip: Start simple. 3–5 high-impact criteria beats a 20-point Frankenstein model every time.
Step 3: Map Criteria to Data Fields in Leadsotters
Log in to your Leadsotters account and look at your available fields. You’ll see standard ones (email, company size, industry) and any custom fields you’ve added.
- Match your criteria to fields. If you want to score on “job title contains ‘VP’,” make sure that’s a field you’re capturing.
- If you’re missing key data, fix your forms or import process first. No sense building a model on missing data.
Honest take: Leadsotters gives you flexibility, but not all data is created equal. “Downloaded a whitepaper” is less valuable than “booked a meeting.” Weight accordingly.
Step 4: Build Your Custom Scoring Model
Here’s where you use Leadsotters’ custom scoring feature. You’ll find this in the admin or settings area, usually under “Lead Scoring” or something similar.
How to set up your model:
- Add scoring rules based on your criteria.
- Example: If “Job Title” contains “Director” or “VP,” +10 points.
- Example: If “Requested Demo” is true, +20 points.
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Example: If “Company Size” is over 500 employees, +5 points.
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Assign weights.
- Give more points to actions or fields that really matter.
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Don’t be afraid to use negative scores. Example: “Student” in job title, -10 points.
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Use AND/OR logic when needed.
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Leadsotters lets you combine conditions. E.g., “Industry is SaaS AND Company Size > 1000.”
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Save and test your model.
- Most platforms (Leadsotters included) let you preview how your score will look on existing leads. Use this before rolling it out.
What works: Keep your weights simple and clear. If you’re debating between +5 or +7, just pick one and move on. You can always tweak later.
What to ignore: Don’t get sucked into the temptation to score every tiny action (like “opened email at 3am”). It adds noise, not insight.
Step 5: Test with Real Leads and Get Feedback
Don’t just go live and hope for the best. Use Leadsotters’ reporting or export features to see how your new scores line up with actual deals.
- Spot-check recent deals. Do high scores actually match leads that closed or moved forward?
- Ask sales reps for gut checks. Do they trust the scores? Anything weird showing up?
- Look for outliers. If someone with a “perfect” score is actually a bad fit, figure out why.
Honest take: No matter how good your model is, the first version is never perfect. Treat this like a draft, not a finished product.
Step 6: Automate Actions Based on Scores (But Don’t Overdo It)
Leadsotters lets you trigger workflows or tasks based on lead scores. This can be useful—but only if you keep it practical.
Useful automations:
- Notify sales when a lead passes a certain score.
- Move leads to a “hot leads” list for quick follow-up.
- Assign to senior reps if over a threshold.
What to avoid:
- Don’t auto-enroll low-scoring leads into endless drip campaigns. You’ll just annoy people.
- Don’t hide low scores from your team. Sometimes, a “C” lead is the start of a big deal.
Pro tip: Start with one or two automations and see how they perform. You can always add more later.
Step 7: Review and Iterate (Monthly, Not Yearly)
Lead scoring isn’t a set-and-forget thing. Every month or so, take a quick look:
- Are your best leads getting high scores?
- Is the sales team ignoring the scores? If so, ask them why and adjust.
- Are you missing new buying signals? Maybe site visits matter more than they used to.
What works: Small, regular tweaks based on real feedback. Don’t wait until something breaks.
What doesn’t: Rebuilding your whole model from scratch every quarter. That’s just procrastination in disguise.
Wrapping Up: Keep It Simple, Stay Skeptical
Custom lead scoring works best when you keep it clear, honest, and tied to what actually matters for your business. Don’t get starry-eyed about “AI” or chase every new metric—just make sure your model actually helps your team focus on the right leads. Start simple in Leadsotters, get feedback, and don’t be afraid to change what isn’t working.
If you’re ever in doubt, ask yourself: Does this lead score help my team take action or just add noise? Iterate, keep it practical, and you’ll be ahead of 90% of teams out there.