If you’re running a B2B sales team, you’ve probably been promised the world by “AI-powered” lead scoring tools—and left feeling underwhelmed. Either the scores are random, or you’re stuck with generic rules that don’t fit your business. This guide is for you if you want something better: a straightforward, hands-on way to build a lead scoring model that actually works for your team using Sailes.
No fluff, no magic bullets—just a practical approach to getting more out of your leads and your sales reps’ time.
Why bother with custom lead scoring anyway?
Let’s be honest: most out-of-the-box lead scoring models are built for “average” companies. Maybe you sell to a niche market, or maybe the way your buyers behave doesn’t fit what some Silicon Valley product manager thinks is “ideal.” Building your own lead scoring model means:
- Your team’s not chasing garbage leads.
- High-value prospects don’t slip through the cracks just because they don’t download whitepapers.
- The scoring evolves as your business does.
But it only works if you put in the work upfront. Here’s how to do it without losing your mind.
Step 1: Get clear on what a “good” lead actually looks like
Don’t skip this step. If you just dump every data point you have into Sailes, you’ll end up with noise, not insight.
Ask yourself: - Which leads have actually converted to revenue in the past 6-12 months? - What do they have in common? (Industry, company size, job title, urgency, tech stack, etc.) - What signals actually matter vs. what just sounds good in a marketing meeting?
Pro tips: - Dig into your CRM. Pull a list of deals closed-won and actually look for patterns. - Talk to your best reps. They know which prospects are worth their time (and which aren’t).
What to ignore: Vanity metrics like email opens or webinar sign-ups. If they don’t correlate with revenue, leave them out for now.
Step 2: Map your key signals to data you actually have
It’s easy to dream up a perfect scoring model that needs data you’ll never get. Stay grounded.
Make two lists: - What you wish you knew (e.g., prospect’s budget, existing pain points) - What you actually know (e.g., job title, company size, web activity, replies to outreach)
Now, focus only on what you really have. In Sailes, all scoring starts with the data you feed in. If you don’t track it, you can’t use it.
Common data sources: - CRM records (industry, revenue, employee count, last activity date) - Website and email engagement (but only if it really matters) - Manual rep notes (if your team actually fills these out—don’t count on it)
Step 3: Clean your data before you even open Sailes
Garbage in, garbage out. If your CRM is a mess, your lead scores will be too.
Quick data hygiene checklist: - Remove obvious duplicates. - Standardize fields (e.g., “VP of Sales” vs “VP Sales” vs “Sales VP”). - Fill in missing values for your must-have signals where possible. - Set up a process to keep this clean, or you’ll be repeating this pain in a few months.
Honest take: If your data is a nightmare, fix the basics first. Don’t expect Sailes or any tool to magically clean it for you.
Step 4: Outline your scoring logic on paper first
Before you start clicking around in Sailes, sketch out how you want to score leads. This saves you hours of rework.
Simple example: - +25 points for company size 100-500 employees - +20 points for job title containing “Director” or “VP” - +15 points for any reply to outbound email - -10 points if company is outside your target industry - -20 points if no activity in past 30 days
Tips: - Start simple. Three to five rules are plenty to begin with. - Weight the biggest “deal-breakers” the most. - If you’re not sure about a signal, leave it out until you see the data.
Step 5: Build your model in Sailes
Now you’re ready to make Sailes do the heavy lifting. The specific steps may change as the product evolves, but here’s the general approach:
- Import your data: Connect your CRM and any other relevant data sources. Make sure your fields map correctly.
- Set up custom scoring rules: In Sailes, you can usually create rule-based scoring (if X, then add/subtract Y points). Start with your paper outline.
- Test and preview: Most tools, including Sailes, let you run a preview. Pick a handful of recent leads and see if the scores make sense.
- Iterate: Expect to tweak your weights. If every lead scores an 85, your ranges are off. If nobody scores over 30, you’re being too strict.
What works: Keeping your model focused on a few high-impact signals. The more complicated you make it, the less your team will trust it.
What doesn’t: Overfitting to edge cases or including every signal you can find. It slows things down and rarely improves results.
Step 6: Roll it out with your team (and actually use it)
Don’t just launch the model and call it a day. Explain to your reps what matters and why—otherwise, they’ll ignore the scores.
How to get buy-in: - Show them how top leads actually match their best accounts. - Ask for feedback on leads that “feel wrong.” Sometimes your model misses the human nuance. - Make the scores visible in your CRM or wherever your reps work.
Warning: If you bury the scores in a dashboard nobody checks, this whole process is a waste. Bring the scores into the tools your team already uses.
Step 7: Review results and keep it honest
Set a calendar reminder to review the model every month or quarter. Look for:
- Are high-scoring leads actually converting more?
- Are any high-value prospects getting low scores?
- Has your ideal customer profile changed?
What to ignore: Don’t obsess over every single outlier. No model is perfect. Look for patterns, not one-off misses.
Pro tip: If you find your model is always being “gamed” (e.g., reps find loopholes to bump scores), tighten your rules or remove the incentive.
Step 8: Iterate—don’t chase perfection
You’ll never have a “finished” lead scoring model. The market changes, your business evolves, and so should your approach.
- Start simple, see what works, and adjust.
- Add new signals only when you have proof they matter.
- Don’t get distracted by shiny features or “AI” hype unless they actually improve your results.
Wrapping up
Custom lead scoring isn’t rocket science, but it does take a bit of thinking and some elbow grease. With Sailes, you can build something that actually helps your sales team focus on the right leads—if you keep it grounded in the reality of your data and your sales process.
Keep it simple, review often, and don’t be afraid to cut what isn’t working. The best models are the ones your team actually trusts and uses—not the ones with the fanciest features.