If you're tired of chasing “leads” that waste your time, you’re in the right place. This guide is for anyone who uses Matchkraft and wants to cut through the noise to find actual B2B prospects—not just anyone who fills out a form. Whether you’re in sales, demand gen, or just stuck managing the CRM, this is how you build a smarter lead scoring system that works for you, not against you.
Let’s get you set up with advanced lead scoring in Matchkraft so you can spend your time on deals that might actually close.
1. Understand What Lead Scoring Actually Does (and Doesn’t)
Before you start dragging sliders or building formulas, let’s get real: lead scoring isn’t magic. It helps you prioritize, yes. But it won’t suddenly make bad leads good, or turn your sales team into wizards.
What it does well: - Saves time by bumping real prospects to the top. - Filters out obvious time-wasters (students, bots, tire-kickers). - Keeps sales and marketing on the same page—mostly.
What it doesn’t do: - Replace good judgment. You still need to review top leads manually. - Guarantee a perfect list. Garbage in, garbage out. - Work instantly. It takes time and iteration to get it right.
Pro tip: The best lead scoring setups are simple and ruthlessly focused on what actually predicts sales, not just what’s easy to measure.
2. Map Out What a “Good Lead” Looks Like for Your Business
Don’t start in the software. First, get clear on what signals a real opportunity for you. This means sitting down with sales (yes, really) and asking:
- Which leads actually close?
- What do they have in common? (Industry, company size, job title, specific behaviors?)
- Which signals are just noise?
Examples of useful signals: - Visited your pricing page more than once. - Has a company email (not Gmail, Yahoo, etc.). - Downloaded a technical whitepaper (not just a blog post). - Job title matches your buyer persona (“VP of IT,” not “Intern”). - Company size fits your sweet spot.
What to ignore: - Likes your company on Facebook. (Almost always useless for B2B.) - Opened a single email. (Too broad.) - Filled out a generic contact form with a throwaway email.
Write this down. You’ll use it to build your scoring rules.
3. Gather Your Data Sources in Matchkraft
Now jump into Matchkraft and look at what data you’re actually capturing. This is where most setups go wrong: if you’re not collecting the right data, you can’t score it.
Check these areas: - Forms: Are you asking for the info you need (company, role, industry)? - Web tracking: Can you see which pages they visit? - Email engagement: Are you logging opens, clicks, replies? - CRM sync: Is Matchkraft pulling in company data from your CRM or enrichment tools?
If you’re missing something critical (like company size or industry), fix that first. Add a form field, connect a data enrichment tool, or update your tracking script. You can’t score on blanks.
4. Build Your Lead Scoring Model in Matchkraft
Here’s where the rubber meets the road. In Matchkraft, go to the Lead Scoring section (names may vary, but you’ll find it under Automation or Settings). You’ll see options to create rules, assign points, and set up thresholds.
A. Create Scoring Rules
Start with two main types:
- Demographic/Firmographic: Who are they?
- Company size
- Industry
- Job title
- Location
- Behavioral: What do they do?
- Visited high-intent pages (pricing, demo)
- Downloaded product materials
- Attended a webinar
- Repeated site visits in a short time
Assign points based on importance. For example: - “Visited pricing page twice in a week” = +15 points - “Has ‘Director’ or above in job title” = +10 points - “Company size 100-1000 employees” = +10 points - “Clicked on ‘Request a demo’” = +20 points
B. Set Negative Scores for Red Flags
Don’t just add points—subtract them for bad signals: - “Email ends with gmail.com” = -10 points - “Downloaded only an eBook, no other activity” = -5 points - “Job title: Student or Intern” = -15 points
C. Set Thresholds for MQLs and SQLs
Decide what score makes someone a Marketing Qualified Lead (MQL) or Sales Qualified Lead (SQL). For example: - MQL = 30 points - SQL = 50 points
Test these out—don’t worry about getting it perfect on day one.
5. Test Your Model: Don’t Trust the Defaults
Here’s where reality checks happen. Run your new scoring model on the last 3-6 months of leads. See who bubbles up—and who gets left behind.
- Spot check the top 10 leads: Are these actually good prospects?
- Look for false positives: Are bad fits sneaking through?
- Ask sales: Did these leads close, ghost, or go nowhere?
What usually needs tweaking: - Too many points for email opens (easy to fake). - Not enough weight on firmographics (company size, industry). - Not scoring negative behaviors (unsubscribe, generic emails).
Ignore “best practice” templates from vendors—they’re generic by design. Your model should match your market, not someone else’s.
6. Automate Actions Based on Scores
Once your scoring is dialed in, make it do real work. In Matchkraft, set up automations like:
- Notify sales when a lead hits SQL status. (Email, Slack, whatever works.)
- Assign leads to the right rep automatically.
- Trigger nurture campaigns for leads that aren’t hot yet.
Don’t overcomplicate. Fancy automations are fun until they break or send alerts at 2am. Start simple: route the best leads to sales, and let the rest drip until they’re ready.
7. Review and Adjust (Seriously, Don’t Skip This)
Lead scoring isn’t “set it and forget it.” Every quarter—or when sales says “the leads are junk” again—review your scoring:
- Are the best leads converting?
- Are you missing hidden gems?
- Are you rewarding the right behaviors?
Pro tip: Watch out for “score inflation”—where everyone becomes an MQL because your thresholds are too low, or your marketing team keeps tweaking for higher numbers. Stay objective.
What Actually Works—and What’s a Waste of Time
Worth your time: - Focusing on signals that match closed-won deals. - Subtracting points for bad fit or sketchy behavior. - Regularly reviewing with sales and updating rules.
Skip or limit: - Overly complex models with 50+ scoring rules. (You’ll never maintain them.) - Relying on email opens/clicks as main signals. - Chasing “engagement” that never leads to revenue.
Keep It Simple, Iterate, and Trust Your Gut
You don’t need a PhD in data science to build a solid lead scoring system in Matchkraft. Get the basics right, stay focused on what’s real, and keep adjusting as you learn. Don’t let “automation” replace common sense. The goal is to help you—and your sales team—focus on leads that actually matter. Start simple, improve as you go, and don’t be afraid to toss out what isn’t working.
Now go sort your leads and get back to work.