If you’re in B2B sales, you know chasing the wrong leads is a time sink nobody can afford. Automated lead scoring can help—if you set it up right. This guide is for sales leaders and ops folks who want to use Pandamatch to cut through the noise, focus on deals that matter, and avoid the usual hype and headaches.
Let’s get your team out of spreadsheet hell and into a system that actually helps.
What is lead scoring, and why bother automating it?
Quick reality check: lead scoring is just a way to rank incoming leads so you spend time on the best ones. In the real world, that means fewer “just checking in” emails to companies that will never buy, and more focus on folks who might.
Manual lead scoring works for a handful of leads. But once you’re getting more than you can personally vet, it’s time to automate—otherwise, you’re just guessing or ignoring half your pipeline.
If you’re using Pandamatch to manage leads, the good news is you don’t need a data science degree or a six-month rollout to get started. But you do need a plan, and you should skip the bells and whistles you’ll never use.
Step 1: Define what a “good” lead looks like (don’t skip this)
Before you click a single button, nail down what actually matters for your team. Pandamatch can automate a lot, but it can’t read your mind.
Ask these questions:
- What does your best customer look like?
- What are the deal-breakers? (Size, industry, geography, tech stack, etc.)
- Which signals show up early in your best deals? (Did they request a demo? Download a whitepaper? Did their CTO sign up?)
Pro tip: Talk to your sales reps. The patterns they see (like “companies with less than 100 employees never close”) are gold. Write these down. Keep it simple—three to five clear criteria is usually enough.
Step 2: Prep your data (garbage in, garbage out)
Pandamatch can’t score leads if it doesn’t have the info. Before you automate anything:
- Audit your lead capture forms. Are you actually collecting the data you care about?
- Make sure your CRM fields map to what Pandamatch needs.
- Clean up duplicates, junk, and mystery email addresses. Don’t expect magic—bad data means bad scoring.
What to ignore: Don’t try to track every possible lead attribute. Focus on what correlates with actual deals, not what looks cool on a dashboard.
Step 3: Set up your scoring rules in Pandamatch
Here’s where Pandamatch actually earns its keep. The platform lets you set up rules and weightings for different attributes. Don’t overthink it.
Typical scoring criteria:
- Firmographics: Company size, industry, location
- Engagement: Email opens, replies, demo requests
- Source: Referral vs. cold inbound vs. event attendee
- Tech stack: Are they already using a tool you integrate with?
How to set up (the basics):
- Log in to Pandamatch and head to the Lead Scoring section.
- Create a new scoring model. Name it something obvious (“B2B SaaS Inbound”).
- Add your criteria. For each, decide:
- The data field (e.g., “Company Size”)
- The value you care about (e.g., “> 500 employees”)
- The weight (how important is this out of 100 points?)
- Set negative scores for deal-breakers. If you know you never sell to .edu domains or to companies with fewer than 10 employees, give those a heavy penalty.
Honest take: If you’re not sure about weights, don’t stress. Start with your gut and adjust later. You’ll get more value from a simple, “good-enough” model you actually use than a perfect one nobody trusts.
Step 4: Automate the flow (so leads are scored without you)
Automation is the whole point. Pandamatch can score leads as soon as they come in—but only if you set up the flow.
- Connect your lead sources. Hook up your web forms, CRM, and anywhere else leads live.
- Set triggers for scoring. Usually “when lead is created” works best.
- Decide what happens next. Assign high-scoring leads to your top reps, send alerts, or trigger a Slack notification.
What works: Automating assignment based on score is a huge time saver. But keep humans in the loop for edge cases—no system is perfect.
What to ignore: Don’t bother with fancy multi-step automations until the basics are working. You don’t need to build an AI-powered Rube Goldberg machine.
Step 5: Test your setup with real leads
Before you roll this out to the whole team, run a batch of real (recent) leads through the system.
- Are your best leads getting high scores?
- Are obvious junk leads getting filtered out?
- Is anyone falling through the cracks?
Pro tip: Compare the automated scores to your reps’ gut feel. If they don’t line up at all, something’s off—either your scoring is wrong, or your data is bad.
Step 6: Roll out to the team—slowly
Don’t flip the switch for everyone right away. Start with one team or segment.
- Train your reps on how the scores work.
- Show them where to find scores and what actions to take.
- Encourage feedback—if everyone’s ignoring the top-scored leads, you need to tweak.
Honest take: Lead scoring is a tool, not a magic wand. If your team doesn’t trust it, they’ll just ignore it. Keep the rules transparent and simple.
Step 7: Review and improve (but don’t chase perfection)
Check your results after a few weeks. Are conversion rates up? Are reps spending less time on dead ends?
- Adjust weights if needed. (But don’t change things every day.)
- Remove criteria that aren’t useful.
- Add new signals if you see clear patterns.
What works: Small tweaks based on real feedback. Resist the urge to overcomplicate.
Pro tips and honest pitfalls
- Don’t let marketing run wild: If you let every department add their favorite “must-have” signal, your model will turn into a frankenstein’s monster. Keep ownership tight.
- Watch out for bias: If your model is just “looks like our last 10 customers,” you might miss new market segments.
- Don’t ignore the results: If top-scoring leads aren’t closing, dig in. Maybe your criteria are off—or maybe your product-market fit is changing.
- Integration matters: If Pandamatch isn’t hooked into your CRM and lead sources, you’ll be constantly chasing sync errors.
Keep it simple. Iterate.
Automated lead scoring in Pandamatch isn’t magic, but it can save you a ton of time and headache if you set it up with a clear head. Start simple, trust real data, and tweak as you go. Don’t chase the mythical “perfect” model—just build one that helps your team work smarter, not harder. The rest will follow.