Setting up lead scoring shouldn’t feel like rocket science, but most teams overthink it (or skip it entirely). If you’re using Bullseye and want to actually help your sales team focus on the right leads, this guide is for you. No fluff—just a straightforward way to get lead scoring working for B2B sales, minus the headaches.
Why Bother With Lead Scoring at All?
Let’s get this out of the way: lead scoring isn’t magic. It won’t turn weak leads into closed deals. But it will help your team stop wasting time on folks who’ll never buy.
- Good lead scoring: Pushes the best-fit prospects to the top, helps sales focus, and gives marketing real feedback.
- Bad lead scoring: Adds noise, gives false confidence, and irritates your team.
So if you want to skip the “spray and pray” approach, let’s get started.
What You Need Before You Start
You’ll need: - Access to Bullseye - A clear idea of your ideal customer profile (ICP)—who actually buys and sticks around - Agreement between sales and marketing on what a “good lead” looks like - Enough data to work with (at least a trickle of leads)
If you’re missing these, don’t bother setting up lead scoring yet. You’ll just be guessing.
Step 1: Audit Your Current Lead Data
Before you build anything, look at what you’ve got.
Action items: - Run a report of your closed/won deals from the past 6–12 months. - List out the traits and behaviors those leads had in common (company size, industry, job titles, web activity, etc.). - Note any “deal-breakers” (traits that always lead to dead-ends).
Pro tip: Don’t make stuff up here. If you don’t have enough historical data, keep your first scoring model simple and gut-driven, then revisit it later.
Step 2: Map Out Your “Fit” and “Behavior” Criteria
Bullseye, like most CRMs, lets you score based on who someone is (“fit”) and what they do (“behavior”).
Fit criteria examples: - Industry (Are they in your sweet spot?) - Company size (Does this match your target deal size?) - Job title (Are you talking to the decision maker?)
Behavior criteria examples: - Email opens/clicks - Website visits to key pages (like pricing or demo) - Downloaded a whitepaper or attended a webinar - Replied to outreach
Don’t overcomplicate it. Pick 2–4 fit criteria and 2–4 behavior criteria max to start.
Step 3: Assign Point Values (Without Guessing Wildly)
Here’s where most teams go off the rails: they assign 100 points to everything and hope for the best.
How to do it right: - Assign more points to “fit” than “behavior”—a great-fit lead who’s lukewarm is worth more than a bad-fit who downloads every eBook. - Use a simple scale: e.g., 5–25 points per criterion. - Make deal-breakers negative (e.g., -100 for students if you only sell to enterprises).
Sample scoring table:
| Criteria | Points | |-------------------------|-----------| | Industry: SaaS | +20 | | Company Size: 100–500 | +15 | | Job Title: Director+ | +25 | | Visited Pricing Page | +10 | | Attended Webinar | +10 | | Opened 3+ Emails | +5 | | Gmail Address Used | -20 |
Pro tip: Don’t assign points to things just because you can track them. Only use data that actually correlates with real deals.
Step 4: Build Your Lead Scoring Model in Bullseye
Now, open Bullseye and put your plan into action.
4.1 Create Custom Fields (if needed)
- Set up custom fields for any traits Bullseye doesn’t track by default (like “Job Title Level” or “Target Industry”).
- Map these fields to your lead capture forms and import processes.
4.2 Set Up Scoring Rules
- Navigate to Lead Scoring settings in Bullseye.
- Add your fit and behavior criteria, point values, and negative scores.
- Set up any “must-have” rules (e.g., leads from specific countries are always disqualified).
4.3 Test With Dummy Data
- Create a few test leads that match different profiles (good fit, bad fit, high activity, etc.).
- Check their scores—do the “best” leads bubble to the top?
- Tweak point values if something looks off.
Honest take: If your first model feels “meh,” that’s normal. Most teams need 2–3 rounds to get it useful.
Step 5: Define What Happens Next (Don’t Skip This)
A lead score is useless if nobody acts on it.
- Decide what score qualifies a lead for sales follow-up. (E.g., above 60 points = send to sales.)
- Set up workflows in Bullseye to notify your sales team, change lead status, or trigger emails when someone crosses the threshold.
- Make sure everyone knows what these scores mean—one short training beats endless confusion.
Don’t: Automatically assign every “high score” to sales. Use this as a signal, not a command.
Step 6: Review, Refine, and Don’t Get Precious
Set a calendar reminder to revisit your scoring every 1–2 months, especially in the first six months.
What to look for: - Are the “hot leads” actually converting, or are they duds? - Are sales folks ignoring high scores? Ask why. - Is any one criterion skewing scores too much (e.g., everyone who downloads your PDF jumps to the top)?
Change it fast: Don’t be afraid to drop points, add new criteria, or remove noise. This isn’t permanent.
What to Ignore (For Now)
- Scoring every possible action: If you need a spreadsheet to track it, you’ve gone too far.
- Predictive AI scoring: Unless you have thousands of deals’ worth of data, this is mostly smoke and mirrors.
- Scoring “engagement” that doesn’t matter: Just because someone visits your blog daily doesn’t mean they want to buy.
Stick to what’s actually shown to matter for your sales team.
Quick Troubleshooting
- Scores feel random? Your criteria are probably too broad or too shallow. Narrow it down.
- Sales still complains about “bad leads”? Ask them to show you real examples, then update your criteria.
- You’re not getting enough “hot” leads? You might be too strict—lower your threshold, but keep an eye on quality.
Wrap-Up: Keep It Simple, Tweak Often
Lead scoring in Bullseye is only as good as the effort you put in—and the willingness to admit when it’s not working. Start simple, use real data, and don’t treat your first model as gospel. Your goal isn’t perfection; it’s to get sales and marketing rowing in the same direction. Iterate, ignore the hype, and your team will thank you.
Now get out there and put those scores to use.