Ever feel like your sales team is chasing ghosts, or wasting time on leads that will never buy? You’re not alone. Manual lead scoring is slow, arbitrary, and usually ignored. If your pipeline is more wishful thinking than reality, it’s time to automate.
This guide is for anyone running B2B sales—especially if you’re using Velaris as your CRM. We’ll cut through the theory and show you, step by step, how to set up lead scoring that actually works. No BS, no magic formulas—just practical steps you can use right now.
Why Automate Lead Scoring (and What Not to Expect)
Let’s get this out of the way: automating lead scoring won’t magically fix a broken sales process, or turn bad leads into good ones. But if you want:
- Less time wasted on dead-end leads
- A clearer sales pipeline
- Fewer “gut feeling” debates in your team meetings
…automation helps. It’s about removing guesswork, so your team can focus on leads that might actually close.
What not to expect: - Crystal balls. Lead scoring is only as good as your data and logic. - Zero maintenance. You’ll need to tweak things as you go. - Sales reps suddenly loving admin tasks. Sorry.
Step 1: Get Your Lead Data in Order
Automated lead scoring is only as good as the data you feed it. Garbage in, garbage out. Before you touch settings, check:
- Are your lead records up to date?
- Are fields like company size, job title, and engagement history actually filled in?
- Is your data coming from reliable sources (website forms, email, LinkedIn, etc.)?
Pro tip: Don’t try to automate scoring if half your leads are missing basic info. Fix your forms and integrations first.
Step 2: Define What a “Good” Lead Looks Like (For Real)
This is where people get lazy—don’t just copy a blog post’s example. Sit down with your best reps and ask:
- What do our top customers have in common?
- What are early warning signs a lead will ghost us?
- Which actions (email opens, demo requests, downloads) actually predict a real deal?
Make a short list of must-haves, nice-to-haves, and red flags. For example: - Must-haves: Company size over 50, decision-maker title, industry fit - Nice-to-haves: Attended webinar, replied to a sales email - Red flags: Gmail address, never opened a single email, “student” in job title
Don’t overcomplicate. Three to five key traits is plenty.
Step 3: Map Out Your Scoring Criteria
Turn your list into scoring rules. Each trait or action gets a point value, positive or negative. Some ideas:
Demographic (Fit) factors: - +10: Company size > 100 - +5: Industry matches your target list - +7: Senior job title (Director+) - -10: Personal email address
Behavioral (Engagement) factors: - +8: Attended a demo - +4: Opened a recent email - +6: Downloaded a whitepaper - -5: No activity in 30 days
Pro tip: Don’t get hung up on perfect numbers. You’ll adjust them later.
Step 4: Set Up Lead Scoring in Velaris
Now it’s time to build your system inside Velaris. (If you’re not familiar, Velaris is a CRM with a decent automation engine—good, but not magic.)
4.1. Find or Create Custom Fields
- In Velaris, create a custom field called “Lead Score” (numeric).
- If you want to track different types of scores (fit vs. engagement), make a field for each.
- Make sure these fields are visible on lead or contact records.
4.2. Build Your Scoring Rules
- Go to the automation or workflow builder in Velaris.
- Set up “if/then” rules to add or subtract points based on your criteria.
- Example: If “Industry” is “Software,” then add 5 points to “Lead Score.”
- Example: If “Email opened” in last 7 days, then add 4 points.
- Chain rules together so scores update as new info comes in.
4.3. Automate Score Updates
- Make sure your rules run automatically when a lead is created or updated.
- Schedule a regular workflow (daily or hourly) to recalculate scores for all leads—so nothing gets stale.
4.4. Set Score Thresholds
- Decide what “hot,” “warm,” and “cold” actually mean in numbers.
- For example: 20+ = hot, 10–19 = warm, <10 = cold.
- Create views or filters in Velaris so reps can quickly see which leads to focus on.
What to ignore: Don’t bother with super granular scoring (like +1 for every web page visit). It just creates noise. Stick to signals that actually matter.
Step 5: Test and Refine (Don’t Set and Forget)
Your scoring model is a guess, especially at first. Here’s how to avoid the common trap of “set and forget”:
- After a week or two, pull a report: Are your hottest leads actually converting?
- Ask sales reps which leads are getting missed or mis-scored. Adjust your rules.
- Drop or change any criteria that aren’t predictive. “Attended a webinar” might sound great, but if it doesn’t correlate with deals, downgrade or remove it.
- Review your scoring every quarter—pipelines and buyer behavior change.
Pro tip: If everyone’s a “hot” lead, your model is too generous. If nobody is, it’s too strict.
Step 6: Put Scoring Into Action
Scoring means nothing if nobody uses it. Here’s how to make it actually help:
- Pipeline views: Set up saved lists in Velaris so reps can see hot/warm/cold leads at a glance.
- Alerts: Use Velaris automations to notify reps when a lead crosses a threshold (e.g., “Lead Score > 20”).
- Handoffs: If you have an SDR/AE split, use scoring to trigger handoffs or stage changes.
- Reporting: Regularly review which scored leads are progressing, and which aren’t.
If reps ignore the score, ask why. Sometimes your model needs fixing; sometimes, it’s a training issue.
Step 7: Avoid the Hype (And the Pitfalls)
A few things to keep in mind:
- Don’t obsess over AI add-ons or predictive scoring promises unless you’ve nailed the basics. Most teams just need a clear, simple model.
- Too many criteria = confusion. If your team can’t explain your scoring, it’s too complex.
- Lead scoring is a tool, not a crystal ball. It helps prioritize, but doesn’t replace human judgment.
- Garbage data will kill you. Keep forms and integrations clean or none of this works.
Wrapping Up: Keep It Simple, Iterate Often
Automating lead scoring in Velaris isn’t rocket science, but it does take some thought. Start simple—use the data you have, focus on what actually predicts a good lead, and make adjustments as you learn. Don’t get sucked into “set and forget” mode or fancy tools you don’t need. The best systems are the ones that get used.
Keep your scoring model tight, your data clean, and your process transparent. Iterate as you go, and you’ll actually see your pipeline improve—no magic required.