If you’re a B2B sales manager or ops lead, you already know the pain: too many leads, not enough time, and half your reps chasing tire-kickers. Automated lead scoring can help—if you set it up right. This guide cuts through the noise and walks you through how to build a practical, automated lead scoring workflow in Jason AI, so you can spend less time sorting and more time selling.
Why bother automating lead scoring?
Manual lead scoring is a slog, and honestly, most people get lazy with it. Worse, when reps have to eyeball every inbound contact, you end up with hot prospects buried under a pile of junk. Automation gets you:
- Consistency. Everyone’s playing by the same rules, not gut feel.
- Speed. Hot leads get action before they go cold.
- Focus. Your team chases real opportunities, not random contacts.
But let’s be real: automation isn’t magic. It needs decent data, clear rules, and tweaks over time. If you don’t have those, no software is going to fix your funnel.
Step 1: Get your lead data in order
Garbage in, garbage out. Before you even open Jason AI, make sure your lead data is accurate and flowing into the system. Here’s what you need to check:
- Are all your lead sources connected? Website forms, LinkedIn, trade shows—whatever you use, they need to pipe into Jason AI.
- Is the data clean? Duplicate contacts, missing emails, or wonky job titles will throw off your scoring.
- Do you have firmographics? Company size, industry, role—these are gold for B2B scoring. If you’re missing them, consider using a data enrichment tool or manual research for top leads.
Pro tip: If your CRM is a mess, fix that first. Jason AI can’t score what it can’t see (or what’s mislabeled).
Step 2: Define your ideal customer profile (ICP)
You can’t score leads if you don’t know what “good” looks like. Sit down with your sales team and nail down your ICP—be honest about who actually buys, not just who you wish did.
Key traits to consider: - Industry/vertical - Company size (employees, revenue, geography) - Seniority/role of the buyer - Tech stack (if relevant)
Make a list. This will be the backbone of your scoring rules.
What to skip: Don’t overcomplicate it with edge cases or vanity traits. Focus on what really predicts a sale. You can always tweak later.
Step 3: Choose your lead scoring criteria
Jason AI lets you score leads on pretty much anything you import or track. That’s both a blessing and a curse. Here’s what typically works for B2B:
Firmographic signals - Industry match (e.g., target industries get +10 points) - Company size in target range (+8 points) - Job title or function (e.g., “Director” or “VP” gets +12)
Engagement signals - Opened an email campaign (+3) - Clicked a link in an email (+5) - Visited your pricing page (+7) - Requested a demo (+15)
Negative signals - Used a personal email address (e.g., Gmail, -5) - Bounced email or bad data (-10) - Unsubscribed or marked as spam (-20)
Pro tip: Don’t get cute. Five to eight criteria is plenty. More than that and you’ll spend all day tweaking and second-guessing.
Step 4: Build your scoring model in Jason AI
Now the hands-on part. In Jason AI, go to the lead scoring module (sometimes called “Scoring Rules” or similar—it may change with updates). Here’s how to set it up:
- Create or edit your lead scoring model.
- Give it a clear name (e.g., “B2B SaaS ICP Scoring Q2 2024”).
- Add your scoring criteria.
- For each rule, set the field (like “Industry”), the value (like “Software”), and the score (maybe +10).
- Use both positive and negative scores. Don’t just reward—penalize bad fits.
- Set up engagement triggers.
- Map actions (email opens, demo requests, site visits) to points.
- If Jason AI integrates with your marketing tools, connect those now. Otherwise, set up manual updates or import engagement data.
- Set your thresholds.
- Decide what’s a “hot,” “warm,” or “cold” lead. Example: 30+ points = hot, 15–29 = warm, under 15 = cold.
- Don’t obsess over the numbers yet; you’ll tweak them after a month or two of real use.
What to ignore: Fancy AI scoring models that claim to “learn” your perfect buyer in a week. Unless you’ve got thousands of closed deals in your database, these are just guesswork with better marketing.
Step 5: Automate your workflows
Scoring is only useful if you actually do something with it. In Jason AI, set up automations so leads move to the right buckets and people:
- Assign “hot” leads to sales reps automatically
- Use round-robin or territory rules
- Send “warm” leads to nurturing campaigns
- Trigger a drip sequence or schedule a follow-up
- Mark “cold” leads for periodic review, not immediate action
- Don’t waste time—let automation keep them on ice
Pro tip: Set up Slack/email alerts for hot leads. But don’t overdo notifications, or reps will start ignoring them.
Step 6: Test, tweak, and get feedback
No scoring model is perfect out of the gate. The real world is messy. Here’s what to do:
- Review closed deals and duds monthly. Did your model flag the right leads? If not, adjust the points or criteria.
- Ask your sales team what feels off. Are they getting too many “hot” leads that are actually junk? Or missing gems?
- Watch for false positives. Sometimes high engagement comes from tire-kickers or competitors. Adjust accordingly.
What works: Iterating every month or two, not daily. Don’t let “perfect” kill “good enough.”
Step 7: Keep it simple (and don’t fall for the hype)
It’s tempting to add more complexity—scoring for every little thing, using “AI intent signals,” or building branching automations for every scenario. But most of that is just noise, especially if your sales cycle is complex or your data is patchy.
- Stick to what actually predicts a sale.
- Update your model once a quarter, not every week.
- Don’t trust a black box. If you can’t explain your scoring to a new rep in five minutes, it’s too complicated.
Common mistakes (and how to avoid them)
- Relying only on engagement. Just because someone opens your emails doesn’t mean they’re a fit.
- Ignoring sales feedback. Your reps know when the scoring model is off—ask them.
- Letting it get stale. If your business shifts, so should your scoring.
- Chasing shiny features. Focus on what’s working, not what’s trendy.
Wrapping up: Keep it practical
Automating lead scoring in Jason AI isn’t rocket science, but it does take some upfront work and a willingness to adjust. Start simple, stay skeptical of hype, and listen to your sales team. You’ll save time, cut down on busywork, and actually close more deals.
Don’t aim for perfect—just get it working, then iterate. The best scoring models are the ones your team actually uses.