If you’re drowning in demo requests and LinkedIn messages, but sales is still chasing their tails, you’re probably not scoring your leads—or you’re doing it by hand. Manual lead scoring is tedious and unreliable. But automating it? That’s a game changer, especially if you use Make to glue together your sales stack. This guide is for sales ops folks, growth hackers, or anyone sick of wasting time on leads that go nowhere.
Here’s how to actually set up automated lead scoring in Make for your B2B sales pipeline, with zero fluff and plenty of real talk about what works, what doesn’t, and what to skip.
Why Automate Lead Scoring (and What Not to Expect)
Let’s be clear: automated lead scoring won’t magically double your close rate or replace sales skills. What it will do:
- Cut out busywork so reps focus on the right leads.
- Give you a repeatable, unbiased system for ranking prospects.
- Make your sales pipeline a lot less chaotic.
But don’t expect it to: - Fix a broken sales process or bad product-market fit. - Read human nuance, like sarcasm in an email or a gut feeling.
If your sales data is a mess, automate later. Clean that up first.
Step 1: Map Out Your Lead Scoring Rules (Don’t Overthink It)
Before you touch Make, decide how you’ll score leads. This is where most people get lost in the weeds. Don’t try to build Salesforce’s scoring model on day one.
Start with simple, obvious rules: - Demographics: Company size, industry, job title. - Engagement: Opened an email, booked a demo, visited pricing page. - Source: Referral, paid ad, inbound form, etc.
Example: - +20 points: Booked a demo - +10 points: Company has 100+ employees - +5 points: Visited pricing page - –10 points: Generic free email (Gmail, Yahoo, etc.)
Write your rules in plain language first. You can always tweak them later.
Pro tip: Get input from sales reps. They know what a good lead actually looks like.
Step 2: Gather Your Lead Data—Don’t Build on Sand
Automation needs fuel: data. Figure out where your lead info lives:
- CRM (HubSpot, Pipedrive, Salesforce, etc.)
- Marketing tools (Mailchimp, Intercom, web forms)
- Website analytics (Google Analytics, Clearbit)
- Spreadsheets (not ideal, but hey, it happens)
The more scattered your data, the more work you’ll need to do. You don’t need everything in one place yet, but you do need to know where everything is and how to pull it into Make.
What to ignore: Don’t try to sync every field. Stick to data that actually feeds your scoring rules.
Step 3: Set Up Your Make Scenario
Here’s where Make comes in. If you haven’t used it, it’s a visual automation tool that connects apps without code. (If you want to see what it looks like, check out their site: Make.)
A lead scoring automation usually looks like this:
- Trigger: New or updated lead in your CRM (or wherever your leads appear first).
- Data Enrichment (optional): Pull extra info from tools like Clearbit or LinkedIn.
- Score Calculation: Apply your rules to the data.
- Update: Tag the lead in your CRM, notify sales, or whatever comes next.
Here’s how to build it:
3.1. Create the Trigger
- In Make, create a new scenario.
- Add a trigger module for your source (e.g., “Watch new records” in HubSpot).
- Set how often to check for new leads—start with every 15 minutes or hourly.
3.2. Pull in Additional Data (If Needed)
- Need more info than your CRM has? Add a module to enrich the lead.
- Clearbit, Hunter.io, or even LinkedIn scraping can fill gaps.
- Only do this if it’s worth the API cost and complexity. Most teams don’t need it at first.
3.3. Calculate the Score
- Add a router or conditional logic module.
- Use Make’s built-in functions to assign points based on your rules.
- For example: If “company size” > 100, add 10 points.
- Sum up the points in a variable.
3.4. Update the Lead Record
- Add a module to update the lead in your CRM with their score.
- Or, add a tag/field (“Hot lead,” “Cold lead,” etc.).
- Optionally, send a Slack alert or email to the assigned rep for leads over a certain threshold.
Pro tip: Always log errors and test with dummy data first. Otherwise, you’ll have no idea if something breaks.
Step 4: Test (and Break) Your Automation
Don’t just hit “run” and hope for the best. Test your scenario with different lead types:
- High-quality leads (should get a high score)
- Junk leads (should get a low score)
- Edge cases (missing data, weird formats)
Check that scores update correctly and that nothing falls through the cracks.
What people mess up: - Forgetting to handle missing data (e.g., no “company size” field). - Not testing with real, messy data (everyone’s test data is too clean).
Step 5: Put Your Scores to Work
Lead scoring is pointless if nobody uses it. Make sure your sales reps:
- See the score front and center in their CRM.
- Know what actions to take (call the “hot” leads first, ignore the “cold” ones).
- Have a way to give feedback if the scores feel off.
Good ways to use scores: - Trigger follow-up tasks for leads above a threshold. - Prioritize outreach for high scores. - Filter newsletters or nurture campaigns based on score.
What to skip: Don’t hide scores in a custom field nobody looks at. Don’t over-automate—reps still need to actually talk to people.
Step 6: Iterate (Seriously, Don’t Skip This)
Your first scoring model will be wrong. That’s fine—just don’t let it rot.
- Review results after a few weeks. Are the right leads being flagged?
- Ask sales if the scores make sense.
- Adjust your rules in Make. It’s usually just tweaking a few numbers or adding a new condition.
Automated doesn’t mean set-it-and-forget-it.
Pro tip: Keep a changelog of your scoring tweaks. It’s easy to lose track of what changed and why.
What Works, What Doesn’t, and Stuff to Ignore
Works well: - Simple point-based rules you can explain to a new hire. - Regular reviews with sales on what “good” leads look like. - Automating just enough to save time but not so much that nobody trusts the system.
Doesn’t work: - Copy-pasting lead scoring templates from the internet. - Overcomplicating with AI or machine learning if you don’t have thousands of leads. - Letting automation replace human judgment.
Ignore: - Fancy dashboards if nobody uses them. - “Predictive” scoring unless you’ve got serious data science chops (most don’t). - Scoring fields that sales never look at.
Keep It Simple and Keep Moving
Automating lead scoring in Make isn’t rocket science, but it does take some clear thinking and a willingness to adjust as you go. Start with the data you have, set up the basics, and let your sales team break it for you. Don’t obsess over perfection—get something live, see what works, and tweak from there.
The fastest path to better sales isn’t more automation. It’s building something your team actually uses. So keep it simple, stay skeptical of hype, and iterate in small steps. You’ll be surprised how much time you free up—and how much less chasing dead leads you’ll do.