Step by step guide to setting up automated lead scoring in HoneyPipe

If you’re swimming in inbound leads, but can’t tell the tire-kickers from the real buyers, you need lead scoring. This guide is for anyone who’s using HoneyPipe and wants to stop guessing who to follow up with. I’ll show you exactly how to set up automated lead scoring that’s useful—without getting lost in a maze of settings or “AI-powered” hype.

Let’s get into it.


Why bother with automated lead scoring?

Before you set up anything, here’s the honest pitch: Automated lead scoring saves you time and helps your sales team focus on leads that might actually buy. It’s not magic, and it won’t fix a broken sales process, but it will stop you from wasting time on leads that’ll never close.

Here’s what works: - Automatic sorting of leads by “fit” or “intent” - Clear, simple rules—no need to overthink it - Ongoing tweaks as you learn

What doesn’t work: - Overcomplicating your score model (trust me, nobody will use it) - Blindly trusting “AI” to figure out your business - Set-it-and-forget-it attitudes

If you’re ready to get your hands dirty, let’s go step by step.


Step 1: Map out what makes a good lead for your business

Don’t even open HoneyPipe yet. The worst thing you can do is start scoring leads before you know what matters.

Ask yourself: - What does a “hot” lead look like? (Company size, job title, industry, actions taken) - What makes a lead a bad fit? - Which actions (like demo requests or pricing page visits) actually predict a real opportunity?

Pro tip: Talk to your sales team. They know which leads are time-wasters. Write down 3-5 traits or actions that matter most. Keep it simple.


Step 2: Clean up your lead data

Automated scoring is only as good as your data. Garbage in, garbage out.

Checklist: - Make sure lead sources, job titles, company names, and emails are coming in cleanly. - Merge duplicates—don’t let the same person show up twice. - Standardize data fields (e.g., “VP Sales” vs “Vice President of Sales” should mean the same thing).

Ignore: Fancy data enrichment tools until you’ve got your basics sorted. Most teams can start with just the data they’re already collecting.


Step 3: Set up basic scoring rules in HoneyPipe

Now, finally, open up HoneyPipe. Navigate to the lead scoring section (usually under “Automation” or “Lead Management”).

Here’s how to build a scoring model that won’t drive you nuts:

  1. Create a new scoring rule set.
  2. Name it something obvious, like “2024 Lead Scoring v1”.

  3. Add criteria based on what matters most. Examples:

  4. Job title: +10 points for “Director” or above
  5. Company size: +5 points for companies with 100+ employees
  6. Industry: +8 points if they’re in your target vertical
  7. Website activity: +7 points for a demo request, +3 points for viewing pricing page

  8. Set negative scores for deal-breakers.

  9. -10 points for generic email domains (e.g., Gmail)
  10. -15 points for industries you never sell to

  11. Choose a scoring range.

  12. Keep it simple, like 0–100. If you go overboard, nobody will understand what the numbers mean.

  13. Save and activate the rule set.

Side note: Resist the urge to add 20 different criteria. Start with 3–5, or your team will ignore the scores.


Step 4: Automate score updates

HoneyPipe lets you automatically update scores as new info comes in.

  • Turn on real-time scoring: So scores update instantly when a lead takes action (fills out a form, visits your site, etc.).
  • Connect your data sources: Plug in your CRM, website analytics, and any forms you use. If it’s not integrated, scores won’t update properly.
  • Test with a dummy lead: Make sure the scores change when you trigger an action.

What to ignore: Don’t waste time integrating every possible data source right away. Start with what your team actually uses. You can add more later.


Step 5: Set up alerts and workflows

You’re scoring leads automatically—now make it useful for sales.

  • Create alerts for high-scoring leads: Set up notifications in HoneyPipe (email, Slack, whatever works) when a lead crosses a threshold (e.g., 70+ points).
  • Assign leads automatically: Use workflows to route hot leads straight to reps, or flag them for a callback.
  • Set up “cold lead” workflows: Automatically move low-scoring leads to nurture campaigns.

Pro tip: Don’t overwhelm your team with noise. Tune the alerts so only truly promising leads trigger them.


Step 6: Train your team (quickly)

This is where most setups fail—the scores are there, but nobody trusts them or knows what to do.

  • Host a short walkthrough: Show reps what the scores mean, what triggers them, and how to use them in conversations.
  • Give examples: “A score over 70 means reach out now. Under 40? Drop them into nurture.”
  • Ask for feedback: If reps say the scores feel off, listen. They’re your reality check.

Ignore: Long-winded documentation. Keep it practical and relevant to daily workflows.


Step 7: Review and tweak regularly

No lead scoring model is perfect out of the box. Expect to adjust as you learn.

  • Check monthly: Are the high scores matching up with closed deals? If not, adjust your criteria or points.
  • Look for false positives/negatives: If you’re getting lots of junk leads with high scores, or good leads with low scores, revisit your rules.
  • Talk to sales: They’ll tell you if the scores are helping or just adding noise.

What to ignore: Don’t chase every outlier. Focus on patterns.


What about “AI-powered” lead scoring?

You’ll see plenty of hype about machine learning picking your best leads for you. Here’s the truth:

  • AI works best when you have tons of data—think hundreds or thousands of deals.
  • If you’re just starting, rules-based scoring is faster, easier, and less likely to go off the rails.
  • Once you’ve got a handle on your process, sure, experiment with AI scoring. Just don’t expect miracles.

Wrapping up: Keep it simple, iterate often

Automated lead scoring in HoneyPipe can be a huge time saver and help you focus on the right leads—but only if you keep it simple and stay flexible. Start with a handful of scoring rules. Get your team using the system. Iterate based on real feedback, not wishful thinking.

If something feels like overkill, it probably is. The best scoring systems are the ones people actually use. Good luck—and don’t be afraid to tweak as you go.