If your sales team is drowning in leads but closing too few, you’re not alone. Most platforms toss you a “lead score” and call it a day—but it’s rarely tuned for what you actually care about. That’s where custom lead scoring in Apollo comes in. This guide is for anyone who wants to stop guessing which prospects deserve attention, and actually put their data to work.
Let’s skip the fluff and get your scoring model working for your real-world pipeline.
Why Custom Lead Scoring Beats the Default
Apollo’s built-in scoring gives every lead the same treatment, based on generic rules. That might help if your business looks like everyone else’s—but let’s be honest, it doesn’t. Custom scoring lets you prioritize leads based on what actually matters to your team: industry, deal size, engagement, whatever moves the needle.
When custom lead scoring is worth it: - You’ve got more leads than your team can handle. - Your default scores ignore key signals (like company size or last reply). - Reps are wasting time on leads that never close.
If that’s you, keep reading. If not, you can probably skip all this.
Step 1: Get Clear on What “Good” Looks Like
Before playing with Apollo’s settings, figure out what defines a high-quality lead for your team.
Questions to ask:
- What traits do your best customers share? (Industry, location, company size, tech stack, decision-maker titles, etc.)
- Are there any red flags that disqualify a lead?
- What engagement signals matter most? (Email opens, replies, demo booked, etc.)
- Do certain products or services have radically different “best fit” profiles?
Pro tip: If you’ve never done this, pull your last 10 closed-won deals and your last 10 duds. Compare them. You’ll spot patterns that are way more useful than a generic scoring template.
Step 2: Map Your Criteria to Apollo’s Data
Apollo can pull in a ton of data—company info, contact details, activity history. Not all of it’s useful for scoring.
Focus on:
- Firmographic data: Industry, employee count, revenue, location.
- Contact data: Job titles, seniority, department.
- Engagement: Last activity, email opens, replies, meetings booked.
- Custom fields: Anything unique you track, like software used or budget.
Skip: Vanity metrics (like “time since creation”) and anything you can’t act on. If you don’t care about a field in your sales calls, don’t let it muddle your score.
Step 3: Set Up Your Custom Lead Scoring Model in Apollo
Here’s where you actually build your scoring logic.
3.1. Find the Lead Scoring Settings
- Log into Apollo.
- Go to “Settings” → “Lead Scoring.”
- Choose to create a new custom scoring model, or edit the default.
3.2. Add Your Scoring Rules
You’ll be able to assign point values to different conditions. Think of it like: “If the lead matches X, add Y points.”
Examples: - +20 points if industry = “SaaS” - +15 points if employee count > 100 - +10 points if job title contains “Director” or “VP” - +25 points if last email reply is within 7 days - -30 points if “Do Not Contact” is checked
Tips: - Don’t go nuts with dozens of rules—the more complex, the harder it is to maintain (and explain). - Assign higher point values to deal breakers or huge positives. - Use negative points for true red flags (wrong industry, no budget, etc).
3.3. Set Score Ranges (Optional, but Useful)
Define what counts as “hot,” “warm,” or “cold.” Example:
- 70+ points = Hot
- 40–69 = Warm
- Under 40 = Cold
You can use these buckets to filter lists, trigger workflows, or just help reps focus.
Step 4: Test Your Model (Don’t Skip This)
Don’t trust your first attempt. Test your scoring model with real data.
How to sanity check:
- Pick 20 random leads you know well. See if the new scores “feel right.”
- Compare recent closed-won deals: Do they score high? If not, why?
- Ask a few reps if the model matches their gut sense of lead quality.
If things look off: Tweak your rules and point values. Expect to do this a few times.
Don’t obsess over perfection: You’ll always be tuning as your business evolves.
Step 5: Put It to Work
Once your model seems solid, roll it out.
How to use your new scores:
- Filtering: Have reps prioritize “hot” leads daily.
- Automation: Trigger sequences or reminders for high-scoring leads.
- Reporting: Track conversion rates by score bucket. If “hot” leads aren’t closing, revisit your model.
- Feedback loop: Every month or quarter, review the model. What’s working? What feels off? Adjust.
What to ignore:
- Don’t create “score inflation” by adding points for everything. Keep it meaningful.
- Don’t let the score be the only thing you look at—some great leads will always slip through the cracks.
- Don’t expect the model to magically fix a broken sales process or bad product-market fit.
Honest Takes: What Works, What Doesn’t
What works well: - Clear, simple rules tied to actual business outcomes. - Regular reviews with the sales team. - Using scores to focus efforts, not replace judgment.
What doesn’t: - Overcomplicating with dozens of tiny rules. - Blindly trusting the score over common sense. - Treating the model as “set and forget.”
What to ignore: - Buzzwords promising “AI-powered scoring” unless you have the data (and patience) to train it. - Any metric you don’t understand or can’t explain to a new rep in 30 seconds.
Keep It Simple—Iterate as You Go
Custom lead scoring isn’t rocket science, but it’s not magic either. Start with a basic model, sanity-check with your team, and tweak as you learn. The goal is to help your reps spend their time wisely—not to chase some mythical “perfect score.”
You can always refine your scoring as your team, product, and market change. Don’t let perfect be the enemy of useful. Keep it simple, listen to feedback, and keep moving forward.