If you’re running B2B sales, you know the pain: too many leads, not enough time, and an inbox full of “maybe later” replies. Lead scoring is supposed to help, but most guides are all theory—no details, lots of hype. This guide is for sales ops folks, marketers, or anyone who actually has to build and use lead scoring in the real world. We’ll dig into how to set up lead scoring models in Saasydb, what’s worth your time, and what you can skip.
Why bother with lead scoring?
Let’s be real: not every lead deserves your team’s attention. The goal isn’t to automate away thinking—it’s to help reps focus on real opportunities and ignore the tire-kickers. A good lead scoring model helps you:
- Prioritize who to call first (and who to ignore)
- Hand off leads to sales at the right time
- Stop wasting everyone’s time on dead ends
If you want to actually close more deals—and keep your reps sane—lead scoring is worth the effort.
Step 1: Get your data in order
Before you even look at Saasydb’s lead scoring features, you need clean, usable data. If your CRM is a mess, your scoring model will be too. Garbage in, garbage out.
What you need: - Up-to-date lead and contact info (company, job title, email, etc.) - Activity data (opens, clicks, pageviews, demo requests, etc.) - Deal history (so you know what a “good” lead actually looks like)
Tips: - Sync your CRM and marketing tools with Saasydb. Don’t rely on CSV imports unless you like pain. - Run a quick audit: Are fields standardized? Any obvious junk data? - If you’re missing activity data, fix that first. Lead scoring without behavior data is pretty weak.
What not to stress about:
Don’t wait for perfect data. Just get the basics right and fix the rest as you go.
Step 2: Define what a “good lead” actually means
This is the step most teams skip, but you can’t build a useful model if you don’t know what you’re aiming for.
Do this: - Pull a list of your last 20–50 closed-won deals. - Look for patterns: company size, industry, job titles, specific actions taken (like requesting a demo). - Ask your best reps what signals they look for.
Key categories: - Demographic fit: Are they in your ICP? (Ideal Customer Profile) - Behavioral signals: Did they attend a webinar? Visit your pricing page 5 times? - Negative signals: Student email addresses, generic company names, spammy domains.
Pro tip:
Write it down. If your team can’t agree on what a good lead is, you’ll never agree on your scoring model.
Step 3: Set up a basic scoring model in Saasydb
Time to log into Saasydb and actually build something you can use. Here’s how to avoid the common traps.
1. Navigate to Lead Scoring
- In Saasydb, go to the “Scoring Models” section under your Sales/Marketing workspace.
- Hit “Create new model.” Name it clearly (e.g., “2024 B2B Lead Score”).
2. Pick your scoring criteria
Saasydb lets you add both demographic and behavioral criteria. Don’t go overboard—start simple.
Examples of criteria to include:
- Job title: +10 for VP or Director roles
- Company size: +5 for 100–1000 employees
- Industry: +8 for your top 3 verticals
- Requested demo: +25 (this is a strong buying signal)
- Clicked email link: +5
- Visited pricing page: +10
- Generic email address: –10 (e.g., Gmail, Yahoo)
- No website or LinkedIn: –10
What works: - Use a mix of firmographic (company details) and behavioral (actions taken) scores. - Start with 5–7 criteria. You can always add more later.
What to ignore: - Don’t bother with vanity metrics (like “followed us on Twitter”) unless you know they correlate with deals.
3. Assign point values
This part is more art than science, at least at first. Don’t obsess over the exact numbers—just make sure the most important signals have higher values.
How to keep it simple: - Use round numbers (+5, +10, +25) - Negative values for big red flags - If you’re unsure, ask: “Would a rep care about this?”
4. Save and activate the model
- Hit “Save” and then “Activate.” Saasydb will start scoring new and existing leads using your model.
Pro tip:
Don’t activate multiple models for the same team unless you want confusion. Start with one, refine as you go.
Step 4: Test and tune your model
Here’s where most teams go wrong: they set it and forget it. Don’t do that.
1. Spot-check the results
- Pull a list of your highest-scoring leads. Do they look like good fits?
- Ask reps: “Do these top leads make sense?”
- Look for false positives (junk leads with high scores) and false negatives (good leads buried at the bottom).
2. Adjust your weights
If you notice patterns—like every student gets a high score—tweak the model: - Lower points for weak signals - Increase penalties for obvious junk - Boost points for behaviors that really move the needle (like a requested demo)
Honest take:
Don’t expect to nail it on the first try. Most teams need 2–3 rounds of tweaks before the scores start feeling right.
3. Automate handoffs
Once your scores look decent, use Saasydb’s automation features: - Set up rules to assign leads to reps when they cross a score threshold (e.g., “Sales Qualified Lead” at 60+ points) - Send alerts or create tasks automatically
What to ignore:
Don’t automate everything. Some leads need a human gut check before they hit sales.
Step 5: Measure what matters
A scoring model is only useful if it helps you close more deals—or at least saves reps time.
Track these: - Are high-scoring leads converting to pipeline faster? - Are reps wasting less time on junk leads? - Are good leads slipping through the cracks?
Saasydb tips: - Use built-in reports to track lead score vs. conversion rates. - Meet with sales and marketing monthly to review what’s working (and what isn’t).
Reality check:
If your top-scoring leads aren’t closing, go back to Step 2 and rethink your criteria.
Step 6: Keep it simple and keep improving
Don’t fall for shiny new features or one-click “AI” scoring unless you’ve nailed the basics. The best lead scoring models are usually simple, clear, and regularly updated. Here’s how to keep things on track:
- Review your model every quarter
- Prune unused criteria—less is more
- Ask for rep feedback often
Skip this:
You don’t need a data scientist or a six-month project plan. Start small, ship something, and adjust.
Wrapping up
Lead scoring in Saasydb isn’t magic, but it does help your team focus on the right prospects if you build it with real-world signals and keep it honest. Don’t overthink it: get your data straight, define what “good” looks like, and start simple. Iterate as your team learns, and don’t be afraid to throw out what isn’t working. The best models are living documents, not set-it-and-forget-it checklists. Now go build something useful—and save your reps a few headaches along the way.