If your sales team is constantly complaining about bad leads, or if you’re not seeing the pipeline you expected, it’s time to get serious about your lead scoring model. This guide is for anyone using Madkudu who wants to stop guessing and actually make their lead scoring work. Whether you’re in marketing ops, demand gen, or just the go-to data person, you’ll find practical steps—no fluff, no nonsense.
1. Know What You Actually Want From Lead Scoring
Before you dive into dashboards, get real about the goal. Lead scoring isn’t magic—it just helps your team prioritize. You want to:
- Surface leads that are likely to convert (not just fill out forms)
- Reduce wasted sales time on junk leads
- Give clear, explainable scores (so sales trusts them)
Don’t aim for “perfect” scores. You’ll never catch every good lead or weed out every bad one. The goal is to help your team make better bets, not predict the future.
2. Get Your Data House in Order
Madkudu is only as good as the data you feed it. Garbage in, garbage out. Before you tweak models:
- Check data quality: Are fields like company, job title, and email being captured reliably? Are enrichment tools (Clearbit, ZoomInfo, etc.) up to date?
- Audit input fields: Make sure things like UTM parameters, signup sources, and product usage events are being tracked accurately.
- Watch for silent failures: Are integrations with your CRM or MAP (like Salesforce or Marketo) breaking? If so, you’ll get weird results or missing leads.
Pro tip: Pull a sample of “high scored” and “low scored” leads. Do they look right? If not, your data might be the culprit—not the model.
3. Benchmark: How Is Lead Scoring Performing Now?
Don’t tweak blindly. First, figure out if your scoring is actually helping. Here’s how:
a. Check Conversion Rates by Score
- Break leads into score bands (e.g., High, Medium, Low).
- For each band, look at:
- Conversion to Opportunity: What % of High leads become opportunities?
- Win Rate: What % of High leads eventually close?
- Speed: How quickly do High leads move through the funnel?
If your “High” leads aren’t converting better (or faster) than “Low” leads, your model isn’t working.
b. Talk to Sales
- Ask: “Are the leads marked High actually good?”
- Gather a short list of recent High/Medium/Low leads and sanity-check with reps.
- If sales is ignoring the scores, find out why. Sometimes it’s trust, sometimes it’s just habit.
c. Look for Red Flags
- Tons of “High” leads but low conversion? Probably too loose.
- Very few “High” leads and most deals come from “Medium”? Your threshold’s too strict.
4. Dig Into Madkudu’s Model Insights
Madkudu isn’t a black box, but you have to know where to look. Spend time in these spots:
a. Signals & Drivers
- Signals are the data points Madkudu uses (industry, company size, job title, product usage events, etc.)
- Drivers show which signals are actually influencing scores.
Check: - Are the top drivers things that make sense for your business? - Are any signals outdated or irrelevant (like “@gmail.com” getting a high score for enterprise deals)?
b. Distribution Charts
- Look at how leads are being scored across your funnel.
- If nearly everyone is “Medium,” your model isn’t separating wheat from chaff.
- If “High” is mostly existing customers or partners, something’s off.
c. Madkudu Predictions vs. Reality
- Madkudu offers “prediction” charts—use them!
- Compare predicted conversion rates to actuals for past cohorts. If the model is way off, it’s time for a tune-up.
5. Identify What (and How) to Optimize
You know what’s broken. Now, here’s what you can actually fix:
a. Fix Data Issues First
- If your best leads aren’t being scored High, check if key fields are missing or mapped wrong.
- Fix enrichment gaps. Sometimes, one wrong field (like “industry”) tanks the whole score.
b. Adjust Signal Weighting
- Madkudu lets you tweak how much weight to give certain signals.
- If “Title: CEO” is overweighted but your buyers are usually Directors, adjust accordingly.
c. Cut Out Noisy or Useless Signals
- Remove signals that add confusion. More isn’t always better.
- If “clicked one email” is a top driver but doesn’t correlate with conversion, turn it down or off.
d. Rethink Score Thresholds
- Don’t be afraid to adjust what counts as High, Medium, or Low.
- Look for a sweet spot: enough High leads for reps to work, but not so many that they ignore the score.
Pro tip: Any change should be small and deliberate. Tweak, check results for a week or two, and iterate. Don’t overhaul everything at once or you’ll lose track of what worked.
6. Test, Measure, Repeat
Optimizing lead scoring isn’t a one-shot deal. Here’s a simple cycle:
- Make a change.
- Give it time. Wait at least a week (ideally a full sales cycle) before judging.
- Re-run your benchmarks: Are High leads converting better? Are reps happier? Is there less junk in the funnel?
- Document what you did. You’ll thank yourself later.
- Repeat. Keep at it until you see real, concrete improvements.
7. What Not to Waste Time On
- Chasing “perfect” accuracy: Lead scoring is rough math, not a crystal ball.
- Overloading with signals: More data isn’t always better—sometimes it’s just noise.
- Over-engineering models: Fancy algorithms can make things worse if you can’t explain them to sales.
- Ignoring sales feedback: If reps don’t trust the scores, your model’s dead on arrival.
8. Pro Tips for a Smoother Process
- Keep sales in the loop: Show your work. Even just a monthly check-in builds trust.
- Automate reporting: Make dashboards that show conversion by score band. Don’t rely on gut feel.
- Document changes: Even a simple Google Doc with “what we changed and when” saves headaches.
- Stay suspicious: If something looks too good to be true (like 80% of High leads converting), dig deeper. It rarely is.
The Bottom Line
Don’t get sucked into endless tweaks or aim for lead scoring perfection. The best models are simple, understandable, and actually help your team work smarter. Fix your data, make one change at a time, and keep a close feedback loop with your sales team. Iterate until the complaints drop and results improve. That’s really all there is to it.