If your sales team is tired of sifting through cold leads or chasing folks who’ll never buy, automated lead scoring is a game changer. It’s especially useful for B2B teams juggling long sales cycles and picky buyers. This guide is for people who want to make their process smarter—not just noisier.
We’ll walk through how to set up automated lead scoring in Xiqinc, using real criteria, not wishful thinking. Whether you’re new to lead scoring or have been burned by “AI magic” promises before, you’ll get a setup that’s practical, flexible, and won’t waste your time.
Step 1: Know What Lead Scoring Is (and Isn’t)
Let’s get something straight: lead scoring is about ranking leads so your team knows who to talk to first. It’s not a crystal ball, and it won’t close deals for you. Good automated lead scoring helps you:
- Prioritize the right prospects
- Spot opportunities before your competitors
- Save time on leads that’ll never convert
But it won’t fix a broken sales process or make up for bad data. If your CRM is a mess or you don’t actually know your best customers, fix that first.
Step 2: Get the Right Data into Xiqinc
Lead scoring is only as good as the data you feed it. Before you set anything up, make sure your contacts and companies in Xiqinc are:
- Clean: Remove duplicates, fix typos, and fill in missing info.
- Segmented: Tag or categorize leads by industry, company size, or any field you actually use.
- Enriched: If you can, pull in data like revenue, tech stack, or recent activity. Don’t overdo it—just what’s useful.
Pro tip: Resist the urge to add 50 data fields just because you can. Most teams only use a handful in real scoring models.
Step 3: Map Your Ideal Customer Profile
Before you create your scoring rules, spend time sketching out what a “good” lead actually looks like for your B2B team. This isn’t about wishful thinking—it’s about real patterns from your closed-won deals.
Ask your team:
- What industries convert best?
- What job titles usually get involved?
- Are there company sizes or locations that never pan out?
- What behaviors (like demo requests or whitepaper downloads) signal actual purchase intent?
Write these down. If you can’t answer them, pull a report from Xiqinc or just talk to your top salespeople. Don’t invent criteria that sound nice but don’t match reality.
Step 4: Set Up Scoring Criteria in Xiqinc
Now, let’s get into the nuts and bolts:
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Go to Settings > Lead Scoring
In Xiqinc, find the lead scoring engine under your account settings or admin panel. If you can’t see it, you might need admin rights. -
Choose Your Scoring Model
Xiqinc usually lets you do either a simple point-based system or a rules-based model (some plans may have “AI” or “predictive” scoring, but start simple first). -
Add Demographic Criteria
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Examples:
- +10 points if industry = SaaS
- +7 points if job title = Director or above
- -5 points if company size < 10 employees
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Add Behavioral Criteria
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Examples:
- +15 points for attending a webinar
- +10 points for opening three emails
- +20 points for requesting a demo
- -10 points for never visiting your pricing page
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Set Score Thresholds
- Decide what score means “hot,” “warm,” or “cold.” Start with rough guesses—e.g., 50+ is hot, 30–49 is warm, below 30 is cold.
- Don’t worry about being perfect. You’ll tweak this later.
What to ignore:
Don’t bother with vanity metrics—like giving points for downloading your PDF if it never leads to real sales. And don’t try to score every possible action. Focus on what’s actually predictive.
Step 5: Automate the Process
Manual scoring is a pain and defeats the point. Xiqinc can recalculate scores automatically when data changes—if you set it up right.
- Turn On Auto-Scoring: Make sure “Automatic lead score updates” (the actual label may vary) is enabled.
- Connect Your Marketing Tools: If you use email marketing, webinars, or chat tools, integrate them with Xiqinc so behaviors sync over automatically.
- Set Notifications: Choose who gets notified when a lead crosses the “hot” threshold. Don’t spam everyone; pick the right sales reps.
Heads up:
Some integrations can be finicky. Test each connection—sometimes fields don’t sync as promised, or you end up with duplicate activities. Clean up issues early.
Step 6: Test with Real Leads (Don’t Rely on Theoretical Models)
Before you roll this out to your whole team, run your scoring model against a few months of real leads:
- Pull up a list of your recent closed-won and closed-lost deals.
- See what scores your new model assigns.
- If your best customers aren’t showing up as “hot”—or you see junk leads at the top—adjust your criteria.
This step matters more than any fancy algorithm. The best scoring models are always a little bit wrong at first.
Step 7: Roll Out and Train Your Team
Automated lead scoring doesn’t do much if your sales team ignores it. Here’s how to get them on board:
- Short Demo: Show how leads get scored and what “hot” actually means.
- Explain the Why: Make it clear that this saves time, not adds busywork.
- Feedback Loop: Encourage reps to flag leads that feel off—this is how you improve the model.
- Update Documentation: Write a one-pager or quick guide so people can check how scoring works. Don’t hide the logic.
What usually goes wrong:
Teams set up scores, never explain them, and then wonder why reps ignore “hot” leads. Or, they make the model a black box and people stop trusting it.
Step 8: Review and Tweak (Regularly)
Lead scoring isn’t a “set it and forget it” thing. Schedule time every month (or quarter, at least) to review:
- Are the “hot” leads actually converting?
- Are good leads getting missed?
- Have your best customer profiles changed?
Make small tweaks, not huge overhauls. If you keep fiddling every week, nobody can trust the scores.
Honest Takes: What Works, What Doesn’t
What works: - Keeping your scoring simple at first—then adding complexity as you learn. - Using real sales feedback instead of marketing “best practices.” - Connecting behavioral data from marketing tools (when it works).
What doesn’t: - Overloading the model with 20+ criteria—nobody can explain or debug it. - Relying on “AI” models without understanding what features they use. - Treating lead scoring as a replacement for real conversations.
Wrapping Up: Start Simple, Improve as You Go
Don’t let perfect be the enemy of good. The best B2B teams use lead scoring as a compass—not a GPS. Get your model live, see what works, and don’t be afraid to tweak it. Focus on criteria that actually predict sales, ignore the rest, and trust your team’s instincts more than any algorithm.
And if you ever feel lost, just ask yourself: “Would I actually call this lead?” If the answer is yes, you’re scoring right.