How we score leads at Belkins: Our CRM model, criteria & real-world logic
Author
Yevheniia Pashaieva
Yevheniia has been a professional copywriter since 2014. Her vast experience in writing for marketing, e-commerce, fin-tech, and pharmaceutical businesses in B2B and B2C enables her to craft resonating content of any complexity.
Reviewed by
Julia Sorokovikova
Julia is a head of sales operation at Belkins, posessing nearly two decades of experience in sales and account management.
Updated:2025-09-05
Reading time:10 min
Most B2B teams have more than enough leads, but they close almost no deals. That’s because they chase the wrong people.
We’ve been there. At Belkins, we’ve worked with thousands of leads across various industries, and we’ve seen what happens when there’s no systematic way to score them:
SDRs waste time.
High-potential leads get buried.
Sales cycles drag on.
After analyzing the situation and B2B best practices, we built our own lead scoring system designed for inbound leads that have already passed our initial qualification criteria. We also help clients implement similar frameworks to improve their inbound pipeline performance.
The goal? Separate the “maybe later” from the “talk-now” leads.
In this article, we’ll break down exactly:
How our scoring system works
Who’s involved
What B2B lead scoring criteria we track
And how it helps us prioritize efforts that actually convert.
For us, scoring is not a replacement for lead qualification. It’s a precision layer on top of it.
Once a lead is in our CRM and qualified (they match our ICP baseline and have responded to our marketing activities in some way), they enter our scoring system. This model evaluates how engaged and sales-ready they are, based on real signals, not gut instinct.
Lead score determines:
How fast we act on the lead
What type of messaging they’ll receive
What level of priority the SDR team assigns
With scoring, we avoid letting high-potential contacts slip through the cracks. It also helps us:
Prioritize high-intent, best-fit leads
Segment contacts based on engagement patterns
Refine and adjust our ideal customer profile (ICP) over time
Avoid relying solely on intuition or assumptions
“High lead scores confirm alignment with our ICP, tech stack, job title, and intent signals. But when high scorers don’t convert, we use that data to audit and refine our criteria. Just the same with low-scoring leads — if they start closing, we reanalyze what signals we may have undervalued. So, for us, scoring also acts as a feedback loop.”
We apply a point-based scoring system in HubSpot, where each lead receives or loses points based on actions and attributes. There are two types of signals:
Positive — indicate interest, fit, or engagement
Negative — signal low intent, bad fit, or deliverability issues
Positive scoring examples
Negative scoring examples
Visiting high-intent pages (pricing, case studies, contact us)
Ignoring emails (0 opens across 5+ sends)
Clicking links in marketing emails
Email bounces
Opening multiple emails
Unsubscribing from marketing communication
Filling out gated forms or downloading content
Attending webinars or virtual events
We assign different weights depending on how strong the signal is. Here are some examples of our B2B lead scoring criteria:
Viewing the pricing page = +10
Filling a download form = +15
Clicking 10+ marketing emails = +10
Email bounced = –25
Check out more lead scoring examples from our HubSpot:
Web events tracking
Sales emails engagement tracking
Marketing emails engagement tracking
Overall, every interaction matters. Behavioral thresholds are tested and adjusted based on what actually correlates with conversions.
If someone visits case studies but never engages beyond that, we lower the score impact over time. Thus, leads accumulate or lose points over time, and their score determines whether they move to the next stage or are deprioritized.
We also don’t use separate scores for company and contact. We focus on the contact’s behavior, as inbound is about personal connection.
Why our B2B lead scoring model works
We don’t aim for a “perfect” model — we aim for a responsive one. That means:
Fast identification of the most promising leads
Adaptive scoring based on conversion feedback
Scalable logic that works across industries
For example:
A B2B SaaS lead visiting the pricing page twice may score higher than a lead from the same industry who only opens emails.
A campaign for fintech flagged unusually high conversions from low-score leads — after investigation, we added a new behavioral signal to improve detection.
In one case, we found that repeat visits to one of our blog posts strongly predicted demo bookings — we added that to the scoring logic.
A mid-level manager who visits our website five times and engages with content scores higher than a C-level exec who visits once and unsubscribes from emails or never opens them. Activity trumps assumptions in our model.
Also, thanks to regular monitoring and adjustments of our scoring model, we manage to react proactively to any market changes. Sometimes it even makes us update the whole strategy, like it was with switching to omnichannel.
“With email privacy updates like Apple Mail’s changes, open rates have become unreliable. That’s why we’re shifting focus toward on-site behavior and deeper engagement signals — things like pricing page views or form submissions. We’re moving away from top-of-funnel vanity metrics and leaning more into bottom-funnel actions that actually predict revenue.”
— Julia Sorokovikova, Head of Revenue Operations at Belkins
Why we tailor scores by industry & ICP
We don’t believe in one-size-fits-all. Over time, we’ve learned that an ICP match is only the start.
Scoring helps uncover patterns, like when we discovered a segment of legal tech buyers who looked cold on paper, but converted at higher rates. Their shared behaviors led us to rethink our baseline criteria.
Now, we look more into the behavior of the lead, depending on their industry and other signals:
In SaaS, we score technographic data higher.
For marketing agencies, website engagement is a stronger indicator — for example, viewing success stories matters more.
In professional services, job titles (Director+ level) heavily influence scoring.
This helps our sales reps and marketers double down efforts on the most promising leads and adjust our communication accordingly.
Email marketers: Set up nurturing sequences based on the score earned by contacts.
Sales reps: Rely on scores to prioritize outreach and follow-ups.
Additionally, lead researchers can tune in to enrich our scoring results with the latest firmographic and technographic data.
“Lead scoring gives sales reps confidence. If someone hits a 70+ score, it means they’re already showing strong buying intent. That changes how fast we reach out and what we say.”
— Julia Sorokovikova, Head of Revenue Operations at Belkins
We review our lead scoring methodology monthly. Sales reps provide feedback on which scored leads convert well and which don’t. If needed, marketing ops will recalibrate thresholds or add/remove scoring triggers.
There’s also a QA layer built in: the marketing ops team tests new logic before it goes live. We never let assumptions run unchecked.
Tools and data sources
We run our scoring model inside HubSpot, and rely on:
Behavioral tracking from HubSpot forms, page views, and email engagement
Marketing automation data (e.g., webinar registrations via Demio)
Every score-triggering behavior is verified. We don’t add points for accidental visits. For example, a contact must visit the pricing page at least twice to count. This protects the model from inflating scores with noise.
What changed in our system over time (and why it matters)
In the past, we used a simpler tiered model (A/B/C leads). But it was too rigid — many B-tier leads were converting, while some A-tier leads went cold.
Since we moved to the weighted point system, we’ve seen:
3x faster SDR follow-up on high-intent leads
21% increase in demo-to-close ratio (in campaigns using the scoring system)
Better alignment between sales reps and marketing with less “Why did you send me this contact?” frustration.
In one inbound campaign, refining lead scoring helped us reallocate 40% of SDR time from dead-end leads to genuinely promising contacts — all without increasing lead volume.
To sum it up
Scoring doesn’t replace qualification. It amplifies it with pattern recognition and behavioral signals human reps might miss.
Strong lead scoring system helps us:
Save SDR time by filtering out non-engaged leads
Improve campaign ROI through better segmentation
Align messaging to the lead's actual journey
Update ICPs based on real-world data, not just assumptions
We’ve built this system to support our own inbound pipeline, but we also help clients implement tailored versions, based on their goals, tech stack, and buyer behavior.
If you’re sitting on hundreds of inbound leads but aren’t sure which ones are worth your time, talk to us. We can help you:
Build a sophisticated scoring model, tailored to your ICP
Define scoring logic that matches your real buyer signals
Yevheniia has been a professional copywriter since 2014. Her vast experience in writing for marketing, e-commerce, fin-tech, and pharmaceutical businesses in B2B and B2C enables her to craft resonating content of any complexity.
Expert
Julia Sorokovikova
Belkins' head of sales operations
Julia brings nearly two decades of experience in sales and account management. She ensures seamless operations at Belkins through CRM optimization, workflow automation, and by enabling high-performing teams through strategic improvements.
