B2B lead scoring criteria examples pulled from real revenue teams, not slide decks.
Fit, intent, engagement, firmographic, behavioral, and negative scoring criteria running on every inbound record, every day, with transparent weights, published thresholds, and the exact routing rule that follows each tier. Strkr ships the scoring engine, the field editor, the audit log, and the recalibration workflow natively, so your model gets smarter every quarter instead of rotting in a spreadsheet.
Fit, intent, engagement, and negative, each with its own math.
A B2B lead scoring model that works in production rests on four axes rather than one. Fit asks whether the account matches the ideal customer shape. Intent asks whether the person or the account is researching the problem right now. Engagement asks whether the person has done anything on your surfaces that signals interest. Negative asks whether a disqualifying signal has fired. Each axis has its own weighting, its own threshold, and its own feedback loop into the next axis. Collapsing them into one number is the shortcut that makes most scoring models useless by month three. Strkr keeps them separate on the record and lets your admin tune each one against the actual conversion data your book throws off.
Fit score
How close the account is to your ICP.
Industry, employee count, revenue band, tech stack, headquarters region, growth rate. Each criterion carries a weight your admin sets. A manufacturing account at 500 employees in your target region scores 72 of 100 before the person ever opens an email. The fit score is firmographic math on account properties, not a reaction to a form submission.
Intent score
The research signal, right now, this week.
Topic-level intent data from your integrated provider, keyword spikes on your category, product-review activity on sites you track, and job postings matching your buyer titles. Intent is time-boxed. A 90-point intent score two months ago is a cold number today, so the engine decays stale intent inside a rolling window your admin configures.
Engagement score
What the person actually did on your surfaces.
Email opens, email replies, meeting booked, pricing-page views, demo-video completion, trial start, feature usage, repeat visits. Engagement weights by recency and by depth. A trial start outweighs ten pricing-page views. A reply outweighs a hundred opens. The engagement score reads the actions, not the vanity metrics.
Negative score
Signals that disqualify, no matter the fit.
Competitor employee domain, student email domain, country outside your coverage, blocked region, prior closed-lost with current competitor in use, hard opt-out on consent. Negative points subtract from the total and can drop a lead below a routing threshold even when every other axis is lit up green.
Account vs person
Score the account, score the person, both.
The account carries fit and intent at the entity level. The person carries engagement and role fit at the individual level. A junior-title person at a perfectly matched account scores differently than the VP of operations at the same account, and the model surfaces both numbers side by side rather than collapsing them.
Tier buckets
A, B, C, D, with thresholds you set.
Numeric totals roll into tier letters your admin configures. Tier A at 85 plus, tier B at 70 to 84, tier C at 50 to 69, tier D below 50 is a common starting point. The tier chip is what the SDR sees first on the inbound queue. The numeric score sits one hover away for the ops team that cares.
Decay windows
A score that does not go stale.
Engagement signals carry age-weighted decay. A pricing-page view from this morning weighs full value, a view from six weeks ago weighs twenty percent of that, a view from six months ago weighs almost nothing. The decay curve prevents a lead that was hot last quarter from sitting at the top of the queue when the moment has passed.
Signal transparency
Every point, traced back to its driver.
Hover any score on any lead and the explanation panel shows the top five contributing signals and the exact point value each one added. "Director title plus twelve. Pricing page three times plus nine. ICP industry plus eight." The SDR sees why the lead got the number and opens the conversation on the right foot.
Nightly recalibration
The model learns from closed-won versus closed-lost.
Every night, Strkr AI compares the scores on recently closed deals against outcomes and surfaces drift. "Leads with intent above 70 that scored tier A converted at 11 percent, down from 18 percent last quarter. Suggested fit weighting change." The admin accepts or rejects the suggestion and the model stays honest.
Firmographic criteria examples with weights
The account-shape math, every field, every weight.
Firmographic fit is the first axis most B2B scoring models get wrong because the vendor ships a one-size-fits-all template. The real version carries weights your admin tunes against the real win rates in your book. These are the fields teams actually score on in Strkr today, with suggested weight ranges that work as starting points before your own data refines them. Industry mix, employee bands, revenue bands, geography, tech stack, growth signals, and funding stage each carry their own weighting rule.
Industry match
Primary SIC code or inferred category.
Target industries score plus fifteen to plus twenty-five. Adjacent industries score plus five to plus ten. Non-target industries score zero or negative five. The industry is the single highest firmographic signal because it drives everything downstream, from the pitch to the pricing plan to the implementation playbook.
Employee band
Count by sweet spot, not just by size.
Your sweet spot band scores plus twenty. One band below scores plus ten. Two bands below scores zero. One band above scores plus fifteen. Two bands above scores plus five. The curve is not linear because enterprise and SMB each carry different conversion patterns and the scoring has to respect the curve your win rates reveal.
Revenue band
ARR estimate from enrichment data.
Public financials or inferred revenue bands from your enrichment provider. Teams selling into mid-market weight revenue bands around 10 million to 250 million at plus fifteen, with higher and lower bands tapering. Revenue bands are a weaker signal than employee count for most B2B SaaS buyers because the correlation to budget is noisy.
Geography
Primary region, secondary region, blocked.
Primary coverage region scores plus ten. Secondary region scores plus five. Non-coverage region scores zero. Blocked region (sanctioned, non-serviceable) scores a negative that disqualifies. The field is operationally critical because the SDR team does not want tier A leads routed from countries the business cannot sell into.
Tech stack match
What they use, what they do not.
Enrichment data on current tools. Using a complementary tool scores plus ten. Using a direct competitor scores plus five or minus five depending on your displacement strategy. Using nothing in the category scores plus fifteen. A tech-stack signal is the strongest leading indicator of implementation friction for most B2B tools.
Growth signal
Hiring pace, funding, headcount delta.
Companies posting target-role jobs score plus ten. Companies with a recent funding round in the past 180 days score plus ten. Companies with net-positive headcount growth over the past quarter score plus five. Growth is a buy-signal multiplier because budget and urgency tend to appear together.
Funding stage
Early, growth, late, public, bootstrapped.
Pattern varies by product. A seat-priced SaaS tool often scores Series B through D highest because budget exists and the org is still agile. An enterprise tool scores late-stage and public highest. The scoring template includes the stage field so admins can tune the curve to the product.
Named account flag
Strategic list overrides the math.
Accounts marked strategic by the account team get a baseline floor regardless of the computed fit. A tier A floor on a strategic account means the SDR layer never under-prioritizes a named target because the enrichment data misread the shape. The flag is auditable and reviewed quarterly.
Blacklist
Current customers, partners, no-fly list.
Existing customers in a non-cross-sell segment score as handled. Partners route to the partnership team. The internal no-fly list (abusive accounts, non-serviceable accounts, legal flags) subtracts enough points to drop the record below any routing threshold. The list is admin-managed and respected by every scoring pass.
Behavioral criteria examples with recency weighting
What the person did, how recently, how deeply.
Behavioral criteria are the second axis and the one most sales teams run out of patience with because the point values get out of hand fast. The honest version weights by three things: the action depth (clicked an email versus booked a demo), the recency of the action (today versus last quarter), and the frequency pattern (one spike or a steady drumbeat). These are the behavioral criteria examples your admin can configure in Strkr with suggested starting weights that production teams run today.
Email engagement
Opens, clicks, replies, forwards.
A reply scores plus fifteen. A forward scores plus ten. A click scores plus five. An open scores plus one. The spread is wide on purpose because replies are intent and opens are noise. Across a 20-email sequence the opens pile up quickly and would otherwise swamp the one reply that actually matters.
Pricing page views
The most honest buying signal there is.
First pricing view scores plus ten. Repeat pricing view inside seven days scores plus eight. Three or more views in seven days scores an additional plus ten modifier. The pricing page is where self-serve buyers reveal themselves, and the scoring respects that pattern rather than treating it as another article view.
Demo video completion
Watched more than seventy percent.
Started the demo video scores plus three. Watched past seventy percent scores plus twelve. Watched to the end scores plus five more. Video intent is a credible mid-funnel signal because the person has already invested the time, and the completion curve correlates with actual demo-request rates in most B2B categories.
Form submissions
Not every form is a hand-raise.
Contact-sales form scores plus thirty. Demo-request form scores plus twenty-five. Trial-signup form scores plus twenty. Newsletter form scores plus three. Gated-content form scores plus five. The spread stops a whitepaper download from sitting at the same tier as a demo request, which is the single most common scoring mistake teams make.
Trial activity
Started, invited a teammate, hit a value milestone.
Trial signup scores plus twenty. Invited a second user scores plus fifteen. Reached the first value milestone (first import, first flow, first dashboard) scores plus twenty-five. Trial behavior is the strongest pre-paid signal and the engagement weighting reflects that.
Case study depth
Which case study, how long on page.
Reading the industry-matched case study scores plus eight. Reading two case studies in one session scores plus six. Reading an unrelated case study scores plus two. The industry match matters because a manufacturing buyer reading the manufacturing case study is doing homework in a way a generic reader is not.
Event attendance
Webinar, roadshow, trade show scan.
Attended live webinar scores plus eight. Watched webinar replay scores plus four. Scanned at a trade show booth scores plus ten. Registered for an event and no-showed scores minus two (the signal is that attention did not clear). Event behavior correlates with intent but not uniformly, so the weights stay modest.
Chat conversation
Started a chat, asked a buying question.
Started chat scores plus five. Asked pricing-related question in chat scores plus fifteen. Booked meeting from chat scores plus twenty. The chat transcript parses in Strkr AI and tags intent-shaped questions automatically, so the scorer reads substance rather than session count.
Recency multiplier
Today weighs more than last month.
Behaviors inside seven days keep full weight. Behaviors between seven and 30 days keep sixty percent. Behaviors between 30 and 90 days keep thirty percent. Beyond 90 days, behaviors keep ten percent or decay out entirely for most categories. The decay curve is tenant-configurable because B2B cycle lengths vary widely.
Intent and negative criteria examples
The signals that qualify and the signals that disqualify.
Intent data is the third axis and the one most teams either over-weight or ignore entirely. The useful version treats intent as a trailing signal that confirms the research moment rather than a leading signal that generates the lead. Negative criteria are the fourth axis and the one teams tend to forget until a competitor employee gets routed to a VP of sales as a tier A inbound. Both axes carry transparent weights and a short list of production-ready criteria examples below.
Topic surge
A tracked topic spiked on this account.
Topic-level surge from your intent provider scores plus fifteen. Topic sustained over 14 days scores an additional plus five. A single-day spike that does not sustain scores half of that. The sustained signal is more credible than the one-day blip because actual buying journeys unfold over weeks, not hours.
Keyword research
The account is reading your category.
Searching your product category scores plus eight. Searching a competitor name scores plus five. Searching a comparison query ("X vs Y") scores plus ten. The comparison query is the strongest keyword signal because it signals late-funnel evaluation rather than top-funnel awareness.
Review-site activity
G2, Capterra, Gartner page views.
Viewing your review page scores plus ten. Viewing a competitor review page in the same category scores plus six. Writing a review request on another vendor scores plus eight. Review-site intent is a reliable late-funnel signal in most B2B software categories and carries weight accordingly.
Job postings
Hiring for a role that uses your product.
A posted job for a role that is a direct user of your product scores plus twelve. Hiring in the broader department scores plus five. Hiring freeze or layoff announcement scores negative five. Hiring is one of the few leading indicators of planned tool spend, so the weight is meaningful.
Competitor employee
The person works at your competitor.
Email domain matches a known competitor scores negative one hundred, which is deep enough to drop the lead below any routing threshold. The admin maintains the competitor list and the scorer blocks demo-request forms from those domains before the SDR layer ever sees them.
Personal email
Free-mail domain on a demo request.
Gmail, Outlook, Yahoo, Hotmail on a form submission scores negative ten. The person might still be a buyer, so the lead routes but to a lower-priority queue. The signal is not disqualifying, but it does downweight because the correlation to serious evaluation is lower than a corporate domain.
Opt-out status
Hard opt-out drops the record.
Any contact who has filed a hard opt-out on marketing communication scores a negative that drops the record and prevents it from re-entering a sequence. The compliance posture carries through to the scorer so the model never promotes a lead the business is not allowed to contact.
Blocked region
Geography the business cannot sell into.
IP geolocation or stated country matches a blocked region. Score drops below routing threshold and the record moves to the suppressed queue. The suppressed queue is auditable so the team can monitor the volume and the fit for later expansion into those regions.
Student or freelancer
Credit-card buyer at a non-ICP title.
Title field matches "student", "intern", "freelance consultant" and the firmographic signal is thin. Score drops a tier and routes to the self-serve queue. Not every low-fit record is useless, but the SDR layer should not spend its capacity on records that are below the self-serve bar.
Routing rules each tier triggers
A score is only useful if a rule reads it.
A scoring model does nothing on its own. The tier the record lands in has to trigger a routing rule, an assignment rule, a cadence, a notification, or a flow. Strkr wires routing to the scoring tier natively, so the admin configures the scorer once and the rest of the stack respects the result. These are the routing rules real revenue teams run on top of a working scoring model in Strkr.
Tier A instant assign
Hot leads land with the right rep in seconds.
A tier A inbound routes to the named account owner if the account is on the strategic list, or to the next SDR in the round-robin for the matching territory otherwise. The assignment fires inside two seconds of the form submission. The SDR gets a Slack notification, the lead carries a 15-minute SLA, and the record opens with the context panel pre-populated.
Tier B queue
Mid-fit leads enter the daily SDR scrub.
Tier B leads land in a daily SDR queue sorted by score and recency. The SDR works the queue top-down each morning. The queue carries a 24-hour SLA. If a tier B lead sits unworked past the SLA, it escalates to the SDR manager and surfaces on the Monday standup list.
Tier C nurture
Low-fit, high-engagement leads join nurture.
Tier C leads route to the marketing nurture track for 60 days. If a tier C lead picks up engagement signal and crosses into tier B, the nurture track pauses and the lead moves to the SDR queue. The transition is a flow trigger and runs without a human loop touching the record.
Tier D suppress
Record created, no human effort spent.
Tier D leads sit in the record store for enrichment and future recalibration but do not land in any human queue. If the fit score improves because the enrichment provider refreshes the firmographic data, the record can promote on the next nightly pass without re-submission.
SDR to AE handoff
Qualified lead becomes a sales opportunity.
When an SDR qualifies a tier A or B lead and books a meeting, the handoff rule fires. The lead converts to a contact plus opportunity, the AE on the account picks it up, and the SDR gets credit on the attribution report. The handoff is one click, not a four-step wizard.
Weekend cover
A tier A lead on Saturday does not wait.
The routing rule respects a weekend cover schedule. Tier A inbounds Friday evening through Sunday evening route to the on-call rep rather than the regular round-robin. The team captures the lead inside the SLA even on weekends, which is the point where most inbound SLAs fall apart.
Named account override
The account owner catches named inbounds.
If an inbound belongs to a named account regardless of tier, the named-account owner is first in line. The round-robin skips the owner and goes to the territory queue only if the owner is out of office or over SLA on previous assignments.
Partner routing
Known partner leads route to the partner team.
A lead from a known partner domain routes to the partner manager instead of the SDR queue. The partner manager owns the follow-up, logs the partner-sourced flag, and the attribution report credits the partner correctly on close. The rule respects the partner list and recalibrates when the list updates.
Enterprise escalation
Tier A at a Fortune 500 brings in the AE.
A tier A inbound at a Fortune 500 account escalates past the SDR and routes directly to the named enterprise AE. The AE accepts the handoff, loops the SDR for context, and the ratchet is set so the enterprise motion starts on the right rep rather than a double-handoff that costs a day.
How scoring plugs into the rest of Strkr
The score is a field, not a feature in its own tool.
Scoring that lives in a separate marketing automation tool and syncs to the CRM once a day is scoring that goes stale, drifts out of the sales workflow, and never gets looked at. Strkr runs scoring on the lead record itself, inside the same object store the pipeline runs on, with the same permission model and the same audit log. These are the surfaces the score touches natively.
Lead record
Score, tier, signals, routing destination.
The lead record carries the four axis scores, the overall tier, the top five contributing signals in plain English, and the routing destination that fired. The SDR reads the score on the same page as the contact info, no second tool, no refresh delay.
List views
Sort, filter, group the queue by score.
Every list view supports score and tier as sort columns and filter facets. The SDR builds a view for "tier A, this week, my territory" and lives inside that view all day. The ops team builds a view for "tier B leads over 24-hour SLA" and audits coverage from the same surface.
Flows on score change
A tier change triggers a sequence.
Strkr flows watch the score field and fire on tier changes. Promote from tier C to tier B and the flow enrolls the lead in the SDR cadence. Drop from tier A to tier B and the flow pauses the hot-lead sequence. The flow engine runs natively, not through a Zapier middle layer, and does not add a syncing delay.
Reports and dashboards
Score as a reportable field, everywhere.
Every report and dashboard builder treats score and tier as first-class fields. Build a report that compares tier A conversion rate month over month, pin it to the sales operations dashboard, and schedule a snapshot to the SDR manager. No separate BI tool, no sync job.
Strkr AI explanations
Ask the assistant why a lead got a score.
The assistant drawer on a lead answers natural-language questions about the score. "Why did this score tier A." "What would move this to tier A." "Which three leads today are highest fit." The assistant reads the scoring config and the audit log and returns answers citing specific signals.
Permissions
Who sees the score, who sees the config.
The permission model controls visibility separately for the computed score, the scoring config, and the audit log. SDRs see the score and the tier. Ops sees the config. Managers see the audit log. Field-level permissions respect the tenant hierarchy so finance partners see their fields and sales partners see theirs.
Audit log
Every weight change, by whom, when, why.
Every change to the scoring configuration lands in the audit log with the user, the timestamp, the before state, and the after state. If a weight change hurts conversion, the team can trace the change, roll it back, and see the comparative before and after. The audit log is non-editable and tamper-evident.
Imports and migration
Bring your current scoring over, do not reinvent it.
The import wizard reads a scoring model exported from HubSpot, Marketo, or a custom spreadsheet and lands it in Strkr as the starting configuration. The admin tunes from a familiar baseline rather than a blank page. Weighting changes layer on top of the migrated config without losing the historical model.
Open API
Push and pull scores through the API.
The REST API exposes the score field for read and write, so an external enrichment pipeline can push data in and an external attribution tool can pull scores out. Webhooks fire on tier changes. The scoring engine is a native primitive, not a locked box, and your data engineering team can wire the surfaces it needs.
A scoring model that stays honest because the data lives on the record.
Fit, intent, engagement, and negative scoring all ship on every paid tier with transparent weights, published thresholds, and nightly recalibration. Open the scoring configuration on your own data, drop in the firmographic and behavioral criteria that match your book, and watch the tier queue sort itself.
What is a lead score and what does a B2B scoring model actually need?
A lead score is a number assigned to a lead or an account that estimates the probability of conversion to a sales opportunity and eventually to a closed deal. A credible B2B scoring model carries four axes rather than one. Fit scores the account against your ICP using firmographic criteria. Intent scores the research signal from topic data, keyword activity, and review-site behavior. Engagement scores what the person did on your surfaces, weighted by recency and depth. Negative scoring subtracts points for disqualifying signals such as competitor domains, blocked regions, or hard opt-outs. Collapsing all four into one number is the single most common scoring mistake and the reason most scoring models get abandoned within a quarter.
What are concrete examples of firmographic criteria and starting weights?
Common firmographic criteria include industry match (plus 15 to plus 25 for primary targets), employee band (plus 20 for your sweet spot, tapering above and below), revenue band (plus 10 to plus 15 depending on category), geography (plus 10 for primary coverage, zero for non-coverage, deep negative for blocked regions), tech stack match (plus 10 to plus 15 for complementary tools), growth signals (plus 5 to plus 10 for hiring pace and funding), and named-account flags (overrides for strategic lists). Starting weights are a baseline rather than a prescription. The scoring engine in Strkr recalibrates nightly against closed-won and closed-lost outcomes, so the initial weights refine themselves against your real conversion data within a quarter or two.
How do behavioral scoring criteria work with recency weighting?
Behavioral criteria measure what a person actually did on your surfaces: email opens, email replies, pricing-page views, demo-video completion, form submissions, trial activity, case-study depth, event attendance, chat conversations. Each action carries a base point value weighted by depth. A reply is worth more than a click. A trial start is worth more than an opened email. On top of that, Strkr applies a recency decay curve. Actions inside seven days keep full value. Actions between seven and 30 days keep 60 percent. Actions between 30 and 90 days keep 30 percent. Beyond 90 days, actions keep around 10 percent or decay out entirely for most categories. The decay curve is tenant-configurable because cycle lengths vary across B2B categories.
What are examples of negative scoring criteria that disqualify a lead?
Negative scoring is the axis most teams forget. Common examples include competitor employee domains (deep negative to drop the record below any routing threshold), personal or free-mail domains on a demo request form (moderate negative that routes to a lower-priority queue), hard opt-out status from a prior unsubscribe, blocked regions outside your coverage, student or intern titles when the ICP is senior, and specific blacklist entries such as prior fraud or non-serviceable accounts. The negative axis is important because without it a competitor employee can land as a tier A inbound and route straight to a VP of sales, which is the kind of embarrassing failure mode that erodes trust in the whole scoring system.
How does the lead score connect to routing and the rest of the CRM?
The score is a native field on the lead record, so every other system in Strkr reads it directly. Routing rules fire on tier changes to assign tier A inbounds to the named account owner or the territory round-robin inside two seconds. Flows watch the score and enroll leads in the appropriate cadence when the tier changes. List views sort, filter, and group by score. Reports and dashboards treat score and tier as first-class fields. Strkr AI explains why a specific lead got the score in plain English. There is no sync job, no separate marketing automation tool, and no middleware layer, which means the score never goes stale between surfaces.
Does lead scoring ship on every tier, or is it an add-on?
Lead scoring is a core capability of Strkr and ships on every paid tier at no extra charge. The scoring engine, the field editor, the audit log, the routing rules, and the nightly recalibration are all native. Strkr AI runs on every tier with no credit meter and no separate SKU, which means the recalibration suggestions and the natural-language score explanations are part of the seat price. The open API exposes the score for read and write so external enrichment and attribution pipelines can wire in without a connector tax. There is no scoring add-on, no premium SKU, and no third-party integration layer required to make the scorer work.
Related features
Pair this with...
Try it free. Bring your team next week.
No sales call, no migration consultant, no four-month implementation. Enter your card, get 14 days of the full Pro tier, cancel any time before day 14 with zero charge. Spin up a workspace, import your CSV, and have something useful before lunch.
We use cookies.
Essential cookies keep the site working. If you accept, we also enable Google Analytics
so we can see which pages help and which don't. Reject to opt out entirely. Details in our
Privacy Policy.
