Feature · Lead Scoring Criteria

Lead scoring criteria that come from close-rate data, not vendor templates.

A scoring model is only as good as the criteria that feed it. The menu below is the set of criteria Strkr customers running production scoring have found predictive, grouped by category (fit, intent, buyer, context, negative). Each criterion lists the weight range, the data source, and the implementation shape in Strkr Flows.

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Fit signals

The criteria that answer "is this Lead inside the ICP."

Fit signals carry the heaviest predictive weight in most B2B scoring models because the sales motion is tuned for a profile. A Lead inside the ICP closes at multiples of a Lead outside; a strong fit signal is often worth more than any single intent signal. The six fit criteria below are the ones production teams score.

Company size

Employee count or revenue in the ICP range.

The Lead's company sits in the segment the sales motion targets (SMB 1-50, Mid-Market 50-500, Enterprise 500+, or whatever your mapping is). Weight: 15 to 25 points. Data source: Clearbit, ZoomInfo, LinkedIn Sales Navigator, or firmographic enrichment at capture.

Industry

A vertical the product fits.

The Lead's industry matches a target vertical. Weight: 10 to 20 points, with the exact value scaled to historical close rate per vertical. Software companies with a horizontal product weight low; vertical SaaS weights high. Data source: enrichment or explicit form field.

Geography

Country, region, or state in the serviceable area.

The Lead sits in a geo the sales team can serve (same timezone, legal entity coverage, language support). Weight: 5 to 15 points. A hard gate for teams without international support; a scalar for teams with partial coverage. Data source: IP geolocation plus form field.

Tech stack

The Lead runs the complementary tools.

The Lead's company runs tools the product integrates with (Salesforce, Hubspot, Slack, Microsoft 365). Weight: 5 to 15 points. The signal predicts adoption friction; a Lead with the right stack has a shorter time to value. Data source: BuiltWith, Clearbit tech stack, or inferred.

Growth stage

Series, hiring trend, or funded status.

The Lead's company is at a stage where the product fits (Series A for growth-stage products, bootstrapped for small-business products, enterprise for enterprise-tier products). Weight: 5 to 10 points. Data source: Crunchbase, LinkedIn hiring trend, PitchBook.

Named account

The Lead's company is on the target-account list.

The Lead belongs to a company on the named-account list (ABM target, strategic account, competitor replacement). Weight: 15 to 25 points, often with named-account routing that overrides the usual territory. Data source: the firm's target-account list in the CRM.

Intent signals

The criteria that answer "is this Lead evaluating."

Intent signals carry the second-heaviest weight. Explicit intent (demo request, pricing-page view, contact-sales click) predicts near-term close reliably. Passive engagement (newsletter open, blog view) correlates with research intent rather than buying intent and should weight low or not at all.

Demo request

The strongest intent signal, by a wide margin.

The Lead requested a demo. Weight: 20 to 30 points. The highest-weighted intent signal in most models; a demo request is the Lead saying "I want to see this work for me." Data source: form submission with the demo-request flag.

Pricing page view

A visitor evaluating cost is close to the buying decision.

The Lead viewed the pricing page (not necessarily submitted a form). Weight: 10 to 20 points. The visitor evaluating cost is late in the research. Data source: web analytics with Lead-to-session tying.

Contact-sales click

Explicit buying intent.

The Lead clicked a contact-sales CTA (contact form, chat initiation, "talk to sales" button). Weight: 15 to 25 points. The CTA signals the Lead wants a human conversation, not more content. Data source: CTA click tracking.

Multiple sessions

A return visitor is an evaluator.

The Lead visited the site multiple times over a short window (three visits in two weeks). Weight: 5 to 10 points. Return visits indicate research intent beyond the single-page skim. Data source: web analytics session tracking.

High-intent content

Downloads tuned to the buying stage.

The Lead downloaded a comparison guide, a buyer's checklist, or an RFP template. Weight: 5 to 15 points. Content tuned to the buying stage signals the Lead is in evaluation. Data source: content download tracking with content classification.

Chat engagement

A real conversation beats a form fill.

The Lead engaged the chat widget with questions (not just hello). Weight: 10 to 20 points scaled by conversation length. A real question in chat is high intent; a quick drop-off is not. Data source: chat transcript with length and keyword analysis.

Buyer signals

The criteria that answer "is this person a decision-maker."

A Lead who fits the ICP and shows intent might still not close because the Lead is not a buyer. Buyer signals (title, decision authority, team context) separate evaluators who can sign from evaluators who cannot. Weight these signals meaningfully.

Decision-maker title

VP, Director, Head of, Owner.

The Lead's title indicates buying authority (VP of Sales, Director of Operations, Head of Marketing, Owner). Weight: 10 to 20 points. The strongest buyer signal; non-decision-maker Leads typically require nurture before they can close. Data source: enrichment or form field.

Buying committee size

The Lead is one of several people from the same company.

Multiple Leads from the same company in a short window indicate a buying committee. Weight: 10 to 15 points on the aggregated opportunity. Data source: company-domain matching across Leads with timestamp proximity.

Budget mentioned

A number has been said out loud.

The Lead mentioned a budget range in the inquiry or demo request. Weight: 15 to 25 points. Budget acknowledgment is a near-universal predictor of close across industries. Data source: form field or email transcript.

Timeline specificity

A date or event, not a season.

The Lead mentioned a timeline (by end of Q3, before board meeting Oct 15, when renewal lands). Weight: 10 to 20 points. Specific timelines correlate with real buying motions. Data source: inquiry text or form field.

Existing stack pain

The Lead named the tool they want to replace.

The Lead mentioned their current tool and the pain driving the switch. Weight: 10 to 15 points. A Lead replacing an existing tool is further along than one evaluating for the first time. Data source: inquiry text.

Decision timeline

The next 90 days versus "someday."

The Lead indicated a decision timeline within 90 days. Weight: 15 to 25 points. Deals with 90-day timelines close at 3-5x the rate of "someday" deals. Data source: form field or follow-up call notes.

Negative signals

The criteria that subtract from the score.

Negative signals are often more valuable than positive signals because they filter the tire-kickers who look engaged but will not close. Weight these honestly; a strong negative signal should move a Lead down a band.

Student or researcher

A .edu email on an enterprise form.

The Lead has a .edu email or indicates student/academic role. Weight: minus 20 to minus 40 points, often enough to disqualify. The exception: edu buyers for legitimate university deployments. Data source: email domain.

Competitor domain

The Lead works at a direct competitor.

The Lead's email domain or company matches a competitor. Weight: minus 30 points or auto-disqualify. The exception: competitors evaluating partnership. Data source: company domain against a maintained competitor list.

Generic email domain

A gmail.com address on a B2B product.

The Lead used a generic email (gmail.com, yahoo.com, hotmail.com) on a B2B product. Weight: minus 10 to minus 20 points. Not disqualifying but a strong fit signal missing. The exception: solo founders or consultants legitimately using personal email.

Too-small company

A single-person LLC on an enterprise product.

The Lead's company has fewer than the ICP minimum (e.g., 1 employee on a 50-employee minimum product). Weight: minus 15 to minus 30 points or auto-cold. Data source: enrichment.

Shopping language

A Lead who announced they are collecting bids.

The Lead opened by saying they are collecting multiple vendors or comparing prices. Weight: minus 5 to minus 15 points depending on category. Price-first buyers close at lower rates and lower prices. Data source: inquiry text.

Response latency

The Lead went dark for a week.

The Lead stopped responding for seven days after the first conversation. Weight: minus 5 to minus 10 points, incrementing on continued silence. The latency growth is a leading indicator of a lost deal.

How Strkr implements the criteria

The configuration surface for every criterion above.

Each criterion above is a configurable rule in Strkr Flows. The admin surface lets a sales ops user add criteria without engineering. The sections below name the mechanism per criterion category.

Fit criteria

Custom fields on the Lead schema, enrichment on capture.

Fit criteria read from Lead fields populated at capture (via Clearbit, ZoomInfo, or form submission). Flows reads the fields and awards points per criterion. The scoring rule is visual: condition, points, comment.

Intent criteria

Web analytics events feeding the Lead timeline.

Intent events (pricing page view, demo request, content download) feed the Lead activity timeline through the Strkr web analytics integration. Flows reads the timeline and awards points. The event-to-criterion mapping configures per criterion.

Buyer criteria

Title parsing, inquiry text analysis, form fields.

Buyer criteria pull from title parsing (enriched title normalized against a buyer-authority ladder), inquiry text analysis (Strkr AI extracts budget mentions and timeline specificity), and explicit form fields. The AI step runs on the first inquiry; the score reflects the extraction.

Negative criteria

Blocklists, generic-domain lists, competitor lists.

Negative criteria check against maintained lists (competitor domains, generic email providers, student indicators). The lists live in the admin surface; adding a new competitor is a single-row insert that applies to future and historical Leads.

Composite scoring

The final score is the sum, visible per Lead.

The Lead score is the sum of positive and negative points, visible on the Lead record with the breakdown by criterion. The band (hot, warm, cold) applies based on configured thresholds. Reps see the score and the breakdown; the magical-score failure mode does not apply.

AI second opinion

Strkr AI emits a confidence rating alongside.

A Strkr AI step reads the Lead's first inquiry (email thread, demo request text, chat transcript) and emits a confidence rating that sits next to the rule-based score. Agreement reinforces; disagreement flags for investigation. The AI does not override the rules; it offers a second opinion.

Pick the ten criteria from this menu that match your motion.

Strkr Flows ships the scoring surface on every paid tier with field-level criterion rules, intent-event mapping, Strkr AI second opinion, and the audit trail that lets sales ops tune the model quarterly.

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Common questions

What buyers ask about this feature.

Which criteria category carries the most weight?

Fit signals, then intent, then buyer, with negative signals subtracting. Fit signals (ICP match, segment, industry, geography) typically account for 40 to 50% of the total score weight because the sales motion is tuned for a profile. Intent signals (demo request, pricing-page view, contact-sales click) account for 25 to 35%. Buyer signals (title, budget mentioned, timeline specificity) account for 15 to 25%. Negative signals subtract and often decide band placement for borderline Leads. The exact split depends on your motion; vertical SaaS leans heavier on fit, horizontal product-led tools lean heavier on intent.

Where does the data for each criterion come from?

Fit criteria pull from enrichment (Clearbit, ZoomInfo, LinkedIn Sales Navigator) applied at capture, plus explicit form fields where enrichment is incomplete. Intent criteria pull from the web analytics event stream feeding the Lead activity timeline. Buyer criteria pull from title parsing, inquiry text analysis (Strkr AI extracts budget mentions and timeline specificity from first-touch emails), and form fields. Negative criteria check against maintained lists (competitor domains, generic email providers, student indicators) and inquiry text patterns.

How do I pick weights for each criterion?

Pull your trailing-twelve-month close rate for Leads with the criterion present versus absent. A criterion that doubles the close rate deserves roughly double the weight of a criterion that moves it 20%. The ranges in the menu above are starting points; the actual weight for your business comes from your data. The quarterly review re-weights based on the past quarter's outcomes. The pattern most teams discover: one or two criteria carry most of the predictive power, and reducing the model to the strongest six to eight loses almost no accuracy.

What about combining criteria (if-this-then-weight-more)?

Combined criteria add value when the combination has significantly more predictive power than the sum of the parts. The common strong combinations: ICP match AND demo request (both strong on their own, extremely strong together), named-account AND decision-maker-title (ABM teams weight this heavily), budget mentioned AND timeline within 90 days (near-automatic hot classification). Avoid combining more than two criteria into one rule; the complexity cost outweighs the accuracy gain. Keep rules single-criterion with transparent weights; compose the picture in the band threshold.

Should I score every criterion, or pick a subset?

Pick a subset. The best practice is five to ten criteria total. The menu above is a catalog of candidates; your model should include the six to ten with the strongest predictive lift for your business. Teams starting out should pick two or three fit criteria, two or three intent criteria, one or two buyer criteria, and one or two negative criteria. Add more only when the quarterly review justifies. Teams running 20-criteria models typically find on review that six of them produce 90% of the discrimination.

How does the AI second opinion interact with the criteria-based score?

The Strkr AI step reads the Lead's first inquiry and emits a confidence rating alongside the rule-based score. The two run independently: the rules sum the configured criteria weights, the AI reads the natural language signals (tone, urgency, decision-maker phrasing, bid-shopping language). When the two agree, the Lead routes cleanly. When they disagree, the Lead flags for sales ops review and the signal in dispute is investigated. The arrangement captures nuance a rule-based model cannot while preserving the transparency reps can read.

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