Feature · Lead Scoring System

A lead scoring system with criteria examples the sales floor can read and the model can score.

Demographic, firmographic, behavioral, intent, and negative-signal rules you can read in English, score in real time against the live record, tier into A through D, route to the right rep, and audit on every change. Twelve criteria groups with explicit example rules, not a generic template.

Start free trial See pricing
Why criteria examples matter more than the model

A lead scoring system fails on the criteria, not the math.

Most lead scoring systems do not fail on the math; the arithmetic that turns a rule set into a 0 to 100 score is trivial. They fail on the criteria. A rule that reads "job title contains manager" fires on three thousand irrelevant records a quarter and the AE loses trust in the score. A rule that reads "visited pricing page in the last seven days AND company size greater than 50 employees" fires on the hundred records that actually look like deals. The difference is specificity. The Strkr lead scoring system ships with concrete criteria examples across six categories, lets the admin edit every rule in a readable editor, and shows the record counts each rule hits during the past 30 days so the business tunes against real data. The sections below walk through each criteria category with the example rules the lead ops lead will actually use on day one.

The criteria debt problem

Generic rules fire on the wrong leads.

Lead scoring platforms that ship with vague defaults; "visited website: +5", "opened email: +3"; spray points across every record and produce a score that correlates with nothing. The sales floor stops reading the score. The marketing spend keeps producing MQLs the floor ignores. The ROI of the scoring system goes negative.

The specificity principle

Each rule names a trait and a threshold.

A good rule reads like a sentence: "Industry is Healthcare AND employee count greater than 500 AND decision maker title AND visited pricing in the last 14 days". The reader knows exactly which records fire. The model scores them deterministically. The AE trusts the score because they can read the reason.

The reversibility check

Every rule has a negative counterpart.

For every positive signal there is a negative version. "Visited pricing" has "did not visit pricing in 60 days". "Opened an email" has "unsubscribed from the newsletter". The scoring system that only accrues points inflates every record toward the top; the system that also subtracts points produces a usable distribution.

Segment-specific tuning

Criteria are per segment, not global.

An enterprise segment weights firmographic fit heavily; a self-serve segment weights product-usage behavior heavily. One global scoring model is a compromise that serves neither segment well. Strkr lets the admin ship a scoring model per segment with its own criteria set and its own tier cutoffs.

The readability constraint

Rules in English, not in a formula box.

The lead ops team is not a data science team. The criteria editor reads like English: a field, an operator, a value, a weight. The admin builds a rule in 30 seconds and tests it against the historical record set. The time from idea to live rule is minutes, not weeks.

The audit requirement

Every rule change leaves a row.

Every edit to a scoring rule; the weight change, the threshold change, the field change; lands in the audit log with the admin, the before-state, the after-state, and the timestamp. A quarterly scoring review reads the audit trail to understand why the distribution shifted between quarters.

The real-time scoring posture

Scores update on the field change, not nightly.

When a lead visits pricing or a contact changes title, the score recomputes on the next read. The routing engine reads the fresh score. The AE sees the current tier on the record. The nightly-batch scoring model that most legacy CRMs ship produces a score that lags reality by a day; Strkr's posture is real time.

The explanation panel

Every score shows the rules that fired.

The contact and account detail show a Score Explanation panel: the current score, the current tier, and the rules that contributed with their point values. The AE reads "scored 72 because: enterprise industry (+15), decision-maker title (+10), visited pricing twice this week (+20), opened recent demo email (+8), existing customer company (+19)".

The tier definition

A, B, C, D with handoff rules per tier.

Scores roll up to tiers (A: 80-100, B: 60-79, C: 40-59, D: 0-39) with handoff rules per tier: A leads route to the top AE queue with a 15-minute SLA, B leads route to the SDR for qualification, C leads enroll in a nurture cadence, D leads suppress from active outreach. The tier structure, not the raw score, drives the operational motion.

Demographic criteria examples

Who the person is; explicit rules with thresholds.

Demographic criteria score the person: title, seniority, department, role responsibility, geography, language, time zone. The rules read as English sentences and apply the same way to a lead, a contact, and a free-trial signup. The examples below are the rules a mid-market B2B scoring model typically uses on day one.

Title contains decision-maker

Example; title keyword tiering.

Title contains VP, Director, Head of, Chief, or Owner: +20 points. Title contains Manager without a seniority modifier: +8 points. Title contains Analyst, Coordinator, Associate, or Intern: 0 points. The rule reads clean and the counts show on the editor so the admin sees the split.

Department match

Example; buying-committee department weight.

Department equals Sales, Marketing, RevOps, or Operations for a sales category product: +15 points. Department equals Engineering, Legal, or Finance where those roles are influencers: +5 points. Department equals HR, Facilities, or Admin on an unrelated product: 0 points.

Seniority tier

Example; seniority ladder tiering.

C-suite seniority: +25. VP: +18. Director: +12. Manager: +6. Individual contributor: 0. The ladder maps to the enrichment provider's seniority field. The admin tunes the step size based on which seniority the deal cycle actually needs.

Target job function

Example; functional ICP match.

Job function equals Revenue Operations: +20. Job function equals Sales Operations: +18. Job function equals Marketing Operations: +12. Job function matches a saved list of 14 target functions imported from the ICP document: +variable.

Geography

Example; region weighting.

Country in United States, United Kingdom, Canada, Australia, or Germany where sales legal supports those jurisdictions: +10. Country in Eastern Europe or APAC where legal is still negotiating MSAs: +3. Country on the OFAC restricted list: hard-exclude (sets tier to D).

Language

Example; language of the lead.

Primary language English on an English-only sales motion: +5. Primary language matches a supported sales language (French, Spanish, German): +3. Primary language outside supported languages: -5 (the AE cannot work the lead at acceptable quality).

Time zone fit

Example; time-zone operational fit.

Time zone within four hours of a staffed sales region: +3. Time zone outside staffed sales hours: -3. The rule prevents a cadence from assigning a 2 a.m. call task to a lead on the opposite side of the world.

Email provider class

Example; personal vs business email.

Email domain is a business domain (not gmail, yahoo, hotmail, outlook.com, protonmail): +8. Email domain is a free personal domain in a B2B motion: -10. The rule filters tire-kicker signups and routes personal-email leads to a different nurture flow.

Contact completeness

Example; enrichment completeness.

Contact has verified work email AND direct phone AND title AND LinkedIn URL: +6. Contact has only email and no other fields: 0. Contact failed enrichment on three providers: -4. A low-completeness record signals a low-quality source.

Firmographic criteria examples

What the company looks like; fit before behavior.

Firmographic criteria score the account: industry, employee count, revenue, growth stage, geography, tech stack, funding, parent company, customer status. A great prospect works at a company that fits the ICP; a bad-fit company dooms the deal regardless of the person's engagement. The examples below are the rules a go-to-market team uses on day one.

Industry match

Example; industry ICP tiering.

Industry in SaaS, Fintech, Professional Services, or Healthcare where the product has strong case studies: +25. Industry in Manufacturing, Retail where the product has one case study each: +10. Industry in Nonprofit, Government where the sales cycle is 3x longer: -5 or route to specialist queue.

Employee count band

Example; company-size tiering.

Employee count 500 to 2500 (ideal mid-market): +25. Employee count 100 to 499 (upper SMB): +15. Employee count 2500 plus (enterprise, deal cycle doubles): +10 and route to enterprise queue. Employee count under 50 (self-serve fits better): -10 and route to product-led queue.

Revenue band

Example; ARR fit.

Annual revenue 10M to 500M: +20. Annual revenue under 10M: +5 (deals close but LTV is low). Annual revenue over 1B: +15 (enterprise, requires named-account handling). Revenue data missing after enrichment: 0 (treat as unknown, not negative).

Growth stage

Example; growth signal.

Growth classified as Hyper-growth or Series B through D: +15. Growth classified as Mature or Stable: +8. Growth classified as Declining or in Restructuring: -10. The growth signal from the enrichment partner materially predicts deal velocity.

Tech stack match

Example; complementary stack signal.

Company uses Salesforce AND HubSpot (both installed): +15. Company uses one of 12 complementary tools from the saved tech-stack list: +8 each (up to +20 cap). Company uses a direct competitor: -20 and route to competitive queue.

Funding stage

Example; recent funding signal.

Company raised Series A, B, or C in the last 180 days: +18 (budget just unlocked). Company IPO'd in the last 365 days: +12. Company laid off 10 percent plus in the last 180 days: -10 (budget frozen). The signal pulls from the enrichment layer.

Named-account match

Example; strategic-account hit.

Account on the named-account list: +40 (immediate A tier) regardless of other scores. Named-account routing to the strategic AE queue with a sub-5-minute SLA. The named-account list ships as a CRM list the strategic AE owns.

Parent-account hierarchy

Example; parent-child relationship.

Account is a subsidiary of a current customer: +25 (expansion play). Account is a subsidiary of an active lost-deal: -15 (recent lost). Account is a subsidiary of a top-10 competitor: -40 (likely a scraping bot or competitive intel).

Existing-customer flag

Example; expansion vs new-logo routing.

Account is already a Strkr customer: route to the CSM queue, not to the AE queue, with Expansion tier. Account is a lost deal within 180 days: suppress from outbound and hold for a 180-day cool-off. Account is an open prospect in another AE's pipeline: route to the owning AE.

Behavioral + intent criteria examples

What the person actually did; real signals, real points.

Behavioral criteria score what the lead actually did on the website, in product, in email, and across the ad surface. Intent criteria pull from third-party signal providers that identify companies researching a category. The combination is where most of the model's predictive power sits. The examples below are the rules the lead ops team ships week one.

Pricing page visit

Example; pricing page tiering.

Visited pricing page once in last 7 days: +12. Visited pricing page three times in last 7 days: +25 (clear buying signal). Visited pricing page once more than 90 days ago: +2 (stale signal, modest weight).

Demo request

Example; demo request weight.

Submitted demo request form in last 7 days: +30 (immediate A tier typically). Submitted contact form in last 7 days: +15. Submitted gated content download: +8 (content lead, not demo lead).

Product trial signup

Example; free trial started.

Signed up for product trial: +25. Invited two or more teammates to the trial: +15 (viral signal). Completed activation checklist (defined set of in-product actions): +20. The product-usage signals compound.

Email engagement

Example; email open and click tiering.

Opened an email in last 14 days: +2. Opened three plus emails in last 14 days: +6. Clicked a link in an email in last 14 days: +8. Replied to a sales email: +15. Unsubscribed from the newsletter: -10.

Content download

Example; content-journey depth.

Downloaded a top-of-funnel ebook: +5. Downloaded a mid-funnel case study: +10. Downloaded a bottom-of-funnel pricing comparison or ROI calculator: +18. The content tier maps to the stage of intent.

Webinar attendance

Example; attended vs registered.

Registered for a webinar: +4. Attended live for 15 plus minutes: +10. Watched the recording within 14 days: +6. Asked a question during the live session: +12 (clear engagement signal).

Third-party intent

Example; Bombora or 6sense signal.

Company shows surging intent on the sales category per the configured intent provider: +20. Company shows surging intent on three plus relevant topics: +30. Intent score decayed below the baseline in last 30 days: -5. The intent provider sits in the enrichment layer.

Chat and conversation

Example; chat conversation depth.

Started a conversation with the AI chat agent on the website: +5. Completed a qualification conversation and asked for a demo: +25. Returned to chat a second time in last 7 days: +12. The chat signals land through the native chat integration.

Negative signals

Example; explicit disinterest.

Replied to an email saying "please remove me": -40 and auto-suppress. Marked a sales email as spam: -30. No activity across any channel for 90 plus days on a cold lead: -15. The negative signals compress inflated scores back toward reality.

How the scoring system runs end to end in Strkr

Rule editor, real-time engine, tiers, routing, and audit.

The scoring criteria themselves are only one piece of a working lead scoring system. The lead ops team needs an editor, a scoring engine that runs on write, tier definitions, routing hooks, and an audit trail that keeps the posture clean. The elements below describe how the Strkr lead scoring system runs end to end.

Rule editor

English rules with weights and thresholds.

The admin builds rules in a visual editor: a field selector, an operator (equals, contains, greater than, in list, within N days), a value, and a weight. Every rule previews its 30-day hit count against the live record set before publish, so the admin tunes against real distributions.

Multi-model support

A scoring model per segment.

The tenant can run multiple scoring models: one for enterprise, one for mid-market, one for self-serve. Each model has its own rule set and tier cutoffs. The engine picks the matching model based on segment membership. One global model does not have to serve every motion.

Real-time scoring

Scores recompute on the write path.

When any field used in a scoring rule updates; title change, pricing page visit, intent signal, trial activation; the score recomputes within seconds on the write path. The AE sees the current tier on the next page load. No nightly batch, no stale signal.

Tier definitions

A, B, C, D with configurable cutoffs.

Scores roll into four tiers (A, B, C, D) with admin-configurable cutoffs. The default cutoffs ship at 80, 60, 40 but the admin tunes them based on volume. The tier is the operational handle; the raw score is only visible when the AE opens the explanation panel.

Routing hooks

Tier changes trigger workflows.

A tier promotion (from B to A) triggers a workflow: route the lead to the top AE queue, start a priority cadence, create a Slack notification, assign a 15-minute SLA. The workflow uses the same primitives as every other automation on the tenant.

Explanation panel

Every score shows the firing rules.

The contact and account detail show which rules fired, the point value each contributed, and the recent changes. The AE opens the panel to understand the score; the manager reviews the panel on a stuck deal to confirm the scoring reflects reality.

Historical score chart

A score trajectory per record.

Each record carries a time-series chart of its score across the last 90 days. The AE sees the acceleration: the record moved from 32 to 78 across the last 10 days. The acceleration itself is a signal; a fast-rising lead often beats a static high-score lead in conversion.

Audit log integration

Every rule edit lands in the log.

Every edit to a scoring rule; the weight change, the threshold change, the field change, the publish, the rollback; lands in the audit log with the admin, the before-state, the after-state, and the timestamp. Quarterly scoring reviews read the audit trail.

A/B testing on rules

Test a rule change before full rollout.

The admin can run a candidate rule set against a 10 percent cohort of records and compare the tier distribution against the control set. If the candidate rule set produces better pipeline conversion, promote it to full rollout; if not, roll back without a side effect.

Build a lead scoring system with criteria the floor can read and the model can score.

Demographic, firmographic, behavioral, intent, and negative-signal rules with explicit example thresholds, real-time scoring on the write path, A through D tier routing, and a full audit log. The rule editor reads English, not formulas.

Start free See lead scoring criteria
Common questions

What buyers ask about this feature.

What is a lead scoring system and how does it differ from simple lead scoring criteria?

A lead scoring system is the end-to-end mechanism that turns a rule set into an operational motion: the criteria themselves, the rule editor that lets admins change them, the engine that runs them on the write path, the tier definitions that compress scores into A through D buckets, the routing hooks that fire when tiers change, and the audit trail that captures every rule change. Lead scoring criteria are the rules alone; a lead scoring system wraps the rules in the operational infrastructure that makes them useful. A spreadsheet with lead scoring rules is not a lead scoring system because no engine runs it, no routing triggers off it, and no audit trail captures its changes. Strkr ships the full system; rules, engine, tiers, routing, audit; natively on every tier.

What are the most useful lead scoring criteria examples to start with on day one?

A mid-market B2B team on day one typically runs ten to fifteen rules across five categories. Demographic: title keyword tiering (VP, Director, Manager vs individual contributor), department match (sales, marketing, revops for a sales-category product), seniority ladder. Firmographic: industry ICP match (saas, fintech, professional services get +25; nonprofit, government get -5), employee count band (500 to 2500 mid-market gets +25), revenue band, named-account hit (+40 immediate A tier). Behavioral: pricing page visit tiering (+12 for one, +25 for three in a week), demo request (+30), email engagement, content download depth. Intent: third-party intent on the sales category (+20 for surging). Negative: unsubscribed from newsletter (-10), marked email as spam (-30), 90 days of no activity (-15). The thresholds tune to the historical pipeline distribution; the editor shows 30-day hit counts so the admin tunes against real data.

How does Strkr handle negative scoring criteria and why do they matter?

Negative criteria subtract points when the lead signals disinterest or when the firmographic profile is wrong for the ICP. Common examples: unsubscribed from the newsletter (-10), replied with a remove-me request (-40 and auto-suppress), marked a sales email as spam (-30), using a personal email domain in a B2B motion (-10), company has an active lost deal within 180 days (-15), industry is misaligned with the ICP (-5 to -10), tech stack includes a direct competitor (-20). Negative criteria matter because a scoring system that only accrues points inflates every record toward the top of the distribution. Teams running positive-only models typically see 60 to 70 percent of records sitting in the top two tiers after six months of operation, and the floor stops trusting the score. A model with negative criteria compresses the distribution back toward reality: A tier represents 10 to 15 percent of records, B tier 20 to 25 percent, C and D together 60 to 70 percent. The AE queue reflects actual priority instead of score inflation.

Can we run different lead scoring criteria for different segments inside Strkr?

Yes. The scoring engine supports multiple models on the same tenant. The admin ships a model for enterprise, a model for mid-market, and a model for self-serve, each with its own rule set and tier cutoffs. The engine picks the matching model based on segment membership (industry, employee count, revenue band, or an explicit segment field). Enterprise typically weights firmographic fit and named-account hits heavily; mid-market weights the balance of firmographic fit and behavioral engagement; self-serve weights product-usage behavior and activation depth heavily. The tier cutoffs also tune per model because the raw score distributions differ. One global model is a compromise; segment-specific models reflect that the buying motion is different per segment.

How does the scoring engine stay fresh; is it batched or real time?

The Strkr scoring engine runs on the write path, not on a nightly batch. When any field used in a scoring rule updates; title change, pricing page visit, intent signal, trial activation, email open, chat conversation; the score recomputes within seconds. The AE sees the current tier on the next page load. The routing engine reads the fresh score. The nightly-batch posture that most legacy CRMs ship produces a score that lags reality by up to 24 hours; a lead that visits pricing at 10 a.m. does not route to the top AE queue until the next day under batch models. Real-time scoring shrinks the signal-to-action gap, which is often the dominant lever on conversion in competitive deals.

How does the audit trail on scoring rule changes support compliance and quarterly reviews?

Every edit to a scoring rule lands in the audit log with the admin who made the change, the before-state, the after-state, and the timestamp. Rule edits include weight changes, threshold changes, field changes, publishes, and rollbacks. The log is searchable and exportable. A quarterly scoring review reads the audit trail to understand why the tier distribution shifted between quarters: which rules were added, which were tightened, which were loosened. For compliance purposes (GDPR Article 22 on automated decision-making, HIPAA scoring on healthcare contacts), the audit trail documents the decision logic that scored each lead at any point in time, so a subject access request can produce the full scoring history for a contact.

Related features

Pair this with...

Lead Scoring Criteria Lead Scoring Models Lead Scoring Best Practices Ai Lead Scoring Automated Lead Scoring Lead Scoring Criteria Examples B2b Lead Scoring

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.

Start free trial See pricing