Feature · SEO Lead Scoring Framework

An SEO lead scoring framework that respects where the lead came from.

An SEO lead scoring framework scores leads by the signals that matter for organic search: the keyword tier that sourced the visit, the page tier that converted the lead, the behavior on the site, and the firmographics that make the lead a fit. Strkr ships the framework as a configurable scoring model inside the CRM.

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What the framework scores on

Four signals that predict pipeline from SEO.

SEO-sourced leads differ from paid-search and inbound-referral leads: the keyword, the content, the time-on-page, and the firmographic fit all carry signal. A good framework scores on all four. The sections below walk the signal groups.

Keyword tier

What they searched to find you.

The keyword tier is the first signal. Branded searches ("Strkr CRM") score highest - the visitor knows the brand. High-intent category searches ("best CRM for mid-market") score next. Informational searches ("what is a CRM") score lowest. The tier is captured on first-touch via the UTM referrer.

Page intent

Which page converted them.

The converting page is the second signal. Pricing-page submits score highest. Demo-request page submits next. Comparison-page submits (vs. HubSpot, vs. Salesforce) score high. Blog-page content opt-ins score lowest. The page intent is the behavior at the moment of conversion.

Behavior

What they did on the site.

Behavior scores pick up return visits, time on site, pricing-table views, and feature-page browsing. A lead that returns three times before converting scores higher than a first-visit convert. Multi-page journeys score higher than single-page journeys.

Firmographics

Who they are, if they tell us.

Firmographic scoring rewards fit: company size in target range, industry in target vertical, title in buying-committee role. Firmographics come from form fields or enrichment lookup. Fit without intent is a nurture; intent without fit is a disqualify; both together is a sales-ready lead.

Negative signals

What subtracts points.

The framework subtracts points on negative signals: competitor email domains (score -50), unsubscribe actions (score -20), bounce behavior (score -10). Negatives prevent false positives and keep the sales team on the honest leads.

Decay

Time without engagement.

Scores decay over time without engagement. A lead that scored 80 and did nothing for 90 days drops to 40. Decay prevents a nurture-stale list from looking active. The decay curve is configurable per rule group.

How the framework is built

Rules, weights, calibration, publish.

The framework is a configurable scoring model inside Strkr. The sales ops and marketing leads build the rules, assign weights, calibrate against historical conversions, and publish. The sections below are the mechanics.

Rule builder

Keyword tier, page tier, behavior, firmo.

The rule builder has four groups: keyword tier, page tier, behavior, and firmographic. Within each group, rules fire and add or subtract points. The builder is readable by the marketing lead without a developer.

Weight assignment

Points per rule, calibratable.

Each rule carries a weight (points). The framework starts with a sensible default and the marketing lead calibrates based on historical conversion. Branded-search inbound converts at 20%, score it 50. Blog-page opt-in converts at 2%, score it 10.

Thresholds

MQL, SQL, hot-lead.

The score crosses thresholds that drive routing. Below 20 is a nurture lead. 20-50 is an MQL - enrolled in nurture, visible to sales. 50-100 is an SQL - routed to a rep. Above 100 is hot - scheduler link on next email.

Historical calibration

Compare predicted to actual.

The calibration report compares predicted score to actual conversion. Rules that over-predict (score high, convert low) get a weight reduction. Rules that under-predict (score low, convert high) get a weight increase. The framework learns from the honest data.

Publish and version

Draft, test, publish.

The framework versions. Changes land as a draft, run against historical leads for the predicted-vs-actual check, and publish when calibrated. Published versions apply to new leads. Historical leads keep their original score and the version used.

Strkr AI enhancement

AI learns the actual signal.

On tiers that bundle Strkr AI, the framework can delegate weight tuning to a model trained on the tenant's conversion history. The model predicts the right weights, the marketing lead reviews, publishes if the predicted lift is credible. The framework is still auditable and tunable by hand.

How teams use the framework

Launch, calibrate, iterate.

The framework is useful at three moments: launch (first SEO motion goes live), calibrate (first quarter of data), iterate (every quarter after). The sections below describe the actual workflow.

First launch

Default framework, published.

The marketing lead opens the framework, reviews the default rules and weights, and publishes. The default framework is Strkr's best starting point for SEO motions. The first SEO campaign goes live scored.

First month

Watch MQL to SQL conversion.

The marketing lead and the sales lead watch MQL-to-SQL conversion for the first month. If MQLs convert to SQL at above 25%, the framework is well-calibrated. If below 10%, the thresholds need tightening.

First quarter

Calibrate against actual close.

By end of quarter one the team has closed-won data. The calibration report compares predicted score to actual close rate per rule. Rules that over-predict are tuned down. The framework version two publishes.

Content expansion

New pages need new page-tier rules.

As the content team ships new pages, the framework needs new page-tier rules. The marketing lead adds the pages to the page-tier group with appropriate weights. The framework stays current with the content.

Quarterly tune

Decay curves, firmo rules, negatives.

Each quarter the marketing lead tunes decay curves, firmographic rules, and negative signals. The honest data makes the framework sharper. The sales team gets scored leads that match conversion reality.

Annual review

Framework version N, publish.

Annually the team reviews the whole framework. Rules that have not fired in a year retire. New signals get added. The framework stays a living artifact, not a one-time install.

What this replaces

Generic scoring and spreadsheet audits.

Most marketing automation tools ship a generic lead-scoring surface. The scoring works for inbound-form motions but miscalibrates for SEO. Strkr's framework replaces the generic scoring with an SEO-aware model. The sections below are the comparison.

HubSpot scoring

Credible, generic, not SEO-aware.

HubSpot lead scoring handles inbound forms well. The keyword-tier signal and the page-intent signal need custom rules the user builds by hand. Strkr's framework is SEO-shaped out of the box.

Marketo scoring

Deep, enterprise, slow to iterate.

Marketo scoring is deep. The build and the iteration cycle are slow. Strkr's framework calibrates quarterly without an admin release train.

Pardot scoring

Clean, Salesforce-bound.

Pardot scoring sits inside Salesforce. Teams on Sales Cloud get native integration. Teams considering consolidation get the SEO-aware framework on Strkr.

Spreadsheet audit

Honest, manual, lossy.

Many marketing teams audit lead quality in a weekly spreadsheet. The spreadsheet is honest and manual. Strkr's framework automates the audit via the calibration report.

Vendor consultant model

Expert, costly, static.

Some teams hire a consultant to build a scoring model. The result is credible and static. Strkr's framework is editable by the marketing lead and tunable quarterly.

Strkr

SEO-shaped, auditable, included.

The SEO lead scoring framework is included in the Lead Scoring surface on every paid Strkr tier. Rule builder, weight assignment, thresholds, calibration report, versioning, and Strkr AI enhancement. The seat price covers it.

Score SEO leads on the signals that actually predict pipeline.

Start a free trial, open the Lead Scoring surface, publish the default framework, and watch the first SEO campaign land scored leads inside an hour.

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Common questions

What buyers ask about this feature.

What is an SEO lead scoring framework?

An SEO lead scoring framework is a model that scores inbound leads by the signals specific to organic search: the keyword tier that sourced the visit, the page tier that converted the lead, the behavior on the site, and the firmographic fit of the lead. The framework assigns points per signal, totals them into a score, and routes based on thresholds (MQL, SQL, hot lead). In Strkr the framework is a configurable scoring model inside the Lead Scoring surface, with a rule builder, weight assignment, calibration report, and version control.

How is SEO lead scoring different from generic lead scoring?

Generic lead scoring treats every inbound lead the same - form submit, email open, link click. SEO-sourced leads carry additional signals that generic scoring misses: the keyword tier (what the visitor searched to find the site), the page tier (which page converted them), and the behavior journey (how many pages they visited before converting). A branded-search visitor who submitted on the pricing page is a very different lead than a top-of-funnel blog-article opt-in; generic scoring often treats them identically. The SEO framework scores them correctly.

What signals does the framework score on?

Four signal groups: keyword tier (branded, high-intent category, informational), page tier (pricing, demo, comparison, blog), behavior (return visits, time on site, pricing-table views, feature-page browsing), and firmographics (company size, industry, title). Negative signals (competitor email domains, unsubscribes, bounces) subtract points. Score decay over time prevents a stale list from looking active. Each signal is a configurable rule with a weight.

How do we calibrate the framework?

The calibration report compares predicted score to actual conversion for closed leads. Rules that over-predict (score high, convert low) get a weight reduction. Rules that under-predict (score low, convert high) get a weight increase. The marketing lead reviews the report quarterly and tunes weights. On tiers that bundle Strkr AI, the model can suggest weight tunings based on the tenant's historical conversion data - the marketing lead reviews and publishes.

Does the framework version?

Yes. Framework changes land as draft, run against historical leads for the predicted-vs-actual check, and publish when calibrated. Published versions apply to new leads from publish time forward. Historical leads keep their original score and the version used to score them. The versioning model lets the team iterate quarterly without rewriting historical analysis.

Is the SEO lead scoring framework included on every tier?

Yes. The Lead Scoring surface, SEO-aware rule groups, weight assignment, thresholds, calibration report, versioning, and historical lead protection are included on every paid Strkr tier. Strkr AI weight suggestions are included on tiers that bundle Strkr AI. Multi-framework support (different frameworks for different motions - SEO vs. paid vs. referral) is included on Pro and above.

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