A score the AE can defend because it explains itself.
Strkr scoring is three transparent components: ICP fit from firmographic enrichment, intent from website and content behavior, engagement decay on no-touch days. Hover the score to see the component math. Tune the weights per segment without a data-science review.
Fit, intent, engagement, each explainable on hover.
Opaque lead scores lose rep trust fast. If an AE cannot explain why a lead scored 87, they stop treating 87 differently from 32. Strkr scoring is three transparent components, each with its own math, each visible on hover. The score is defensible in a 1:1, which is the baseline test for whether AEs will act on it.
ICP fit
Firmographic match to the ideal customer.
Fit scores the lead against the configured ICP: company size band, industry (NAICS plus custom tags), geography, tech stack (from enrichment), funding stage, and ARR band. Each dimension carries a configured weight. Hover shows the per-dimension match: "Industry +15 (healthcare, matches ICP), Size +10 (120 FTE, matches), Geography 0 (New Zealand, not in current coverage)."
Intent
Website and content behavior signals.
Intent sums signals from inbound website behavior (pricing page views, demo page views, repeat visits), content engagement (whitepaper downloads, webinar attendance), and external intent providers (G2 search, Bombora topic surges). Hover shows the events: "Pricing page x3 last 7 days +12, G2 competitor comparison +8, demo-request abandoned +5."
Engagement decay
Score falls on days of silence.
Engagement decays on no-touch days with a configurable half-life. A lead at score 80 on day one falls to 72 on day seven of no touch, 64 on day fourteen. Decay prevents the common failure mode where a six-month-old high-fit lead with no recent activity still shows up at the top of the queue blocking fresher high-intent leads.
Component weights
Tunable per segment, versioned on edit.
The three component weights (fit 50 percent, intent 35 percent, engagement 15 percent is the default) are tunable per segment: inbound SMB uses more intent weight, outbound enterprise uses more fit weight. Weight edits are versioned; the admin sees the weight history and can revert if a change tanks qualify rate.
Hover explanation
Why this lead got this score.
Hovering the score anywhere on the surface (lead list, deal timeline, routing audit) opens a popover with the three-component breakdown and the top five contributing signals. The AE sees exactly which signals produced the number, which is the information they need to decide the right first touch angle. No black box.
Score history
The curve on the lead timeline.
The lead detail page shows a score history curve with the three components overlaid. Spikes on the curve correspond to specific events (pricing page visit on day 3, demo booked on day 5). AEs read the curve the way a doctor reads a chart; the shape tells the story faster than a static number.
Write back to record
Score drives routing and sequences.
The score writes to the lead record on every component change. Routing rules read the score live. Sequence enrollment reads the score. Dashboard filters read the score. The score is a first-class field, not a reporting-only metric. Changes propagate without a sync layer.
Segment thresholds
Named bands with configured actions.
Score ranges carry named segments: Hot (85+), Warm (65-84), Cool (40-64), Cold (under 40). Each segment has configured actions on entry: Hot opens an immediate Slack notification to the owner, Warm enrolls in the nurture sequence, Cool stays in the newsletter list, Cold drops from active segments. One scoring model drives multiple downstream flows.
What the model learns
Three signals the engine tracks across tenants.
Within a tenant, the admin tunes component weights explicitly. Behind the explicit weights, the engine tracks three cross-signal patterns per tenant and surfaces calibration suggestions on the admin dashboard. The admin accepts or rejects suggestions explicitly; nothing changes silently.
Win rate by band
Hot leads should close faster than Cool.
The engine tracks win rate by score band over the trailing six months. If Hot (85+) leads close at 42 percent and Warm (65-84) leads close at 38 percent, the model is poorly calibrated at the top end. The dashboard surfaces the gap and suggests a weight shift (raise fit weight, lower intent weight) with the expected calibration improvement.
Signal lift
Which specific events predict win.
The engine measures the win-rate lift from each tracked signal. A pricing page view in the trailing 7 days might show a 3.2x win lift; a webinar attendance might show 1.1x. The admin dashboard shows the lift table and highlights signals that are overweight (contributing to score without predicting win) and underweight (predicting win without contributing).
Decay rate fit
Is the half-life right for your motion?
If engagement decay is too fast, high-fit leads churn out of the Hot band before the AE touches them. If too slow, stale leads clog the queue. The engine measures the median time from first-signal to close and recommends a half-life that lands fresh high-fit leads in Hot and ages out cold ones. Admin accepts or rejects.
Segment drift
Weights that fit SMB can drift on mid-market.
The engine flags when a weight set calibrated on SMB inbound starts to misfire on mid-market inbound (common after a Q2 shift upmarket). The admin either tunes a mid-market-specific weight set or re-balances the universal weights. The flag catches drift before it costs a full quarter of pipeline quality.
Rep acceptance
Measured by first-touch rate on Hot.
If AEs accept the scoring model, they first-touch Hot leads within the SLA at a high rate. The engine measures first-touch rate on Hot specifically. A drop below 85 percent is a trust signal; the admin dashboard surfaces it with a recommended action (open a 1:1 to understand which signals the AEs are ignoring).
Explainability check
Random spot audit of five scores a week.
The engine suggests five random lead scores a week for the admin to audit: open the hover, verify the explanation makes sense, confirm the component math matches the admin's intuition. The audit habit catches configuration drift (a new signal was added but miscategorized) in the first week it occurs.
Where this lands in the day
The surfaces where a score matters most.
A lead score is useful only if it shows up at the moment the rep makes a decision. The scoring engine surfaces the number and the explanation in six specific places that match the rep workflow. The surfaces matter more than the model itself.
Lead list
Default sort on score desc.
The lead list ships default-sorted by score descending. The AE starts the day at the top of their queue. Score bands render with color coding (Hot red, Warm orange, Cool blue, Cold grey). Filters support "Hot and Warm only," which produces the AE focus list in one click.
Routing input
High-fit beats territory on fit-first teams.
The routing engine reads score components directly. A tenant can configure routing to prefer high-fit over strict territory, which produces the "lift the SMB rep to mid-market when the lead profile justifies it" pattern without an exception rule for every case.
Sequence enrollment
Nurture vs direct outreach by band.
The sequence enrollment logic reads the score band. Hot gets direct AE outreach. Warm gets a nurture sequence with the AE copied on reply. Cool stays in the marketing list. Cold drops. One scoring model drives three different outreach behaviors from one trigger.
Pipeline dashboard
Pipeline weighted by score, not stage.
Pipeline summary dashboards include a score-weighted pipeline view that applies the lead score as a confidence multiplier. Managers see a more honest pipeline number than the pure sum of open deal amounts; the number lands closer to actual close.
Marketing report
MQL definition is a score threshold.
MQL to SQL handoff is a score threshold in Strkr Marketing. The threshold is one place to tune the gate (raise or lower based on sales capacity). No "MQL definition shared across five Google docs" problem; one definition in one place drives both marketing attribution and sales handoff.
ICP audit
Low-fit high-pipeline deals surface.
The ICP audit view surfaces deals in the pipeline that scored low on fit at creation. These are either exceptions worth closing (strong signals of expansion into a new segment) or shouldn't-have-pursued cases (sunk-cost pipeline the team keeps alive). The audit prompts a weekly conversation that was previously implicit.
The numbers from live customers
Ten mid-market teams measured scoring impact.
Ten mid-market B2B customers measured scoring deployment outcomes against a pre-deployment baseline. The numbers below are the median; the tightest and loosest customers are noted where variance was material. Scoring is one of the five highest-ROI changes a mid-market inbound team ships.
Qualify rate
Up eight points on inbound.
Median qualify rate on inbound rose from 24 percent to 32 percent after scoring deployment. The lift came from AEs prioritizing the top of the queue (Hot and Warm) instead of first-in-first-out. The tightest customer saw a 14-point lift (SMB-heavy, high-noise inbound); the loosest saw a 3-point lift (enterprise-only, already-triaged inbound).
Time to close
Hot leads close in 23 days vs 41 for Warm.
Within the scored pipeline, Hot leads close in 23 days median; Warm in 41 days. The 18-day differential is the practical value of scoring: AEs invest the faster motion in Hot leads and the longer nurture in Warm, matching effort to opportunity. The differential held across all ten customers within +/- 4 days.
Marketing spend efficiency
Cost per MQL down 22 percent.
When scoring tightened the MQL definition (from "any form fill" to "any Warm or Hot form fill"), marketing shifted budget from top-funnel to mid-funnel. The result: cost per MQL fell 22 percent across the sample and the MQL-to-SQL conversion rose from 31 percent to 44 percent. The ROI delta is marketing budget redirected to signal-rich channels.
Rep trust
94 percent said they use the score daily.
A rep survey across the sample asked "do you consult the lead score daily." Pre-deployment baseline (teams on HubSpot predictive scoring): 41 percent said yes. Post-Strkr: 94 percent said yes. The gap is almost entirely the hover-explanation feature; reps use what they can defend.
Weight tuning cadence
Admin edits weights 1-2 times per quarter.
The admin dashboard's calibration suggestions produce a 1-to-2-edit-per-quarter cadence for most customers. Not weekly, not never. The right tuning rhythm. Teams that edit more frequently are often chasing noise; teams that never edit are missing drift. The 1-to-2 cadence is the healthy range.
Low-fit pipeline
Pursuit of under-30 scores dropped 68 percent.
Pre-scoring, roughly 18 percent of AE time was spent on leads that would retroactively have scored under 30 (low fit, low intent, low engagement). Post-scoring, the number dropped to 6 percent. The AE time saved reinvested into Warm nurture, which produced the mid-term qualify-rate lift.
Setup in one afternoon
The five configuration steps that cover 80 percent.
Scoring deployment takes an afternoon for a tenant with a clear ICP document and an enrichment feed. The five steps below cover the standard setup. Everything beyond the five is tuning; the first live score lands within four hours of starting.
Step one
Define the ICP dimensions.
The admin picks the six ICP dimensions that matter (industry, size band, geography, tech stack, funding stage, ARR band). Each dimension carries a weight. The admin UI shows a worked example score for a sample lead so the weights make sense before saving. Fifteen minutes.
Step two
Pick the intent signals.
The admin selects which intent events contribute: pricing page views (yes, weight 8), demo page views (yes, weight 12), G2 search (yes if the integration is live, weight 6), content downloads (yes, weight 3 each). The signals come from the integrated enrichment and marketing module feeds. Twenty minutes.
Step three
Set engagement half-life.
The admin picks a half-life (default 14 days). Most SMB-focused teams land at 7-10; most enterprise teams land at 21-30 because the sales cycle is longer. The engine shows a decay curve preview against a sample high-intent lead so the choice is intuitive. Ten minutes.
Step four
Name the segment bands.
The admin names and sets thresholds for Hot (85+), Warm (65-84), Cool (40-64), Cold (under 40). Each band gets configured actions on entry (Slack notify, sequence enroll, newsletter stay, drop). The configured actions call into the standard Flow engine. Thirty minutes.
Step five
Dry-run against the last 90 days.
The admin runs the scoring model against the trailing 90 days of leads (dry-run only, no writes) and reviews the score distribution. The dry-run flags skew issues (every lead scores Hot; nothing scores Cold) before deployment. The admin tunes weights and re-runs. Forty-five minutes.
See lead scoring explain itself on five live leads in ten minutes.
Live walkthrough from signal ingestion to score hover to routing handoff. Bring your ICP; we will configure the model on screen.
Does the scoring model use Strkr AI or a simple weighted sum?
The default scoring model is a transparent weighted sum of the three components (fit, intent, engagement) because transparency is more valuable than a 2-point accuracy lift from a black-box model. Teams on the Pro tier and above can enable the Strkr AI calibration layer, which runs a per-tenant model that suggests weight adjustments based on the historical win data. The AI layer never changes weights silently; it suggests, the admin accepts or rejects. The underlying hover explanation remains readable regardless of whether the AI layer is active.
Can scoring handle multiple ICPs for a horizontal product?
Yes. Teams with multiple ICPs (horizontal product with distinct verticals, product-led plus sales-led motions, SMB plus enterprise segments) configure one scoring model per segment and route leads to the right model based on segment classification at lead creation. The classification step uses firmographic rules (industry, size) to pick the model. Each model has its own weights, its own bands, and its own thresholds. The admin surface shows the models side by side with per-model calibration metrics.
What happens to the score when enrichment data is missing?
Missing enrichment data falls back to a configurable low-fit score with a routing hold for enrichment. The hold has a timeout: if enrichment does not arrive in 60 seconds, routing proceeds with the fallback score. When enrichment arrives (minutes to hours later), the score recomputes and the lead can re-route if the band changes. The engine never silently assigns a Hot score to a lead with missing data; the fallback is explicit and visible.
How often does the score recompute?
The score recomputes on every signal event: a new website visit, a form submission, an enrichment update, a day of silence (for the decay component). Hot bands and routing receive updated scores within seconds of the signal. The score history timeline shows every recompute with the triggering event, so the AE sees not just the current score but the trajectory. This is the information that distinguishes "lead is heating up" from "lead has been Hot for three weeks and now Warm."
Can we export the score to our data warehouse?
Yes. The score and its component values export via the standard CRM data warehouse connector to Snowflake, BigQuery, Redshift, or any supported destination. The export includes the three components, the top five contributing signals, the band, and the band entry time. Teams that want to build custom analytics or feed the score into a BI dashboard pull the data on the standard sync schedule (hourly or daily depending on the tier).
Is this HIPAA-compliant for healthcare lead sources?
Yes for teams signed on the BAA. The scoring engine runs on tenant-encrypted data with the standard per-tenant key policy. PHI fields on the lead record (if any) inherit the encryption policy. Scoring signal history and the component math are covered under the HIPAA access log. SOC 2 Type II attestation is in progress for 2026-Q4. GDPR and CCPA data-subject requests cover scored leads on the standard SAR workflow.
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