This page walks through one complete lead scoring model as if the sales operations lead were building it from scratch. The example tenant is a mid-market B2B SaaS company selling into technology leadership at companies with 200 to 2,000 full-time employees. The model carries seven signal branches, a weight set calibrated against twelve months of closed-won data, an explicit hand-off threshold, cited drivers, and an audit routine. Each step shows the field, the configuration, the resulting record page, and the counterfactual the sales team reads. The model is the one you can build on the Strkr admin page in an afternoon.
The example tenant is a mid-market B2B SaaS company whose primary ICP is technology leadership (VP of engineering, chief information officer, chief technology officer) at companies with 200 to 2,000 full-time employees in North America. Secondary ICP: operations leadership (chief operations officer, VP of operations) at the same company size. Buying cycle: four to six months. The historical closed-won pattern across the trailing twelve months is the data basis for the initial weight set.
Primary ICP: VP of engineering, chief information officer, chief technology officer at B2B SaaS, fintech, or developer-tool companies with 200 to 2,000 FTE in North America. The ICP definition lives on the admin page and the firmographic branch reads it.
Secondary ICP: chief operations officer, VP of operations at the same company size band. The segment weighting treats secondary ICP leads at ninety percent of the primary ICP weight. The admin page handles the split.
The weight calibration pulls from the trailing twelve months of closed-won deals. The pattern that correlates with won deals becomes the data basis for the initial weight set. The operations lead reviews and tunes from there.
The buying cycle length drives the recency windows for the engagement and timing branches. A behavioral event from twelve months ago is noise; an event from six weeks ago is a signal. The admin page configures the windows.
Marketing and sales agreed on a hand-off threshold of composite 70. Leads at 70 or higher flow to the sales development queue with drivers cited. Leads below 70 stay in the nurture lane. The threshold is on the admin page and the audit log records any change.
Monthly signal audit by branch owner, weekly drift report, quarterly weight recalibration. The operations lead owns the schedule. The model stays current rather than drifting.
Each branch below shows the configuration the operations lead set, the signal fields the branch reads, and the owner. The branches are visible on the admin page and the vocabulary is shared across marketing operations and sales operations. Nothing is tribal memory.
Reads industry (SaaS, fintech, developer tools), FTE band (200-2,000), geography (North America), and tech stack signals (cloud provider, CI tool, auth provider). Owner: marketing operations lead. Refresh cadence: weekly from the enrichment provider.
Reads product documentation reads, pricing page visits (seven-day rolling window), demo request forms, campaign email reads and clicks, documentation downloads. Owner: marketing operations lead. Each event is cited with timestamp.
Reads reply rate on outbound touches (thirty-day rolling), meeting booking rate, and response latency. Owner: sales operations lead. The common failure of over-rewarding stale engagement is handled by the rolling window.
Reads engaging contact role. VP-plus titles weight at 1.0, director titles at 0.75, manager titles at 0.40, individual contributor at 0.20. Owner: sales operations lead. Role mapping is tunable per segment.
Reads trigger events (funding announcement, exec hire, product launch, M&A, large hiring push). Each trigger fires an intra-day re-score. Owner: sales operations lead. Budget cycle timing weights fiscal Q4 at 1.1x for the ICP segment.
Reads per-tenant custom fields: strategic account flag, partner-sourced flag, prior pilot history. Owner: sales operations lead. The field inventory lives on the admin page and the weights are tunable without a data science engagement.
Reads the shape of the closed-won deals over the trailing twelve months. The weight starts low in the first quarter after launch (thin data) and increases as the model accumulates confirmed outcomes. Owner: sales operations lead.
ScalePilot is a 420-FTE developer-tool company in Austin that fits the primary ICP. The VP of engineering submitted a demo request last Monday after reading two product documentation pages and visiting the pricing page three times in a week. The outbound touch two weeks ago got a reply from a director of platform engineering within six hours. The model reads nine signal fields and produces a composite of 76. The record page cites every driver.
ICP fit: SaaS, 420 FTE, Austin (fits North America), modern cloud stack. Hit value 0.90 against the ICP definition. Contribution 22.5 points. Cited field: enrichment record last refreshed three days ago.
Demo request form submitted, two documentation reads, three pricing page visits in seven days. Hit value 0.80. Contribution 20.0 points. Each event cited with timestamp.
Reply from director of platform engineering within six hours of outbound touch. Reply length: substantive. Hit value 0.70. Contribution 10.5 points. Reply thread cited.
VP of engineering submitted the demo request (role weight 1.0); director of platform engineering replied (role weight 0.75). Combined hit value 0.75. Contribution 11.3 points.
Recent engineering hire announcement (trigger fire). No strategic account flag. Combined hit value 0.60. Contribution 9.6 points.
Closed-won pattern match: moderate alignment on behavior shape and champion role. Hit value 0.50. Contribution 2.0 points. Total composite: 76. Hand-off threshold 70 met; routes to the sales development queue with the drivers cited.
The record page is the surface the sales development representative opens in the morning. It shows the composite, the drivers, the counterfactual, the engagement timeline, the nurture state, and the opener draft. The common failure of a scoring tool whose output lives in a separate dashboard gets closed off.
The composite 76 lives at the top of the record page with the segment context. The rep sees the number scoped to the mid-market segment, not against unrelated verticals.
The top five drivers (firmographic match, demo request, pricing visits, director reply, VP demo) are cited with timestamps. The rep clicks through to the field to verify any one of them.
The counterfactual panel shows that an engaged chief information officer would move the lead from 76 to 85. The rep works the next touch around the CIO rather than guessing where to invest effort.
The engagement timeline shows every signal and every touch over the last ninety days. The rep reads the context in one view rather than clicking across campaign, nurture, and outbound tools.
The current nurture track (Education Track, step 3 of 7) is visible. The next scheduled send is in four days. The rep times the first-touch call around the nurture rather than cutting across it.
Strkr AI drafts the opener referencing the documentation the VP read, the trigger event (recent hire), and the director reply. The rep reviews, edits, and sends. The opener is a draft, not a sent email.
The hand-off rule is explicit, agreed between marketing and sales, and recorded on the admin page. Leads at composite 70 or above with the champion branch value at 0.50 or above flow to the sales development queue. Leads below the threshold stay in the nurture lane. Exceptions are routed to the review queue. The rule is auditable and the audit log records any change.
The threshold is composite 70 with a champion branch value at 0.50 or above. The compound condition prevents the common failure of promoting a lead whose high score comes from firmographic and behavioral signals but whose engaging contacts are too junior to move the deal.
The sales development team commits to a first touch within one business day of hand-off. The service level is agreed and visible on the manager rollup. Breaches route to review.
Leads that cross the threshold but look wrong to the rep (unexpected segment, non-ideal context) route to the review queue with notes. Marketing operations and sales operations review together on the weekly standup. The exception is tracked rather than silently rejected.
When sales rejects a lead, the reason is recorded and feeds back to the model. Marketing operations sees the rejection pattern on the next calibration. The common failure of marketing and sales arguing without a shared view gets avoided.
Every hand-off decision (promote, reject, hold, route to review) is recorded in the audit log with the user, the timestamp, and the reason. The quarterly review runs against the log.
The hand-off agreement on the admin page shows the signers (sales operations lead, marketing operations lead, sales management, marketing management). The rule survives leadership changes because the agreement is documented.
The audit routine keeps the model honest after the launch. Monthly signal audit by branch owner, weekly drift report, quarterly weight recalibration, annual full review. The schedule is on the admin page and the ownership is explicit. The common failure of a model that quietly degrades over a year gets closed off.
Every month, each branch owner audits the fields they own: cited sources working, refresh cadence hit, no renamed fields, no stale enrichment. The audit log records the pass or the issues found.
Every week, the drift report shows the score distribution across the pipeline versus the segment benchmark. Shifts outside the expected range flag for review. The operations lead sees drift before the sales team notices.
Every quarter, the recalibration pulls the latest closed-won pattern and recommends weight tunes. The operations lead reviews, approves, and promotes. The model stays current with the actual pipeline shape.
Every quarter, the hand-off review checks whether the threshold still fits the current pipeline. Marketing and sales re-ratify or tune. The common failure of a hand-off rule that quietly stops fitting gets avoided.
Once a year, the operations lead runs a full review: signal inventory, weight set, hand-off agreement, audit routine. The model gets a scheduled refresh rather than drifting for a year.
Every audit event, every weight change, every hand-off decision is exportable through the open API. The compliance review runs against the exported history rather than tribal memory.
Strkr runs the full example model (signal branches, weights, hand-off, audit) on the admin page in an afternoon. Fifty dollars per seat flat. No data science engagement required.
Yes. The signal inventory, weight calibration, record wiring, and hand-off agreement all live on the admin page. A sales operations lead familiar with the pipeline can configure the model in an afternoon. The tuning and sign-off between marketing and sales is the schedule, not the configuration.
The historical branch starts at a low weight when the model launches because the data is thin. As the model accumulates confirmed outcomes over the first six months, the operations lead increases the historical weight and reduces the firmographic weight proportionally. The weight evolves with the data.
A lead whose score comes almost entirely from firmographic and behavioral signals but whose engaging contacts are too junior to move the deal is a common false positive. The compound condition requires both a composite above threshold and a champion branch value at 0.50 or above. The lead that fits the ICP and shows intent but has no champion stays in the nurture lane until a champion engages.
When a previously rejected lead crosses the threshold again (new trigger event, new champion engagement), the record carries the prior rejection history and the new context on the record page. The sales development representative sees both the previous rejection and the new signals that produced the resurface.
Yes. The admin page supports multiple weight sets per tenant. The example above runs primary and secondary ICPs with a 10% weight differential. Tenants with distinct enterprise, mid-market, and SMB segments run three separate weight sets side by side.
Strkr is HIPAA compliant with a Business Associate Agreement available, and the SOC 2 Type II audit is in progress through 2026-Q4. Every change to the signal taxonomy, the weights, the ICP, and the hand-off threshold is recorded in the audit log. The configuration is exportable through the open API.
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