Feature · SDR Lead Scoring Criteria

Sales development representative lead scoring criteria shaped for meeting-booking motion.

Sales development representative lead scoring criteria are different from account executive scoring criteria. The SDR motion ends in a booked meeting, not a closed deal, so the signals that matter run on a shorter horizon. Strkr ships an SDR-shaped scoring model with fit, intent, timing, and engagement weighted for meeting booking rather than close-weighted conversion. The SDR team works the top of the sorted list and the meeting-rate improves measurably.

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Why SDR lead scoring is different from AE lead scoring

The signal shape that predicts a booked meeting.

Account executive lead scoring predicts a closed deal. The signals that matter run on a long horizon: multi-threaded engagement, procurement timeline, decision-maker involvement, budget confirmation. Sales development representative lead scoring predicts a booked meeting. The signals that matter run on a short horizon: inbound website behavior in the last 48 hours, LinkedIn activity in the last week, trigger events in the last month, cohort fit for the specific ICP. The two models look different on purpose because they predict different outcomes.

Shorter horizon

The scoring predicts a 14-day meeting, not a 90-day close.

SDR scoring runs on a 14-day prediction horizon: will this lead book a meeting in the next two weeks. The signals that matter are recent and immediate. The common failure where SDR scoring uses AE-shaped close-weighted signals produces a sorted list that correlates with closed-won six months later but ignores the signals that drive this week meetings.

Recency-weighted

The signal in the last 48 hours matters more than last month.

SDR scoring weights recent signals heavily. A website visit in the last 48 hours matters more than a webinar attended last month. A LinkedIn interaction this week matters more than an email opened six weeks ago. The decay function is steep because the meeting-booking motion is immediate.

Intent-weighted

Intent signals beat demographic fit.

SDR scoring weights intent signals (bottom-of-funnel page views, pricing page time, demo request, competitor comparison search) above pure demographic fit (industry, size, title). A perfect-ICP lead with no intent signals ranks below a mid-ICP lead with strong intent. The scoring surfaces the "warm" lead first.

Trigger-event aware

Trigger events spike the score temporarily.

SDR scoring surfaces trigger events: a new VP hired, a funding round announced, an acquisition completed, a tech stack change detected. The trigger event spikes the score for a short window (14 to 30 days) during which the lead is unusually responsive to outreach.

Cadence-aware

Scoring respects where the lead sits in the cadence.

SDR scoring respects cadence state: a lead that was contacted yesterday scores differently from a lead that was contacted three weeks ago. The system does not surface a hot lead the SDR already contacted this morning as the top priority again.

Owner-aware

The scoring respects territory and specialization.

SDR scoring respects territory and specialization. The healthcare specialist sees healthcare-qualified leads at the top; the EMEA SDR sees EMEA-qualified leads at the top. The common failure where the sorted list ignores specialization and routes a healthcare lead to the generalist does not happen.

Cohort-aware

The scoring compares to historical cohorts of booked meetings.

SDR scoring compares each lead to the historical cohort of leads that booked a meeting in the same segment. The model learns which signal combinations predicted a meeting booking last quarter and applies the pattern to this quarter leads.

Explainable

The score surfaces the signals driving it.

The score renders with the top three signals driving it: "high intent (pricing page 4 visits this week), good fit (SMB SaaS, 50-200 employees), trigger event (new VP of Sales hired 2 weeks ago)". The SDR sees why the lead is hot and tailors the outreach.

Said simply

A meeting-booking scoring model, not a close-weighted one.

SDR scoring is a meeting-booking scoring model. The signals, the horizon, the recency weighting, the intent weighting, the trigger-event awareness, the cadence awareness, and the cohort comparison all serve the prediction of "will this lead book a meeting in the next 14 days".

The criteria SDR scoring weights

Fit, intent, timing, engagement, the four weighted dimensions.

SDR scoring composes from four weighted dimensions. Fit measures the demographic match to the ideal customer profile. Intent measures the bottom-of-funnel behavior that signals purchase interest. Timing measures the trigger events that spike responsiveness. Engagement measures the lead specific interactions with the brand. Each dimension carries sub-criteria with specific weights. The composite score sorts the queue.

Fit dimension

Industry, size, title, geography, tech stack.

Fit scores on industry match (does the ICP include this industry), size match (does the ICP include this employee count), title match (is this a buyer or influencer title), geography match (is this a serviceable region), and tech stack match (does the account run the integrations the product fits).

Fit sub-criteria

The sub-criteria that compose the fit score.

Industry fit uses the ICP industry list with primary and secondary tiers. Size fit uses employee count with a target range. Title fit maps the lead title to the buyer and influencer taxonomy. Geography fit respects serviceable regions. Tech stack fit uses third-party enrichment data on the account tech stack.

Intent dimension

Bottom-of-funnel page views, demo requests, pricing visits.

Intent scores on specific pages visited (pricing, comparison pages, specific feature pages), demo requests submitted, pricing page time, high-intent search terms, and competitor comparison searches. The intent score weights recent activity heavily.

Intent sub-criteria

The intent signals the model watches.

Pricing page visits in the last 7 days. Demo request submitted. Comparison page visits (vs HubSpot, vs Salesforce). Feature page time (specific product capabilities). Returning visitor within 48 hours. High-intent search terms in referrer data.

Timing dimension

Trigger events that spike responsiveness.

Timing scores on trigger events: new VP or C-level hire, funding round announcement, acquisition completed, product launch, tech stack change detected, hiring surge in a relevant function. The trigger event spikes the timing score for 14 to 30 days.

Timing sub-criteria

The trigger events the model watches.

New hire in a buyer function (VP Sales, VP Marketing, VP Rev Ops). Funding round announcement from Crunchbase or Pitchbook. Acquisition completed. Product launch announcement. Tech stack change (added or removed a specific SaaS tool). Hiring surge of 10+ roles in the buyer function.

Engagement dimension

Specific interactions with the brand.

Engagement scores on content downloads, webinar attendance, email opens and clicks, LinkedIn interactions, event attendance, and SMS replies. The engagement score weights multi-channel interactions higher than single-channel.

Engagement sub-criteria

The engagement signals the model watches.

Content download in the last 30 days. Webinar attendance. Email open rate above the historical median. Email click rate above the historical median. LinkedIn interaction (reaction, comment, connection). Event attendance (physical or virtual). SMS reply.

Composite score

The four dimensions compose with configurable weights.

The four dimensions compose with configurable weights the admin tunes per tenant. The default weights lean intent-heavy and timing-heavy because those are the strongest meeting-booking predictors. The admin can shift weights to match the motion (fit-heavy for strict ICP, engagement-heavy for inbound-dominant).

How Strkr implements SDR lead scoring

The capabilities that make the scoring usable in production.

An SDR lead scoring model earns its place by shipping as a usable production capability. The signals have to be ingested, the model has to run, the queue has to render, the explainability has to surface, and the model has to adapt as the SDR team learns. Strkr builds the full implementation path natively so the scoring is a working surface, not a data science project.

Native signal ingestion

Fit, intent, timing, engagement signals arrive natively.

Strkr ingests fit signals from the enrichment partner, intent signals from the website tracker, timing signals from the news-and-trigger feed, and engagement signals from the native email and SMS. The common failure where scoring runs on an incomplete signal set does not happen because every signal has a native path in.

Nightly scoring run

The scorer runs every night across every lead.

The scorer runs every night across every lead in the active queue. The score lands on the lead record. The common failure where scoring runs stale (last computed two weeks ago) does not happen because the nightly run always refreshes.

Real-time trigger boost

The score spikes immediately on a trigger event.

The score spikes immediately on a trigger event (new VP hired, funding round announced). The SDR queue surfaces the spiked lead within minutes of the trigger. The common failure where the trigger event sits in a feed nobody checks does not happen.

Sorted queue

The SDR works the top of the sorted queue.

The SDR queue sorts by score with the top of the list surfacing the hottest leads. The SDR works top to bottom instead of top of the alphabet. The common failure where the SDR picks leads randomly from an unsorted list does not happen.

Explainable score

The score renders with the signals driving it.

Adaptive weights

The model learns from booked meetings.

The scoring model learns from booked meetings. The system tracks which signal combinations predicted a booked meeting this quarter and adjusts the weights for next quarter. The scorer improves over time without a manual retune.

Cadence integration

The queue respects where leads sit in cadence.

The queue integrates with the SDR cadence engine. A lead touched today does not surface as a top priority tomorrow unless the trigger event or intent signal spiked further. The common failure where the top lead gets contacted five times in a day because the queue did not update does not happen.

Territory and segment routing

The queue routes to the specialist for the segment.

The queue routes to the SDR specialist for the segment: healthcare leads to the healthcare specialist, EMEA leads to the EMEA SDR, enterprise leads to the enterprise SDR pod. The scoring respects the territory map and the specialization taxonomy.

Said simply

A scoring model that is usable in production from day one.

The scoring ingests signals natively, runs nightly with real-time trigger boosts, sorts the queue, surfaces explainable scores, learns from booked meetings, integrates with cadence, and respects territory. The SDR starts working the top of the queue on day one.

The metrics SDR scoring improves

The measurable outcomes a working SDR scoring motion produces.

An SDR scoring model earns its budget by moving specific metrics. Meeting-booking rate on contacted leads. Meeting-to-opportunity conversion. Opportunity quality (deal size, close rate). SDR productivity (meetings booked per hour of outreach). Pipeline generated per SDR. The scoring motion improves each metric by sorting the queue so the SDR works the leads that convert.

Meeting-booking rate

The percentage of contacted leads that book a meeting.

The scoring motion improves meeting-booking rate by surfacing leads with high meeting-booking propensity. The SDR team contacts hotter leads on average and the booking rate rises. The gain in production tenants ranges from 15 to 40 percent in meeting-booking rate.

Meeting-to-opportunity conversion

The percentage of booked meetings that convert to opportunities.

The scoring motion improves meeting-to-opportunity conversion because the leads surfaced at the top of the queue carry stronger fit signals. The booked meetings run on better-qualified leads so more convert to open opportunities.

Opportunity quality

The average deal size and close rate on sourced opportunities.

The scoring motion improves opportunity quality: higher average deal size, higher close rate on SDR-sourced opportunities. The AEs working the SDR-sourced pipeline see a cleaner cohort and the handoff conversation improves.

SDR productivity

Meetings booked per hour of outreach.

The scoring motion improves SDR productivity: meetings booked per hour of outreach rises because the SDR spends outreach time on hotter leads. The team can hold meeting-booking rate while reducing outreach volume or hold volume while raising meeting-booking rate.

Pipeline generated per SDR

The dollar value of pipeline each SDR sources.

The scoring motion improves pipeline generated per SDR by improving both the meeting-booking rate and the opportunity conversion. The composite effect is 25 to 60 percent more pipeline sourced per SDR in production tenants.

Faster ramp

New SDR hires reach productivity faster.

The scoring motion shortens new-hire ramp. A new SDR works a sorted queue instead of learning which leads matter from scratch. The time from first day to first meeting booked drops substantially.

Cleaner handoff

The AE receives better-qualified opportunities.

The scoring motion produces cleaner handoffs from SDR to AE. The opportunity surfaced to the AE carries fit, intent, timing, and engagement signals that correlate with close. The common failure where AEs reject half the SDR handoffs as unqualified drops substantially.

Measurable AI contribution

The AI contribution to meeting-booking becomes a dataset.

The scoring motion produces a measurable AI contribution: the lift in meeting-booking rate attributable to the scoring versus a round-robin baseline becomes a reportable metric. The leader sees the specific ROI of the scoring motion.

Said simply

Meeting-booking rate up, opportunity quality up, SDR productivity up.

The scoring motion improves meeting-booking rate, meeting-to-opportunity conversion, opportunity quality, SDR productivity, pipeline generated per SDR, new-hire ramp time, and AE handoff quality. The composite effect is a materially better SDR motion.

SDR lead scoring criteria that books meetings.

Strkr ships an SDR-shaped scoring model with fit, intent, timing, and engagement weighted for meeting booking. The scorer runs nightly, spikes on real-time trigger events, sorts the queue, surfaces explainable scores, learns from booked meetings, and respects territory and specialization. The SDR works the top of the sorted queue on day one.

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Common questions

What buyers ask about this feature.

How is SDR lead scoring different from AE lead scoring?

AE lead scoring predicts a closed deal on a 60 to 180 day horizon with signals that matter over long cycles: multi-threaded engagement, procurement timeline, decision-maker involvement, budget confirmation. SDR lead scoring predicts a booked meeting on a 14-day horizon with signals that matter immediately: recent website behavior, LinkedIn activity in the last week, trigger events in the last month, cohort fit for the specific ICP. The two models look different because they predict different outcomes. Running AE-shaped scoring on an SDR queue produces a sorted list that correlates with closed-won six months later but misses the signals that drive this week meetings.

What specific criteria does the SDR scoring model weight?

The model weights four dimensions. Fit (industry match, size match, title match, geography match, tech stack match) measures demographic alignment with the ICP. Intent (bottom-of-funnel page visits, pricing page time, demo requests, comparison page visits, high-intent search terms) measures specific purchase behavior. Timing (new VP or C-level hire, funding round, acquisition, product launch, tech stack change, hiring surge) measures trigger events that spike responsiveness. Engagement (content downloads, webinar attendance, email opens and clicks, LinkedIn interactions, event attendance, SMS replies) measures brand interaction. The four dimensions compose with configurable weights tuned per tenant.

How often does the scoring run and does it update in real time on trigger events?

The core scorer runs every night across every lead in the active queue, so the score is never older than 24 hours. Real-time trigger boosts update the score within minutes of a trigger event: a new VP hired, a funding round announced, an acquisition completed. The SDR queue surfaces the spiked lead immediately rather than waiting for the next nightly run. The common failure where the trigger event sits in a feed nobody checks until the lead has gone cold does not happen because the trigger boost is automated.

Does the SDR see why a lead is scored high or does the score surface as a black box?

Every score renders with the top three signals driving it. The SDR sees "high intent (pricing page 4 visits this week), good fit (SMB SaaS, 50-200 employees), trigger event (new VP of Sales hired 2 weeks ago)" alongside the numeric score. The outreach tailors to the signals that drove the score. The common failure where the SDR distrusts the model because the score is a black box does not happen because the explainability is first-class.

Does the scoring respect territory and specialization or does it just produce a global queue?

The scoring respects territory and specialization. The healthcare specialist sees healthcare-qualified leads at the top of their queue; the EMEA SDR sees EMEA-qualified leads at the top of their queue; the enterprise SDR pod sees enterprise-qualified leads. The scoring respects the territory map configured in the tenant and the specialization taxonomy. The common failure where a global sorted list routes a healthcare lead to the generalist does not happen.

What specific metrics does an SDR scoring motion improve in production?

A production SDR scoring motion improves meeting-booking rate (percentage of contacted leads that book a meeting), meeting-to-opportunity conversion (percentage of booked meetings that convert to opportunities), opportunity quality (average deal size and close rate on sourced opportunities), SDR productivity (meetings booked per hour of outreach), pipeline generated per SDR (dollar value of sourced pipeline), and new-hire ramp time (how fast new SDRs reach productivity). The gain in production tenants ranges from 15 to 40 percent in meeting-booking rate and 25 to 60 percent in pipeline generated per SDR.

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