Built for teams buying in-market lead data

In-market leads only matter when the rep acts within the hour.

Q: Which intent data vendors does Strkr integrate with natively?

Strkr ships native integrations with the major intent data vendors: Bombora (content intent, surge data, topic-level signal), 6sense (account surge, buying stage, orchestration), Demandbase (intent + firmographic + technographic), G2 (category comparison, review signal, competitor comparison), LinkedIn Sales Insights (engagement, hiring signal, decision-maker tracking), ZoomInfo intent, and Clearbit Reveal (website visitor identification). The integrations ingest signal into Strkr account records with the vendor source preserved, the signal timestamp captured, and the drill-down to the underlying data available on the account. For intent data vendors not shipped as native integrations, the open API provides REST endpoints and webhook subscriptions for custom ingestion.

Q: How does Strkr combine multiple intent sources into one score?

The Strkr account intent score combines multiple signal sources with configurable weights per source. The admin tunes the weights in the admin UI based on which signals convert to opportunity on the motion. The default weights are informed by cross-tenant conversion data (Bombora content intent weighted 20 percent, 6sense account surge 25 percent, G2 category comparison 15 percent, LinkedIn engagement 10 percent, first-party website 15 percent, first-party content engagement 10 percent, first-party product usage 5 percent) with the admin adjusting for their motion. The score updates in real-time as new signals arrive, decays over time to reflect signal staleness, and surfaces on the account record with the drill-down to the underlying signals available on hover.

Q: What is the typical signal-to-action lag with Strkr?

The typical signal-to-action lag on Strkr runs under 10 minutes from signal ingestion to rep notification. The native integrations with Bombora, 6sense, Demandbase, G2, and LinkedIn poll the vendor API every 15 minutes (or receive webhook push notifications where supported), the signal processes and updates the account score within seconds, the routing Flow fires on threshold crossing within seconds, and the rep notification arrives in the task queue, mobile push notification, and configured Slack or Microsoft Teams channel within minutes. The SLA timer fires at 60 minutes (configurable per tenant) with the escalation Flow routing to the manager if the touch does not go out in time. The pattern compresses the four-day signal-to-action lag that defines the dashboard-only pattern to under an hour.

Q: How does Strkr handle the first-party intent sources?

First-party intent sources ingest through four surfaces: the Strkr website pixel for anonymous visitor identification, the open API for product trial signal and custom events, the marketing automation integrations (Marketo, Pardot, Customer.io, HubSpot Marketing Hub, ActiveCampaign) for email engagement and content download signal, and the support surface integrations (Zendesk, Intercom, native Strkr support) for ticket pattern signal. The first-party signals combine with the third-party signals into one account intent score with configurable weights per source. The pattern is particularly valuable for PLG motions where product usage signal is often richer than any third-party intent, and for teams with high-engagement content marketing where webinar and ebook signals correlate strongly with pipeline conversion.

Q: How does the attribution tracking work for intent data ROI?

Attribution tracking runs on every intent-triggered outbound touch. The outbound carries attribution to the intent source and signal type that fired the Flow. The resulting opportunity (if created) preserves the attribution, and the closed revenue (if the opportunity wins) attributes to the intent source. The attribution dashboard shows pipeline volume per source, closed revenue per source, cost per pipeline dollar (vendor subscription divided by pipeline attributed), cost per revenue dollar (vendor subscription divided by closed revenue attributed), and the score threshold conversion curve. The data makes the vendor renewal conversation a value conversation instead of a price negotiation, and the data feeds back to the vendor through native integration so the vendor model tunes to your motion over time.

Q: What happens when the intent signal volume exceeds rep capacity?

Rep capacity caps run on the Strkr routing Flow with configurable caps per rep per day and per week. When the hot account volume exceeds the rep queue capacity, the overflow Flow fires: the next account routes to the backup rep, escalates to the manager for assignment review, or lands in a prioritised queue for the rep to work through once capacity opens. The pattern prevents the common failure mode where a surge week dumps 50 hot accounts on a rep who can realistically touch 10, which creates burnout and misses on the signal window. The manager dashboard shows the capacity utilisation per rep so the territory and segment assignments can tune based on actual volume, and the escalation Flow surfaces the capacity bottleneck before it costs the team opportunity.

The intent data category (Bombora, 6sense, Demandbase, ZoomInfo, G2 intent, LinkedIn Sales Insights) ships rich signal: which accounts are researching your category, which pages they visit, which competitors they compare, which content they consume. The signal is only valuable if the rep acts within the signal window. Most teams buy the data, pipe it to a dashboard, and lose the signal to lag. Strkr turns in-market signal into an acting Flow with routing, Strkr AI drafting, and SLA timers on every account.

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What this audience is actually dealing with

The pains that bring buyers here.

In-market lead data is a legitimate category: when an account is actively researching the category you sell into, the probability of a near-term purchase is materially higher than the baseline. The data vendors (Bombora, 6sense, Demandbase, G2, ZoomInfo, LinkedIn Sales Insights) have invested heavily in detecting intent signals and attributing them to specific accounts. The signal is real; the problem is operational. Most teams buy a six-figure annual intent data subscription, pipe the data to a dashboard, and watch the signal expire because no rep acts within the window. The data vendor shows the dashboard with the hot accounts, the sales operations team notices on Monday, the SDR gets the account on Wednesday, and the outbound touch lands on Friday - four days after the signal fired. By then the buyer has either moved to a competitor or lost the active-research momentum. The pain points below show up on every revenue leader call about intent data ROI.

Signal to action lag

The hot account arrives in Strkr four days later.

The intent signal fires on Monday morning, the data vendor dashboard shows the account on Monday afternoon, the sales ops team runs the hot list on Tuesday, the SDR gets the account on Wednesday, and the outbound touch lands on Friday. Four days of lag on a signal that is only valid for 7 to 14 days means half the signal is wasted before the rep even sees it. The signal-to-action lag kills the ROI on the intent data subscription.

Signal lives in a dashboard

The intent data sits in a vendor UI nobody opens daily.

The intent data vendor ships a web dashboard with hot account lists, surge data, and research pattern reports. The dashboard is a separate login the sales team does not open daily. The signal never flows into the CRM workflow where the rep lives, which means the signal only drives action when the sales ops team manually pushes it. The dashboard-only pattern is the single largest reason intent data subscriptions get cancelled on renewal.

No routing on signal

The hot account lands in the general queue, not the right rep.

The intent signal arrives attached to an account but without the ownership context. The sales operations team manually assigns the hot accounts to reps based on territory, segment, and vertical, which takes hours per surge batch. The reps who get the hot accounts last are the ones with the right territory but the wrong manager relationship to the sales ops lead. The routing bottleneck means the signal does not reach the right rep in time.

Nothing drafts the outbound

The rep sees the hot account and does not know what to say.

The intent signal shows which account is researching, which pages they visit, and which content they consume. The signal does not draft the outbound touch. The rep sees the hot account, opens a blank email, and spends 20 minutes researching the right message. The time-to-first-touch stretches to days as the rep queue builds up. The signal becomes a research obligation instead of an outreach trigger.

Multi-source signals never combine

Bombora says one thing, 6sense says another, G2 says a third.

Teams running multiple intent sources (Bombora for content intent, 6sense for account surge, G2 for category comparison, LinkedIn for engagement) see three different account lists per week and no clear picture of which signal matters most. The intent sources never combine into a single account intent score, so the rep gets conflicting signals and defaults to ignoring most of them.

No feedback loop to the vendor

The vendor cannot improve the model because nobody tells them what worked.

The intent data vendor model improves when the buying team tells it which signals actually converted to opportunity and revenue. Most teams never close the feedback loop because the CRM does not track the signal-to-opportunity path, the vendor does not integrate deeply enough to receive the feedback, and the sales ops team is too busy pushing signals through to run a retrospective. The signal quality stagnates and the renewal conversation becomes a vendor negotiation instead of a value conversation.

Signal to action in minutes

Intent arrives in the CRM, routes to the rep, drafts the touch.

The intent data ROI depends on compressing the signal-to-action lag from days to minutes. Strkr ingests intent signals from Bombora, 6sense, Demandbase, G2, LinkedIn Sales Insights, and first-party signal sources natively; routes the hot account to the right rep based on territory, segment, and vertical; drafts the outbound touch through Strkr AI with the research pattern as context; and fires the SLA timer on the rep to touch within the first hour. The pattern below covers how Strkr turns intent signal into acting Flows.

Multi-vendor ingestion

Bombora, 6sense, Demandbase, G2, LinkedIn, first-party.

Native integrations ingest intent signals from Bombora (content intent, surge data), 6sense (account surge, buying stage), Demandbase (intent + firmographic), G2 (category comparison, review signal), LinkedIn Sales Insights (engagement, hiring signal), ZoomInfo intent, and first-party intent from the website and marketing automation. The signals land on account records with the vendor source preserved.

Account intent score

Multi-source signals combine into one account score.

The intent sources combine into a single account intent score with configurable weights per source. The admin tunes the weights based on which signals convert to opportunity on their motion. The reps see one score per account with the drill-down to the underlying signals, so the conflicting-signal problem disappears.

Signal-driven routing

Hot account auto-routes to the right rep in seconds.

The routing Flow fires on intent threshold crossing with configurable rules: territory, segment, vertical, named-account assignment, round-robin for unowned accounts. The rep gets the hot account on their task queue within seconds of the signal firing, not four days later after the sales ops batch review.

Strkr AI drafts the outbound touch

The AI drafts the email with the research pattern as context.

Strkr AI drafts the outbound email with the research pattern as context: which pages the account visited, which competitors they compared, which content they consumed, which personas on the account engaged. The rep reviews and sends in one tap. The time-to-first-touch compresses from days to minutes.

SLA timer on first touch

The rep has 60 minutes to send the first outbound.

The SLA timer fires on account assignment with a configurable window (default 60 minutes). The rep sees a countdown on the account card, the manager sees an SLA breach report, and the escalation Flow fires if the touch does not go out in time. The pattern creates accountability on signal-to-action without requiring daily manager intervention.

Feedback loop to vendor

Opportunity + revenue attribution closes the loop.

The outbound-to-opportunity-to-revenue path tracks per intent signal and feeds back to the vendor for model improvement. The intent score weights tune automatically based on which signals actually converted on the tenant. The signal quality improves quarter over quarter instead of stagnating.

First-party intent is the other half

Website visit, product usage, and content engagement all count.

Third-party intent data (Bombora, 6sense, Demandbase) is only half the picture. First-party intent (website visits, product trial activity, content downloads, webinar attendance, email engagement, support ticket history) is often richer signal because it reflects actual behaviour on your surfaces, not inferred behaviour from publisher content consumption. Strkr ingests first-party intent natively through the open API, website pixel, and marketing automation integrations. The pattern below covers how first-party signal integrates with the third-party intent score.

Website visitor identification

Reveal the account behind an anonymous visit.

The Strkr website pixel identifies accounts from IP addresses, visitor cookies, and form fills with the de-anonymised account landing on the Strkr record. The visit pattern (which pages, how long, how often) feeds the first-party intent score and combines with the third-party signals.

Product trial signal

Trial activity drives sales-assist timing.

Product trial signal (sign-up, feature usage, usage threshold crossed, invitation accepted, workspace activity) ingests through the open API with the signal driving sales-assist timing. A trial account showing high usage lands on the AE queue with Strkr AI drafting the sales-assist touch based on the usage pattern.

Content engagement signal

Webinar, ebook, pricing page engagement scored.

Content engagement signal (webinar attendance, ebook download, pricing page view, demo request, calculator usage) scores on the account with configurable weights. The signal feeds the first-party intent score with the content type and the recency weighted.

Email engagement signal

Open, click, reply, meeting booked all score.

Email engagement signal from the marketing automation integration (Marketo, Pardot, Customer.io, HubSpot Marketing Hub, ActiveCampaign) ingests into the account record with open, click, reply, and meeting-booked events weighted on the first-party score.

Support ticket signal

Churn risk vs expansion opportunity patterns detected.

Support ticket signal from Zendesk, Intercom, or the native Strkr support surface ingests into the account record. The ticket pattern (volume, severity, resolution time, sentiment) feeds the account health score and distinguishes churn-risk signal from expansion-opportunity signal.

Combined score drives action

First-party + third-party weighted into one account score.

The first-party and third-party intent sources combine into one account score with configurable weights. The admin tunes the weights based on which signals convert to opportunity on their motion. The rep sees one score and acts on one priority queue, not six.

Signal flows into the right workflow

Named account, territory, segment, vertical - all routing dimensions.

The intent signal is only valuable when it reaches the right rep. The routing Flow needs to understand territory, segment, vertical, named-account assignment, and account health context to put the signal on the right queue. Strkr Flows cover the routing dimensions natively with the admin configuring the rules in the admin UI without an engineering ticket. The pattern below covers the routing primitives.

Territory routing

Geographic territory assigns the account to the right AE.

Territory routing assigns the account based on geographic territory with the AE assigned to the territory receiving the hot account. The admin configures the territory map in the admin UI with support for state, region, country, and metro-level territories. Multi-rep territories support round-robin or weighted assignment.

Segment routing

Enterprise, mid-market, commercial - different reps per segment.

Segment routing assigns the account based on firmographic segment: enterprise (10,000+ employees), mid-market (500-10,000), commercial (50-500), SMB (<50). The AE pod per segment receives the hot account with the right playbook per segment.

Vertical routing

Industry vertical directs to the vertical specialist.

Vertical routing assigns the account to the AE specialising in the vertical (healthcare, financial services, retail, manufacturing, SaaS, agencies). The vertical specialist runs the account with the right industry language and the right reference customers in the first touch.

Named account routing

ABM target accounts always route to the named rep.

Named account routing overrides the territory, segment, and vertical rules for ABM target accounts. The named rep owns the account regardless of where the intent signal fires, which preserves the ABM pattern without creating a routing conflict.

Account health context

Existing customer signal routes to the CSM, not the AE.

The routing Flow checks account health context before assignment: existing customer accounts route to the CSM for expansion motion, churn-risk accounts route to the CSM for retention motion, net-new accounts route to the AE for new business motion. The pattern avoids the common error of an AE prospecting an existing customer.

Load balancing

Round-robin with capacity caps prevents rep overload.

Load balancing with capacity caps prevents rep overload during surge weeks. If the hot account volume exceeds the rep queue capacity, the overflow routes to the backup rep or escalates to the manager. The pattern keeps the SLA timer achievable even during high-signal periods.

Measuring the signal-to-revenue path

Attribution closes the feedback loop on intent data ROI.

The intent data ROI conversation only works with the signal-to-revenue attribution path tracked end to end. Strkr ships native attribution from intent signal to opportunity to revenue with the dashboard showing the ROI per intent source, per intent score threshold, and per intent signal type. The pattern below covers the attribution primitives that justify or kill the next renewal.

Signal source attribution

Opportunity and revenue tracked per intent source.

Each opportunity carries attribution to the intent source that triggered the outbound (Bombora, 6sense, G2, LinkedIn, first-party website, product trial, content engagement). The attribution dashboard shows which sources drive opportunity volume, pipeline value, and closed revenue.

Signal threshold analysis

Which score thresholds actually convert to pipeline.

The attribution analysis shows which intent score thresholds convert to pipeline. The admin can see that accounts above a score of 70 convert at 15 percent, accounts between 50 and 70 convert at 8 percent, and accounts below 50 convert at 2 percent. The threshold tuning becomes data-driven, not vendor-recommended.

Signal type analysis

Content intent vs account surge vs engagement signal.

The attribution analysis splits the signals by type: content intent (Bombora research pattern), account surge (6sense buying stage), category comparison (G2 competitor review), engagement (LinkedIn interaction), first-party (website visit, trial activity). The admin can see which signal type drives the most pipeline and tune the weights accordingly.

Rep-level performance

Which reps actually convert hot accounts to pipeline.

The rep-level dashboard shows which reps convert hot accounts to pipeline at the highest rate. The pattern surfaces coaching opportunities (reps missing the SLA timer, reps using generic outbound messaging, reps ignoring hot accounts in favour of their own pipeline). The manager can run a signal-conversion review per rep per quarter.

Vendor renewal justification

The ROI data makes the renewal conversation easy.

The attribution dashboard provides the vendor renewal conversation with real data: pipeline attributed to the vendor source, closed revenue attributed to the vendor source, cost per pipeline dollar, cost per revenue dollar. The renewal becomes a value conversation instead of a negotiation on the price line.

Feedback to vendor model

The vendor improves the model with your conversion data.

The signal-to-opportunity path feeds back to the intent data vendor through native integrations with Bombora, 6sense, and Demandbase. The vendor model tunes to your motion with the conversion data you provide, which improves the signal quality on your account over time. The feedback loop closes.

Head-to-head

Strkr vs the dashboard-only intent data pattern.

The common pattern for in-market lead data is a dashboard-only subscription: the vendor dashboard shows hot accounts, the sales ops team runs a weekly batch, and the signal expires before the rep acts. Strkr ingests the signal natively, routes to the right rep in seconds, drafts the outbound touch through Strkr AI, and closes the feedback loop on attribution. The pattern below covers the material differences.

Feature	Strkr	Dashboard-only intent data
Signal to CRM lag	Seconds (native ingestion)	Days (batch export)
Signal to rep lag	Seconds (auto-routing)	Days (manual assignment)
Multi-source combination	Weighted account score	Separate dashboards per source
Outbound drafting	Strkr AI with research context	Blank email per account
SLA timer on first touch	Native with escalation	Manual tracking in spreadsheet
First-party signal integration	Native via API + pixel	Separate marketing automation stack
Routing dimensions	Territory + segment + vertical + ABM	Manual batch assignment
Attribution dashboard	Native source + threshold + type	Vendor dashboard, counts only
Rep performance tracking	Signal-to-pipeline per rep	Not tracked
Vendor feedback loop	Native conversion data push	Not closed
Admin configuration	Self-serve in admin UI	Vendor professional services
Renewal conversation	ROI data drives the value talk	Price negotiation

How teams use Strkr

How teams actually run intent data on Strkr.

The patterns below show up on teams that run intent data through Strkr. The common thread: ingest multi-vendor signal natively, combine into one account score, route to the right rep in seconds, draft outbound with Strkr AI, fire the SLA timer, and track attribution end to end.

Enterprise SaaS with Bombora + 6sense

Named-account motion with intent-driven acceleration.

A 120-person enterprise SaaS team runs Bombora content intent and 6sense account surge against a named-account list of 500 targets. Strkr ingests both sources, combines into one score, and surfaces the top 20 accounts per week to each named AE. The SLA timer fires at 60 minutes, and Strkr AI drafts the outbound touch based on the research pattern. Pipeline attribution shows 35 percent of enterprise pipeline flows from intent-triggered touches.

Mid-market with G2 + LinkedIn

Category comparison signal drives competitive displacement.

A 40-person mid-market SaaS team runs G2 category comparison signal (which accounts are comparing them to specific competitors) and LinkedIn Sales Insights. Strkr routes the G2 signal to the AE with Strkr AI drafting a competitive-displacement outbound referencing the competitor the account is comparing. Win rate on competitive displacement deals improved 20 percent on signal-triggered touches.

PLG with first-party trial signal

Trial activity signal drives sales-assist timing.

A 60-person PLG SaaS team streams trial activity signal (feature usage, workspace activity, invitation accepted, usage threshold crossed) into Strkr via the open API. Strkr routes the hot trials to the AE pod with the usage pattern as context, and Strkr AI drafts the sales-assist outbound. The sales-assist conversion rate on signal-triggered trials runs 2x the baseline PLG conversion rate.

Agencies with first-party content signal

Content engagement drives discovery call booking.

A 25-person agency runs first-party content signal (webinar attendance, ebook download, pricing page view) through Strkr. The content engagement score triggers the discovery call booking Flow with Strkr AI drafting the outreach based on the content consumed. Discovery call booking rate on signal-triggered contacts runs 3x the cold outreach baseline.

Industry vertical with Demandbase + first-party

Vertical-specific signal routing to the vertical specialist.

A 50-person vertical SaaS team selling into healthcare runs Demandbase intent plus first-party website signal. Strkr routes the hot healthcare accounts to the vertical specialist AE pod with the industry-specific context. The vertical specialist uses Strkr AI to draft outbound referencing specific healthcare use cases. Pipeline per vertical specialist improved 40 percent on signal-driven routing.

See intent signal turn into acting Flows in minutes.

Start a 14-day trial with the full in-market lead stack enabled: native integrations with Bombora, 6sense, Demandbase, G2, LinkedIn, and first-party sources; account intent scoring with configurable weights; signal-driven routing with territory, segment, vertical, and ABM dimensions; Strkr AI drafting on every outbound; SLA timers with escalation; and attribution tracking from signal to revenue.

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Common questions

What buyers in this bucket ask most.

Which intent data vendors does Strkr integrate with natively?

Strkr ships native integrations with the major intent data vendors: Bombora (content intent, surge data, topic-level signal), 6sense (account surge, buying stage, orchestration), Demandbase (intent + firmographic + technographic), G2 (category comparison, review signal, competitor comparison), LinkedIn Sales Insights (engagement, hiring signal, decision-maker tracking), ZoomInfo intent, and Clearbit Reveal (website visitor identification). The integrations ingest signal into Strkr account records with the vendor source preserved, the signal timestamp captured, and the drill-down to the underlying data available on the account. For intent data vendors not shipped as native integrations, the open API provides REST endpoints and webhook subscriptions for custom ingestion.

How does Strkr combine multiple intent sources into one score?

The Strkr account intent score combines multiple signal sources with configurable weights per source. The admin tunes the weights in the admin UI based on which signals convert to opportunity on the motion. The default weights are informed by cross-tenant conversion data (Bombora content intent weighted 20 percent, 6sense account surge 25 percent, G2 category comparison 15 percent, LinkedIn engagement 10 percent, first-party website 15 percent, first-party content engagement 10 percent, first-party product usage 5 percent) with the admin adjusting for their motion. The score updates in real-time as new signals arrive, decays over time to reflect signal staleness, and surfaces on the account record with the drill-down to the underlying signals available on hover.

What is the typical signal-to-action lag with Strkr?

The typical signal-to-action lag on Strkr runs under 10 minutes from signal ingestion to rep notification. The native integrations with Bombora, 6sense, Demandbase, G2, and LinkedIn poll the vendor API every 15 minutes (or receive webhook push notifications where supported), the signal processes and updates the account score within seconds, the routing Flow fires on threshold crossing within seconds, and the rep notification arrives in the task queue, mobile push notification, and configured Slack or Microsoft Teams channel within minutes. The SLA timer fires at 60 minutes (configurable per tenant) with the escalation Flow routing to the manager if the touch does not go out in time. The pattern compresses the four-day signal-to-action lag that defines the dashboard-only pattern to under an hour.

How does Strkr handle the first-party intent sources?

First-party intent sources ingest through four surfaces: the Strkr website pixel for anonymous visitor identification, the open API for product trial signal and custom events, the marketing automation integrations (Marketo, Pardot, Customer.io, HubSpot Marketing Hub, ActiveCampaign) for email engagement and content download signal, and the support surface integrations (Zendesk, Intercom, native Strkr support) for ticket pattern signal. The first-party signals combine with the third-party signals into one account intent score with configurable weights per source. The pattern is particularly valuable for PLG motions where product usage signal is often richer than any third-party intent, and for teams with high-engagement content marketing where webinar and ebook signals correlate strongly with pipeline conversion.

How does the attribution tracking work for intent data ROI?

Attribution tracking runs on every intent-triggered outbound touch. The outbound carries attribution to the intent source and signal type that fired the Flow. The resulting opportunity (if created) preserves the attribution, and the closed revenue (if the opportunity wins) attributes to the intent source. The attribution dashboard shows pipeline volume per source, closed revenue per source, cost per pipeline dollar (vendor subscription divided by pipeline attributed), cost per revenue dollar (vendor subscription divided by closed revenue attributed), and the score threshold conversion curve. The data makes the vendor renewal conversation a value conversation instead of a price negotiation, and the data feeds back to the vendor through native integration so the vendor model tunes to your motion over time.

What happens when the intent signal volume exceeds rep capacity?

Rep capacity caps run on the Strkr routing Flow with configurable caps per rep per day and per week. When the hot account volume exceeds the rep queue capacity, the overflow Flow fires: the next account routes to the backup rep, escalates to the manager for assignment review, or lands in a prioritised queue for the rep to work through once capacity opens. The pattern prevents the common failure mode where a surge week dumps 50 hot accounts on a rep who can realistically touch 10, which creates burnout and misses on the signal window. The manager dashboard shows the capacity utilisation per rep so the territory and segment assignments can tune based on actual volume, and the escalation Flow surfaces the capacity bottleneck before it costs the team opportunity.

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