Feature · Sales and Marketing Analytics

Sales and marketing analytics joined on the same record, not stitched after the fact.

Sales and marketing analytics only works when the two funnels share a data model. Strkr reads campaigns, forms, SMS, email, meetings, and closed-won deals as rows on the same account, so attribution lands on the dollar, not on the MQL. The marketing team sees which campaigns produced revenue; the sales team sees which sources closed; the leader sees the full lifecycle in one surface.

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The gap between marketing analytics and sales analytics

Why joined analytics is a different product than two dashboards side by side.

Most teams run marketing analytics in one tool, sales analytics in another, and reconcile the two in a spreadsheet once a quarter. The reconciliation never actually works because the two tools model the funnel differently: marketing counts leads and MQLs, sales counts opportunities and closed-won, and nobody can tie a campaign to a dollar of revenue with any credibility. Joined analytics means the same record carries every touch, every stage, and every dollar. The attribution math stops being a hand-wave and becomes a query.

One record

Lead to opportunity to closed-won without a copy step.

The contact that filled the form, booked the demo, converted to an opportunity, and signed the contract lives on a single record. The analytics engine walks the whole lifecycle in one query. The common failure where the marketing tool loses the thread the moment the lead converts to an opportunity does not happen.

Every touch counted

Email opens, page views, SMS replies, meetings, all on the record.

Marketing touches land on the same record the sales rep sees. The campaign that generated the lead, the nurture emails the lead opened, the webinar they attended, and the demo they booked all render as timeline events on the opportunity. The attribution model has real data to work with.

Attribution to the dollar

The campaign that produced the revenue, not the MQL.

Attribution renders in revenue, not in MQL count. The marketing lead sees that the retargeting campaign produced 180 MQLs that converted to 42 opportunities that produced 1.2M in closed-won. The ROI math stops at the money, not at the funnel midpoint.

Multi-touch models

First-touch, last-touch, linear, time-decay, position-based.

The attribution engine supports multiple attribution models side by side. The team sees first-touch for brand investment, last-touch for closing-channel investment, and time-decay for ongoing nurture. Different models answer different questions and the dashboard renders them without a separate tool.

Source quality

Which lead source converts, not just which produces volume.

The source quality tile ranks lead sources by MQL-to-closed-won conversion rate and average deal size. The marketing team sees that one high-volume source converts at 3 percent and another low-volume source converts at 18 percent. The budget conversation reads from quality, not volume.

Cost per opportunity

Campaign spend divided by opportunities produced.

The cost tile divides campaign spend by opportunities produced and by closed-won revenue. The marketing lead sees that one channel costs 1,200 per opportunity and another costs 4,800 per opportunity. The spend allocation reads from CAC per opportunity, not from CPL.

Lifecycle funnel

The full funnel in one chart.

The lifecycle funnel renders every stage from first touch through closed-won: form fill, MQL, SQL, opportunity, proposal, closed-won. Each stage shows the conversion rate and the time between stages. The leak surfaces visually before any analysis.

Account-based rollup

The account view across every stakeholder.

The account rollup renders every touch, every stage, and every closed-won amount for every contact tied to the account. ABM teams see the full account surface without stitching contact records manually. The account-level analytics match the account-level motion.

Cohort revenue

Signup cohorts tracked through expansion and churn.

Cohorts group accounts by first closed-won month and track expansion, cross-sell, and churn over subsequent months. The marketing lead sees that the campaign-sourced cohort from Q1 is expanding faster than the SDR-sourced cohort from Q2, which is a signal about the quality of the pipeline the two motions produced.

The data model that makes joined analytics work

How Strkr unifies the sales and marketing data without a sync job.

Joined analytics breaks the moment the two systems need to sync. Marketing automation in one tool, CRM in another, warehouse in a third, and the join happens through a nightly ETL that nobody trusts. Strkr avoids the join by running one data model: the account, the contact, the opportunity, and the campaign are all first-class objects in the same database. The analytics engine queries the live relational store without a reporting cube in the middle.

Shared account record

Marketing updates and sales updates on the same row.

The account carries both marketing-defined fields (lifecycle stage, campaign history, intent score) and sales-defined fields (opportunity count, closed-won revenue, renewal date). The marketing team and the sales team read from the same row and update the same row with different permissions.

Shared contact record

Form fills and SDR calls on the same timeline.

The contact timeline carries form submissions, email opens, campaign engagement, SDR dials, demo attendance, and discovery notes in chronological order. The sales rep walks into a call knowing the full marketing history on the contact; the marketing team sees whether the campaign actually produced a conversation.

Native campaigns

Campaigns live in Strkr, not in a separate tool.

Campaigns are a native object in the Strkr data model with their own spend fields, source UTMs, and attribution rules. The campaign to lead to opportunity to closed-won chain exists as a navigable graph in the same database. The common failure where campaigns live in a marketing tool with a brittle sync to CRM does not apply.

Native email and SMS

The engagement events are first-class rows.

Strkr Messaging and Strkr Email run inside the platform, so email opens, link clicks, SMS replies, and MMS sends are first-class rows the analytics engine can query. The attribution model does not depend on a third-party integration dropping events into a webhook queue.

Native lead scoring

The score lives on the contact, queryable by analytics.

The lead score lives on the contact record as a numeric field with the component factors. The analytics engine queries the score the same way it queries any other field. The marketing lead can slice closed-won by lead score bucket to see whether the scoring model actually predicts revenue.

Live operational queries

The analytics reads the live record, not a BI replica.

The analytics engine runs on the live operational database. A campaign that launched this morning renders on the attribution dashboard this afternoon. The common failure where the analytics dashboard lags the operational system by 24 hours does not happen because there is no ETL step.

Deterministic snapshots

Point-in-time views that reconcile cleanly.

For historical questions ("what was pipeline value from this campaign on April 1") the engine persists deterministic daily snapshots. The analytics walks the historical record without reconstructing from an audit log.

Permission-aware

The marketing view and the sales view respect roles.

The analytics dashboard renders what the user can see. A marketing analyst sees campaign and lifecycle data; a sales manager sees pipeline and closed-won data; a regional VP sees the regional slice of both. The same dashboard surface renders different data based on role.

Audit log on every write

Every attribution rule change tracked.

Attribution rules are configuration, not code. Every change to a rule, every reassignment of a touch, every edit to a lifecycle stage is tracked in the audit log with user and timestamp. The compliance team walks the full change history for the attribution engine.

The ten questions joined analytics has to answer

The questions revenue leaders ask and nobody can answer today.

The real test of joined sales and marketing analytics is whether a revenue leader can answer the ten questions below without a quarterly BI project. Most teams can answer two or three with confidence, four or five with asterisks, and five with no credibility at all. Strkr pre-wires the ten questions as default views, so the leader starts from an answer instead of a build.

Which campaign produced revenue

Closed-won revenue by original campaign.

The campaign ROI tile ranks every active campaign by closed-won revenue produced, with cost per opportunity and cost per closed-won dollar. The marketing lead sees that the field event in Austin produced 420K in closed-won against 38K in spend, which is the line that justifies the next event.

Which source converts at scale

Source quality ranked by closed-won conversion.

Source quality ranks every lead source by MQL-to-closed-won conversion rate and average deal size. Low-volume high-quality sources surface against high-volume low-quality sources. The budget conversation anchors on quality rather than top-of-funnel volume.

Which content moved deals

Content assets ranked by influenced revenue.

Content assets rank by influenced revenue: deals where the asset appeared in the engagement timeline before closed-won. The content team sees that one case study appeared on 70 percent of enterprise closed-won deals in the quarter, which is the asset to double down on.

Which SDR motions worked

SDR cadences ranked by booked meeting and converted opportunity.

SDR cadences rank by meeting-booked rate, opportunity-converted rate, and closed-won revenue. The SDR leader sees that the LinkedIn-first cadence produced three times the conversion of the email-first cadence for the enterprise segment. The playbook stops being a vibe and becomes a measurement.

Which lifecycle stage leaked

The stage where the funnel narrowed.

The lifecycle funnel highlights the stage with the largest drop in conversion. The analytics specifies that lead-to-MQL held but MQL-to-SQL fell from 48 percent to 32 percent, which is the stage for a cross-functional investigation.

Which account tier bought

Revenue by tier against pipeline investment by tier.

The tier analysis renders revenue produced by tier against the pipeline investment the team made by tier. The ABM lead sees that tier-1 accounts produced the expected revenue against the heavy investment, or that tier-2 accounts actually outperformed on ROI.

Which cohort retained

Expansion and churn by signup cohort.

Cohort analysis tracks expansion and churn by signup month and signup campaign. The leader sees whether the Q1 campaign cohort is expanding faster than the Q2 cohort, which is a signal about the quality of the acquisition and the fit of the ICP.

What is the real CAC

Blended CAC and CAC by source.

Customer acquisition cost renders as blended CAC and as CAC by source. The analytics includes marketing spend, SDR cost, AE cost, and tool stack. The leader sees that enterprise CAC is 42K against an ACV of 92K and SMB CAC is 1,900 against an ACV of 3,800. The ratio reads for every segment.

What is the LTV ratio

Lifetime value divided by CAC, by segment.

The LTV ratio renders LTV divided by CAC for every segment. The leader sees whether enterprise is 4.2x and SMB is 2.1x. The segment with the highest LTV ratio is the one to invest in next quarter. The analytics reads directly from closed-won, expansion, and churn data on the record.

How Strkr delivers joined analytics without a BI project

The surfaces and the compliance posture that make it usable.

The reason most teams never ship real joined analytics is the time cost. A BI project takes a quarter of engineering, a quarter of analyst time, and a quarter of data governance review. Strkr ships joined analytics as a default view the moment the tenant signs up. The same account record carries marketing and sales data. The same analytics engine renders both. The same compliance posture governs both. The common three-quarter rollout becomes a day-one default.

Day one default

The joined dashboard ships pre-built.

The joined analytics dashboard ships as a default surface on day one. The lifecycle funnel, source attribution, campaign ROI, and cohort view render without configuration. The team starts from a working surface and customizes from there, instead of building from a blank canvas.

Configurable attribution

Pick the model, pick the window, pick the touches.

The attribution engine is configurable from the admin surface: pick first-touch, last-touch, linear, time-decay, or position-based; pick the lookback window; pick which touch types count. The marketing lead configures the model without an engineering ticket.

Shared permissions

The dashboard respects the row-level permission model.

The joined dashboard inherits the full permission model. Marketing analysts see their data, sales managers see theirs, regional VPs see their region. The common failure where joined analytics requires an all-or-nothing permission exemption does not happen.

Scheduled snapshots

A weekly email to the revenue team.

The dashboard schedules to email on any cadence. The marketing lead subscribes to the Monday morning snapshot. The CRO subscribes to the Monday executive snapshot. The weekly ritual becomes a passive subscription instead of a scramble to assemble numbers.

AI commentary

Strkr AI narrates the weekly delta.

Strkr AI reads the joined dashboard and posts a narrative: campaign CAC dropped 18 percent driven by the retargeting shift, enterprise closed-won expanded three deals outside the forecast, SMB lifecycle leaked at MQL-to-SQL. The narrative renders at the top of the dashboard every Monday.

Export to the board

A PDF the leader drops into the quarterly review.

The dashboard exports to PDF with the brand chrome, the key tiles, and the AI narrative. The CRO drops the PDF into the board deck. The marketing lead drops the attribution section into the campaign review. The board story reads from live data.

No warehouse required

The analytics runs on the live operational database.

Joined analytics ships without requiring a separate data warehouse. The analytics engine queries the operational database directly. The team that wants to export to Snowflake or BigQuery can; the team that does not want the warehouse overhead does not have to.

HIPAA and SOC 2 posture

The compliance envelope is one envelope.

Strkr signs HIPAA BAAs and is undergoing SOC 2 Type II audit. The joined analytics runs inside the same compliance envelope as the rest of the platform. Regulated tenants can run real marketing and sales analytics without a separate security review.

Said simply

One model, one dashboard, one audit log.

Sales and marketing analytics runs on one data model, renders on one dashboard, writes to one audit log, and inherits one permission model. The reconciliation step most teams spend quarters trying to fix stops being a project because the join never has to happen.

Sales and marketing analytics on one record.

Strkr joins marketing and sales analytics on the same operational database. Attribution renders in revenue. The lifecycle funnel reads from first touch through closed-won. Source quality, cost per opportunity, cohort expansion, and campaign ROI all ship as default views. No BI project required.

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Common questions

What buyers ask about this feature.

How is joined sales and marketing analytics different from two dashboards in separate tools?

Two dashboards in separate tools reconcile through an export and a VLOOKUP in a spreadsheet, usually once a quarter. The reconciliation is error-prone because the marketing tool models leads and MQLs while the sales tool models opportunities and closed-won, and the join column rarely matches cleanly. Joined analytics means the same record carries every touch and every stage from first form fill through closed-won renewal, so the analytics engine walks the whole lifecycle in one query. The attribution math stops being a reconciliation project and becomes a database query.

Does Strkr support multi-touch attribution or only first-touch and last-touch?

The attribution engine supports first-touch, last-touch, linear, time-decay, and position-based models side by side. The marketing lead configures which model applies to which campaign type from the admin surface. Different models answer different questions: first-touch for brand investment, last-touch for closing-channel investment, time-decay for ongoing nurture. The dashboard can render multiple models at once so the team sees the attribution picture from several angles instead of being locked into one.

Can we see which specific content assets or campaigns moved specific deals?

Yes. Every touch (form fill, email open, link click, webinar attendance, content download, SMS reply, demo booking) lands on the contact timeline that connects to the opportunity. The attribution engine queries which assets appeared on which deals before closed-won. The content team sees that a case study appeared on 70 percent of enterprise closed-won deals in the quarter; the campaign team sees that a field event produced 42 opportunities that converted to 1.2M in revenue.

Do we need a separate data warehouse to run joined analytics?

No. The analytics engine queries the live operational database directly, so the joined dashboard renders without a warehouse step. For teams that want to export to Snowflake, BigQuery, or Redshift for downstream analysis, Strkr supports a dataset export, but the operational analytics do not depend on the warehouse. The common failure where joined analytics requires a quarter of data engineering work before the first dashboard renders does not apply.

Does the dashboard respect the permission model for marketing analysts versus sales managers?

Yes. The joined dashboard inherits the full row-level and field-level permission model. A marketing analyst sees campaign and lifecycle data for the segments they own; a sales manager sees pipeline and closed-won data for their team; a regional VP sees the regional slice of both. The same dashboard surface renders different data based on the role of the viewer. The common failure where joined analytics requires an all-or-nothing permission grant does not happen.

Is sales and marketing analytics included on every tier or does it cost extra?

The core joined analytics dashboard is included on every paid Strkr tier: lifecycle funnel, source attribution, campaign ROI, cost per opportunity, cohort revenue, and shared permissions. Configurable attribution models and scheduled snapshots are included on all paid tiers. AI commentary is included on tiers that bundle Strkr AI. There is no separate BI SKU, no analytics-specific tier upgrade, and no per-query charge. The seat price listed on pricing is the full price.

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