Sales analytics that live inside the CRM, not a separate BI product.
Pre-built pipeline, rep performance, forecast, territory, and cohort reports. Customizable dashboards for every team. Raw SQL for RevOps analysts who want to go deeper. All running on the same database that stores your deals, so a number on a dashboard matches the number on the record every time.
The 7 sales analytics every revenue team should run
The reports a sales leader cannot operate without.
Most revenue teams run three or four reports well and five or six badly. The gap is not analyst time, it is picking the right seven measurements and keeping them in front of the team every week. Pipeline velocity, conversion by stage, stuck-deal count, win rate by segment, forecast accuracy, rep attainment, and cycle time are the seven that compound into predictable revenue. If a weekly leadership meeting cannot cite the current number for each of these, the team is running on vibes and the quarter will surprise someone. Strkr ships every one of them pre-built, filterable, exportable, and scoped to the viewer so a rep sees their own slice and a VP sees the whole division.
Pipeline velocity
How fast dollars convert, by segment and by rep.
Velocity equals open deals times average deal size times win rate divided by average cycle time. Strkr computes it nightly across every filter you care about, including segment, product, source, rep, and territory. The chart is a line, not a vanity number, because the derivative matters more than the current value. A dip in velocity three weeks before a bad quarter is the signal most teams miss, and the velocity line is where it shows up first. Running velocity by segment is how you find the sub-market that is quietly dragging the whole team down.
Conversion by stage
The funnel chart that catches a leaking stage.
For every pipeline stage, Strkr reports the share of deals that advance to the next stage versus the share that stall or lose. A single stage with below-benchmark conversion is almost always a product or messaging problem that nobody has named yet, and the funnel chart is the first place it surfaces. Benchmarked against your own 90-day rolling baseline, not a vendor average that was never relevant to your motion. Click any stage bar to drill through to the deals currently living in it, which is where the real root cause gets found.
Stuck deals
Every deal past its in-stage threshold, listed by rep.
A deal that has sat in one stage past its stage-specific threshold with no activity is stuck. Strkr lists every one of them, groups by owner and by stage, and lets a manager drill into the record from the cell. The weekly 1:1 cover list writes itself, because every stuck deal is a conversation worth having before it ages into a lost deal. No more "I think about ten deals are stuck" guesswork, no more spreadsheet export at the end of the quarter. Thresholds are configurable per stage, which matters because a 10-day threshold in Prospecting means something different than 10 days in Negotiation.
Win rate by segment
Where you win 60 percent, where you win 10.
Win rate as a single number lies. Win rate by segment, by deal size band, by competitor present, by source, and by product mix tells the real story and is almost always the first analysis a new VP asks for. Strkr breaks it down across every dimension and surfaces the ones with meaningful sample size so an analyst does not have to filter manually. The segments where you win big get more marketing dollars and should see expanded quota. The segments where you lose get a product review, a messaging rewrite, or a disqualification criterion added to the inbound filter.
Forecast accuracy
Last quarter commit versus last quarter booked.
Forecast accuracy is the difference between what the team committed four weeks out and what the team actually booked. Teams that measure this learn whose forecasts to trust and whose to discount, and the chart stops being political within one quarter of having the data in front of everyone. Strkr snapshots every forecast weekly, so a quarter-end accuracy report is one query away, broken down by rep, manager, and segment. A rep whose commits land within 5 percent for three straight quarters becomes the baseline. A rep whose commits land 30 percent high becomes a coaching conversation.
Rep attainment
Pace against pro-rated quota, every rep every week.
Monthly or quarterly quotas, pro-rated by business day, compared to booked bookings for the period. Strkr reports current attainment, projected attainment at current pace, and the gap in both bookings and pipeline terms so a rep knows exactly what they need to close and how much pipeline they need to build. Reps behind pace get a per-rep remediation view with the deals most likely to move. Managers see the full team on one page with sort by gap-to-quota, which is the right view for Monday morning triage.
Cycle time
Days from created to closed, trended quarter over quarter.
Average cycle time is a lagging operational indicator for product-market fit. When it shortens, friction is dropping and the message is landing faster. When it stretches, something upstream broke and the team usually finds out two quarters later than they should have. Strkr reports cycle time as a median plus a 90th percentile tail, because the long tail of six-month deals is the number worth optimizing, not the median that a few lucky quick wins can distort. Breaking cycle time by segment catches the enterprise motion stretching while the SMB motion stays flat.
How Strkr sales analytics works
Pre-built reports, custom dashboards, raw SQL.
Most CRM analytics ship as a shallow set of pre-built reports plus a visual builder that stops short the moment a RevOps analyst asks a real question. The analyst exports to CSV, loads it into a spreadsheet or a separate BI tool, and spends the rest of the afternoon rebuilding the same chart with a different filter. Strkr ships three layers that stack and share one data model. Fifty-plus pre-built reports cover the common questions the moment a tenant is provisioned. A drag-and-drop dashboard builder covers the ones specific to your team, your segments, and your custom fields. A raw SQL console covers everything else, from one-off board questions to recurring executive queries. Every layer runs on the same production database with the same permissions as the records they draw from.
Pre-built library
Fifty-plus reports, no configuration required.
Pipeline, forecast, activity, rep, cohort, territory, product, and churn reports ship on day one with sensible defaults a new tenant can start running immediately. Every report is filterable by date range, segment, team, product, and custom tags, and every filter persists per user so a VP sees their saved view and a rep sees theirs. Clone any report to a tenant copy to tweak the filters without losing the original, which matters when a RevOps lead wants a modified version for the board without stepping on the day-to-day dashboard. The library covers the common ninety percent of what a sales org asks for in year one.
Dashboard builder
Drag tiles onto a grid, publish to a team.
A visual builder with chart, table, metric, and funnel tiles. Each tile is backed by a saved report or a dashboard-level filter, so a cross-tile filter change (segment, date range, product) applies everywhere without a rebuild. Dashboards publish to a team, a role, or the whole tenant, and the viewer sees their permission-scoped slice automatically. The sales leader starts the day on the leadership dashboard. Each rep opens their personal attainment dashboard. One source of tiles, many permission-scoped views, zero duplicate maintenance.
Raw SQL console
A read replica for the RevOps analyst.
The SQL console runs against a read replica with the full schema exposed, including every custom field a RevOps lead has added, every pipeline snapshot, and every activity log row. Every join a RevOps analyst would want is already defined as a view, so the first query is a SELECT and not a schema archeology project. Save a query, publish it as a tile, schedule it to a Slack channel. No separate warehouse to sync, no stale data, no additional per-row charge for touching your own records, and no third-party ETL vendor to manage.
Snapshots and history
Every record versioned, time travel included.
Strkr snapshots every deal, forecast, and pipeline nightly. A report can ask "what did pipeline look like on the 15th of the month" and get the right answer, with the right stages and amounts as of that date, not the current amount with current stages reconstructed. Point-in-time reporting is the difference between a forecast meeting that converges and one that gets re-opened every week with new numbers that nobody can reconcile. Retention is 24 months on the mid tier and unlimited on the top tier, which covers almost any board-level lookback question.
Permission-aware
A rep sees their own deals, a manager sees their team.
Every report inherits the CRM permission model with no additional configuration. A rep looking at the pipeline-by-owner chart only sees their own deals. A manager sees their team, resolved through the manager hierarchy on the user record. A VP sees the division. The admin sees everything. One report, many permission-scoped views, no separate role model to maintain. No accidental leak of a strategic deal to a sibling rep, which is the kind of mistake that happens exactly once in a bolted-on BI tool and then triggers a six-month security review.
Scheduled delivery
Monday morning digest, no analyst required.
Any report or dashboard snapshot can be scheduled to email, Slack, or an in-app digest. Weekly leadership roll-up on Monday at 7 AM. Daily rep nudge every weekday at 8. Monthly board deck on the first of the month, auto-formatted as a PDF with every tile embedded. The analyst stops being the delivery mechanism and gets back to answering the questions that actually need an analyst. Schedules can be user-scoped (each rep gets their own numbers) or tenant-scoped (everyone sees the same leadership view).
Drill-through everywhere
Click a bar, land on the deal list behind it.
Every chart and every metric tile is clickable. Click the Qualified bar on the stage-conversion chart, land on the filtered deal list with every filter preserved from the chart. Click a rep tile, land on their pipeline. Click a segment on the win-rate heatmap, land on the deals in that segment ranked by close date. The chart is never a dead end. The analyst does not need to re-build a filter in a different page to see the underlying records, which is the single biggest time sink in a bolted-on BI tool.
CSV and API export
Every report pours out through CSV or REST.
Every report has a CSV download. Every report also has a REST endpoint that returns the same rows as JSON, authenticated with the same tenant API key as the rest of the Strkr API. A board deck pulls fresh numbers via a Google Sheets import that refreshes every morning. A data warehouse pulls the same rows via a nightly API job without a separate connector contract. The API is paginated, cursor-based, and rate-limited at a level most tenants never hit, and every endpoint is documented in the same place as the rest of the Strkr API.
Freshness guarantees
Real-time where it matters, cached where it does not.
Pipeline-by-stage, rep attainment, and open-deals reports refresh in real time against the primary database, so a deal that moved at 10:03 shows up at 10:03. Historical cohort and territory reports refresh hourly against the read replica, because trending the last 24 months of cohorts does not need sub-second freshness and a hot query on the primary would hurt everyone. The analyst never wonders "is this the latest number" because every report says when it last ran and whether it ran against primary or replica.
The hidden cost of analytics-as-separate-SKU
Why bolt-on analytics is more expensive than it looks.
Salesforce Analytics Cloud is a separate SKU priced per user, usually several hundred dollars a seat on top of the base CRM license, and the upsell conversation happens roughly nine months into a Salesforce contract. Tableau CRM is powerful but is a different product with a different data model that has to be hydrated from the CRM, which means an ETL job that breaks every time a field is added. HubSpot reporting is included at the base tier but the useful reports move behind the Enterprise paywall quickly, and the Enterprise paywall is a step function, not a trickle. Gong and Clari sit on top of your CRM as revenue intelligence players, syncing data in both directions and charging another per-seat line that scales with headcount, not usage. Looker is beautiful and is also a six-figure annual commitment with its own admin cost. The sticker price on any of these is only part of the real cost of running a bolted-on analytics stack.
Seat multiplication
The analytics seat is a second seat.
A rep already pays for a CRM seat. Adding analytics as a separate SKU means they also pay for an analytics seat, usually at a comparable price. For a 50-rep team at 300 dollars a seat extra, that is 180,000 dollars a year on top of the CRM line, which is a hire that never got hired. Strkr ships every report and dashboard under the standard seat price, so a rep is one bill, not two. The analytics multiplier line simply does not exist in the invoice, which is often the single biggest line item savings in a competitive replacement.
Data sync tax
A second database stays out of date.
Tableau CRM, Looker, and most warehouse-based analytics stacks live on a different database than your CRM. That database has to be kept in sync, usually via nightly ETL that runs on a schedule set by whichever engineer owns the pipeline. The report you look at Monday morning is sixteen hours stale. Deals that moved on Friday night do not show up until Tuesday, and the Monday pipeline meeting runs on last-week numbers. Strkr runs on the same database as your records, so the lag between a deal moving and the chart updating is zero.
Model drift
Two models of the same deal.
When analytics lives in a second system, the second system has its own definition of a deal, a stage, and a win. Over time those definitions drift from the CRM definitions as new stages get added, old stages get merged, and win criteria get redefined. A win rate in Looker and a win rate in the CRM slowly stop matching, and reconciling them becomes a quarterly fire drill. Strkr avoids this by not having a second model. The report queries the same tables the records write to, so a change to the stage list on Monday shows up in the funnel chart on Monday.
Admin overhead
The BI tool wants its own admin.
A standalone BI product needs an admin to manage data sources, dashboards, user access, performance tuning, and training. That admin salary alone usually runs 90,000 to 140,000 dollars a year, and bigger teams add a second analyst before the first year is up. Strkr dashboards are configured by a RevOps lead in the same admin console that owns the rest of the CRM, no separate skill set required and no new hire needed. The CRM admin and the analytics admin are the same person, which is the right staffing level for most sub-500-rep teams.
Change-management hit
A field change ripples through two systems.
Add a custom field to a deal in a bolted-on stack and you also change it in the BI tool, re-map the ETL, re-publish the dashboard, re-train the users, and update the documentation. That is a half-day of work for a change that should take ten minutes. Multiply that across the twenty custom fields a growing team adds in a year and the hidden maintenance cost is a full-time job. Strkr custom fields appear in the report builder the moment they are created on the record. One change, one place, one audit trail, zero re-publishing cycles.
Permission split
Two permission models, two attack surfaces.
Every bolted-on analytics tool has its own permission model, often with a service account that has full read on the CRM. The audit trail is split across two systems and does not reconcile cleanly during a SOC audit. The attack surface doubles because any credential compromise on the analytics side exposes the CRM data wholesale. Strkr runs analytics inside the CRM under the same permissions as the record access, so the audit trail is one trail, the attack surface is one surface, and the compliance review is one review.
Revenue intelligence markup
Gong and Clari price per rep, not per report.
Revenue intelligence overlays sit on top of your CRM and charge per rep, usually in the 1,200 to 1,800 dollars per seat per year range. A 100-rep team at 1,500 dollars a seat a year is 150,000 dollars for a layer that mostly does forecast scoring and call transcription, and most of the scoring logic is reproducible from the CRM data directly. Strkr ships forecast-category rollups and risk flagging on the base plan and adds call transcription as an optional add-on, not a bundled multiplier. Teams that bought Gong purely for pipeline inspection can retire the line item.
Where native analytics beats a bolted-on BI tool
The things only a native stack can do.
Price and admin overhead are the obvious differences between native and bolted-on analytics, and they are the arguments that win most procurement conversations. The deeper difference, and the one that matters more over the long run, is the things a native stack can do that a bolted-on stack cannot do at all. Running a report at the moment a record changes. Writing a dashboard tile that respects a per-rep permission in real time. Firing a workflow when a metric crosses a threshold. Clicking a chart bar and landing on the records behind it in the same application. These are not harder in a bolted-on stack, they are structurally impossible without a round-trip through a sync layer, a webhook, or a cross-product deep link that breaks every six months.
Record-change triggers
Metric crosses a threshold, flow fires.
When a rep crosses 100 percent of quota, fire a flow that posts in #sales-wins and emails the leadership team. When a segment win rate drops below a floor for two weeks, flag the segment for a product review and open a task on the RevOps lead. When pipeline coverage drops below 3x for the next quarter, auto-escalate to the VP with the shortfall math already calculated. A native stack ties record changes, aggregate metrics, and workflow actions together without a webhook, a cron, or a cross-product integration contract.
Live permission scope
A rep sees their deals, a manager sees the team, instantly.
A bolted-on BI tool compiles dashboards per audience and caches them. When a rep moves teams, the dashboard they see is stale until the next ETL run, which might be the next day or the next week depending on the sync schedule. Strkr evaluates permission scope at report-run time, so a rep who was promoted to manager this morning sees the right view this afternoon, with the right team roster and the right pipeline. Reorganizations, team splits, and manager changes propagate instantly because there is no cached permission snapshot to invalidate.
Zero-lag reporting
A deal moved thirty seconds ago shows up now.
The pipeline-by-stage report counts the deal the moment it moves. The attainment chart updates when a deal closes. The forecast adjusts when a commit changes. The activity feed shows the call that just logged. On a bolted-on stack, the earliest the chart updates is the next ETL run, which is typically overnight, so Monday morning dashboards run on Friday evening data. Native beats bolted-on by a shift in work, which is the difference between a reactive forecast meeting and one that is actually looking at the current week.
Shared custom fields
Your custom field is a reportable field.
The custom field a RevOps lead added for competitor-present tracking last week is in the report builder today, as a filter, group-by, and column. The custom object a team added for partnerships last quarter is a reportable entity today, with every custom field exposed. On a bolted-on stack, that field is in the BI tool whenever the ETL maps it, which is a change request to the data engineering team and a week of waiting in the best case. Native eliminates the waiting entirely because there is nothing to map.
Record drill-through
A chart bar is a filtered record list.
Click the Qualified bar on the stage-conversion chart, land on the filtered deal list. Click a rep tile, land on their pipeline. Click an account on the churn risk list, land on the account page with the full timeline. Chart and record live in the same application, so the drill-through is a route change, not a cross-product deep link that requires SSO back to the CRM and loses the filter context on the way. The chart is a navigation surface for the records, not a dead-end report.
Snapshot history
What pipeline looked like on June 1.
Point-in-time reporting is the ability to ask what the pipeline looked like on an arbitrary past date and get the right answer, not the current amount reconstructed from current records. Strkr snapshots every record nightly, so a quarter-end lookback report is a filter, not a research project with a spreadsheet reconstruction. Most bolted-on stacks lack snapshots entirely because the sync is a one-way replica and does not preserve history. Strkr lookbacks match the records as of that date, including stage, amount, owner, and close date on every deal.
Workflow integration
A report row is a flow input.
Any report row can trigger a flow. The weekly "deals with no activity for 10 days" report fires a per-row flow that drops a task on each owner, so stale-deal cleanup stops being a Monday morning email thread. The daily "accounts crossing churn threshold" report fires a per-row flow that notifies the CSM, opens a renewal task, and schedules a check-in call. Reports stop being inert artifacts that a VP looks at once a week and become part of the operating cadence that runs the business every day.
Five analytics dashboards already in production
The exact dashboards our customers run every day.
A sales analytics product is only as good as the dashboards teams actually open on a Monday morning. A dashboard that nobody looks at is a cost center, not a tool. Here are five Strkr customers use today, built on the standard tile library, published to the standard roles, running on the standard database. Each one solves a specific operating problem and has an owner who is accountable for the numbers on it. None of them required a BI consultant, a data engineer, or a six-month implementation project. All of them are available as starter templates a new tenant can clone and modify in an afternoon.
Sales leadership
Monday morning executive roll-up.
Twelve tiles on one page. Current-quarter booked, open pipeline, pipeline coverage ratio, forecast commit versus most-likely, this-week movement by stage, top ten deals at risk, rep attainment leaderboard, segment win-rate heatmap, cycle-time trend, source-of-pipeline donut, net-new logos, and expansion revenue. The VP opens one tab and runs the Monday meeting from it, drilling into any tile when a question comes up. The meeting shortens from 90 minutes of slide-building to 30 minutes of actual decisions, which is the whole point of the dashboard.
Rep personal dashboard
Pipeline, pace, and next-best-action.
Each rep lands on a personal dashboard. Current attainment versus quota pace. Open pipeline by stage. Deals at risk flagged by Strkr. The three activities most likely to advance a deal this week based on their own historical win patterns. Last week's activity count against the activity target. The rep sees their own number and the smallest set of next actions that moves it, without having to filter a shared report or interpret a leaderboard. The dashboard is the first page they open every morning and the last page they close every evening.
RevOps weekly
Hygiene, data quality, and leading indicators.
Deals with missing close dates. Deals with missing amount. Accounts with no primary contact. Stage-conversion trend over the trailing eight weeks. Rep activity against expected activity. Forecast-accuracy history for every rep. The RevOps lead opens this dashboard every Friday and closes the loop on anything red before Monday, so the leadership meeting is not derailed by data quality issues. Every red cell is clickable, so fixing a bad record takes a click, not a filter rebuild in another tool.
Manager mid-week
Team pace, coaching flags, and 1:1 prep.
Every front-line manager runs a mid-week team dashboard. Attainment by rep, sorted by gap-to-quota so the biggest intervention surfaces first. Pipeline coverage per rep against their own quota, not the team aggregate. Deal risk flags grouped by rep so a Thursday 1:1 is a conversation about three specific deals, not a general check-in. Activity totals so a rep with low call volume gets a specific coaching nudge. The dashboard becomes the 1:1 agenda, which is the right job for a dashboard a manager uses every week.
Board quarterly
The slide-free quarterly review.
A board-ready dashboard with ARR waterfall, net revenue retention, logo retention, new-logo ACV trend, win rate by segment, average contract value trend, sales efficiency ratio, and payback period. Every number tied directly to the records that produced it, so a board member asking "what is in that top ten deals" clicks through and sees the deals. The CFO runs the slide prep in an hour instead of a week, and the review itself gets back to the strategic questions instead of reconciling numbers across three systems.
Sales analytics on every paid tier, raw SQL on Scale.
Pre-built reports and dashboards ship on Starter. Custom dashboards and scheduled delivery ship on Pro. Raw SQL console and API access ship on Scale. Most teams never need to add a BI product on top. The analytics line on the bill stops being its own line.
How is Strkr sales analytics different from Salesforce Analytics Cloud or Tableau CRM?
Salesforce Analytics Cloud and Tableau CRM are separate SKUs priced per user on top of the base CRM license, typically several hundred dollars a seat, and the upsell conversation starts about nine months into a Salesforce contract. They also run on a separate data model that is hydrated from the CRM via nightly sync, so dashboards lag a shift behind the records and any field change requires an ETL update. Strkr runs analytics inside the CRM against the same database as the records, included under the standard per-seat subscription with no sync lag, no separate SKU, and no second permission model to maintain alongside the first.
Can a RevOps analyst write custom queries against the Strkr database?
Yes. The Scale tier includes a raw SQL console that queries a read replica of the production database with the full schema exposed, including every custom field, every pipeline snapshot, and every activity log row. A saved query can be published as a dashboard tile, scheduled to Slack, or exposed through the REST API with the same tenant authentication as every other endpoint. The console respects tenant isolation, so an analyst can only query their own tenant's data and cross-tenant joins are impossible by design. For teams that want to pipe into a warehouse, every report has a REST endpoint and every table has a nightly export in CSV or JSON.
Does Strkr replace Gong or Clari for revenue intelligence?
For forecast rollup, deal risk flagging, pipeline hygiene, and rep attainment, yes. Strkr ships every one of these as a native report and dashboard tile under the base plan, running on the same records as the rest of the CRM so the numbers always reconcile. For call transcription and conversation intelligence specifically, Strkr has an optional add-on that integrates with Zoom, Google Meet, and Microsoft Teams, priced per rep but only on users who actually use it. A team that bought Gong purely for pipeline inspection and forecast scoring can retire that line item on day one. A team that bought Gong for its call-coaching library can keep it alongside Strkr without data duplication.
How current are the numbers on a Strkr dashboard?
Pipeline-by-stage, rep attainment, open-deals, and forecast rollup refresh in real time against the primary database. A deal that moves at 10:03 shows up at 10:03, and the manager who was looking at the chart at 10:02 sees the new state the moment they refresh. Historical cohort, territory, and multi-month trend reports refresh hourly against a read replica for performance, because trending the last 24 months of cohorts does not benefit from sub-second freshness. Every tile displays its last-run timestamp so an analyst never wonders whether a number is stale. By comparison, Looker and Tableau typically run on nightly ETL, so the earliest a chart updates is the morning after the record change.
What point-in-time reporting does Strkr support?
Strkr snapshots every deal, forecast, pipeline, and attainment record nightly, with full state captured including stage, amount, owner, close date, and every custom field. A report can ask "what did pipeline look like on June 15 of last year" and get the right answer, with the right stages, amounts, and owners as of that date, not the current record reconstructed from today's data. Snapshot history retention is 24 months on Pro and unlimited on Scale and Enterprise, which covers almost any board-level lookback question. Point-in-time reporting is the difference between a forecast meeting that converges and one that keeps re-opening the same questions week after week.
Can non-technical users build their own dashboards?
Yes. The dashboard builder is a visual drag-and-drop grid with chart, table, metric, and funnel tile types. Add a tile, pick a saved report, pick a chart type, position it on the grid, done. Dashboards publish to a user, a team, a role, or the whole tenant, with permission scope evaluated at view time so each viewer sees their own slice. A RevOps lead with no SQL background builds the first ten production dashboards in under a week, usually within an afternoon for the most common ones. SQL is only required for the long tail of custom queries that pre-built reports do not already cover, which is maybe five percent of what most teams actually need.
What is the cost of Strkr sales analytics versus HubSpot Reporting or a Salesforce plus Tableau stack?
Strkr includes pre-built reports and dashboards on every paid tier with no per-seat analytics multiplier, so analytics does not scale with headcount independently of the base plan. HubSpot Reporting is included at the base tier but the useful reports (custom funnel, attribution, revenue analytics) sit behind the Enterprise paywall at roughly 3,600 dollars a month, which is a step function most growing teams hit in the second year. A Salesforce plus Tableau stack typically runs 150 dollars a seat for the base CRM plus 300 dollars a seat for Analytics Cloud or Tableau CRM, which is more than three times the Strkr line before considering admin overhead. For a 50-rep team the gap compounds to six figures a year.
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