A KPI dashboard your revenue team opens every Monday, not a BI project.
Twenty standard revenue KPIs preconfigured on day one. Bookings, pipeline coverage, win rate, cycle time, forecast accuracy, rep attainment, SDR activity ratio, AE pipeline coverage. Target vs actual with pacing math, trend arrows against the prior period, cohort and segment filters, alert thresholds that fire into Flows. No SQL, no data engineer, no second tool.
A KPI dashboard is not a BI report, and it is not a sales dashboard. A sales dashboard shows the operational view: a list of this week's deals, a funnel, a leaderboard, a feed. A KPI dashboard shows the twenty numbers a revenue leader is accountable for and the pace they are hitting against target. Metric-tile first. Target line visible on every tile. Trend arrow against last period. One click to drill into the records behind the number. Strkr ships every revenue KPI a mid-market B2B team needs on day one, preconfigured against the schema the CRM already stores, so the first tile goes live the hour you turn the module on. The twenty tiles are not a vendor opinion. They are the list that a sales operations lead at a growing B2B company reaches for every single Monday, derived from how real revenue teams run their forecast cadence. If a weekly leadership meeting cannot cite the current reading for each of these numbers, the team is running on vibes and the quarter will surprise someone. The purpose of the KPI dashboard is to make that surprise impossible.
Bookings
New ARR closed, month-to-date and quarter-to-date.
The headline tile every revenue leader checks first. Sum of Closed Won amount in the current period, broken down by new business, expansion, and renewal. Target vs actual, trend arrow against the prior period, pacing math against the day-of-period line. Click through to the deal list for the slice you clicked on.
Pipeline coverage
Open pipeline divided by remaining quota.
The ratio that tells a VP whether the quarter is already decided. Three-times coverage is the industry default, but your historical win rate determines the real floor. Strkr computes coverage per segment, per team, and per rep, and raises an alert the moment any slice drops below the configured threshold.
Win rate
Deals won over deals that reached a decision.
Win rate as a single number is a vanity metric. Win rate by segment, by deal size band, by competitor present, and by source is the measurement a VP actually uses to decide where the next marketing dollar goes. Strkr breaks it down across every dimension and ranks the slices by sample size.
Cycle time
Median days from Qualified to Closed Won.
The number that determines how much pipeline you need. A 90-day cycle at 20 percent win rate requires fifteen times more coverage than a 30-day cycle at 40 percent. Strkr computes median and ninetieth percentile cycle time by segment, by rep, and by stage transition, with the trend line that catches a cycle creeping up.
Forecast accuracy
How close last quarter's call landed to actual.
A forecast at 92 percent accuracy is operationally reliable. A forecast at 68 percent is a liability. Strkr compares the submitted forecast at the start of each period to the actual bookings at the end, by rep and by team, and surfaces the pattern of over-call versus under-call that most teams never measure.
Rep attainment
Percent of quota each rep has closed.
The grid that every VP reviews before the Monday forecast call. Each row is a rep, each column is the current period, the cell is a percentage against the pro-rated quota pace. Color coded against the pacing threshold. The reps in red are the forecast conversation; the reps in green are the stretch conversation.
AE pipeline coverage
Per-rep ratio of open pipeline to remaining number.
Coverage at the team level hides the three reps who are already dead for the quarter. AE-level coverage puts every rep on the same page: here is your remaining number, here is your open pipeline, here is your ratio. Below two-times, the manager owns a conversation this week.
SDR activity ratio
Meetings held per hundred outbound touches.
The leading indicator of SDR productivity, and the one that catches a messaging change working or failing before the pipeline tile moves. Strkr tracks dials, emails sent, LinkedIn messages, and meetings held, and reports the ratio by SDR, by cadence, and by segment.
What makes Strkr KPI dashboards different
Shipped KPIs for revenue, not an empty canvas.
Tableau and Looker and Mode can build any KPI dashboard, provided someone writes the SQL, models the data warehouse, and keeps the pipeline alive. For a revenue team of fifty people, that is a six-figure data-engineering line item plus three months of lead time. Strkr ships every revenue KPI preconfigured against the schema the CRM already stores. The first tile goes live the hour you turn the module on, and the custom KPI builder covers the dozen or so metrics unique to your motion that the preconfigured list does not. The design brief for the Strkr KPI dashboard was deliberate: a sales operations lead with no SQL background should be able to launch the board in an afternoon, hand it to a VP by Monday, and never open a ticket with the data team to add a tile. Every design decision below falls out of that brief, from the formula-field custom KPI builder to the role-scoped viewer context to the alert threshold that fires into Flows the moment a number crosses the line.
Twenty preconfigured KPIs
Live on day one, no modeling required.
Bookings, pipeline, pipeline coverage, win rate, loss rate, cycle time, average deal size, forecast accuracy, rep attainment, AE pipeline coverage, SDR activity ratio, lead response time, demos held, qualified opportunities created, stage conversion, stuck deal count, renewal rate, net retention, churn rate, and quota attainment. All running against your real data from the first login.
Target vs actual
Every tile has a line, not just a number.
A KPI without a target is a number nobody is accountable for. Every Strkr tile shows the target line (monthly, quarterly, annual, or custom cadence), the actual value, and the gap in both absolute and percentage terms. Set targets per team, per rep, per segment, or at the top level, and roll them down.
Pacing math
Where you should be on the day you are looking.
A tile showing 60 percent of quota on the first of the quarter is a different conversation than 60 percent on the last day. Pacing math computes where you should be as of the current day based on linear pace, weighted pace, or historical seasonality, and colors the tile against the pacing threshold, not the end-of-period threshold.
Trend arrows
Direction matters more than the current reading.
Every tile carries a trend arrow against the prior comparable period: week over week, month over month, or quarter over quarter. The arrow is up, flat, or down, with the delta in both absolute and percentage terms. The derivative is where the signal lives, not the point value.
Cohort and segment filters
Slice every KPI the way the business runs.
Filter the entire dashboard by segment, team, territory, product, source, deal size band, or any custom field on the record. Save filter sets as named views so the SMB tab, the Enterprise tab, and the EMEA tab are one click each. Filters compose and persist across sessions.
Alert thresholds
A KPI that crosses a line fires into Flows.
Set a threshold on any tile: pipeline coverage drops below 2.5, win rate falls under 25 percent, forecast accuracy breaks 85 percent. When the KPI crosses the threshold, Strkr fires a flow. Notify Slack, create a task, open an incident, email the VP. The dashboard becomes an operational system, not a passive report.
Custom KPI builder
Formula field with helpful functions.
Define a custom KPI with a formula over any record field: sum(deals.amount where stage equals Closed Won) divided by count(reps where status equals active). Date windows, filter expressions, cross-object joins, percentage and ratio helpers. Function allowlist, no raw SQL exposed, versioned and auditable per workspace.
Role-scoped views
Rep sees own slice, VP sees the whole division.
A rep opening the KPI dashboard sees their quota, their pipeline, their activity. A manager sees their team roll-up and the per-rep drill. A VP sees every team and every segment. Row-level security is enforced at the KPI engine, not in the UI, so a rep cannot URL-hack their way to the full board.
No SQL required
Point, click, filter, done.
Every preconfigured KPI is wired to the CRM schema out of the box. Custom KPIs use a formula field with autocompleted function names and field pickers. No SELECT statements, no JOIN syntax, no data warehouse to keep alive. If you can describe the number in English, you can build the tile.
The buyer's math
Why native KPIs beat Tableau, Looker, and Mode.
The real question is not whether a BI tool can build the dashboard. Tableau and Looker and Mode can build anything, given enough analyst time. The real question is what the total cost looks like for a mid-market revenue team that needs a KPI dashboard this quarter, not next year. For a sales-ops-led deployment, native always wins. The reasons compound, and most of them do not show up on the first pricing page comparison. They show up three months in, when the ETL pipeline is still being debugged, the analyst is still writing LookML, and the forecast call is still running on a Google Sheet nobody trusts. The six reasons below are what separate a KPI dashboard that is live by Friday from a BI project that is live next quarter.
Time to first tile
An hour instead of a quarter.
A preconfigured Strkr KPI tile is live the hour you log in. A Tableau-first deployment usually means a data warehouse (Snowflake or BigQuery), an ETL pipeline (Fivetran or Airbyte), a semantic model (LookML or dbt), and an analyst to connect it all. Average time to first production tile is eight to twelve weeks in our prospect data.
Analyst dependency
Native KPIs do not require a data engineer.
A sales ops lead can build a Strkr dashboard in an afternoon. A BI dashboard typically requires at least one dedicated analyst or data engineer to keep the pipeline alive, resolve schema changes, and respond to one-off requests. Fully loaded cost of that role in the US is $120,000 to $160,000 per year. That is more than the full Strkr license.
Freshness
Native reads the live record, not a nightly snapshot.
A Strkr KPI reads the live deal record and reflects the stage change instantly. A warehouse-backed BI dashboard typically runs on a nightly ETL, which means a deal that moved to Closed Won at 9 AM does not show up until tomorrow. Teams running a weekly forecast call cannot rely on numbers that are already a day stale.
Alignment with the record
The number matches the deal, every time.
A native KPI reads the same row the rep is looking at. A BI tile reads a replica of that row in a warehouse. When the two disagree, which happens every time an ETL fails or a column rename lands, the forecast call stops to reconcile. The disagreement is a weekly tax that nobody tracks until a quarter closes short.
Permission alignment
KPIs inherit the CRM's access model.
A Strkr KPI runs under the viewer's CRM permissions. A rep sees their deals, a manager sees their team, a VP sees everyone. A BI tool runs under its own service account with full access to the warehouse, and permission enforcement has to be rebuilt separately in the BI layer. The attack surface and the admin burden both double.
Cost
Native bills under the seat price.
Strkr KPIs ship on every paid tier at no additional cost. A production BI stack for a mid-market team typically runs $800 to $2,500 per month in Tableau or Looker licenses plus warehouse compute plus ETL subscription plus the analyst headcount. The gap is a line item on the finance budget that is hard to justify once the native alternative is on the table.
Beyond the preconfigured twenty
Custom KPIs, cohort analysis, and alerting.
Every revenue team has a dozen metrics unique to their motion that no vendor can ship preconfigured. Maybe your pricing model turns on expansion revenue inside the first ninety days of a logo. Maybe your SDR team is measured on connected calls per hour instead of total dials. Maybe your renewal motion includes a usage-weighted health score that only your product team computes. The custom KPI builder is the escape hatch, and it was designed from day one for the non-technical sales ops lead who is going to own the dashboard long after the launch team moves on. The ingredients below are what let a formula field cover the dozen metrics that are unique to how your business runs.
Formula field
English-shaped expressions, not SQL.
Write a KPI as a formula: sum(deals.amount where stage equals Closed Won and segment equals Enterprise) divided by count(reps where team equals Enterprise and status equals active). Autocomplete on field names, function names, and operator keywords. Preview the result against the current period before saving.
Date windows
Rolling 7, 30, 90, QTD, YTD, custom.
Every KPI carries a date window configuration: trailing 7 days, trailing 30, trailing 90, month to date, quarter to date, year to date, or an explicit range. Rolling windows refresh on the schedule you choose, and the window label renders on the tile so viewers never guess what period the number covers.
Cohort grouping
Compare this quarter's cohort to last.
Group deals by the quarter they were created, the campaign that sourced them, or the stage they entered pipeline from, and compare win rate and cycle time across cohorts. The cohort view is where you catch a messaging change that lifted conversion for Q3-created pipeline but has not yet moved the overall tile.
Threshold alerts
Fire a flow when a KPI crosses a line.
Set upper and lower bounds on any tile. When the KPI crosses the bound, Strkr fires an attached flow. Common uses: Slack ping when pipeline coverage drops below 2.5, create a task when a rep's attainment falls under 60 percent on the fifteenth of the month, email the CRO when forecast accuracy breaks 85 percent.
Comparison periods
Last period, same period last year, custom.
Every tile supports a comparison period: previous period (last week, last month, last quarter), same period last year (year over year), or a specific explicit range. The delta renders in absolute and percentage terms, and the trend arrow colors against the configured direction (up is good for bookings, down is good for cycle time).
Target rollups
Set at the top, inherit down the tree.
Set an annual bookings target at the company level, and Strkr rolls it down to teams, segments, and reps based on quota distribution. Override at any level. The pacing math and the gap math all respect the hierarchy, so a rep tile and a VP tile tell the same story at different scopes.
Snapshotting
Every KPI history preserved automatically.
Strkr snapshots every KPI value nightly. The trend chart on every tile walks that history, and the historical table is available for export, backfill, or audit. A number on a dashboard a year ago is still reproducible, which matters for board decks and comp true-ups.
Export + API
Every tile is also a REST endpoint.
Every KPI is exposed as a REST endpoint and a CSV export, so a finance team can pull the current bookings number into their consolidation model, or a BI team can land it in the warehouse alongside their other sources. The dashboard is the primary surface; the data is yours either way.
How a KPI dashboard fits the broader stack
Sibling to sales dashboards and sales analytics.
Strkr ships three related revenue surfaces, and knowing which one to open depends on what question is in front of you. A KPI dashboard answers "where are we against target." A sales dashboard answers "what is happening operationally this week." A sales analytics surface answers "why did that number move." Together they are the full stack a revenue leader needs to run a quarter. The three surfaces share a schema, share a permissions model, and share a drill-through, so a tile on the KPI board links to a saved report in sales analytics and a card on the sales dashboard. Nothing is replicated into a parallel system, and nothing has to be reconciled between surfaces. The decision tree below is how a revenue team actually uses the three together on a weekday.
KPI dashboard
Target vs actual, metric-tile first.
The surface for the weekly leadership meeting. Twenty tiles, each with a target, an actual, a pacing read, a trend arrow, and a drill. The question it answers is binary: are we on pace or not? And if not, which slice is the drag? Open it Monday morning before the forecast call and know the answer in sixty seconds.
Sales dashboard
The operational view, lists and feeds.
The surface for a rep or manager running their book. This week's deals, this week's activities, this week's stuck records, the funnel chart, the leaderboard, the activity feed. It is the opening screen for day-to-day execution, not the roll-up for the leadership meeting.
Sales analytics
The engine underneath, deep slice-and-dice.
The surface for the RevOps analyst answering a specific question. Pipeline velocity by product by rep, conversion by stage by cohort, forecast accuracy by segment by period. Full filter composition, raw SQL available for analysts who want it, saved as a report that can feed a KPI tile.
When to open which
A simple decision tree.
Running the Monday forecast call? KPI dashboard. Opening Strkr to work your deals? Sales dashboard. Writing the quarterly business review deck? Sales analytics. The three surfaces compose: a KPI tile links to a sales analytics report; a sales dashboard list card can be derived from a KPI slice; a sales analytics saved report can be pinned as a KPI tile.
Shared schema
Same database, same numbers, every surface.
All three surfaces run against the same CRM database. A deal moved to Closed Won at 9 AM moves the KPI tile, the sales dashboard list, and the sales analytics report at the same instant. There is no reconciliation step between surfaces, because there is no replica step between surfaces.
Shared permissions
A rep sees their slice everywhere.
A rep logging into Strkr sees their deals, their KPIs, and their analytics scoped to what they own. A manager sees their team scoped everywhere. The row-level security is enforced at the data layer, so the scope matches across surfaces by construction, not by every surface having to re-implement the rule.
How teams actually run it
Three KPI dashboards in production today.
VP of Sales
Monday 7 AM board, nine tiles.
Bookings QTD, pipeline coverage, win rate by segment, cycle time, forecast accuracy from last period, rep attainment grid, AE pipeline coverage, SDR activity ratio, stuck deal count. Opened before the Monday forecast call. The one slice in red is the conversation that gets ten minutes; everything else is a status update.
RevOps lead
Daily pacing board, built in two hours.
Daily snapshot of booked, booked + commit, pipeline added, pipeline removed, meetings held, qualified opportunities created. Filtered by this week versus the trailing four-week average. The RevOps lead opens it twice a day during the last week of the quarter and knows which rep to call before the manager does.
CRO monthly
Executive KPI board for the board deck.
Net retention, gross retention, logo churn, ARR by segment, pipeline coverage by quarter forward, average deal size by product. Snapshotted on the first of each month, exported as CSV into the board deck template, with a trend chart that walks the last six months on every tile. The board deck becomes a copy-paste job.
KPI dashboard on every paid tier. Twenty preconfigured, unlimited custom.
Starter ships with the full twenty preconfigured KPIs and five custom tiles. Pro and above are unlimited. Alert thresholds, flow attachment, cohort filters, and export are included on every tier. No separate BI SKU, no revenue intelligence tax.
How is a KPI dashboard different from a sales dashboard in Strkr?
A KPI dashboard is metric-tile first and target-vs-actual primary. Twenty tiles, each with a target, an actual, a pacing read, a trend arrow, and a drill. The question it answers is "are we on pace against the number." A sales dashboard is the operational view: this week's deals, this week's activities, the funnel chart, the leaderboard, the activity feed. The question it answers is "what is happening right now." Both ship on every paid tier, and both run against the same CRM database so the numbers match by construction.
Can non-technical users build custom KPIs?
Yes. The custom KPI builder is a formula field with autocompleted function names and field pickers, not a SQL editor. A sales ops lead with no coding background can define sum, count, average, ratio, and conditional KPIs across any object in the CRM. Common patterns like "sum of Closed Won amount this quarter divided by sum of quota for active reps" compose in a single expression. Preview the result against the current period before saving. The function allowlist keeps the formulas safe and the engine fast.
Does Strkr replace Tableau, Looker, or Mode for revenue KPIs?
For every revenue KPI that reads from the CRM, yes. Strkr ships twenty revenue KPIs preconfigured against the schema the CRM already stores, with target vs actual, pacing math, trend arrows, cohort filters, and alert thresholds that fire into Flows. Tableau, Looker, and Mode can build the same KPIs, but doing so requires a data warehouse, an ETL pipeline, a semantic model, and an analyst to connect it all. For a KPI dashboard specifically, native is faster to launch, cheaper to run, and freshes on every record change instead of every nightly ETL. For cross-source analytics that span finance, product, and marketing data outside the CRM, a BI tool may still earn a seat; the two coexist and the KPI dashboard exposes every tile as a REST endpoint for the warehouse team.
What happens when a KPI crosses a threshold?
Set an upper or lower bound on any tile. When the KPI crosses the bound, Strkr fires an attached flow. Common uses: Slack ping to #sales-ops when pipeline coverage drops below 2.5, create a task on the manager when a rep's attainment falls under 60 percent on the fifteenth of the month, email the CRO when forecast accuracy breaks 85 percent, open an incident when SDR meetings-held falls more than 20 percent week over week. Flows attached to KPIs run atomically with full retry, DLQ, and failure-replay, same as any other flow in the system.
How does pacing math work for a KPI tile?
Pacing computes where you should be as of the current day, so a 60 percent of quarter number on day 15 is a different color than the same 60 percent on day 85. Three pacing modes ship by default: linear (quota divided evenly across the period), weighted (configurable curve for seasonal businesses), and historical (where the same relative day of the prior period landed). The tile colors green, amber, or red against the pacing threshold, not the end-of-period threshold, so the signal arrives before the quarter is already lost.
What is the cost of a Strkr KPI dashboard versus a BI stack?
Strkr KPI dashboards ship on every paid tier at no additional cost: 20 preconfigured KPIs, unlimited custom tiles on Pro and above, alert thresholds, flow attachment, cohort filters, and export all included. A production BI stack for a mid-market revenue team typically runs $800 to $2,500 per month for Tableau or Looker licenses, plus warehouse compute (Snowflake or BigQuery at $500 to $2,000 per month), plus an ETL subscription (Fivetran or Airbyte at $500 to $3,000 per month), plus one dedicated analyst or data engineer at $120,000 to $160,000 per year fully loaded. For a KPI dashboard specifically, Strkr is a line on the seat price; the BI alternative is a line on the finance budget plus a hiring plan.
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