Feature · Sales Reporting

A sales reports tool that answers the pipeline question before the standup starts.

Strkr sales reporting ships scheduled email delivery, two-click drill-down from any tile to the underlying rows, saved views scoped per rep and per manager, cohort reports that follow a vintage of deals across months, win/loss reports with reason-code analysis, activity rollups by rep and by segment, pipeline velocity by stage and by segment, and custom metrics anyone on the ops team can author. Every report runs on the same Postgres as the CRM. No warehouse sync, no BI vendor, no "contact sales" tier.

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What a credible sales reporting tool has to do in 2026

The checklist most CRMs quietly fail on their own data.

Every CRM claims a reporting module. The honest grading starts with a specific list of workloads that a modern sales organization runs every week: scheduled delivery that lands in the right inbox at the right time, drill-down from any aggregated tile to the underlying rows without a second query tool, saved views that are per-user and per-role, cohort reports that follow a specific vintage of deals through time, win/loss reports with reason codes and segment splits, activity rollups by rep and by team, pipeline velocity by stage and by segment, and a custom-metric builder that an ops lead can use without opening a ticket for engineering. If a sales reporting tool cannot do all eight without an external BI stack bolted on top, the module is a lightweight dashboard, not a reporting system. The eight capabilities below are the ones Strkr ships natively on every paid tier.

Scheduled reports

The report lands before the meeting starts.

Any saved report can be scheduled to run on an interval and email the result to a list of recipients, with the output rendered as an HTML table in the message body and a CSV attached for the ones who want it in a spreadsheet. The schedule carries a timezone, a day-of-week pattern, and an on-weekday-only option so a Monday pipeline brief never arrives on a holiday. Recipients can be individual users, Strkr roles, or external email addresses for a shared ops distribution list. The schedule includes an on/off switch, a last-run timestamp, and a per-report failure log so an ops lead can see which schedules have been quietly broken for two weeks.

Drill-down from any tile

Two clicks from a number to the rows that drove it.

Every aggregated number on a dashboard or report tile is clickable. The click opens the underlying rows in a sortable table with the same filter context the tile was computed under, and the row set is exportable to CSV in one more click. There is no second query tool. There is no "ask the data team" step. A manager who sees pipeline growth drop six percent clicks the tile, sees the sixteen deals that regressed, and emails the three reps who own them, all without leaving the browser tab.

Saved views

Per-rep and per-manager, with inherited defaults.

Every report page supports saved views, scoped per user. A rep saves "my open deals, sorted by close date, past-due flagged red" and it is their default when they land on the page. A manager saves "my team's open deals, grouped by rep, sorted by stage age" and the view belongs to them. Admins publish shared views at the tenant level, roles inherit the defaults, and individuals override. The result is that nobody rebuilds the same filter stack twice a week.

Cohort reports

A vintage of deals, followed through time.

A cohort report groups deals by the month they were created and tracks a chosen metric across subsequent months: how many converted to Stage 2 by month 1, how many closed won by month 6, what the average deal size was by month 3. The pattern surfaces whether this quarter's pipeline is actually faster or just louder, and whether the segment-split cohorts (Enterprise, Mid-Market, SMB) are converging or diverging. Standard reports show the current state; cohort reports show the trend inside the state.

Win/loss reports

Reasons, segments, and the delta against last quarter.

Every closed deal in Strkr carries a win reason or a loss reason from a tenant-defined picklist, plus optional free-text context. The win/loss report pivots the reasons by segment, by rep, by product line, and by competitor. The delta column shows how each reason shifted versus the last quarter, so a product leader learns that "price" moved from 18 percent to 26 percent of losses between Q1 and Q2 without having to run a bespoke query. The report ships with a comment thread per reason, so the ops lead can annotate root causes directly on the data.

Activity reports

The input metric nobody tracks until it is too late.

Pipeline is the output. Activities are the input. The activity report rolls up calls, emails, meetings, and tasks per rep per week, with target lines overlaid, with productivity by day-of-week visible, and with a flag on reps whose output is drifting below the floor. The report groups by team, by segment, by role, and by manager. A VP looking at a dip in next-quarter pipeline opens the activity report first and finds the two reps whose outbound volume collapsed four weeks ago, which is the root cause the pipeline tile cannot show.

Pipeline velocity

Days per stage, by stage and by segment.

The velocity report computes the median and 90th-percentile time a deal spends in each stage, split by segment, by product line, and by rep. Stage 2 to Stage 3 is seventeen days for Enterprise and five days for SMB; that is the baseline, and the report flags any deal currently exceeding its own segment baseline by more than one standard deviation. Velocity trends are plotted over a trailing twelve months so an ops lead can see whether a stage is quietly bloating, which is almost always the first signal of a late-quarter miss.

Custom metrics

An ops lead builds a KPI without a Jira ticket.

Every custom metric is a short formula over the Strkr object graph: a SUM, COUNT, AVG, or ratio across any object, filtered by any field, grouped by any dimension. The editor is a point-and-click form with a live preview. An ops lead builds "closed-won ARR per manager, trailing 90 days, excluding renewals" in four minutes. The metric becomes a reusable tile that any report can embed, a KPI that any dashboard can show, and a trigger that any flow can watch. No engineering, no SQL, no warehouse round trip.

Export and share

CSV, PDF, link, and embed, all one click.

Every report exports to CSV with the current filter context, exports to PDF with the current chart layout, generates a shareable link that respects the recipient's own permissions, and offers an iframe embed for the internal wiki. Shared links honor the viewer's row-level security, so a rep clicking a manager's link sees only the rows they are allowed to see. The pattern collapses the usual "can you screenshot and send that" workflow into a one-click share that reflects live data.

How Strkr sales reporting works under the hood

Live Postgres, native permissions, no warehouse detour.

Most CRM reporting modules route the query through a separate analytics layer: a materialized cube, a nightly sync into a warehouse, a secondary BI vendor stapled on top. The trade is predictable performance in exchange for always-stale data. Strkr sales reporting runs its queries against the live operational Postgres that the rest of the CRM writes into, with the same row-level security, the same field-level visibility, and the same audit trail as every other read in the system. The number on the dashboard is the number in the record, computed in the same transaction, no sync lag, no stale-data footnote, no "report refresh at 2 AM" window where leadership cannot trust the morning number.

Live query

No overnight cube, no sync lag.

Every report query runs against the live Postgres on the same server that handles CRM reads. A deal updated at 10:03 shows up in the 10:04 pipeline report, not in tomorrow morning's refresh. Index strategy is tuned for the aggregate shapes reports actually run, which keeps a trailing-twelve-month pivot under a second on a tenant with half a million deal rows. The query path is the same path the dashboard uses, so the number on the dashboard matches the number on the detail row to the dollar.

Permission-aware

A report shows only what the viewer can see.

Every row returned by a report query is filtered through the same row-level security the UI uses. A rep running a team-level report sees aggregated numbers computed over only their own rows. A manager sees their direct reports plus their own. An admin sees everything in the tenant. The permission model is one surface; the report is a view onto that surface, not a bypass. Compliance review is a one-paragraph attestation instead of a six-page questionnaire about the analytics subsystem.

Field-level security

Columns respect visibility rules.

If a rep cannot see the Amount field on an Enterprise deal, the Amount column on the pipeline report is blank for that row, not just the aggregated total. Field-level security is enforced in the query, not in the UI, so an export to CSV carries the same redactions. The pattern means a finance team can share a report with a rep layer without worrying that an aggregated number leaks a per-row value they were not supposed to see.

Audit trail

Every scheduled run shows up in the log.

When a scheduled report runs, the activity log records the schedule, the recipients, the row count delivered, and the execution time. When a manual report is exported, the log records the user, the filter context, and the destination (CSV, PDF, share-link, embed). An admin running a quarterly compliance review can answer "who exported what, when, under which permission context" from a single filter in the activity log.

Row-level drill

The aggregate links to the rows that make it up.

The drill-down link on every tile is deterministic. The tile says "Closed Won, Q2, $4.1M" and the click opens the exact set of deal rows the aggregate was computed over, in the exact filter context the tile was defined under. There is no "approximate" link, no "see similar deals" shortcut, no second query that returns a different row count. The reconciliation that most ops teams spend an hour a month doing against the warehouse is a non-task on Strkr.

Snapshot history

Point-in-time pipeline, preserved by default.

Strkr snapshots the pipeline at the end of every business day, every Friday, and every month-end. The snapshot is a frozen row-level copy of the deals in open stages with their value and stage at that moment. The pattern unlocks "what did pipeline look like three Fridays ago" and "how much pipeline moved out of Stage 3 last month" as native report dimensions. No warehouse ETL, no slow-changing-dimension modeling, no custom script.

Custom objects

Reports read your schema, not a fixed menu.

When an admin adds a custom object (Projects, Vehicles, Loans, Policies), the reporting module sees it the moment it is published. The report builder lists the object, the fields, the relationships to standard objects, and the picklists. There is no "wait for the vendor to ship schema support" step. If you can model it in Strkr, you can report on it on the same day.

Formula fields

Computed columns, reusable across every report.

A formula field defined on an object is available to every report that reads the object. Define "Days To Close" once as (close_date - created_at) and every pipeline, cohort, and velocity report can group, filter, or sort by it. The formula engine supports arithmetic, logic, date math, text operations, and cross-object joins. No per-report formula sprawl.

Export fidelity

CSV and PDF match the UI to the pixel.

CSV export carries the same column order, the same filter context, and the same conditional formatting state as the UI. PDF export carries the same chart layout, the same color encoding, and the same legend. The export is "the report as I was looking at it," not "a different query the backend ran against the same data." Finance teams stop sending screenshots because the exports are good enough to paste into the board deck.

Scheduled reports in production

The five report cadences every sales team ends up running.

Scheduled delivery is the feature that turns a reporting tool from a dashboard people visit into a system that pushes the right number to the right inbox. The five schedules below are the ones every production sales organization settles into within sixty days of adopting Strkr. All five are built in the UI, no code, no vendor engagement.

Monday pipeline brief

Sunday night, each manager's inbox.

A manager-scoped pipeline report runs at 9 PM Sunday tenant time and emails each manager a one-page brief: open deals, movement since last Monday, deals at risk, deals that moved stage, this week's expected closes. The manager walks into Monday standup already briefed, and the first five minutes of standup are action instead of status recital. The schedule respects the manager's timezone, which matters once a team crosses the two-timezone threshold.

Rep daily digest

Weekday mornings, each rep's inbox.

A rep-scoped digest runs at 7 AM weekdays and emails each rep their open task list for the day, their deals that need an activity, their past-due commitments, and any inbound leads routed overnight. The schedule is one tenant-wide definition that renders per-rep by filter context. Reps start the day with a plan instead of fifteen minutes of inbox-sorting.

VP weekly rollup

Friday afternoon, exec distribution.

A tenant-wide rollup runs at 3 PM Friday and emails the exec distribution a one-page: closed-won versus plan, pipeline coverage, forecast call versus model call, segment splits, win-rate trend, top-ten deals of next quarter. The rollup includes the model-called forecast number alongside the rep-called number, and flags the deals driving the gap. The exec team starts the weekend already knowing which questions to ask Monday.

Monthly cohort pass

First business day, ops lead.

A cohort report runs on the first business day of each month and lands in the ops lead's inbox: last month's created-deal cohort compared to the trailing six, broken out by segment and by lead source, with conversion-at-N-days numbers. The ops lead spots the lead sources whose cohorts are regressing before the regression shows up in next quarter's closed-won.

Quarterly win/loss

Last business day of quarter, product and sales leaders.

A win/loss report runs on the last business day of each quarter and emails the product and sales leaders: reason-code distribution, delta versus the prior quarter, competitor splits, segment splits, with free-text annotations surfaced in a bulleted list. The product team walks into the next quarter already knowing which objections are rising and which are falling. The report pairs with the quarterly roadmap review.

Activity floor alert

Rolling 7-day, triggered on floor breach.

The activity floor is a tenant-defined minimum (e.g., 25 outbound touches per rep per week). A rolling report runs every weekday morning and alerts the manager when any direct report drops below the floor for the trailing seven days. The alert is a one-line notification plus a drill-down to the activity rows. The manager intervenes the week it happens, not the quarter it shows up in pipeline.

Cohort reports, the signal most dashboards miss

Pipeline growth that is real versus pipeline growth that is noise.

A sales dashboard shows the current pipeline number. A cohort report shows whether this quarter's pipeline is actually healthier than last quarter's, or whether it just looks bigger because reps created more deals that will not close. Cohort analysis is the single highest-leverage report shape most sales organizations under-use, and almost nobody runs it without a BI stack. Strkr ships it natively. The patterns below are the ones ops teams return to most often.

Conversion-at-N-days

How fast each vintage moves from Stage 2 to Stage 3.

The cohort axis is the month the deal was created. The metric is percent of the cohort that reached Stage 3 by day N. Comparing the trailing six months of cohorts shows whether Stage 2-to-3 conversion is accelerating or slowing. A dip of three percentage points in a single month is early signal that the top of pipeline is softening, usually ninety days before closed-won reflects it.

Average deal size by vintage

Are new deals landing bigger or smaller than three months ago.

Group deals by created-month and compute average amount at close. Rising average-at-close with flat deal count is a sign that the sales motion is moving upmarket. Falling average-at-close with rising deal count is a sign of a drift down to smaller opportunities, which the pipeline total alone will not reveal.

Lead-source vintages

Which inbound channels are regressing.

Split cohorts by lead source. The paid search cohort for last February converted at 11 percent to Stage 3 by day 30. The paid search cohort for last August converted at 7 percent. The report surfaces the drop before the next quarter's pipeline reflects it, which is the window where the marketing team can intervene on channel allocation.

Segment vintages

Enterprise versus mid-market versus SMB over time.

The same cohort analysis, split by segment, exposes which part of the business is driving aggregate trend. "Pipeline is healthy" turns out to be "Enterprise is strong and SMB is collapsing," and the two require entirely different responses. The segment split is a one-click dimension on every cohort report.

Rep vintages

Which reps' recent cohorts are regressing.

Group by rep and by create-month. A rep whose last three cohorts have each converted five points lower than their trailing average is in a quiet slump, and a manager who sees the pattern can coach before the pipeline tile shows it. The report flags cohort regressions automatically on the exec rollup.

Campaign vintages

Which marketing drops produced the best quality.

When a campaign is tagged on deals created from its touches, the cohort pivot shows which campaign vintages produced the highest-converting pipeline. A spring campaign with great volume but poor conversion is distinguishable from a modest-volume campaign with excellent conversion, and the marketing team allocates next cycle's budget on the second one.

Win/loss reporting that actually drives changes

Reason codes, segment splits, and the quarterly delta.

Most CRM win/loss reports are a pie chart of reason codes with no context. A credible win/loss report pairs reason codes with segment splits, competitor splits, free-text annotations, and a delta against the prior quarter. The point is not to describe the past; the point is to drive product, pricing, and segment decisions. Strkr's win/loss shape ships all four dimensions by default.

Reason-code pivot

The tenant-defined picklist, pivoted by segment.

Every closed deal captures a win or loss reason from a tenant-defined picklist. The pivot splits reason by segment, by product line, and by competitor. "Price" is 18 percent of SMB losses but 7 percent of Enterprise losses; "Missing feature X" is 24 percent of Enterprise losses but 4 percent of SMB. The split tells product which gap matters where.

Quarterly delta

Each reason, this quarter versus last quarter.

The delta column shows how each reason moved in share quarter over quarter. "Price" moved from 18 percent to 26 percent of losses. "No decision" moved from 11 percent to 15 percent. The deltas are the agenda items for the next product and pricing review. The report highlights deltas larger than three points automatically.

Competitor splits

Who you lose to, and why, by segment.

When a loss reason is "competitor," the deal captures which competitor won. The report pivots loss-to-competitor by segment and by product line. The sales enablement team sees that Enterprise losses to Competitor A are up eight points while mid-market losses to Competitor B are down four, and the battle cards get rewritten accordingly.

Free-text annotations

Rep context, surfaced in a scrollable list.

Every closed deal's free-text win/loss context is surfaced in a scrollable list within the report, grouped by reason code. Reading the actual rep notes, in bulk, catches patterns that the picklist cannot encode (e.g., a specific objection phrasing that is new this quarter). The patterns feed into enablement, messaging, and roadmap.

Won deals too

The win-side is where the ICP learnings live.

Win reasons get the same treatment as loss reasons. "Fastest deployment" won 34 percent of Enterprise against Competitor A; "Price advantage" won 51 percent of SMB. The win-reason pivot is where the sharpest ICP signal lives, and most teams under-use it because it is less emotionally compelling than losses. Strkr pins the win pivot next to the loss pivot by default.

Rep-level win rates

With volume gated to avoid noise.

The report shows win rate per rep, gated to reps with at least a tenant-defined minimum of closed deals in the window. The gate avoids the usual trap of a rep with four deals and 100 percent win rate anchoring the leaderboard. The report flags both the top performers and the reps whose win rate dropped most quarter over quarter, with the drop broken out by reason.

Pipeline velocity, the metric that explains late-quarter misses

Days per stage, by segment, with the trend line visible.

Pipeline velocity is the single most predictive metric for whether a quarter closes on plan. It is also the metric most CRM reporting modules fail to compute correctly, usually because they average across segments and lose the signal in the aggregate. Strkr's velocity report splits by segment, by product line, and by rep, with a trailing-twelve-month trend overlay so a stage that is quietly bloating is caught before it shows up in the forecast.

Median and 90th percentile

Both numbers, both visible.

For each stage, the report shows median days and 90th-percentile days a deal spends in the stage. The median is the center; the 90th-percentile is the tail. A rising 90th percentile with a flat median is a sign that specific deals are stuck, which is a different problem than a sales motion slowing down overall. Both numbers inform different interventions.

Segment splits

Enterprise and SMB are different motions.

Enterprise Stage 3 is seventeen days median; SMB Stage 3 is five days median. Averaging them into one number is a reporting failure. Strkr splits velocity by segment on every stage, which means the manager looking at Enterprise sees the Enterprise baseline and the manager looking at SMB sees the SMB baseline. A deal is slow against its own segment, not against the global average.

Trailing-12 trend

A stage that is quietly bloating is caught early.

Each stage shows a twelve-month trend line of median days. A stage that is drifting from twelve days to seventeen days over eight months is the kind of regression that aggregates can hide. The trend view is the ops lead's early-warning system for a motion that is losing crispness.

Per-rep velocity

Which reps are faster, which are slower.

Group velocity by rep, scoped to each segment, to see which reps move deals through specific stages faster than their peers. The rep who closes faster in Stage 4 is a candidate for a close-coach session with reps whose Stage 4 is dragging. The report is a coaching input, not just a performance dashboard.

Stuck-deal flagging

Deals currently exceeding their segment baseline.

Any open deal currently sitting more than one standard deviation beyond its segment's median for the current stage is flagged on the live pipeline report. The flag is per-stage and per-segment, which keeps the signal clean. Managers see a sorted queue of deals that need intervention first.

Velocity by lead source

Which inbound channels produce faster-moving deals.

Deals created from referrals close in a different timeframe than deals from cold outbound. Splitting velocity by lead source surfaces the channels that produce faster pipeline, which is a direct input to marketing budget allocation. The report pivots lead source against each stage's velocity.

Custom metrics without a Jira ticket

An ops lead builds a KPI in four minutes.

The usual failure mode of CRM reporting is that every non-standard metric requires an engineering ticket, a sprint, and a release. The ops team learns to ask for less, and the sales organization runs on a shrinking set of metrics the vendor pre-shipped. Strkr's custom-metric builder is a point-and-click form that an ops lead uses directly, with a live preview, saved definitions, and reusable tiles. The three patterns below are the ones ops teams build within their first week.

Weighted pipeline

Amount times stage probability, grouped by rep.

Define Weighted Pipeline as SUM(Amount * Stage.Probability) over Open Deals, grouped by Owner. The metric is a reusable tile on every pipeline dashboard. It stays accurate as stages are added, renamed, or re-probabilitized, because the formula references the stage object, not a hard-coded list. The ops lead builds it in four minutes without engineering involvement.

Pipeline coverage

Open pipeline divided by remaining quota.

Define Coverage as SUM(Open Deal Amount with close date in current quarter) divided by (Quota minus Closed-Won This Quarter). The number is a per-rep, per-manager, per-team metric. The exec rollup shows the Coverage trend over the trailing twelve weeks, with a flag when it drops below the tenant-defined floor (commonly 3x). No SQL, no ticket.

Days since last activity

An input metric that flags cold deals.

Define Days Since Last Activity as (today minus deal.last_activity_at), a formula field on the Deal object. Every report that reads deals can group, filter, or sort by it. The pipeline report flags deals red when the number exceeds a tenant threshold; the manager's 1:1 agenda includes a filter on the metric; the Monday brief surfaces the top ten coldest deals. One definition, many surfaces.

Renewal pipeline ARR

Open renewal deals, by segment, due in next 90 days.

Define Renewal Pipeline ARR as SUM(Deal.ARR) over deals where Type = Renewal and close_date within next 90 days, grouped by Segment. The metric is the input to the renewal manager's weekly review. The segment split surfaces where the renewal risk sits before the finance team asks.

Expansion ratio

Expansion ARR divided by renewal ARR, by quarter.

Define Expansion Ratio as SUM(Expansion ARR) divided by SUM(Renewal ARR) for the current quarter. The ratio is the single most honest growth metric for a seat-based business. The report tracks it quarter over quarter, with segment splits so the exec team can tell whether expansion is coming from Enterprise upsell or SMB cross-sell.

Lead-to-opp conversion

New leads turned into opps within 30 days, by source.

Define Lead-To-Opp 30d as (opps created from leads whose creation was within last 30 days) divided by (leads created in the same window), split by lead source. The metric is the marketing team's cleanest input to channel ROI. The quarterly cohort pivot pairs it with eventual closed-won to compute source-level LTV.

How Strkr sales reporting compares

The honest grading against the big vendors.

The sales reporting market has three honest categories: native modules inside the CRM, embedded BI tools licensed on top, and standalone warehouses the ops team maintains. The Strkr line is the first category done well. The three alternatives are the pattern that most sales organizations end up running when the native module falls short. The grading below is the one buyers should walk every vendor through before procurement.

Salesforce Reports

Capable, slow, admin-gated.

Salesforce Reports is the deepest native reporting module on the market, with cross-object joins, matrix reports, and formula fields. The trade is that non-trivial reports require a certified admin, custom report types are a schema modeling exercise, and the UI remains visibly slow on large datasets. The total cost is the Salesforce license plus the admin salary plus (frequently) a Tableau CRM add-on for the shapes the native builder cannot express. Strkr ships the common 90 percent of those shapes natively, with a point-and-click builder, at a seat price that includes the capability.

HubSpot Reports

Clean mid-market reporting, caps show on scale.

HubSpot Reports is a well-designed mid-market reporting module with scheduled delivery, saved dashboards, and a usable custom-report builder. The limits appear when teams want cross-object pivots on custom objects, multi-step cohort analysis, or formula fields that span relationships. For a 20-rep team, HubSpot is sufficient. For a 100-rep team with product-led signals and multi-year contracts, the reporting layer becomes a bottleneck inside twelve months. Strkr ships the deeper shapes at every tier.

Pipedrive Insights

Operational dashboards, not analytical reports.

Pipedrive Insights is a clean operational dashboard with pipeline visualization and a usable activity view. It is explicitly not an analytical reporting tool, and cohort or velocity analysis requires either a Zapier-wired warehouse or an external BI purchase. Strkr covers both the operational view and the analytical shapes, in one module, included.

Embedded BI (Looker, Tableau)

Powerful, expensive, slow to iterate.

Embedding Looker or Tableau into a CRM gives a sales organization warehouse-grade analytics at the cost of a parallel data pipeline, a separate security review, a separate per-seat license, and a two-week turnaround on every new report. The pattern makes sense for teams north of 500 reps with a dedicated analytics engineering function. Below that threshold, the cost outweighs the benefit, and the native Strkr module covers the common shapes without the pipeline overhead.

Standalone warehouse

Snowflake plus dbt plus Metabase.

The standalone pattern (Snowflake plus dbt plus Metabase or Mode) is the pattern for sales organizations that have given up on native CRM reporting. The upside is unlimited analytical ceiling. The downside is a seven-figure annual commitment, a full-time analytics engineering team, and a 24-hour sync lag on every number the sales floor sees. Strkr's live-query Postgres model collapses the sync lag to zero for the 90 percent of reports that do not need warehouse-grade modeling.

Spreadsheet exports

The pattern teams fall back on when nothing works.

When native reporting fails, teams export CSVs into spreadsheets, build pivot tables by hand, and share Google Sheets as the source of truth. The pattern is universal and universally regretted. The ops team spends twenty hours a week keeping the spreadsheet aligned with the CRM, the numbers in the spreadsheet are stale by Friday, and nobody can audit who changed what. Strkr's native scheduled reports collapse the spreadsheet workflow into a one-time schedule setup, with CSV export preserved for the week the ops team still wants it.

What ships on which tier

Reporting on every paid tier, no "contact sales" gating.

The common CRM pattern is to put real reporting behind the two highest tiers. Strkr's pattern is the opposite: scheduled delivery, drill-down, saved views, win/loss, and activity reports are on every paid tier, and cohort reports, pipeline velocity, custom metrics, and snapshot history unlock at Pro. The decision was deliberate. Reporting is where sales teams make decisions, and gating it behind an upgrade means the smallest customers run their business on worse data, which is the opposite of what a modern tool should encourage.

Starter

Scheduled delivery, drill-down, saved views.

Every Starter seat gets scheduled report delivery, two-click drill-down from any tile, per-user saved views, CSV and PDF export, and shareable links with permission inheritance. Win/loss and activity reports are included. The tier is designed so a 10-rep team can run a complete reporting cadence (Monday brief, daily digest, Friday rollup) without upgrading.

Pro

Cohort reports, pipeline velocity, custom metrics.

Pro adds cohort analysis, pipeline velocity by stage and segment, the custom-metric builder, snapshot history, formula fields, and the full win/loss delta reporting. Pro is the tier where a 30-to-100-rep team gets the analytical depth that would otherwise require Looker or Tableau on top of a competing CRM.

Scale and Enterprise

Unlimited scheduled reports, warehouse export, custom retention.

Scale and Enterprise unlock unlimited scheduled reports per tenant, a native warehouse export (Snowflake, BigQuery, Redshift) for teams that want to blend CRM data with other sources, custom retention windows on snapshots, SSO-bound share-link policy, and a dedicated performance envelope. The warehouse export is included; no middleware vendor required.

Sales reporting on every paid tier. Scheduled delivery, drill-down, cohorts, win/loss, velocity, and custom metrics, included.

Starter includes scheduled delivery, drill-down from any tile, saved views, win/loss, and activity reports. Pro adds cohort analysis, pipeline velocity, custom metrics, snapshot history, and formula fields. Scale and Enterprise unlock unlimited schedules, warehouse export, and custom retention. The seat price is the reporting price, every tier, every month, with no per-report meter and no "analytics" add-on SKU. Open a trial and ship your first scheduled report before Friday.

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Common questions

What buyers ask about this feature.

Does Strkr sales reporting require a separate BI tool like Looker or Tableau?

No. Strkr sales reporting ships the common 90 percent of what sales organizations ask of a BI tool natively, inside the CRM, running on the same Postgres as the rest of the product. Scheduled delivery, drill-down from any tile, saved views per user and per role, cohort reports, win/loss reports with reason codes and segment splits, activity rollups, pipeline velocity by stage and segment, and a point-and-click custom-metric builder are all included on paid tiers. For teams north of 500 reps that want warehouse-grade modeling across non-CRM sources, Scale and Enterprise include a native warehouse export to Snowflake, BigQuery, or Redshift, so you can run Looker or Tableau on top without licensing a middleware vendor.

How does Strkr sales reporting compare to Salesforce Reports and Dashboards?

Salesforce Reports is the deepest native reporting module on the market, with cross-object joins, matrix reports, and powerful formula fields. The trade is that non-trivial reports almost always require a certified admin, custom report types are a schema modeling exercise in their own right, and the UI stays visibly slow on large datasets. The total cost of running Salesforce reporting at scale is the Salesforce license plus the admin salary plus (frequently) a Tableau CRM add-on for the shapes the native builder cannot express. Strkr ships the common 90 percent of those shapes natively, with a point-and-click builder an ops lead can run themselves, at a seat price that includes the capability.

How does Strkr sales reporting compare to HubSpot Reports?

HubSpot Reports is a well-designed mid-market reporting module with scheduled delivery, saved dashboards, and a usable custom-report builder. For a team under 20 reps selling simple SaaS, HubSpot is sufficient. The limits show up when a team wants cross-object pivots on custom objects, multi-step cohort analysis, formula fields that span relationships, or pipeline velocity split by segment and product line. HubSpot gates several of these shapes behind Professional and Enterprise tiers, and the deepest ones require purchasing Operations Hub. Strkr ships cohort reports, pipeline velocity, and the custom-metric builder on Pro, and the schedule-delivery plus drill-down baseline on Starter.

Can scheduled reports deliver to recipients outside the CRM?

Yes. Scheduled report delivery supports Strkr users (by individual, by role, or by team), external email addresses for shared ops distributions, and shareable links that respect the recipient's own permissions when they are a Strkr user. The CSV attachment and HTML body are rendered server-side. The schedule carries a timezone, a day-of-week pattern, an on-weekday-only option so a Monday brief never arrives on a holiday, and a failure log so an ops lead can see which schedules have been quietly broken for two weeks.

How is drill-down different from a regular report filter?

Drill-down is the two-click path from an aggregated tile to the exact row set the aggregate was computed over, in the exact filter context the tile was defined under. A regular report filter narrows the rows a user sees before the aggregate is computed; drill-down expands an aggregate into the rows it is already made of. The practical difference is reconciliation. On most CRMs, a "see similar deals" link opens an approximate query that returns a different row count than the aggregate. On Strkr, the drill-down link is deterministic: the tile says $4.1M and the click opens the exact 73 deal rows summing to $4.1M. The reconciliation hour that most ops teams spend each month against a warehouse is a non-task.

Can an ops lead build custom metrics without engineering help?

Yes. The custom-metric builder is a point-and-click form that walks an ops lead through aggregation (SUM, COUNT, AVG, ratio), object selection, filter context, grouping dimensions, and a live preview against the live Postgres. No SQL, no engineering ticket, no vendor engagement. The metric saves as a reusable tile that any report can embed, a KPI any dashboard can show, and a trigger any flow can watch. The formula engine supports arithmetic, logic, date math, text operations, and cross-object joins. The common patterns (Weighted Pipeline, Pipeline Coverage, Days Since Last Activity, Renewal Pipeline ARR, Expansion Ratio, Lead-To-Opp Conversion) typically take an ops lead under five minutes to define.

Does the reporting module respect field-level and row-level security?

Yes. Every report query runs through the same row-level security the UI uses, and every column in a report respects the same field-level visibility rules. A rep running a team-level report sees aggregated numbers computed only over the rows they are allowed to see. If a rep cannot see the Amount field on an Enterprise deal, the Amount column is blank for that row on the report, and the redaction carries into CSV and PDF exports. The permission model is one surface; the report is a view onto that surface, not a bypass. Compliance review is a one-paragraph attestation rather than a six-page questionnaire about the analytics subsystem.

How long is snapshot history retained?

Pro retains end-of-day snapshots for 90 days, end-of-week snapshots for 12 months, and end-of-month snapshots for 36 months by default. Scale and Enterprise unlock custom retention windows on all three cadences, which teams typically extend to seven years for finance and audit purposes. Snapshots are row-level copies of open-stage deals with their stage, value, and ownership at the moment of capture, so point-in-time pipeline reporting and historical trend analysis are first-class rather than an ETL exercise.

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