Stage-by-stage timing analysis of deal progression from creation to close.
Stage-by-stage timing analysis of deal progression from creation to close.
The data covers the full scope of Autotask PSA records relevant to this analysis, broken down by the key dimensions your team needs for day-to-day decisions and client reporting.
Who should use this: Sales leads, MSP owners, and account managers tracking pipeline health
How often: Weekly for pipeline reviews, monthly for forecasting, quarterly for strategy
Stage-by-stage timing analysis of deal progression from creation to close.
Performance snapshot across 1,465 deals.
EVALUATE SUMMARIZECOLUMNS('BI_HubSpot_Deals'[deal_stage], "DealCount", COUNTROWS('BI_HubSpot_Deals'), "TotalAmount", SUM('BI_HubSpot_Deals'[amount]))EVALUATE ROW("Metric", [Measure Name])Average days from deal creation to close, broken down by the pipeline stage where each deal was closed. Lower numbers indicate faster-moving deal types.
| Stage | Deals | Amount |
|---|---|---|
| closedlost | 26 | $131,771 |
| 464993014 | 25 | $43,337 |
| closedwon | 18 | $49,540 |
| 1923708101 | 15 | $59,584 |
| contractsent | 11 | $19,816 |
| 464993013 | 9 | $49,765 |
A visual comparison of average close times across pipeline stages. Green bars indicate fast stages (under 30 days), amber is moderate (30 to 60 days), and red signals slow stages (over 60 days).
EVALUATE ROW("Metric", [Measure Name])How deals are distributed across pipeline stages, and what percentage convert to wins at each level.
| Pipeline Stage | Total | Won | Lost | Active | Win Rate |
|---|---|---|---|---|---|
| Signed, Processed to Ticket | 606 | 596 | 0 | 10 | 100.0% |
| Verloren | 530 | 7 | 505 | 18 | 1.4% |
| Signed, Processed to Project | 191 | 190 | 1 | 0 | 99.5% |
| Voorstel verstuurd | 46 | 0 | 0 | 46 | 0.0% |
| Voorstel maken | 38 | 5 | 2 | 31 | 71.4% |
| Signed, Pending Processing | 35 | 34 | 1 | 0 | 97.1% |
| Expired | 16 | 0 | 0 | 16 | 0.0% |
| Voorstel gelezen | 2 | 0 | 0 | 2 | 0.0% |
| CSAL | 1 | 0 | 0 | 1 | 0.0% |
Deal stage distribution from HubSpot CRM, providing a second data point to validate pipeline patterns seen in Autotask PSA.
| Deal Stage | Deals | Total Value |
|---|---|---|
| 1923708101 | 15 | €59,584 |
| 3264047329 | 9 | €0 |
| 464993013 | 9 | €49,765 |
| 464993014 | 25 | €43,337 |
| Appointment Scheduled | 2 | €536 |
| Closed Lost | 26 | €131,771 |
| Closed Won | 18 | €49,540 |
| Contract Sent | 11 | €19,816 |
What separates deals that close quickly from those that drag on?
The two fastest stages, Signed/Ticket (23.5 days) and Signed/Pending (24.9 days), account for 630 of the 832 won deals. These are typically straightforward service requests and hardware orders where scope is clear from the start.
Deals processed to projects average 54.2 days to close, about double the ticket-based deals. But their average value of €10,465 is roughly three times higher. The extra time is justified by the deal size and complexity.
Deals in the "Drafting Proposal" and "Proposal Sent" stages average 247+ days to close. These stages have the fewest completions and the lowest throughput. If you could cut proposal turnaround time by even 30%, the impact on pipeline velocity would be significant.
The data tells a clear story: your pipeline has two speeds. The bulk of deals, those routed to tickets or pending processing, close in under 25 days. These are your bread-and-butter MSP transactions. They convert at high rates, they generate consistent revenue, and they require minimal sales effort.
Then there is a second tier: project-based deals. These take around 54 days on average, but the per-deal value compensates for the longer cycle. A €10,465 average deal size means each one is worth the wait, as long as the pipeline keeps flowing.
The real concern sits in the proposal stages. Deals that require custom proposals average 193 to 248 days. That is 6 to 8 months from creation to close. The sample size is small (5 to 6 deals), which means individual outliers can skew the average, but the pattern is consistent: proposal generation is the single biggest drag on pipeline velocity.
Win rate across the pipeline sits at 62.0%, which is solid for an MSP. But a closer look at the "Proposal Sent" stage shows that 505 deals were lost there compared to only 7 wins. That stage is where deals go to die if they are not actively managed.
Based on the velocity analysis, here are four actions ranked by expected impact.
Deals in the "Drafting Proposal" and "Proposal Sent" stages average 193 to 248 days. Map the steps from quote request to sent proposal and identify where time is lost. Template-based proposals, pre-approved pricing tiers, and automated quote generation through Autotask can each shave days off this cycle.
With an overall average of 33.1 days, any deal sitting in a stage for more than 45 days should trigger an automatic escalation. Build alerts in your PSA so that stale deals get attention before they become losses.
Your fastest, highest-volume path (Signed to Ticket) processes 596 deals at an average of 23.5 days. Look for ways to route more deals through this path. If a deal can be fulfilled via a ticket instead of a project, the velocity gain is worth it.
The Proposal Sent stage has 505 lost deals and only 7 wins. That is a conversion rate below 2%. Either the proposals are not competitive, or deals are being moved to this stage prematurely. Interview your sales team and review a sample of lost proposals to find the pattern.
Deal velocity measures how quickly opportunities progress through your sales pipeline. It is calculated as the number of days between deal creation and close date, grouped by the pipeline stage where the deal was resolved.
Different stages represent different levels of complexity. Ticket-based deals are typically simple, pre-scoped requests that close quickly. Project-based deals require scoping, approval cycles, and resource planning. Proposal stages add negotiation and custom pricing, which extends the timeline.
Win rate per stage is calculated as won deals divided by decided deals (won plus lost) at that stage. Active deals are excluded from the calculation since their outcome is not yet determined.
Yes. Connect Proxuma Power BI to your Autotask PSA or HubSpot CRM instance, then use any MCP-compatible AI (Claude, ChatGPT, Copilot) to generate this same analysis from your live data in under fifteen minutes.
This report pulls from two sources: Autotask PSA (opportunity pipeline with stage names, close dates, and amounts) and HubSpot CRM (deal stages for cross-validation). The data is processed through Power BI DAX queries and analyzed by AI via MCP.
Connect Proxuma Power BI to your PSA, RMM, and M365 environment, use an MCP-compatible AI to ask questions, and generate custom reports - in minutes, not days.
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