This report crosses HubSpot deal pipeline data (115 deals), HiBob employee records (75 employees across 14 departments), and Autotask time entries & opportunity data (50,752 hours tracked, 124 active opportunities worth €3.94M) to answer a single question: does your team have the capacity to deliver what your sales pipeline is selling?
This report crosses HubSpot deal pipeline data (115 deals), HiBob employee records (75 employees across 14 departments), and Autotask time entries & opportunity data (50,752 hours tracked, 124 active opportunities worth €3.94M) to answer a single question: does your team have the capacity to deliver what your sales pipeline is selling?
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
This report crosses HubSpot deal pipeline data (115 deals), HiBob employee records (75 employees across 14 departments), and Autotask time entries & opportunity data (50,752 hours tracked, 124 active opportunities worth €3.94M) to answer a single question: does your team have the capacity to deliver what your sales pipeline is selling?
Support accounts for 27% of total headcount with 20 employees. That is the largest department by a wide margin. Engineering follows at 9 people, then Operations at 8. The delivery side of the business (Support + Engineering + Operations) holds 37 of 75 seats, just under half.
Sales has 4 people managing a pipeline worth nearly 4 million euros. That is roughly 1 million per sales rep in active pipeline value. Whether that number is healthy depends entirely on the close rate and the delivery capacity behind it.
EVALUATE
SUMMARIZECOLUMNS(
BI_HiBob_Employee[department],
"Headcount", COUNTROWS(BI_HiBob_Employee),
"Managers", CALCULATE(
COUNTROWS(BI_HiBob_Employee),
BI_HiBob_Employee[is_manager] = TRUE
)
)| Stage | Opportunities | Total Value | Avg Deal |
|---|---|---|---|
| Proposal Sent | 46 | €3.06M | €66,511 |
| Signed → Project | 191 | €1.97M | €10,317 |
| Signed → Ticket | 606 | €1.84M | €3,034 |
| Proposal Draft | 38 | €762K | €20,058 |
| Signed → Processing | 35 | €133K | €3,787 |
| Expired | 16 | €38K | €2,355 |
The funnel shows a concentration pattern. 46 proposals worth 3.06 million euros sit in "Proposal Sent" status. That is 39% of the total pipeline value sitting in a single stage, waiting for client signatures. If even half of those convert, the delivery team needs to absorb 1.5 million in new project work.
606 opportunities already signed and converted to tickets at an average deal size of 3,034 euros. These are small recurring items that are already consuming support capacity. The 191 signed-to-project deals at 10,317 euros average are the mid-range commitments that need project management bandwidth.
The 16 expired deals at 38K total are a minor concern, but they represent opportunities where proposals went unanswered. Tracking why those expired could improve the conversion on the 46 currently outstanding proposals.
| Resource | Billable Hrs | Total Hrs | Billable % | Status |
|---|---|---|---|---|
| Resource A | 1,749 | 2,400 | 72.9% | Medium |
| Resource B | 1,838 | 2,050 | 89.7% | High |
| Resource C | 1,416 | 1,862 | 76.0% | Medium |
| Resource D | 1,527 | 1,888 | 80.9% | High |
| Resource E | 1,157 | 1,780 | 65.0% | Medium |
| Resource F | 1,308 | 1,433 | 91.3% | High |
| Resource G | 1,304 | 2,136 | 61.0% | Low |
| Resource H | 1,145 | 2,060 | 55.6% | Low |
| Resource I | 1,254 | 1,290 | 97.2% | High |
| Resource J | 1,344 | 1,418 | 94.8% | High |
The overall billable ratio sits at 75.6%, which is 4.4 points below a typical MSP target of 80%. That gap represents roughly 2,200 hours of non-billable time that could have been recovered, or about 1.2 FTE worth of capacity.
Resource G and Resource H stand out with billable ratios of 61% and 55.6%. Between them, that is over 1,700 hours of non-billable work across about 4,200 total hours. Before adding headcount to handle pipeline growth, it is worth investigating what is driving the non-billable time for those two resources. If it is internal project work, that may be intentional. If it is poorly categorized time, fixing the tracking frees up apparent capacity.
On the other end, Resource I at 97.2% and Resource F at 91.3% are running at or near maximum billable capacity. These resources have no room to absorb new pipeline work without something else giving way.
EVALUATE TOPN(15,
SUMMARIZECOLUMNS(
BI_Autotask_Time_Entries[resource_id],
"BillableHours", SUM(BI_Autotask_Time_Entries[Billable Hours]),
"TotalHours", SUM(BI_Autotask_Time_Entries[hours_worked])
),
SUM(BI_Autotask_Time_Entries[hours_worked]), DESC
)Here is the math that matters. The active pipeline holds 124 opportunities worth 3.94 million euros. With a 15.7% close rate from HubSpot, the expected conversion value is roughly 619K euros. That is the realistic revenue target, not the headline 3.94M number.
The delivery concern is not the expected revenue. It is what happens if close rates improve. If sales execution tightens and the close rate climbs to 25% (a reasonable improvement goal), the expected delivery load jumps to 985K euros. With 75 employees running at 75.6% billable ratio and 844 open tickets already in the queue, absorbing a 60% increase in new work is not straightforward.
The 4.3-year average tenure is a positive signal. Experienced teams deliver faster per project hour than new hires. But experienced teams also reach capacity walls faster because they are already operating efficiently. The slack in the system sits with the underutilized resources (Section 4.0), not with the high performers.
HubSpot tracks 115 total deals while Autotask holds 124 active opportunities. The numbers are close but not identical, which is normal since HubSpot includes all deal stages (including lost) while the Autotask count filters to active only.
The 15.7% HubSpot close rate is the most important number on this page. It means roughly 6 out of 7 deals that enter the pipeline never convert. Before investing in more delivery capacity, the more immediate question is whether improving sales conversion (through better qualification or faster proposal turnaround) would generate more revenue from the existing pipeline than adding delivery headcount.
EVALUATE ROW("TotalDeals", COUNTROWS('BI_HubSpot_Deals'), "TotalAmount", SUM('BI_HubSpot_Deals'[amount]), "Opportunities", COUNTROWS('BI_Autotask_Opportunities'))The team runs at 75.6% billable against an 80% target. That 4.4-point gap equals roughly 2,200 hours or 1.2 FTE of unrealized capacity. Two resources (Resource G at 61%, Resource H at 55.6%) account for a large portion of that gap. Fixing the ratio is cheaper than hiring.
46 proposals totaling 3.06 million euros sit in "Proposal Sent" status. If a cluster of these close in the same quarter, the delivery team faces a spike it may not be sized for. Tracking proposal age and setting conversion forecasts per quarter would give operations a 30-60 day early warning.
The current ticket backlog of 844 sits on top of 67,521 total tickets created. Support (20 employees) handles the bulk of this. Any pipeline conversion that generates project work requiring support involvement will compete directly with the existing backlog for the same people.
High tenure means lower onboarding costs and faster delivery per hour worked. The team knows the clients, the tools, and the patterns. This is a structural advantage when absorbing new pipeline work, since experienced staff deliver faster than new hires learning the environment.
1. Close the billable ratio gap before adding headcount. Moving from 75.6% to 80% billable unlocks roughly 2,200 hours of effective capacity without a single new hire. Start with the two resources running below 62% billable and understand what is driving the non-billable time. Internal projects, poor time categorization, and excessive meeting load are the usual suspects.
2. Build a pipeline conversion forecast by quarter. The 3.06M in outstanding proposals will not all close at once, but some clustering is inevitable. Create a simple quarterly conversion forecast using the Autotask opportunity close probabilities and share it with operations leads so they can plan resource allocation 30-60 days ahead of delivery demand.
3. Address the HubSpot close rate as a revenue lever. At 15.7%, roughly 6 out of 7 pipeline deals are wasted effort. Moving the close rate to 20% (a modest improvement) on the current 3.94M pipeline would add approximately 170K in expected revenue. Better deal qualification at the top of the funnel has a higher return than increasing pipeline volume.
4. Create a capacity dashboard that combines all three sources. HiBob headcount, Autotask utilization, and HubSpot pipeline should feed a single view that operations and sales leadership review weekly. The data already exists in Power BI. Connecting it into one dashboard removes the guesswork from "can we deliver what we are selling" and replaces it with numbers.
Three sources: HubSpot (CRM deal pipeline and close rates), HiBob (employee headcount, department structure, tenure data), and Autotask (time entries with billable/non-billable split, opportunity pipeline with stage-level detail, and ticket volumes). All data is queried live from the Power BI semantic model via DAX.
The overall ratio of 75.6% is pulled down by a handful of resources with high non-billable hours. Two resources in particular run below 62% billable, which could indicate internal project assignments, training time, or time entry categorization issues. Investigating those specific cases is the fastest path to improvement.
The 3.94M pipeline value comes from Autotask opportunities filtered to status "Active" only. It sums the amount field across all 124 active opportunities. This excludes won, lost, and implemented deals. The HubSpot pipeline tracks separately with 115 total deals including all stages.
It means the realistic expected conversion from the 3.94M pipeline is approximately 619K euros, not the full pipeline value. Capacity planning should use the expected conversion value rather than the headline number. If the close rate improves to 25%, the expected delivery load rises to 985K, which would require either higher utilization or additional headcount.
Individual utilization data is sensitive. Showing real names in a report that may be shared across leadership creates unnecessary risk. The purpose of the resource table is to identify patterns (high vs low billable ratios), not to single out individuals. Managers with access to Autotask can map resource IDs to names if needed for follow-up.
Monthly for the full cross-source analysis. Pipeline values and utilization rates shift week to week, but the strategic alignment between sales and delivery is best assessed on a monthly cadence. The DAX queries in this report are ready to run against the live semantic model at any time.
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