This report crosses HiBob employee data (75 employees across 10+ departments) with HubSpot deal pipeline (115 deals, 1,465 open opportunities worth $14.97M) to test whether headcount growth keeps pace with sales pipeline growth. Two data sources, one question: is your team large enough to deliver on what sales is selling?
This report crosses HiBob employee data (75 employees across 10+ departments) with HubSpot deal pipeline (115 deals, 1,465 open opportunities worth $14.97M) to test whether headcount growth keeps pace with sales pipeline growth. Two data sources, one question: is your team large enough to deliver on what sales 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 HiBob employee data (75 employees across 10+ departments) with HubSpot deal pipeline (115 deals, 1,465 open opportunities worth $14.97M) to test whether headcount growth keeps pace with sales pipeline growth. Two data sources, one question: is your team large enough to deliver on what sales is selling?
Support is the largest department at 20 people, making up 26.7% of the entire workforce. That tracks with what you would expect from an MSP: the delivery side of the business carries the most headcount. Engineering and Operations together add another 17, bringing delivery-adjacent roles to 60% of the company.
Sales has just 4 people but 2 managers. That 50% manager ratio stands out compared to Support's 10%. Whether that structure makes sense depends on how much of the sales motion is manager-led versus rep-driven, but it is worth a second look.
EVALUATE
SUMMARIZECOLUMNS(
BI_HiBob_Employee[department],
"Count", COUNTROWS(BI_HiBob_Employee),
"Managers", CALCULATE(
COUNTROWS(BI_HiBob_Employee),
BI_HiBob_Employee[is_manager] = TRUE
)
)366 opportunities per sales rep is an unworkable number. Even with automation and lead scoring, no rep can meaningfully advance that many conversations. This points to either a qualification problem (too many low-quality opportunities staying open) or a staffing gap (not enough reps to work the pipeline properly).
On the delivery side, the picture looks different. At 5.75 deals per support person, the team has reasonable capacity to onboard new clients. But that number assumes current deal volume. If the 15.7% win rate improves to even 25% with better sales coverage, support would need to absorb roughly 29 deals instead of 18, pushing the ratio above 1 new deal per support person per quarter.
| Metric | Value |
|---|---|
| Employees | 75 |
| New Hires | 18 |
| Active Contracts | 1,377 |
| Tickets | 67,521 |
| Tickets/Employee | 900 |
96 of 115 deals (83.5%) have no company linked in HubSpot. That is the single biggest data quality issue in this pipeline. Without company associations, you cannot track account-level engagement, identify multi-deal accounts, or build a proper account-based strategy. The 19 deals that do have company links show a scattered pattern of single deals per company.
EVALUATE ROW("CurrentEmployees", [Total Employees], "NewHires", COUNTROWS(FILTER('BI_HiBob_Employee_History', 'BI_HiBob_Employee_History'[work_change_type] = "New Employee")), "TotalTickets", [Tickets - Count - Created], "ActiveContracts", COUNTROWS(FILTER('BI_Autotask_Contracts', 'BI_Autotask_Contracts'[contract_status_name] = "Active")))The company-wide manager ratio of 18.7% is within healthy range for a 75-person MSP. Industry benchmarks typically put this between 15-25%. The outlier is Sales at 50% managers: 2 managers overseeing 2 individual contributors. That structure makes sense only if those managers are player-coaches carrying their own quota. If they are purely managing, the span of control is too narrow and the cost per rep is too high.
96 of 115 deals sit in HubSpot without a linked company. This blocks account-level reporting, makes it impossible to calculate revenue per account, and breaks cross-source analysis with HiBob. Fixing this is a data hygiene task that takes hours, not weeks, and unlocks every other metric in this report.
4 sales staff managing 1,465 opportunities works out to 366 per rep. Even accounting for automation and pipeline stages, that ratio means most opportunities get no meaningful human touch. If even 10% of those opportunities are qualified, that is still 37 active conversations per rep, which is at the upper end of what research shows is manageable for complex B2B sales.
Only 18 of 115 deals have closed won. For MSP services with typical sales cycles of 30-90 days, a healthy win rate sits between 20-35%. The gap could reflect poor lead qualification, insufficient follow-up capacity (see the rep-to-opportunity ratio), or pipeline bloat where stale deals are not being closed out.
At 5.75 deals per support person, the delivery side has room to absorb new wins. Even a doubling of the win rate would keep the ratio manageable. The 4.3-year average tenure means these are experienced staff who can onboard new clients without heavy ramp-up time.
1. Link all HubSpot deals to their company records. This is the fastest-return action in this report. Have someone spend a day associating the 96 unlinked deals with their proper company records. Until that happens, you cannot do account-level pipeline analysis, which means you cannot spot multi-deal accounts, calculate true deal concentration, or connect pipeline data to HiBob workforce planning.
2. Audit the 1,465 open opportunities and close out stale ones. A pipeline with 1,465 opportunities and a 15.7% win rate likely contains hundreds of deals that will never close. Set a rule: any opportunity with no activity in 90 days gets moved to closed-lost. This cleans up the pipeline, gives you an accurate forecast, and reduces the noise your sales team has to wade through.
3. Add 1-2 sales reps before expanding pipeline further. At 366 opportunities per rep, your current team is spread too thin to properly qualify and advance deals. Before investing in more lead generation, bring the ratio down to under 100 active opportunities per rep. That usually means hiring 1-2 reps focused on pipeline management and qualification, not just net-new prospecting.
4. Review the Sales department manager ratio. Two managers for two individual contributors is unusual. If those managers carry quota and close deals themselves, the structure works. If they are purely managing, consider whether one manager could cover Sales and Marketing combined, freeing budget for an additional IC who carries a bag.
5. Build a quarterly headcount-vs-pipeline review. Run this cross-source report every quarter. Track whether the pipeline-per-employee ratio is growing, shrinking, or stable. If it climbs above $250K per employee (gross pipeline), that is an early signal that the team will struggle to deliver on what sales is closing. The DAX queries are ready to go.
EVALUATE ROW(
"TotalEmployees", [Total Employees],
"TotalManagers", [Total Managers],
"ManagerPct", [Manager Ratio],
"AvgTenure", [Average Tenure Years],
"DealsTotal", [HubSpot - Deals Total],
"DealsWon", [HubSpot - Deals Won]
)
-- Pipeline detail:
EVALUATE ROW(
"AvgProbability", AVERAGE(BI_Autotask_Opportunities[probability]),
"TotalOppValue", SUM(BI_Autotask_Opportunities[amount]),
"OppCount", COUNTROWS(BI_Autotask_Opportunities),
"AvgDealSize", DIVIDE(
SUM(BI_Autotask_Opportunities[amount]),
COUNTROWS(BI_Autotask_Opportunities)
)
)It divides total pipeline value by total headcount. A higher number means each employee must support more potential revenue. For MSPs, a ratio above $200K gross pipeline per employee typically signals that delivery teams will get stretched if close rates improve. The weighted version ($93K here) gives a more realistic picture by factoring in deal probability.
Three common causes: (1) Pipeline bloat with stale opportunities that should be closed-lost. (2) Insufficient sales capacity to follow up on leads, which means good opportunities go cold. (3) Poor lead qualification at the top of the funnel, letting unqualified prospects into the pipeline. The 366 opportunities per rep ratio suggests causes 1 and 2 are the primary drivers here.
Both sources feed into the Power BI data model through separate data pipelines. HiBob provides employee records (department, tenure, manager status). HubSpot provides deal records (amount, stage, company). They connect through Bridge_All_Companies using proxuma_company_id. The cross-source analysis depends on both sources being properly mapped through this bridge table.
Industry benchmarks for tech companies put manager ratios between 15-25% of total headcount. At 18.7%, the overall ratio is healthy. The department-level view is where it gets interesting: Support at 10% is lean, while Sales at 50% is heavy. Most MSPs target a 1:6 to 1:10 manager-to-IC span of control in delivery teams.
Deals in HubSpot can be created without associating them to a company record. This often happens when deals are created from forms, imports, or quick-add flows. The fix is straightforward: review each unlinked deal, match it to an existing company (or create one), and associate them. HubSpot also supports bulk association via import, so this can be done in one session.
Quarterly is the right cadence for most MSPs. Monthly is overkill because headcount does not change that fast. Annual is too slow because pipeline can shift dramatically in a quarter. A quarterly run lets you spot trends early enough to start hiring before delivery gets overwhelmed, while keeping the reporting overhead low.
Yes. The DAX queries in this report execute against the live Power BI dataset via MCP. Once scheduled, the report regenerates with fresh HiBob and HubSpot data each quarter. The generation process takes under 15 minutes. Set it to run the first Monday of each quarter and you will always have a current snapshot for leadership review.
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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