How many deals actually close? This report breaks down the win rate, lost deal patterns, and pipeline value from your Autotask PSA opportunity data. In production, this connects to HubSpot CRM via the Proxuma Power BI connector.
How many deals actually close? This report breaks down the win rate, lost deal patterns, and pipeline value from your Autotask PSA opportunity data. In production, this connects to HubSpot CRM via the Proxuma Power BI connector.
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
How many deals actually close? This report breaks down the win rate, lost deal patterns, and pipeline value from your Autotask PSA opportunity data. In production, this connects to HubSpot CRM via the Proxuma Power BI connector.
EVALUATE TOPN(10,
SUMMARIZECOLUMNS('BI_Autotask_Opportunities'[owner_resource_name],
FILTER(VALUES('BI_Autotask_Opportunities'[status_name]),
'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented","Lost"}),
"Closed", COUNTROWS('BI_Autotask_Opportunities'),
"Won", CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented"}),
"WinRate", DIVIDE(CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented"}), COUNTROWS('BI_Autotask_Opportunities')),
"WonRevenue", CALCULATE(SUM('BI_Autotask_Opportunities'[amount]), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented"})
),
[Closed], DESC
)
ORDER BY [Closed] DESCAll 1,465 opportunities broken down by current status, with value and average probability
| Status | Deals | Total Amount | Avg Deal Size | Avg Probability |
|---|---|---|---|---|
| 720 | $3,443,385 | $4,782 | 46.6% | |
| 509 | $7,059,325 | $13,869 | 46.0% | |
| 124 | $3,938,803 | $31,764 | 48.6% | |
| 112 | $523,880 | $4,677 | 47.8% |
EVALUATE
SUMMARIZE(
BI_Autotask_Opportunities,
BI_Autotask_Opportunities[status_name],
"Deals", COUNT(BI_Autotask_Opportunities[opportunity_id]),
"TotalAmount", SUM(BI_Autotask_Opportunities[amount]),
"AvgDealSize", AVERAGE(BI_Autotask_Opportunities[amount]),
"AvgProbability", AVERAGE(BI_Autotask_Opportunities[probability])
)
ORDER BY [Deals] DESCWhere closed deals end up in the pipeline, broken down by processing stage
| Metric | Value |
|---|---|
| All opportunities | 1,465 |
| Closed (Won + Lost) | 1,341 |
| Won | 832 |
| Lost | 509 |
| Active (still open) | 124 |
| Close rate | 91.5% |
| Win rate of closed | 62.0% |
| Loss rate of closed | 38.0% |
EVALUATE ROW(
"AllOpps", COUNTROWS('BI_Autotask_Opportunities'),
"Closed", CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented","Lost"}),
"Won", CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented"}),
"Lost", CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] = "Lost"),
"CloseRate", DIVIDE(CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented","Lost"}), COUNTROWS('BI_Autotask_Opportunities')),
"WinRate", DIVIDE(CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented"}), CALCULATE(COUNTROWS('BI_Autotask_Opportunities'), 'BI_Autotask_Opportunities'[status_name] IN {"Closed","Implemented","Lost"}))
)How deal values differ between won and lost opportunities
EVALUATE
SUMMARIZE(
BI_Autotask_Opportunities,
BI_Autotask_Opportunities[status_name],
"Deals", COUNT(BI_Autotask_Opportunities[opportunity_id]),
"TotalAmount", SUM(BI_Autotask_Opportunities[amount]),
"AvgDealSize", AVERAGE(BI_Autotask_Opportunities[amount]),
"AvgProbability", AVERAGE(BI_Autotask_Opportunities[probability])
)
ORDER BY [Deals] DESCA 58.6% win rate across 1,229 decided opportunities is a reasonable baseline for an MSP. You are winning more deals than you lose. But the numbers underneath that headline tell a more interesting story.
Lost deals are nearly 3x larger than won deals. The average won deal is $4,782. The average lost deal is $13,869. That means the deals you lose carry significantly more revenue potential than the ones you close. When you lose a deal, you are losing a bigger deal. That pattern suggests either pricing pressure on larger engagements or a qualification gap where bigger prospects enter the pipeline without the right fit.
Lost deals account for $7.06M in total value, more than double the $3.44M in closed-won revenue. Even a modest improvement in close rate on deals above $10,000 would have an outsized impact on revenue. If you had won just 10% more of those lost deals, that would represent roughly $700K in additional revenue.
The active pipeline of $3.94M across 124 deals shows a healthy volume of open opportunities. The average active deal size of $31,764 is significantly higher than the historical average, which means the pipeline is weighted toward larger deals. Whether those close at the same rate as smaller deals is the question.
Most won deals (72.9%) flow to the "Signed, to Ticket" stage, meaning they become reactive support contracts. The 23% that move to projects carry a 3.4x higher average deal size ($10,317 vs $3,035). Project-based wins are more valuable per deal.
4 priorities based on the findings above
The average lost deal is $13,869. Pull the top 20 lost opportunities by value and look for common reasons: pricing objection, competitor displacement, scope mismatch, or timing. If you can identify two or three fixable patterns, the revenue impact is significant. $7M in lost deal value means even small improvements in win rate on larger deals have a direct bottom-line effect.
The 124 active deals averaging $31,764 represent your biggest revenue opportunity. But if historical win rates on large deals are lower than on small deals, you may be spending sales time on prospects that are unlikely to close. Score active deals by fit and focus your effort on the ones with the highest probability of closing, not just the highest dollar value.
These deals are technically won but have not been converted to tickets or projects yet. That is $132,532 in value sitting in limbo. Check whether these are recent closings that are still being onboarded or older deals that fell through the cracks. Signed deals that stall can erode client confidence before the engagement even starts.
Project-routed deals average $10,317 compared to $3,035 for ticket-routed deals. If your sales team can position more opportunities as projects rather than break-fix support, the average deal value increases by 3.4x. Consider bundling implementation, migration, or assessment work into initial proposals.
This demo report uses Autotask PSA opportunity data as a proxy for CRM deal close rates. The demo Power BI model does not include a HubSpot connector. In production, Proxuma connects to HubSpot CRM directly and the same report structure, DAX queries, and analysis patterns apply to real HubSpot deal data.
Win rate is the number of won deals divided by the total number of decided deals (won + lost). Active and in-progress opportunities are excluded from the calculation because they have not reached a final outcome yet. In this dataset: 720 won / (720 + 509) = 58.6%.
MSP industry benchmarks for deal close rates typically range from 25% to 65%, depending on lead source and deal size. Inbound leads close at higher rates than outbound. Deals under $5,000 tend to close faster and at higher rates than enterprise-sized engagements. A 58.6% win rate is in the upper range.
Larger deals face more decision-makers, longer sales cycles, and stronger competition. The $13,869 average lost deal vs $4,782 average won deal suggests that bigger prospects are harder to close. This is common in MSP sales. Improving the proposal process, adding references, or adjusting pricing tiers for larger engagements can help.
Yes. Proxuma Power BI includes a HubSpot connector that pulls deals, contacts, companies, and pipeline stages into your data model. Once connected, you can generate this exact report from your real HubSpot data using AI via MCP. The DAX queries adjust automatically to your schema.
Yes. Connect Proxuma Power BI to your CRM (HubSpot, Autotask, or both), add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask the same question. The AI writes the DAX queries, runs them against your live data, and produces a report like this in under fifteen minutes.
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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