A cross-platform analysis comparing HubSpot deal pipeline data with actual Autotask billing. This report maps 115 deals against $17.6M in billed revenue across the Bridge_All_Companies link to identify where CRM promises and PSA delivery diverge. HubSpot Autotask PSA
A cross-platform analysis comparing HubSpot deal pipeline data with actual Autotask billing. This report maps 115 deals against $17.6M in billed revenue across the Bridge_All_Companies link to identify where CRM promises and PSA delivery diverge. HubSpot Autotask PSA
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: MSP owners, finance leads, and operations managers tracking profitability
How often: Monthly for financial reviews, quarterly for strategic planning, on-demand for pricing decisions
A cross-platform analysis comparing HubSpot deal pipeline data with actual Autotask billing. This report maps 115 deals against $17.6M in billed revenue across the Bridge_All_Companies link to identify where CRM promises and PSA delivery diverge. HubSpot Autotask PSA
High-level metrics from the combined HubSpot CRM and Autotask PSA dataset.
Actual billed revenue from Autotask PSA across the 10 highest-revenue clients. None of these top billing accounts have matched HubSpot deals.
Client H is an outlier. It generates $416K from just 84 hours of labour, pointing to a licensing or product-heavy account. The remaining top 10 clients show a clearer correlation between hours worked and revenue billed.
EVALUATE
TOPN(10,
ADDCOLUMNS(
SUMMARIZE(Bridge_All_Companies,
Bridge_All_Companies[company_id]),
"CompName", CALCULATE(MAX('BI_Autotask_Companies'[company_name])),
"DealsWon", [HubSpot - Deals Won],
"Revenue", [Revenue - Total],
"HoursWorked", [Company - Hours Worked]
),
[Revenue], DESC
)Of 18 won deals in HubSpot, only 3 could be matched to companies with Autotask billing. The remaining 15 are either unmatched or logged under a catch-all company record.
| Company Match | Deals Won | Close Rate | CSAT |
|---|---|---|---|
| No Match Unmatched | 16 | 16.7% | 90.3% |
| Matched Smith and Sons | 1 | 100% | -- |
| Matched Kennedy Inc | 1 | 100% | -- |
The 88.9% unmatched rate is the headline number here. If the CRM and PSA use different company names, different IDs, or no linking field at all, you lose the ability to compare what was sold against what was delivered. The top 10 revenue accounts in section 2.0 generate $9.2M combined, and not a single one has a matching HubSpot deal.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(Bridge_All_Companies,
Bridge_All_Companies[company_id]),
"CompName", CALCULATE(MAX('BI_Autotask_Companies'[company_name])),
"DealsWon", [HubSpot - Deals Won],
"CSAT", [SmileBack - CSAT],
"ClosedRate", [HubSpot - Closed Rate]
)
ORDER BY [DealsWon] DESCComparing the HubSpot pipeline funnel against what Autotask actually billed. The disconnect between the two systems makes a direct deal-to-revenue comparison impossible for most clients.
The gap between the two bars tells the whole story. $17.6M in billed revenue sits in Autotask with no link back to the CRM pipeline. Only about $300K of revenue can be traced back to a specific HubSpot deal through the Bridge_All_Companies table. That means 98% of your revenue has no sales pipeline context -- you cannot tell whether what was delivered matched what was sold.
How revenue distributes across the client base. Concentration in a small number of accounts increases business risk, especially when those accounts lack CRM visibility.
Three clients account for nearly two-thirds of the top-10 revenue. Client A alone pulls $2.32M, which is 13.2% of total billed revenue. If any of these three clients churns, you lose between $1.4M and $2.3M with no pipeline data to predict or prevent it. The 15.7% close rate on HubSpot deals suggests the pipeline itself needs work, but without matched company data, you cannot connect won deals to actual delivery outcomes.
EVALUATE
ROW(
"TotalLicenses", [Total Licenses],
"TotalDeals", [HubSpot - Deals Total],
"DealsWon", [HubSpot - Deals Won],
"ClosedRate", [HubSpot - Closed Rate],
"TotalRevenue", [Revenue - Total]
)The CRM-to-PSA bridge is broken. Of 18 won HubSpot deals, 16 land in an unmatched company bucket. That means the Bridge_All_Companies table cannot connect the majority of sales activity to billing activity. The top 10 revenue clients -- responsible for over $9.2M -- have zero HubSpot deals attached to them. This is not a reporting gap; it is a process gap.
Revenue concentration is high and unmonitored. Clients A, B, and C together generate $5.97M. Without deal data linked to these accounts, you cannot see renewal risk, upsell potential, or whether the original deal scope matches what gets delivered. These are your most important accounts operating in a blind spot.
The HubSpot close rate of 15.7% is low for an MSP. With 115 deals in the pipeline and only 18 won, either the pipeline includes stale or unqualified deals, or the sales process needs tightening. A healthy MSP pipeline typically closes between 25% and 40%. The current number suggests deal hygiene problems alongside the matching issues.
Client H represents a hidden dependency. At $416K from 84 hours, this account runs almost entirely on product or licensing revenue. If that revenue stream changes, the financial impact is immediate and significant. This type of account needs separate tracking from labour-driven clients.
16 of 18 won HubSpot deals cannot be linked to an Autotask billing company. The Bridge_All_Companies table is either missing entries or using mismatched identifiers. Until this is fixed, sold-vs-delivered analysis is impossible for most of your revenue.
Your biggest billing accounts appear nowhere in the HubSpot pipeline. Either these clients were acquired before the CRM was adopted, or deals are being created with company names that do not match the PSA. This creates a complete blind spot for account management and renewal tracking.
The pipeline of 115 deals with 18 wins suggests qualification problems. Without matched delivery data, you cannot assess which types of deals convert into profitable, long-term clients. The pipeline needs both better hygiene and a feedback loop from PSA data.
The 16 unmatched won deals still show a 90.3% CSAT score, which means delivery quality is not the problem. Customers are satisfied with the work; the issue is tracking and connecting the dots between what was sold and what was billed.
Steps to close the CRM-PSA gap and enable real sold-vs-delivered reporting.
Run a manual reconciliation of the top 25 Autotask billing companies against HubSpot. Map company IDs, fix name mismatches, and create missing bridge entries. This single action will unlock deal-to-billing tracking for the majority of your revenue. Target: complete within 2 weeks.
Every won deal should require a verified Autotask company match before it is marked as closed-won in HubSpot. Add this as a mandatory field or workflow step. Without it, the gap will keep growing with every new deal.
Review all 97 non-won deals. Archive anything older than 90 days without activity. The 15.7% close rate may partly reflect dead deals inflating the denominator. A clean pipeline gives a more accurate read on sales performance and makes forecasting possible.
Once the bridge table is repaired, create a Power BI view that compares deal value at close with actual billed revenue over 3, 6, and 12 months. This shows whether delivery matches the sales promise and surfaces scope creep or under-delivery early.
Bridge_All_Companies is a linking table in the Proxuma Power BI data model that maps company records across different platforms (Autotask, HubSpot, IT Glue, etc.). When a company exists in multiple systems, this table creates a single identifier that ties all platform data together.
Deals are unmatched when the HubSpot company associated with the deal does not have a corresponding entry in Bridge_All_Companies. This typically happens when company names differ between systems, when the HubSpot company was never linked to Autotask, or when deals were created without a company association.
CRM match rate is the percentage of won HubSpot deals where the associated company has a valid entry in Bridge_All_Companies with a linked Autotask company_id. In this dataset, only 2 of 18 won deals (Smith and Sons, Kennedy Inc) had a proper match, plus 16 under an unmatched bucket.
Most MSPs target a deal close rate between 25% and 40%. The 15.7% rate here could indicate pipeline hygiene issues (stale deals inflating the total) or qualification gaps. Cleaning the pipeline is the first step to getting an accurate benchmark.
Yes. Copy any query from the toggles above and paste it into DAX Studio or the Power BI Desktop performance analyzer. The queries reference standard Proxuma data model tables and measures that exist in every Proxuma Power BI deployment with HubSpot integration enabled.
Monthly, alongside your pipeline review. Once the bridge table is repaired, this report becomes a tool for tracking whether sales commitments are translating into actual billing. Quarterly reviews are useful for spotting trends in revenue concentration and client dependency.
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