This report crosses HubSpot deal pipeline data (115 deals across the portfolio) with Autotask PSA ticket metrics (67,521 tickets, 844 currently open) to identify clients approaching contract renewal while showing worsening service levels. Two sources, one question: which clients are most likely to churn at renewal time?
This report crosses HubSpot deal pipeline data (115 deals across the portfolio) with Autotask PSA ticket metrics (67,521 tickets, 844 currently open) to identify clients approaching contract renewal while showing worsening service levels. Two sources, one question: which clients are most likely to churn at renewal time?
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: Account managers, finance teams, and MSP owners managing renewals
How often: Monthly for pipeline review, 90 days before expiry for renewal preparation
This report crosses HubSpot deal pipeline data (115 deals across the portfolio) with Autotask PSA ticket metrics (67,521 tickets, 844 currently open) to identify clients approaching contract renewal while showing worsening service levels. Two sources, one question: which clients are most likely to churn at renewal time?
Client A at 43.2% first response is a red flag that needs immediate attention. With 6,381 tickets and 113 currently open, more than half of all first responses miss the SLA window. At that volume, this is not a one-off spike. It is a structural problem with capacity or routing.
Five clients (A, D, H, I, J) fall below the 80% portfolio average for first response. Together they account for 14,445 tickets. If any of these five sit within a renewal window, the risk of losing them is significantly higher than the rest of the portfolio.
EVALUATE ROW("ActiveContracts", COUNTROWS(FILTER('BI_Autotask_Contracts', 'BI_Autotask_Contracts'[contract_status_name] = "Active")), "InactiveContracts", COUNTROWS(FILTER('BI_Autotask_Contracts', 'BI_Autotask_Contracts'[contract_status_name] = "Inactive")), "CSAT", [CSAT - Average Rating], "ResolutionMet", [Tickets - Resolution Met %])| Client | Tickets | Resolution % | Overdue | Open Now | Risk |
|---|---|---|---|---|---|
| Client A | 6,381 | 79.3% | 67 | 113 | High |
| Client B | 5,458 | 91.7% | 23 | 65 | Medium |
| Client C | 5,290 | 93.7% | 22 | 40 | Medium |
| Client D | 2,775 | 88.3% | 11 | 33 | Medium |
| Client E | 2,376 | 92.5% | 13 | 20 | Low |
| Client F | 2,364 | 99.9% | 0 | 0 | Low |
| Client G | 2,180 | 90.9% | 8 | 25 | Low |
| Client H | 1,803 | 87.1% | 11 | 20 | Medium |
| Client I | 1,758 | 86.0% | 5 | 13 | Medium |
| Client J | 1,728 | 93.1% | 15 | 36 | Medium |
Client A stands alone with 67 overdue tickets and a 79.3% resolution rate. No other client comes close to that combination. Client A also holds the highest open ticket count at 113. If they have any deal activity in HubSpot, this account should be the top priority for a service recovery conversation before renewal.
Client F runs at near-perfect SLA with zero overdue and zero open tickets. That kind of operational consistency makes renewal conversations straightforward. The gap between Client A and Client F tells you exactly where to focus limited account management time.
A 15.7% win rate across 115 deals sits below the typical MSP benchmark of 20-30%. That gap could mean pricing issues, qualification problems, or slow follow-up. But for this report, the more relevant question is: what happens to won deals when the service experience starts declining?
The 18 won deals represent clients who chose you. If their ticket SLA numbers start dropping after they sign, they are the ones most likely to reconsider at renewal. A won deal with worsening SLA is more dangerous than a lost prospect, because the revenue is already on the books.
EVALUATE ROW(
"TotalDeals", [HubSpot - Deals Total],
"WonDeals", [HubSpot - Deals Won],
"OverallFirstResponse", [Tickets - First Response Met %],
"OverallResolution", [Tickets - Resolution Met %],
"TotalTickets", [Tickets - Count - Created],
"OpenTickets", [Open Tickets (Current)]
)Client D demands 0.74 hours per ticket on average, the highest in the portfolio. Combined with a 73.7% first response rate and 88.3% resolution SLA, this client generates both high effort and poor outcomes. That is a costly combination during renewal negotiations.
Client A shows a different pattern: extremely low per-ticket effort (0.17 hrs) but the worst SLA numbers in the portfolio. That usually means tickets are being touched but not properly resolved, leading to repeat contacts and the high overdue count we see in Section 3.
EVALUATE TOPN(10,
SUMMARIZECOLUMNS(
BI_Autotask_Companies[company_name],
"AvgHours", [Tickets - Avg Hours Per Ticket],
"TicketCount", [Tickets - Count - Created]
),
[Tickets - Avg Hours Per Ticket], DESC
)43.2% first response | 79.3% resolution | 67 overdue | 113 open. Worst SLA in the portfolio at the highest ticket volume. Any active deal should trigger an immediate service recovery plan.
68.6% first response | 86.0% resolution | 5 overdue | 13 open. Second-worst first response rate. The lower ticket volume (1,758) masks how poor the experience is per interaction.
73.7% first response | 88.3% resolution | 11 overdue | 33 open. High effort per ticket (0.74 hrs) combined with below-average SLA. Renewal pricing discussions will be tough.
70.1% first response | 93.1% resolution | 15 overdue | 36 open. Resolution is solid, but first response lags badly. Clients notice wait times more than resolution quality.
75.4% first response | 87.1% resolution | 11 overdue | 20 open. Both SLA metrics below portfolio average. Steady decline rather than a dramatic failure.
98.0% first response | 99.9% resolution | 0 overdue | 0 open. The gold standard. Use this as the benchmark for what every account should look like at renewal.
With a 43.2% first response rate, 79.3% resolution SLA, 67 overdue tickets, and 113 open tickets, Client A sits in a category of its own. No other client combines this level of volume (6,381 tickets) with this level of service failure. If there is an active renewal deal for this account, it needs a dedicated escalation path before the renewal conversation happens.
Clients A, D, H, I, and J all miss the 80% first response target. Together they represent 14,445 tickets and carry the bulk of the open and overdue backlog. First response time is the single metric clients feel most directly: every missed SLA is a real person waiting longer than promised. These five accounts need priority routing or capacity adjustments.
The HubSpot pipeline shows 115 deals with only 18 won. A win rate well below the 20-30% MSP benchmark could mean over-qualification of the pipeline, slow follow-up, or pricing misalignment. For renewal risk analysis, the concern is different: if acquisition is already hard, losing existing clients through poor SLA makes the revenue impact worse.
At 90.2% overall, the resolution SLA target of 90% is being met. Eight of twelve top clients exceed it. The problem is concentrated in first response, not resolution, which points to capacity or queue management rather than technical skill. That is a more fixable issue for the operations team.
1. Build a renewal risk dashboard that combines HubSpot deal stage with Autotask SLA trends. This report is a snapshot, but the real value comes from tracking SLA trajectory in the 90 days leading up to each renewal date. A client whose first response rate drops from 85% to 70% in the quarter before renewal is signaling trouble. Automate this cross-source view so account managers see it in real time, not after the fact.
2. Launch a service recovery program for Client A immediately. Do not wait for the renewal conversation. Assign a dedicated escalation point, clear the 113 open tickets with priority triage, and schedule a weekly service review with the client contact. A proactive reach-out about service improvements lands very differently than a defensive conversation during renewal negotiations.
3. Fix the first response bottleneck for the five under-performing accounts. Consider dedicated queue routing for these five clients, temporary capacity boosts, or auto-escalation rules that trigger after 50% of the SLA window elapses. The 80.1% portfolio average for first response is being dragged down by these five accounts. Fixing them lifts the entire number above target.
4. Cross-reference HubSpot renewal dates with this SLA data monthly. Schedule this report to regenerate on the first of each month. Any client with an active deal in HubSpot whose SLA metrics dropped below 85% first response or 90% resolution should automatically flag for account manager review. The DAX queries are already built. The bridge mapping through proxuma_company_id makes the join possible.
The report crosses two data sources. HubSpot provides deal pipeline data showing which clients have active commercial relationships. Autotask PSA provides operational data showing ticket volumes, SLA compliance, overdue tickets, and resolution times. A client that appears in both systems with declining SLA metrics is flagged as a renewal risk. The risk level depends on the combination of low first response rates, missed resolution SLAs, and high overdue ticket counts.
Two metrics define SLA health: first response met percentage and resolution met percentage. "Worsening" means either metric drops below the portfolio target (90% for resolution, 80% as the current portfolio average for first response). Overdue tickets (resolved_due_age_days greater than 0) add another signal. A client with declining percentages and growing overdue counts shows a clear pattern of deteriorating service quality.
The connection runs through Bridge_All_Companies, a mapping table in the Power BI data model. Each company gets a proxuma_company_id that links its records across HubSpot, Autotask, Microsoft 365, and other sources. When a HubSpot deal is associated with a company that also has Autotask tickets, the bridge enables cross-source analysis. Companies not yet mapped will not appear in the cross-source view.
Overdue tickets are resolved tickets where the resolution happened after the SLA due date. In the data model, this is identified by resolved_due_age_days being greater than zero. A value of 3 means the ticket was resolved 3 days past the SLA deadline. These tickets count against the resolution SLA percentage and are a direct indicator of service delivery problems.
A low win rate means acquiring new clients is already difficult. When you combine that with the risk of losing existing clients due to poor SLA performance, the revenue impact multiplies. Replacing a churned client at a 15.7% conversion rate requires roughly 6-7 qualified prospects in the pipeline. Retaining existing clients through better service is significantly more cost-effective than finding replacements.
Monthly is the recommended cadence. SLA trends need at least 30 days of data to show a meaningful pattern. Running this on the first of each month gives account managers a full month of service data to act on before renewal conversations. The DAX queries execute in under a minute via MCP, so the overhead is minimal.
Yes. All DAX queries in this report are production-ready and run against the live Power BI semantic model via MCP. The report generation process takes under 15 minutes. Scheduling a monthly run that auto-flags clients with active HubSpot deals and declining Autotask SLA would turn this from a one-time analysis into a continuous early warning system.
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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