This report crosses HubSpot deal data (115 deals, 18 won) with SmileBack CSAT surveys (8,900+ rated responses across 20 active companies) and Autotask ticket volume (67,521 tickets, 844 currently open) to identify clients showing the triple churn signal: high commercial value combined with declining satisfaction and growing support demand. Three data sources, one question: which valuable clients are quietly heading for the exit?
This report crosses HubSpot deal data (115 deals, 18 won) with SmileBack CSAT surveys (8,900+ rated responses across 20 active companies) and Autotask ticket volume (67,521 tickets, 844 currently open) to identify clients showing the triple churn signal: high commercial value combined with declining satisfaction and growing support demand. Three data sources, one question: which valuable clients are quietly heading for the exit?
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, MSP owners, and service delivery leads
How often: Monthly for client reviews, quarterly for QBRs, on-demand when client signals change
This report crosses HubSpot deal data (115 deals, 18 won) with SmileBack CSAT surveys (8,900+ rated responses across 20 active companies) and Autotask ticket volume (67,521 tickets, 844 currently open) to identify clients showing the triple churn signal: high commercial value combined with declining satisfaction and growing support demand. Three data sources, one question: which valuable clients are quietly heading for the exit?
10 of the top 20 surveyed companies fall below the 85% positive rate threshold. That is not a handful of outliers. It is half the client base with meaningful survey data sending a clear dissatisfaction signal. Clients D and F are particularly concerning because they combine low scores (73.6% and 79.4%) with the highest survey volumes (382 and 384 ratings). These are not thin-sample anomalies.
EVALUATE TOPN(15,
SUMMARIZECOLUMNS(
BI_Autotask_Companies[company_name],
"CSATAvg", [CSAT - Average Rating],
"TotalRatings", [CSAT - Total Ratings]
),
[CSAT - Total Ratings], DESC
)| Client | Tickets | Open Now | CSAT Positive | Ratings | First Resp. Met | Risk |
|---|---|---|---|---|---|---|
| Client N | 6,381 | 113 | 88.6% | 79 | 43.2% | High |
| Client F | 5,458 | 65 | 79.4% | 384 | 88.2% | High |
| Client D | 5,290 | 40 | 73.6% | 382 | 87.5% | High |
| Client M | 2,775 | 33 | 89.4% | 104 | 73.7% | Medium |
| Client L | 2,376 | 20 | 89.4% | 142 | 86.0% | Medium |
| Client G | 2,180 | 25 | 80.6% | 62 | 84.9% | High |
| Client C | 1,803 | 20 | 70.0% | 30 | 75.4% | High |
| Client J | 1,758 | 13 | 84.0% | 50 | 68.6% | Medium |
| Client A | 1,728 | 36 | 52.5% | 59 | 70.1% | High |
| Client E | 1,317 | 18 | 75.0% | 44 | 83.9% | Medium |
The pattern is clear. The six clients flagged as High risk all share at least two of the three churn signals: high ticket volume, low CSAT positive rate, or poor first response SLA compliance. Client A is the most alarming case with only 52.5% positive CSAT, 1,728 tickets, 36 still open, and a first response rate of just 70.1%. Client N generates the most tickets (6,381) with 113 open right now and a first response met rate of only 43.2%.
The lowest CSAT score in the entire portfolio. More than half of all survey responses from this client are neutral or negative. Combined with 36 open tickets and a first response rate well below target, this is the most urgent churn risk. Every day without intervention deepens the damage.
This is the highest-volume CSAT client (382 ratings) with one of the lowest positive rates. At 5,290 total tickets, they are also one of the heaviest ticket generators. The first response rate is acceptable, but the satisfaction gap with 382 data points behind it is not a fluke. This client is telling you something through hundreds of surveys.
All three signals firing at once. A 70% positive rate means roughly 1 in 3 interactions leaves this client dissatisfied. First response SLA compliance at 75.4% means one in four tickets does not get a timely initial response. That combination accelerates frustration.
The CSAT looks acceptable at first glance, but look at the volume: 6,381 tickets with 113 still open and a catastrophic first response met rate of 43.2%. More than half of all tickets from this client miss the first response SLA. That kind of responsiveness gap will erode even strong satisfaction numbers over time.
There is a visible pattern here. Clients where the first response SLA dips below 80% tend to show lower or declining satisfaction scores. Client N is the starkest example: 43.2% first response met with 6,381 tickets. Their CSAT is still 88.6%, but that number is living on borrowed time when more than half of all tickets start with a missed SLA.
First response time is the canary in the coal mine. Client D is an interesting counterpoint: 73.6% CSAT despite an 87.5% first response rate. That tells you the satisfaction issue there is not about speed. It is about resolution quality or something else entirely. Fixing the first response gap would not rescue Client D's CSAT, but it would protect Client N's before it erodes.
EVALUATE TOPN(15, ADDCOLUMNS(VALUES(BI_Autotask_Companies[company_name]), "CSATAvg", [CSAT - Average Rating], "TotalRatings", [CSAT - Total Ratings], "TicketCount", [Tickets - Count - Created], "HoursWorked", [Tickets - Hours Worked]), [TicketCount], DESC)The HubSpot data tells a story about commercial activity, but it is mostly disconnected from the operational data in Autotask. 96 of 115 deals sit under a null company mapping, meaning HubSpot knows about these prospects but Autotask does not link them to a service record. Only 2 companies with HubSpot deals also have CSAT data in the model.
This data silo is a blind spot. When a deal closes in HubSpot, the account team has no automated way to see that client's ticket history or satisfaction trajectory. And when a service manager sees declining CSAT for a client, they cannot check what the commercial relationship looks like in HubSpot. Bridging these two would let you catch churn signals before renewal conversations start.
Clients A, C, D, F, G, and N all combine below-target CSAT or poor first response rates with significant ticket volume. Client A at 52.5% positive rate is the most critical. These clients need immediate account review before the next renewal cycle.
43.2% first response met across 6,381 tickets. That is not an occasional miss. More than half of all tickets from this client start with a broken SLA promise. With 113 tickets currently open, the operational pressure is ongoing.
96 of 115 HubSpot deals have no Autotask company link. Commercial and operational data cannot be cross-referenced for the vast majority of accounts. This makes it impossible to correlate deal value with service quality at scale.
Client L (89.4% positive, 2,376 tickets, 86% FRM) and Client M (89.4% positive, 2,775 tickets) prove that high ticket volume does not automatically mean low satisfaction. The difference is operational execution, specifically first response speed and resolution quality.
1. Schedule immediate account reviews for the 6 high-risk clients. Start with Client A (52.5% CSAT) and Client N (43.2% FRM). Pull the last 90 days of ticket data, identify recurring issue categories, and build a remediation plan before the next QBR. For Client A specifically, anything below 60% positive rate warrants a face-to-face conversation with the decision maker.
2. Fix the first response SLA process for Client N. A 43.2% first response rate across 6,381 tickets is a staffing or routing problem, not a one-off miss. Check whether their tickets are landing in the right queue, whether the assigned resources have capacity, and whether the SLA target itself is realistic for this account. The CSAT is still 88.6% today. It will not stay there.
3. Map HubSpot deals to Autotask companies. 96 unmapped deals represent commercial relationships without operational context. Start with the 18 won deals. For each one, find the matching Autotask company and create the bridge link. This gives you immediate visibility into whether your closed-won accounts are getting good service or quietly becoming churn risks.
4. Build a monthly churn risk scorecard. Use the three signals from this report (CSAT positive rate, ticket volume trend, first response SLA) to score every client monthly. Any client below 80% on two or more signals gets flagged for proactive outreach. The DAX queries are already built. This just needs a Power BI page and a monthly review cadence.
5. Investigate the root cause behind Client D's low CSAT. At 73.6% positive with a healthy 87.5% first response rate and 382 survey responses, Client D's dissatisfaction is not about response speed. Dig into the ticket categories and resolution quality. This client is being responded to on time but still not satisfied, which points to a deeper service delivery gap.
EVALUATE ROW(
"TotalDeals", [HubSpot - Deals Total],
"WonDeals", [HubSpot - Deals Won],
"CSATAvg", [CSAT - Average Rating],
"CSATLastMonth", [CSAT - Average Rating - Last Month],
"TotalTickets", [Tickets - Count - Created],
"OpenTickets", [Open Tickets (Current)],
"AvgHoursPerTicket", [Tickets - Avg Hours Per Ticket]
)SmileBack uses a three-point scale: +1 (positive/thumbs up), 0 (neutral), and -1 (negative/thumbs down). The "CSAT Positive Rate" in this report is the percentage of all responses that scored +1. An 87.7% positive rate means roughly 88 out of every 100 survey responses are thumbs up. The remaining 12 are either neutral or negative.
The triple threat combines three signals: (1) CSAT positive rate below 85%, (2) high or rising ticket volume relative to the client's size, and (3) first response SLA met rate below 80%. Any client showing two or more of these signals is flagged as a churn risk. All three firing at once, as with Client C, represents the highest urgency.
HubSpot and Autotask use different company identifiers. The data model connects them through BI_Autotask_Companies, but 96 of 115 deals sit under a null company mapping. This means the HubSpot deal records have not been linked to their corresponding Autotask company records. Fixing this requires matching company names across both systems and writing the link.
First Response Met % is the proportion of tickets where the first technician response occurred within the SLA-defined timeframe. A rate of 87.5% means roughly 7 out of 8 tickets got their initial response on time. Below 80% indicates a systemic responsiveness issue that typically correlates with declining client satisfaction.
Not on its own. Client L generates 2,376 tickets with an 89.4% positive rate and 86% first response met. High ticket volume simply means the client is active and engaged with support. It becomes a churn signal only when combined with declining satisfaction or missed SLAs. That is why the triple threat framework uses all three signals together.
Schedule account reviews for Client A (52.5% CSAT) and Client N (43.2% FRM) within the next two weeks. These are the two most acute risks. Then map the 18 won HubSpot deals to their Autotask companies to close the biggest data gap. Everything else can follow on a monthly review cadence.
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