Cross-referencing SmileBack CSAT ratings with Autotask ticket resolution speed across 67,521 tickets. This report examines whether faster resolution times correlate with higher customer satisfaction, and identifies clients where survey coverage gaps make the picture incomplete.
Cross-referencing SmileBack CSAT ratings with Autotask ticket resolution speed across 67,521 tickets. This report examines whether faster resolution times correlate with higher customer satisfaction, and identifies clients where survey coverage gaps make the picture incomplete.
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: Service delivery managers, operations leads, and MSP owners tracking service quality
How often: Weekly for operational adjustments, monthly for client reporting, quarterly for contract reviews
Cross-referencing SmileBack CSAT ratings with Autotask ticket resolution speed across 67,521 tickets. This report examines whether faster resolution times correlate with higher customer satisfaction, and identifies clients where survey coverage gaps make the picture incomplete.
Key metrics across all clients over the last 12 months, combining SmileBack survey data with Autotask ticket resolution metrics.
Top 10 clients ranked by average hours per ticket. Clients with the slowest resolution times, cross-referenced with their CSAT scores and SLA compliance. Cells marked "--" have no SmileBack survey data.
| Client | CSAT Pos% | Last Month | Avg Hrs/Ticket | Res. SLA Met |
|---|---|---|---|---|
| Client A | -- | -- | 9.67 | -- |
| Client B | -- | -- | 6.83 | -- |
| Client C | -- | -- | 4.86 | 100% |
| Client D | -- | -- | 4.06 | 80% |
| Client E | -- | -- | 3.00 | -- |
| Client F | 100% | -- | 2.98 | 100% |
| Client G | -- | -- | 2.60 | 77.8% |
| Client H | -- | -- | 1.64 | 80% |
| Client I | -- | -- | 1.50 | -- |
| Client J | -- | -- | 1.46 | 50% |
The slowest clients are mostly invisible on satisfaction. Of the 10 clients with the highest average hours per ticket, only Client F has any CSAT data at all. That means the accounts consuming the most labour per ticket are the same ones where you have no idea if the customer is happy or frustrated. Client A averages 9.67 hours per ticket with zero survey responses. That is a blind spot worth investigating.
EVALUATE
TOPN(10,
ADDCOLUMNS(
SUMMARIZE(Bridge_All_Companies,
Bridge_All_Companies[company_id]
),
"CompName", CALCULATE(MAX('BI_Autotask_Companies'[company_name])),
"CSAT", [CSAT - Average Rating],
"CSATLastMonth", [CSAT - Average Rating - Last Month],
"AvgHoursPerTicket", [Tickets - Avg Hours Per Ticket],
"ResMet", [Tickets - Resolution Met %]
),
[AvgHoursPerTicket], DESC
)Monthly CSAT positive rate (left axis) plotted against average hours per ticket (right axis). The question: when resolution speeds up, does satisfaction follow?
The two lines move in near-lockstep. From May through March, both CSAT and resolution speed improved steadily. CSAT climbed from 84.2% to 90.1%, while average hours per ticket dropped from 0.92h to 0.68h. The April dip in both metrics (CSAT back to 87.7%, hours up to 0.75h) reinforces the correlation. When tickets take longer, satisfaction drops. The Pearson correlation between these two series is roughly -0.94, which is about as strong as it gets.
EVALUATE ROW("CSATAvg", [CSAT - Average Rating], "CSATLastYear", [CSAT - Average Rating - Last Year], "ResolutionMet", [Tickets - Resolution Met %], "SameDayRes", [Tickets - Same Day Resolution %], "FirstHourFix", [Tickets - First Hour Fix %], "TotalTickets", [Tickets - Count - Created], "HoursWorked", [Tickets - Hours Worked])Comparing first response SLA compliance (80.1%) and resolution SLA compliance (90.2%) against overall CSAT. The donut charts show the gap between the two SLA metrics and where the satisfaction target sits.
Resolution SLA outperforms first response SLA by 10 percentage points. The gap suggests that while initial pickup can be slow, the team recovers well during the resolution phase. CSAT at 87.7% sits between the two SLA metrics, which makes sense: customers feel the initial wait, but the end result still lands well. Closing the first response gap from 80.1% toward the 90% mark would likely push CSAT above 90%.
Clients without CSAT data also have worse SLA performance. The group with no survey responses runs 8 points lower on first response and 5 points lower on resolution compared to clients who do return surveys. This is not a coincidence. The least-engaged clients tend to get less proactive attention, which drags down both their SLA numbers and their willingness to respond to satisfaction surveys.
EVALUATE
ROW(
"AvgCSAT", [CSAT - Average Rating],
"AvgFRMet", [Tickets - First Response Met %],
"AvgResMet", [Tickets - Resolution Met %],
"TotalTickets", [Tickets - Count - Created]
)Survey response coverage across the top 10 slowest clients. Green cells indicate CSAT data is available; red cells indicate no survey data exists.
9 out of 10 of the slowest clients have zero CSAT data. Only Client F provides survey feedback, and they happen to be 100% satisfied with a 100% resolution SLA. The pattern is clear: clients that consume the most support hours are the least likely to respond to satisfaction surveys. This creates a feedback loop where the accounts that need the most attention are the ones you know the least about.
Client J stands out for a different reason. At 1.46 hours per ticket, the resolution time is not extreme. But a 50% resolution SLA rate on top of zero CSAT data is a warning sign. This client might be silently unhappy.
Over 12 months, every month where average hours per ticket decreased also showed a CSAT improvement. The relationship is almost perfectly inverse. Faster resolution is the single strongest lever for improving customer satisfaction in this dataset.
The top 10 clients by hours per ticket represent the heaviest support load. Of these, 9 have returned zero SmileBack surveys. You are spending the most time on accounts where you cannot measure satisfaction. That is a risk.
Resolution SLA sits at a healthy 90.2%, but first response lags by 10 points. Since customers feel the initial wait most acutely, this gap likely accounts for the difference between the 87.7% CSAT and what could be a 90%+ score. The team fixes things well; they just need to pick up the phone faster.
The no-CSAT group runs at 76.4% first response and 87.8% resolution, compared to 84.3% and 93.1% for clients with survey data. These accounts are getting objectively worse service and you have no satisfaction signal to flag it. Double blind spot.
The core finding is simple: speed drives satisfaction. The -0.94 correlation between resolution hours and CSAT positive rate is about as definitive as it gets in operational data. This is not a "maybe." Every month that resolution times dropped, CSAT went up. Every month they crept back up, CSAT fell.
But there is a second story underneath the first. The CSAT data only covers a fraction of the client base. The clients who respond to surveys tend to be the ones already getting better service (higher SLA compliance, lower resolution times). The clients who consume the most support hours and miss SLA targets the most often are invisible in the satisfaction data. This means the 87.7% CSAT figure is likely overstating true satisfaction across the full client base.
The April dip is worth watching. After 10 months of steady improvement, both CSAT and resolution speed reversed in April. CSAT dropped from 90.1% to 87.7%, and average hours rose from 0.68h to 0.75h. This could be seasonal (end of fiscal year, staff turnover), a one-time event, or the start of a trend. One month is not a pattern, but it is worth flagging for next month's review.
Client F is an interesting outlier. They are the only high-hours client (2.98h per ticket) with 100% CSAT and 100% resolution SLA. This suggests that some clients with complex issues can still be perfectly happy, as long as the SLA commitments are met. Resolution time alone does not determine satisfaction; meeting the promised timeline matters just as much.
Practical steps to close the CSAT coverage gap and keep the speed-satisfaction trajectory moving in the right direction.
Verify that Clients A through J all have SmileBack survey triggers active on ticket closure. If the surveys are active but not returned, switch to a different delivery method (inline email vs. separate survey email). The goal: get at least one data point per client within 30 days.
Average hours per ticket jumped from 0.68h to 0.75h in April, breaking 10 months of improvement. Pull the ticket data for April and check for: staffing changes, a spike in complex tickets, or a specific client driving the increase. If it is a one-time event, document it. If it is structural, fix it before May.
The 10-point gap between first response (80.1%) and resolution (90.2%) SLA is the biggest operational gap in this report. Set a 90-day target to close half that gap. Review auto-assignment rules, check for tickets sitting in queues without pickup, and consider adding first-response alerts for tickets approaching SLA breach.
Track the percentage of clients with at least one survey response per month. Set a target of 70% client coverage within 6 months. Right now, the heaviest clients are invisible. A simple monthly report that shows which clients have zero responses will keep this on the agenda.
SmileBack uses a -1 (negative), 0 (neutral), +1 (positive) scale. The positive rate is the count of +1 responses divided by total responses. An 87.7% rate means that of all surveys returned, 87.7% were positive. Neutral and negative responses make up the remaining 12.3%.
Several reasons: the client may have opted out of surveys, the survey trigger might not be configured for their ticket types, or they simply do not respond. In some cases, tickets are closed without reaching the end-user (e.g., internal escalations or monitoring tickets). Check the SmileBack configuration per client to identify the root cause.
First response SLA measures how quickly the team acknowledges a ticket (first human reply or status change). Resolution SLA measures how quickly the ticket is fully resolved. A ticket can meet the resolution SLA while missing the first response SLA if it sits in queue but is then resolved quickly once picked up.
Not always. Client F averages 2.98 hours per ticket but has 100% CSAT and 100% SLA compliance. Some clients have genuinely complex environments that require more time per ticket. The problem arises when high hours combine with missed SLAs and absent CSAT data, because then you have no way to know if the time investment is paying off.
The Pearson correlation coefficient is approximately -0.94. This is a very strong inverse relationship: as resolution hours go down, CSAT goes up. In practical terms, every 0.1h reduction in average resolution time has historically corresponded to roughly a 2.5 percentage point increase in CSAT positive rate.
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.
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.
See more reports Get started