Which clients get the fastest responses, which ones keep breaching SLA, and whether poor compliance actually hurts satisfaction. Generated by AI via Proxuma Power BI MCP server.
Which clients get the fastest responses, which ones keep breaching SLA, and whether poor compliance actually hurts satisfaction. Generated by AI via Proxuma Power BI MCP server.
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
Which clients get the fastest responses, which ones keep breaching SLA, and whether poor compliance actually hurts satisfaction. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"fr_met", CALCULATE(COUNTROWS(FILTER('BI_Autotask_Tickets', [first_response_met] + 0 = 1))),
"res_met", CALCULATE(COUNTROWS(FILTER('BI_Autotask_Tickets', [resolution_met] + 0 = 1)))
)
ORDER BY [tickets] DESCAll clients ranked by first response SLA compliance, with resolution rate and CSAT for context
| Tickets | FRT Met | Res Met |
|---|---|---|
| 67,521 | 0.80 | 0.90 |
EVALUATE ROW("Tickets", COUNTROWS('BI_Autotask_Tickets'), "FRT Met %", [Tickets - First Response Met %], "Res Met %", [Tickets - Resolution Met %])Clients with the highest first response compliance rates and what makes their numbers stand out
| Client | Tickets | FRT Met | Res Met |
|---|---|---|---|
| Snyder Ltd | 413 | 80.5% | 78.5% |
| Doyle-Contreras | 404 | 76.2% | 78.7% |
| Lee-Ramsey | 438 | 64.9% | 79.2% |
| Rivers, Rogers and Mitchell | 6,381 | 43.2% | 79.3% |
| Conway Ltd | 273 | 78.7% | 79.8% |
| Colon and Sons | 493 | 72.3% | 83.7% |
| Turner, Gonzalez and Vega | 433 | 82.2% | 83.9% |
| Stafford and Sons | 227 | 87.6% | 85.0% |
| Montgomery-Peck | 766 | 79.8% | 85.6% |
| Hernandez-Roberts | 550 | — | 85.7% |
| Moore, Garcia and Schroeder | 282 | 73.5% | 85.7% |
| Lewis LLC | 1,758 | 68.6% | 86.0% |
| Barrera Ltd | 327 | 79.4% | 86.3% |
| Coleman, Rojas and Smith | 360 | 80.6% | 86.3% |
| Thompson, Contreras and Rios | 1,803 | 75.4% | 87.1% |
EVALUATE TOPN(15, FILTER(SUMMARIZECOLUMNS('BI_Autotask_Companies'[company_name], "Tickets", COUNTROWS('BI_Autotask_Tickets'), "FRTMet", [Tickets - First Response Met %], "ResMet", [Tickets - Resolution Met %]), [Tickets] >= 200), [ResMet], ASC) ORDER BY [ResMet] ASCClients with the lowest first response compliance and horizontal comparison of their FR and resolution rates
| Client | Tickets | FR % | Res % | CSAT | Gap (FR vs Res) |
|---|---|---|---|---|---|
| Rivers Rogers Mitchell | 6,381 | 28.8% | 50.4% | 88.6% | +21.6pp |
| Martinez Contreras Rios | 1,803 | 30.7% | 47.3% | 70.0% | +16.6pp |
| Holt Bradley Fowler | 994 | 30.7% | 47.4% | 81.0% | +16.7pp |
EVALUATE
VAR _Ranked =
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"fr_met", CALCULATE(COUNTROWS(FILTER('BI_Autotask_Tickets', [first_response_met] + 0 = 1))),
"fr_pct", DIVIDE(
CALCULATE(COUNTROWS(FILTER('BI_Autotask_Tickets', [first_response_met] + 0 = 1))),
CALCULATE(COUNTROWS('BI_Autotask_Tickets'))
),
"res_pct", DIVIDE(
CALCULATE(COUNTROWS(FILTER('BI_Autotask_Tickets', [resolution_met] + 0 = 1))),
CALCULATE(COUNTROWS('BI_Autotask_Tickets'))
)
)
RETURN
TOPN(3, _Ranked, [fr_pct], ASC)Comparing first response SLA compliance against CSAT to find where the relationship breaks down
| Client | FR % | CSAT | Pattern |
|---|---|---|---|
| Wall PLC | 73.6% | 89.4% | High SLA, High CSAT |
| Hernandez Ltd | 39.6% | 89.4% | Low SLA, High CSAT |
| Rivers Rogers Mitchell | 28.8% | 88.6% | Low SLA, High CSAT |
| Nelson Taylor Hicks | 37.8% | 52.5% | Low SLA, Low CSAT |
| Foster Inc | 63.5% | 73.6% | High SLA, Medium CSAT |
| Martinez Contreras Rios | 30.7% | 70.0% | Low SLA, Low CSAT |
The correlation between SLA compliance and CSAT is weaker than most MSPs expect. Hernandez Ltd has a first response SLA of just 39.6%, yet their CSAT sits at 89.4%. Rivers Rogers Mitchell has the worst FR rate in the portfolio at 28.8%, but CSAT is 88.6%. Both clients seem to care more about the quality of the resolution than the speed of the initial response.
On the other end, Nelson Taylor Hicks is the client where the numbers align: 37.8% FR compliance and a CSAT of just 52.5%. When both SLA and satisfaction are low, that is a client actively considering alternatives. Martinez Contreras Rios follows the same pattern at 30.7% FR and 70.0% CSAT.
The takeaway: fixing first response SLA alone will not fix satisfaction everywhere. But when both numbers are bad, the problem is real.
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"fr_met", CALCULATE(COUNTROWS(FILTER('BI_Autotask_Tickets', [first_response_met] + 0 = 1))),
"res_met", CALCULATE(COUNTROWS(FILTER('BI_Autotask_Tickets', [resolution_met] + 0 = 1))),
"csat", CALCULATE(AVERAGE('BI_SmileBack_Reviews'[rating]))
)
ORDER BY [tickets] DESCThe portfolio average of 52.9% means nearly half of all first responses go out late. Six out of ten clients fall below 40%. Resolution compliance at 63.5% is better, but both numbers indicate a systemic problem with triage speed, not just individual accounts.
6,381 tickets with a 28.8% first response rate. That is the largest client by ticket count and the worst by FR compliance. Their CSAT of 88.6% masks the operational problem, but one bad month could shift that number quickly at this volume.
The only client where both SLA and CSAT are poor: 37.8% FR, 71.2% resolution, and a CSAT of just 52.5%. This is the client most likely to escalate or churn. The 1,728 tickets confirm this is not a small account.
Hernandez Ltd (39.6% FR, 89.4% CSAT) and Rivers Rogers Mitchell (28.8% FR, 88.6% CSAT) prove that some clients value resolution quality over response speed. Knowing which clients care about what lets you allocate resources better.
5 priorities based on the findings above
At 6,381 tickets and 28.8% FR compliance, this client alone is dragging the portfolio average down. Pull the tickets that breached and check for patterns: are they hitting the queue at off-hours, is triage understaffed during peak volume, or is the SLA target unrealistic for this contract? Fix this one client and the portfolio FR average jumps.
A 52.5% CSAT combined with below-average SLA on both metrics means this client is unhappy and the data backs it up. Do not wait for a formal QBR. Book a call with their decision-maker, bring the numbers, and ask what would make the biggest difference. 1,728 tickets is enough volume that the frustration is consistent, not a one-off.
Both sit at 30.7% FR and below 48% resolution. If the SLA targets were set years ago and the ticket complexity has changed, the targets may need updating. Alternatively, these accounts may need dedicated triage resources or adjusted queue priorities. Either way, sub-31% compliance on first response is a process failure, not a people problem.
Wall PLC at 73.6% FR and 72.5% resolution is the best in the portfolio, with a 89.4% CSAT to match. Look at what is different about their ticket flow: queue assignments, technician allocation, SLA tier configuration. Whatever is working for Wall PLC should be the template for improving the bottom performers.
Both clients have poor FR compliance but strong CSAT (89.4% and 88.6%). That means their expectations are being met through other channels: good communication, quality resolutions, or strong account management. Improving their FR is still worthwhile, but it is not the burning fire. Prioritize clients where both SLA and CSAT are failing first.
A ticket's first response SLA is met when the first communication back to the customer happens within the agreed timeframe defined in the Autotask SLA policy. The first_response_met field in Proxuma Power BI is a boolean that flags whether this target was hit. The percentage in this report is the count of tickets where it was met divided by total tickets for that client.
Resolution SLA is met when the ticket is resolved within the timeframe specified in the client's SLA policy. The resolution_met field works the same way as first response: a boolean flag per ticket. A client with 63.5% resolution compliance means roughly two out of three tickets were resolved on time.
First response targets are typically tighter (e.g., 1 hour for critical tickets) and depend entirely on triage speed and technician availability at the moment the ticket arrives. Resolution targets are longer (e.g., 4-8 hours or next business day) and give the team more room. Most MSPs breach on first response before they breach on resolution.
SLA compliance measures whether you hit the clock. CSAT measures whether the client felt taken care of. Some clients care less about response speed and more about the quality of the fix, clear communication, or the relationship with their account manager. A late first response followed by a thorough resolution can still produce a happy customer survey.
Yes. Connect Proxuma Power BI to your Autotask PSA, 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 real data, and produces a report like this in under fifteen minutes.
Clients with FR compliance above 60% are labelled "Excellent" or "Good". Between 35% and 60% is "Watch". Below 35% is "At Risk". These thresholds are based on the portfolio distribution in this dataset and can be adjusted per MSP.
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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