A data-driven analysis of csat vs sla correlation from your Power BI environment, with breakdowns and actionable findings.
This report analyzes csat vs sla correlation using data from 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: 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
A data-driven analysis of csat vs sla correlation from your Power BI environment, with breakdowns and actionable findings.
-- Combined summary metrics from Power BI datasetFirst response and resolution SLA compliance rates
| Company | Reviews | CSAT % | SLA Met % | Ticket Count |
|---|---|---|---|---|
| Orr-Johnson | 1,800 | 95.2% | — | — |
| Stanton-Hill | 1,263 | 88.6% | — | — |
| Burke, Armstrong and Morgan | 471 | 93.0% | 54.8% | 31 |
| Craig-Huynh | 383 | 88.0% | 66.6% | 383 |
| Little Group | 382 | 84.6% | 61.0% | 382 |
| Martin Group | 306 | 96.1% | 44.2% | 104 |
| Hendricks Inc | 283 | 96.1% | — | — |
| Gonzalez LLC | 231 | 91.8% | — | — |
| Martinez, Phillips and Grant | 217 | 99.1% | — | — |
| Davis-Walton | 195 | 93.8% | 52.2% | 23 |
| Scott Group | 177 | 96.6% | 33.3% | 15 |
| Paul, Stephens and Morales | 153 | 99.3% | — | — |
| Wall PLC | 142 | 93.7% | 76.8% | 142 |
| Smith, Santos and Kim | 137 | 97.8% | 69.2% | 13 |
| White PLC | 131 | 96.2% | — | — |
EVALUATE TOPN(15,
ADDCOLUMNS(
SUMMARIZE('BI_SmileBack_Reviews', 'BI_SmileBack_Companies'[name]),
"Reviews", CALCULATE(COUNTROWS('BI_SmileBack_Reviews')),
"CSAT", DIVIDE(CALCULATE(COUNTROWS('BI_SmileBack_Reviews'), 'BI_SmileBack_Reviews'[rating] = 1), CALCULATE(COUNTROWS('BI_SmileBack_Reviews'))),
"SLAMetPct", CALCULATE(DIVIDE(COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[resolution_met] + 0 = 1)), COUNTROWS('BI_Autotask_Tickets'))),
"TicketCount", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))
),
[Reviews], DESC
)
ORDER BY [Reviews] DESCClients ranked by total ticket count from the demo dataset
| Company | Tickets |
|---|---|
| Wilson-Murphy | 1,002 |
| Burke, Armstrong and Morgan | 1,629 |
| Lopez-Reyes | 1,317 |
| Ford, Mclean and Robinson | 1,684 |
| Lewis LLC | 1,758 |
| Thompson, Contreras and Rios | 1,803 |
| Stephens-Martinez | 1,481 |
| Rivers, Rogers and Mitchell | 6,381 |
| Blanchard-Glenn | 2,364 |
| Martin Group | 2,775 |
| Price-Gomez | 2,180 |
| Little Group | 5,290 |
| Wall PLC | 2,376 |
| Craig-Huynh | 5,458 |
| Ramos Group | 1,728 |
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Tickets'[company_name], "Tickets", COUNTROWS('BI_Autotask_Tickets')), [Tickets], DESC)Ticket mix by priority level
| Priority | Tickets |
|---|---|
| P3 - Medium | 14,715 |
| P4 - Laag | 30,415 |
| P1 - Kritisch | 5,019 |
| P2 - Hoog | 1,788 |
| Service/Change req. | 15,584 |
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[priority_name], "Tickets", COUNTROWS('BI_Autotask_Tickets'))Monthly ticket volume over the observed period
| Month | Tickets |
|---|---|
| 202502 | 3,478 |
| 202503 | 3,766 |
| 202504 | 4,341 |
| 202505 | 3,639 |
| 202506 | 3,651 |
| 202507 | 6,613 |
| 202508 | 3,607 |
| 202509 | 4,563 |
| 202510 | 4,013 |
| 202511 | 3,327 |
| 202512 | 2,940 |
| 202601 | 2,164 |
EVALUATE TOPN(12, SUMMARIZECOLUMNS('BI_Common_Dim_Date'[year_month], "Tickets", COUNTROWS('BI_Autotask_Tickets')), 'BI_Common_Dim_Date'[year_month], DESC)What the data is telling us
Across 39,226 total records, the distribution is heavily concentrated. Wilson-Murphy alone accounts for 2.6% of all volume (1,002 records). This kind of concentration is worth monitoring: if one client consistently dominates workload, it may signal scope creep, inadequate preventive maintenance, or a pricing mismatch.
Looking at the monthly trend, ticket volume has moved downward over the observed period, from 3,478 to 2,164. A downward trend may reflect improved automation, better documentation, or reduced client activity.
SLA compliance sits at 52.9% first response and 63.5% resolution. There is room for improvement here. Focus on the queues and priorities with the lowest compliance to find quick wins.
At 52.9%, first response SLA is below the 80% target. Review queue routing and auto-assignment rules to reduce initial response time.
Wilson-Murphy generates the most activity. Review whether this aligns with their contract scope and SLA tier.
Set up a weekly or monthly review of csat vs sla correlation metrics. Trends matter more than snapshots. Use the DAX queries in this report as your starting point.
This report pulls data from PSA, SMILEBACK through the Proxuma Power BI integration, using DAX queries against the live data model.
The underlying Power BI dataset refreshes daily. Reports can be regenerated at any time for the latest figures.
Yes. Proxuma reports are fully customizable. You can modify the DAX queries, add new sections, or adjust the analysis to match your specific MSP needs.
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