A data-driven analysis of csat trend over time from your Power BI environment, with breakdowns and actionable findings.
This report analyzes csat trend over time using data from Autotask PSA, SmileBack CSAT.
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 managers, account managers, and MSP leadership tracking customer experience
How often: Weekly for trend monitoring, monthly for team reviews, quarterly for QBRs
A data-driven analysis of csat trend over time from your Power BI environment, with breakdowns and actionable findings.
EVALUATE ROW("CSATAvg", [CSAT - Average Rating], "CSATLastYear", [CSAT - Average Rating - Last Year], "Ratings", [CSAT - Total Ratings])Customer satisfaction scores and response rates
| Metric | Value |
|---|---|
| Average Score | 88% |
| Total Responses | 10,178 |
| Positive (≥80%) | 9,385 |
EVALUATE ROW("AvgScore", AVERAGE('BI_SmileBack_Reviews'[rating]), "Total", COUNTROWS('BI_SmileBack_Reviews'), "Positive", CALCULATE(COUNTROWS('BI_SmileBack_Reviews'), 'BI_SmileBack_Reviews'[rating] >= 0.8))Clients 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)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.
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 trend over time metrics. Trends matter more than snapshots. Use the DAX queries in this report as your starting point.
This report uses demo data. Connect Proxuma Power BI to your own Autotask PSA to generate this analysis from your real numbers.
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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