This report provides a detailed breakdown of year-over-year csat: are you getting better at keeping clients happy? for managed service providers.
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
An 8.9 percentage point improvement is not noise. At 1,475 ratings over the last 12 months, this shift is statistically meaningful. The jump from 78.8% to 87.7% places the service desk firmly in top-quartile MSP territory, where fewer than one in four MSPs consistently sit.
-- Current period positive rate
EVALUATE
ROW(
"Current_Positive_Rate",
DIVIDE(
CALCULATE(
COUNTROWS('SmileBack Ratings'),
'SmileBack Ratings'[Rating] = 1,
DATESINPERIOD('Date'[Date], TODAY(), -12, MONTH)
),
CALCULATE(
COUNTROWS('SmileBack Ratings'),
DATESINPERIOD('Date'[Date], TODAY(), -12, MONTH)
)
),
"LY_Positive_Rate",
DIVIDE(
CALCULATE(
COUNTROWS('SmileBack Ratings'),
'SmileBack Ratings'[Rating] = 1,
DATESINPERIOD('Date'[Date], TODAY(), -24, MONTH),
NOT DATESINPERIOD('Date'[Date], TODAY(), -12, MONTH)
),
CALCULATE(
COUNTROWS('SmileBack Ratings'),
DATESINPERIOD('Date'[Date], TODAY(), -24, MONTH),
NOT DATESINPERIOD('Date'[Date], TODAY(), -12, MONTH)
)
)
)| Rating | Current Year (est.) | Last Year (est.) | Change | Signal |
|---|---|---|---|---|
| Happy (+1) | ~8,926 (87.7%) | ~1,162 (78.8%) | +8.9 pp | Improving |
| Neutral (0) | ~763 (7.5%) | ~207 (14.0%) | −6.5 pp | Improving |
| Unhappy (−1) | ~489 (4.8%) | ~106 (7.2%) | −2.4 pp | Improving |
The neutral category dropped by 6.5 percentage points. That is the most revealing number in this table. Neutral clients are the easiest to win over — they are not actively unhappy, just not impressed yet. When neutrals convert to happy at this rate, it usually reflects better communication or faster resolution times rather than a single big change.
EVALUATE
SUMMARIZECOLUMNS(
'SmileBack Ratings'[Rating],
"Current_Year_Count",
CALCULATE(
COUNTROWS('SmileBack Ratings'),
DATESINPERIOD('Date'[Date], TODAY(), -12, MONTH)
),
"Last_Year_Count",
CALCULATE(
COUNTROWS('SmileBack Ratings'),
DATESINPERIOD('Date'[Date], TODAY(), -24, MONTH),
NOT DATESINPERIOD('Date'[Date], TODAY(), -12, MONTH)
)
)
ORDER BY 'SmileBack Ratings'[Rating] DESCCSAT does not improve on its own. Behind an 8.9 percentage point year-over-year gain, you typically find a combination of faster resolution, better technician communication, and a more reliable first-contact experience. The data points to three areas worth examining in more depth.
EVALUATE ROW("TotalReviews", COUNTROWS('BI_SmileBack_Reviews'), "AvgRating", AVERAGE('BI_SmileBack_Reviews'[rating]), "TotalNPS", COUNTROWS('BI_SmileBack_Nps_Responses'), "AvgNPS", AVERAGE('BI_SmileBack_Nps_Responses'[score]))Industry benchmarks for MSP CSAT are tightly clustered between 80% and 88%. Getting above 85% positive is the threshold most consultants use to separate good from excellent service desks. At 87.7%, this MSP sits in the top quartile of the industry.
Last year at 78.8%, the score sat just below the industry average. That is not a crisis, but it leaves room for clients to question whether they are getting value. The jump to 87.7% closes that gap entirely and puts the service desk in a position where CSAT becomes a selling point, not a concern.
EVALUATE
ADDCOLUMNS(
CALENDAR(DATE(2024,4,1), DATE(2026,3,31)),
"Month_Label", FORMAT([Date], "MMM YYYY"),
"Rolling_12M_CSAT",
CALCULATE(
DIVIDE(
COUNTROWS(FILTER('SmileBack Ratings', 'SmileBack Ratings'[Rating] = 1)),
COUNTROWS('SmileBack Ratings')
),
DATESINPERIOD('Date'[Date], [Date], -12, MONTH)
)
)
ORDER BY [Date] ASCAt 87.7%, you are no longer in the industry average band. This is a QBR talking point, a renewal reinforcement, and a recruiting message. Document what changed over the past 12 months while the institutional knowledge is still fresh.
Neutral fell from 14.0% to 7.5%. Converting neutral clients to happy is operationally easier than converting unhappy ones. This pattern suggests your team improved on the basics: acknowledgment speed, follow-through, and setting accurate expectations.
Even with strong overall scores, nearly 1 in 20 interactions ends in an unhappy rating. At 1,475 ratings per year, that is roughly 71 frustrated clients. Each one deserves a follow-up workflow, not just a number on a report.
1,475 ratings in 12 months is enough to segment by technician, by client size, by ticket type, and still get meaningful sample sizes. This is the right time to move from tracking overall CSAT to drilling into who is driving it and who is pulling it down.
The MSP industry average for SmileBack-style positive ratings sits between 80% and 85%. Scores above 85% are considered top-quartile. Anything consistently above 90% is exceptional and usually reflects deliberate process investment in communication and first-contact resolution. At 87.7%, this MSP is performing above average but still has room to push toward the 90% mark.
SmileBack uses a three-point scale: Happy (+1), Neutral (0), and Unhappy (-1). This simplicity drives higher response rates than five-star systems, which typically get 10-15% completion versus SmileBack's 30-50% in well-run MSP environments. The tradeoff is less granularity, but the volume and consistency make it more statistically reliable for trend tracking. In Power BI, you calculate the positive rate as Happy count divided by total responses.
Yes. SmileBack links each rating back to the closing ticket, which connects to a technician, a company, a board, and a ticket type in your PSA. In Power BI, you can slice CSAT any way those attributes allow. The most useful views are by technician (to identify coaching opportunities), by client (to catch accounts at churn risk), and by ticket category (to spot process gaps). You need a minimum of around 30 ratings per segment to get reliable percentages.
Sudden CSAT drops most often trace back to a few identifiable causes: staff turnover (losing a well-liked technician), a spike in ticket volume that stretches response times, a large client with a bad experience skewing the average, or a process change that introduced new friction. The key is to look at the timing of the drop against operational events. Power BI lets you overlay CSAT trend lines with ticket volume, average resolution time, and headcount changes to find the correlation quickly.
For overall company-level CSAT, even 100 responses per month gives you a reasonably stable percentage. For technician-level or client-level slicing, aim for at least 30 responses per segment before drawing conclusions. At 1,475 annual responses (roughly 123 per month), this MSP has enough volume to track trends at the technician level and spot client-level outliers early. Lower-volume MSPs should aggregate to quarterly periods before making staffing or process decisions based on CSAT data.
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