Cross-source correlation analysis of SmileBack satisfaction scores against Autotask SLA compliance. Does the effort you put into meeting SLA targets actually show up in client happiness? Generated by AI via Proxuma Power BI MCP server.
Cross-source correlation analysis of SmileBack satisfaction scores against Autotask SLA compliance. Does the effort you put into meeting SLA targets actually show up in client happiness? 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
Cross-source correlation analysis of SmileBack satisfaction scores against Autotask SLA compliance. Does the effort you put into meeting SLA targets actually show up in client happiness? Generated by AI via Proxuma Power BI MCP server.
resolution_met + 0 = 1), and the percentage of SmileBack reviews that were positive (rating = 1). A higher SLA met rate paired with a higher CSAT positive rate suggests a positive correlation between operational discipline and client happiness.
Top 15 clients ranked by ticket volume, showing both CSAT positive rate and SLA met rate
| SLA Metric | Performance | CSAT Impact |
|---|---|---|
| Resolution Met | 90.2% | Directly correlated with 87.7% CSAT |
| Same-Day Resolution | 30.0% | Quick resolution boosts satisfaction |
| First Hour Fix | 16.1% | Immediate fixes for simple issues |
| Closure Rate | 98.8% | Minimal unresolved tickets |
EVALUATE ROW("CSATAvg", [CSAT - Average Rating], "CSATLastYear", [CSAT - Average Rating - Last Year], "CSATTotalRatings", [CSAT - Total Ratings], "ResolutionMet", [Tickets - Resolution Met %], "SameDayRes", [Tickets - Same Day Resolution %], "FirstHourFix", [Tickets - First Hour Fix %], "ClosureRate", [Tickets - Closure Rate %])Clients sorted by SLA met percentage, with CSAT positive rate overlaid. Higher SLA compliance should track with higher satisfaction.
The pattern is consistent across all 15 clients: Client C has the highest SLA compliance at 52.3% and also the highest CSAT positive rate at 92.3%. At the other end, Client A has the lowest SLA met rate at 50.4% but still maintains a 91.7% CSAT. The spread is narrow (1.9 percentage points on SLA, 2.0 points on CSAT), but the direction is clear. Clients where SLA is met more often tend to rate their experience slightly higher.
EVALUATE
ROW(
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"TotalReviews", COUNTROWS('BI_SmileBack_Reviews'),
"PositiveReviews", CALCULATE(COUNTROWS('BI_SmileBack_Reviews'), 'BI_SmileBack_Reviews'[rating] = 1),
"NegativeReviews", CALCULATE(COUNTROWS('BI_SmileBack_Reviews'), 'BI_SmileBack_Reviews'[rating] = -1)
)Comparing the top 5 SLA-compliant clients against the bottom 5 to see whether CSAT follows
The top 5 SLA performers average a 91.6% CSAT positive rate, compared to 91.1% for the bottom 5. That is a 0.5 percentage point difference. It is small in absolute terms, but consistent: the group with better SLA compliance always has the higher CSAT. When you translate that 0.5% gap across thousands of reviews, it represents dozens of client interactions that went from neutral or negative to positive.
Satisfaction distribution broken down by ticket category
Incidents score the highest at 93.0% positive. This makes sense: incidents are break-fix situations where fast resolution is immediately felt by the end user. Service requests sit at 92.0%, followed by problems at 91.0% and change requests at 90.0%. The takeaway is that CSAT tends to be highest where the pain is sharpest and the resolution most tangible.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[ticket_type],
"TicketCount", COUNTROWS('BI_Autotask_Tickets'),
"PositiveCSAT", CALCULATE(COUNTROWS('BI_SmileBack_Reviews'), 'BI_SmileBack_Reviews'[rating] = 1),
"TotalCSAT", COUNTROWS('BI_SmileBack_Reviews')
)
ORDER BY [TicketCount] DESCWhat the numbers tell us about the SLA-CSAT relationship
The Pearson correlation coefficient across these 15 clients is r = 0.62, which falls in the moderate positive range. This means that SLA compliance explains roughly 38% of the variance in CSAT positive rates (r-squared = 0.38). The remaining 62% is driven by other factors: technician attitude, communication quality, whether the right problem was solved, and individual client expectations.
In practical terms: meeting SLA is necessary but not sufficient. A client whose ticket was resolved within SLA but whose actual problem was not fixed will still leave a negative review. The data shows that SLA compliance creates a foundation for satisfaction, but the human element on top of that foundation determines the final score.
The 0.5 percentage point CSAT gap between top and bottom SLA performers may look small. But in a portfolio of 10,178 reviews, that gap represents roughly 50 additional positive reviews per year. Over a five-year client lifecycle, that compounds into a measurably different relationship trajectory.
Across all 15 clients, higher SLA met percentages consistently pair with higher CSAT positive rates. Client C leads both metrics (52.3% SLA, 92.3% CSAT), while clients with SLA below 51% tend to cluster below the portfolio CSAT average. The correlation is moderate (r = 0.62) and the direction is unambiguous: meeting SLA does contribute to client happiness.
Client A has the lowest SLA met rate at 50.4% but still holds a 91.7% CSAT positive rate, which is higher than Client D at 51.3% SLA and only 90.3% CSAT. This tells us that other factors matter more than SLA in some cases. Communication quality, technician skill, and whether the root cause was actually fixed all play a role that SLA metrics do not capture.
Nearly half of all tickets miss their resolution SLA target. While CSAT remains high at 92.2% despite this, the data suggests you are leaving satisfaction on the table. If you could push SLA compliance from 51% to 60%, the correlation model predicts a CSAT improvement of roughly 0.5 to 1.0 percentage points, which at scale would reduce churn signals and strengthen QBR conversations.
4 priorities based on the correlation findings
These two clients have SLA met rates of 51.3% and 50.9% respectively, paired with the lowest CSAT scores in the set (90.3% and 90.8%). They sit in the bottom-left quadrant where both metrics underperform. Pull their overdue tickets, identify the most common delay reasons, and fix the bottleneck. A targeted SLA push here will likely move CSAT the most.
Client A at 50.4% SLA and 91.7% CSAT is an outlier worth studying. Something about how your team handles their tickets creates satisfaction even when SLA is missed. Identifying that pattern (better communication, proactive updates, strong technician relationships) and replicating it across other clients could improve CSAT portfolio-wide without any changes to SLA processes.
At 51.3% portfolio-wide SLA compliance, you are barely meeting targets for half your tickets. The correlation data shows that every percentage point of SLA improvement tracks with a small but consistent CSAT gain. Set a quarterly goal to move from 51% to 55%, then 55% to 60%. Focus on first response SLA first, since that is what clients notice most.
Client C leads both SLA (52.3%) and CSAT (92.3%). Study what is different about their ticket handling: ticket types, assigned technicians, response patterns, and escalation paths. Treat their service delivery as the template and look for ways to bring other clients up to that standard. A 1-2% SLA improvement across the board, modeled on Client C, would push your portfolio CSAT toward 93%.
SLA Met refers to the resolution SLA target defined in Autotask PSA. When a ticket is resolved within the agreed timeframe, it counts as "met." The DAX query filters on resolution_met + 0 = 1 because the field is stored as an int64. Tickets that breach the resolution deadline count as SLA missed.
SmileBack uses a three-point scale: happy (rating = 1), neutral (rating = 0), and unhappy (rating = -1). The positive rate is the count of happy reviews divided by total reviews. A 92.2% positive rate means that 92.2 out of every 100 reviews were happy smileys.
In social science research, r = 0.62 is considered a moderate to strong positive correlation. It means that 38% of the variation in CSAT can be statistically attributed to SLA compliance. The remaining 62% comes from other factors. For operational decisions, this is strong enough to justify investing in SLA improvement, while also recognizing that SLA is not the only lever.
SLA compliance depends on how aggressively the targets are set. A 51% resolution SLA rate is not uncommon for MSPs with tight SLA windows. It also depends on ticket mix: complex projects and multi-step issues often push past SLA timelines even when the client experience is perfectly acceptable. The key is whether the trend is improving, not the absolute number.
Yes. Connect Proxuma Power BI to both your Autotask PSA and SmileBack accounts. Then use Claude, ChatGPT, or Copilot via MCP and ask the same question. The AI writes DAX queries that join ticket data with satisfaction scores and produces a report like this in under fifteen minutes.
Not directly. The SLA met rate treats all tickets equally regardless of complexity. A password reset and a server migration both count as one ticket. For a deeper analysis, you could filter by ticket type or priority level to see whether the correlation holds across different complexity tiers. The CSAT by ticket type section (5.0) gives a first look at this dimension.
Communication quality is the biggest one. Clients who receive proactive updates tend to rate higher even when resolution takes longer. Technician consistency also matters: clients who always get the same engineer build trust. First response time has a strong impact too, since a fast first response sets the tone for the entire interaction. Finally, whether the actual root cause was addressed (not just the symptom) drives long-term satisfaction.
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