AI-generated loyalty analysis from SmileBack NPS surveys and CSAT ticket data
AI-generated loyalty analysis from SmileBack NPS surveys and CSAT ticket data
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
AI-generated loyalty analysis from SmileBack NPS surveys and CSAT ticket data
Key NPS and satisfaction metrics at a glance.
EVALUATE ROW("CSATAvg", [CSAT - Average Rating], "Ratings", [CSAT - Total Ratings])How responses split across Promoters, Passives, and Detractors.
NPS = % Promoters minus % Detractors = 16.9% − 37.9% = -21.0
EVALUATE
VAR AllResponses = COUNTROWS('BI_SmileBack_Nps_Responses')
VAR Promoters = CALCULATE(COUNTROWS('BI_SmileBack_Nps_Responses'), 'BI_SmileBack_Nps_Responses'[score] >= 9)
VAR Detractors = CALCULATE(COUNTROWS('BI_SmileBack_Nps_Responses'), 'BI_SmileBack_Nps_Responses'[score] <= 6)
VAR Passives = AllResponses - Promoters - Detractors
VAR NPS = DIVIDE(Promoters - Detractors, AllResponses) * 100
VAR AvgScore = AVERAGE('BI_SmileBack_Nps_Responses'[score])
RETURN ROW(
"Total", AllResponses,
"Promoters", Promoters,
"Passives", Passives,
"Detractors", Detractors,
"PromoterPct", DIVIDE(Promoters, AllResponses) * 100,
"PassivePct", DIVIDE(Passives, AllResponses) * 100,
"DetractorPct", DIVIDE(Detractors, AllResponses) * 100,
"NPS", NPS,
"AvgScore", AvgScore
)Response count for each score value (0 through 10).
EVALUATE
ADDCOLUMNS(
VALUES('BI_SmileBack_Nps_Responses'[score]),
"Count", CALCULATE(COUNTROWS('BI_SmileBack_Nps_Responses'))
)
ORDER BY 'BI_SmileBack_Nps_Responses'[score]Performance comparison across survey campaigns.
| Campaign | Responses | Avg. Score | Promoters | Detractors | NPS |
|---|---|---|---|---|---|
| Q4 Annual Survey | 99 | 6.72 | 19.2% | 27.3% | -8.1 |
| Mid-Year Check-in | 83 | 6.95 | 20.5% | 25.3% | -4.8 |
| Service Review v3 | 52 | 5.35 | 13.5% | 55.8% | -42.3 |
| Service Review | 21 | 4.71 | 4.8% | 81.0% | -76.2 |
| Welcome/Exit | 16 | 5.63 | 6.3% | 56.3% | -50.0 |
Comparing ticket-level satisfaction with long-term loyalty.
SmileBack collects two types of feedback: CSAT ratings on individual tickets (thumbs up/neutral/down) and NPS surveys sent periodically via campaigns. Comparing the two reveals whether day-to-day satisfaction translates into long-term loyalty.
The gap is significant: 92% of ticket interactions end positively, yet the NPS score sits at -21. This tells us that clients are satisfied with individual ticket resolutions but hold broader concerns about the overall relationship. The 45% passive segment (scoring 7-8) represents the biggest opportunity: they are not unhappy, but they are not enthusiastic either.
What each score range means for client behavior and retention.
| Score Range | Category | Count | % of Total | Typical Behavior |
|---|---|---|---|---|
| 9 - 10 | Promoter | 46 | 16.9% | Actively recommends your MSP. Likely to renew and expand. |
| 7 - 8 | Passive | 123 | 45.2% | Satisfied but not loyal. Vulnerable to competitor offers. |
| 0 - 6 | Detractor | 103 | 37.9% | At risk of churn. May share negative experiences publicly. |
The data paints a clear picture: operational satisfaction is high, but strategic loyalty is low. With a CSAT positive rate of 92.2%, day-to-day ticket work meets expectations. The NPS of -21, on the other hand, signals that clients do not feel strongly enough to recommend the service.
The core issue sits in the passive segment. At 45.2% of all responses, this group scored 7 or 8. They are not dissatisfied. They are simply indifferent. Converting even a third of passives into promoters would shift the NPS from -21 to roughly +3, crossing into positive territory.
Campaign data adds context. The most recent surveys ("Q4 Annual Survey" at -8.1 and "Mid-Year Check-in" at -4.8) show improvement over older campaigns like "Service Review" at -76.2. This suggests that service quality has been trending upward, but legacy detractor responses still weigh down the overall score.
The recommended path forward: focus on the 123 passive respondents. Proactive account reviews, quarterly business reviews, and personalized follow-ups on ticket resolutions can move these clients from "fine" to "great."
Prioritized recommendations based on the NPS data.
123 clients scored 7-8. Schedule quarterly business reviews with each, focusing on their specific pain points. A 30% conversion rate would bring NPS above zero.
The "Service Review" (NPS -76.2) and "Welcome/Exit" (NPS -50.0) campaigns carry the worst scores. Analyze what changed between these and the newer, better-performing campaigns.
Only the score is captured. Adding a required comment field for scores below 7 would provide qualitative context for each detractor response.
The 92.2% positive CSAT rate is strong. Maintaining and publicizing this during account reviews reinforces the operational quality that NPS alone does not capture.
NPS equals the percentage of Promoters (score 9-10) minus the percentage of Detractors (score 0-6). Scores of 7-8 are classified as Passives and do not affect the calculation directly. The result ranges from -100 to +100.
Industry benchmarks vary, but most IT service providers consider an NPS above +30 to be strong and above +50 to be excellent. A negative NPS (below zero) indicates more detractors than promoters and typically signals retention risk.
CSAT measures satisfaction with individual tickets. NPS measures overall loyalty and willingness to recommend. Clients can be satisfied with how tickets are resolved but still feel that the broader relationship lacks proactive communication, strategic value, or competitive pricing.
Most MSPs run NPS surveys quarterly or biannually. Running them too frequently causes survey fatigue and lower response rates. The key is consistency: same cadence, same audience segmentation, so you can track trends over time.
This report uses SmileBack NPS survey responses (BI_SmileBack_Nps_Responses) for the 0-10 scores, SmileBack CSAT ticket reviews (BI_SmileBack_Reviews) for the positive/neutral/negative ratings, and Autotask PSA for client and ticket context.
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