CSAT vs SLA Performance Correlation

SmileBack reviews are linked to Autotask tickets through a relationship in the Power BI data model. Each review is associated with a ticket ID, which allows the model to filter SLA columns (first_response_met, resolution_met) by the rating attached to each review. The DAX query uses VALUES(BI_SmileBack_Reviews[rating]) to create rating-level filter context, which propagates through the relationship to the ticket table — enabling SLA rate calculations per satisfaction tier.

The data shows correlation, not causation. Tickets with negative CSAT ratings have lower SLA rates on average — but this doesn't prove that missing SLA caused the dissatisfaction. It could also mean that difficult or complex tickets (which are more likely to miss SLA) are also more likely to generate dissatisfied clients regardless of response time. The relationship is consistent enough to act on (improving SLA should improve CSAT on average), but individual negative reviews should also be analysed for root cause.

Work backwards from the data: positive-rated tickets average 86.5% first-response SLA. If your current overall first-response rate is, say, 79%, identify the queue or priority tier dragging it down. Setting a target to move that segment from 79% to 84% is a concrete, measurable goal that has a documented correlation with CSAT improvement. Pair this with a communication quality standard — response templates, technician notes — since SLA alone explains roughly half the CSAT variance.