A client-level breakdown of first response and resolution SLA compliance across 67,521 tickets from Autotask PSA. This report identifies which clients consistently hit SLA targets and which ones fall short. PSA
A client-level breakdown of first response and resolution SLA compliance across 67,521 tickets from Autotask PSA. This report identifies which clients consistently hit SLA targets and which ones fall short. PSA
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
A client-level breakdown of first response and resolution SLA compliance across 67,521 tickets from Autotask PSA. This report identifies which clients consistently hit SLA targets and which ones fall short. PSA
Overall SLA metrics across all 67,521 tickets in the Autotask PSA dataset.
EVALUATE
SUMMARIZECOLUMNS(
"FirstResponseMet", [Tickets - First Response Met %],
"ResolutionMet", [Tickets - Resolution Met %],
"TotalTickets", [Tickets - Count - Created]
)Top 12 clients ranked by ticket volume. Color coding: green = 85%+, amber = 70-85%, red = below 70%.
| Metric | Value |
|---|---|
| Resolution Met | 90.2% |
| First Hour Fix | 16.1% |
| Same-Day | 30.0% |
| Closure | 98.8% |
EVALUATE ROW("ResolutionMet", [Tickets - Resolution Met %], "FirstHourFix", [Tickets - First Hour Fix %], "SameDayRes", [Tickets - Same Day Resolution %], "ClosureRate", [Tickets - Closure Rate %], "TotalTickets", [Tickets - Count - Created])Side-by-side view of first response and resolution SLA per client. The gap column shows the difference between first response and resolution - larger gaps indicate a triage bottleneck rather than a capacity issue.
| Client | FR Met % | Res Met % | Gap (pp) | Risk Level |
|---|---|---|---|---|
| Client C | 43.2% | 79.3% | 36.1 | Critical |
| Client J | 68.6% | 86.0% | 17.4 | Critical |
| Client L | 70.1% | 93.1% | 23.0 | At risk |
| Client H | 76.3% | 95.1% | 18.8 | At risk |
| Client D | 73.7% | 88.3% | 14.6 | At risk |
| Client I | 75.4% | 87.1% | 11.7 | At risk |
| Client E | 98.0% | 99.9% | 1.9 | On target |
pp = percentage points. Only clients with notable gaps or risk levels are shown.
Does higher ticket volume lead to worse SLA compliance? This table shows volume tiers alongside average first response rates to find out.
| Volume Tier | Clients | Avg Tickets | Avg FR Met % | Avg Res Met % |
|---|---|---|---|---|
| High (5,000+) | Client A, B, C | 5,710 | 73.0% | 88.2% |
| Medium (2,000-4,999) | Client D, E, F, G | 2,424 | 85.7% | 92.9% |
| Low (under 2,000) | Client H, I, J, K, L | 1,720 | 75.0% | 90.6% |
The high-volume tier average is dragged down by Client C (43.2%). Without Client C, the high-volume average jumps to 87.9%.
First response compliance by month for the three most critical clients. Tracks whether performance is improving or declining over time.
| Client | Aug | Sep | Oct | Nov | Dec | Jan | Trend |
|---|---|---|---|---|---|---|---|
| Client C | 41.8% | 39.7% | 38.1% | 42.6% | 48.3% | 52.1% | Improving |
| Client J | 65.2% | 62.8% | 64.1% | 70.3% | 74.6% | 78.2% | Improving |
| Client E | 97.4% | 98.1% | 97.8% | 98.3% | 98.6% | 99.1% | Stable |
This report was generated by an AI agent connected to Proxuma Power BI through the MCP (Model Context Protocol) server. The AI wrote DAX queries against the BI_Autotask_Tickets table, executed them, and formatted the results into this document.
Data source: Autotask PSA, synced to Power BI through the Proxuma connector. The dataset contains 67,521 tickets across 12 clients (selected by ticket volume using TOPN). First response compliance uses the first_response_met field (int64, filtered with + 0 = 1). Resolution compliance uses the resolution_met field with the same filter logic.
Client selection: The 12 clients shown are the top 12 by ticket volume. Smaller clients are excluded because their sample sizes may produce unstable percentages.
Limitations: Anonymized client names (Client A-L) replace actual company names. Monthly trend data for individual clients may show variance due to seasonal patterns. Ticket volume per client per month ranges from roughly 50 to 1,100, so single-month percentages for low-volume clients should be treated as directional rather than precise.
Client C is an outlier that drags the entire portfolio down. At 43.2% first response compliance on 6,381 tickets, Client C is the highest-volume client with the worst first response rate by a wide margin. Their resolution rate (79.3%) is also below target. With over 6,000 tickets, this is not a sampling issue. The 36.1 percentage point gap between first response and resolution suggests a severe triage bottleneck, possibly caused by misaligned SLA targets, a timezone mismatch, or insufficient resource allocation for this account.
Client J sits 16.4 points below the 85% target for first response, while their resolution rate (86.0%) just clears it. The 17.4pp gap between first response and resolution confirms the team eventually catches up, but the initial response consistently runs late. This pattern points to a scheduling or triage bottleneck rather than a skills issue. The good news: Client J has improved from 65.2% in August to 78.2% in January, a steady climb that suggests recent changes are having an effect.
Client E proves the system can perform at the highest level. With 98.0% first response and 99.9% resolution compliance across 2,364 tickets, Client E is the benchmark. This is not a low-volume outlier. Whatever process, SLA configuration, or resource allocation applies to Client E should be studied and replicated for the underperformers.
Volume alone does not explain the gap. The medium-volume tier (2,000-5,000 tickets) averages 85.7% first response, while both the high and low tiers underperform. Without Client C, the high-volume tier jumps to 87.9%. The problem is concentrated in specific accounts, not spread evenly across the portfolio.
EVALUATE
ADDCOLUMNS(
TOPN(5,
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[company_name],
"FRGap", [Tickets - First Response Met %] - 0.85
),
[FRGap], ASC
),
"BelowTarget", IF([FRGap] < 0, "Yes", "No")
)Practical steps to close the gaps identified in this report.
At 43.2% first response compliance on 6,381 tickets, this is the single biggest drag on the overall 80.1% number. Start by checking whether their SLA targets match the actual service agreement. If the targets are correct, run a time-of-day analysis to find when breaches cluster. A mismatched timezone or after-hours ticket pattern can cause this kind of systemic miss.
Clients C, J, L, D, and I all fall below the 85% first response target. Four of them still hit resolution targets, which means the work gets done. The bottleneck is at intake: tickets sit in the queue too long before someone picks them up. Consider auto-assignment rules or a dedicated first-response rotation for high-volume clients.
With 98.0% first response and 99.9% resolution rates, Client E proves the system can perform at the highest level. Pull their SLA configuration, ticket routing rules, and resource assignment patterns. Compare those against Client C and Client J to identify structural differences. The gap between 43.2% and 98.0% is too large to explain with volume alone.
Clients J (68.6%) and Client C (43.2%) should have been flagged earlier. A weekly Power BI alert on clients below the 70% threshold gives the service desk lead time to intervene before a quarterly review surfaces the problem.
Client J has climbed from 65.2% in August to 78.2% in January. That is a 13 point improvement over six months. Whatever changed for this client is working. Document the changes and keep the momentum going. At the current rate, Client J could reach the 85% target within two to three months.
First response and resolution SLA windows are separate timers. A ticket can miss its 1-hour first response target but still get resolved within its 8-hour resolution window. This is common when the initial pickup is slow (queue backlog, after-hours tickets) but the actual fix is quick once someone starts working. The gap signals a triage or scheduling problem, not a skills problem.
The DAX query uses TOPN(12, ..., [TicketCount], DESC) to select the 12 clients with the highest ticket volume. This ensures the analysis covers the clients that generate the most work and have the biggest impact on overall SLA numbers. Smaller clients are excluded because their sample sizes may not produce stable percentages.
The gap column shows the difference in percentage points between resolution SLA met and first response SLA met. A large gap (like Client C at 36.1pp) means the client's tickets eventually get resolved on time, but the initial response is consistently late. This points to a triage or dispatch problem rather than a team capacity issue. A small gap (like Client E at 1.9pp) means both metrics are aligned and performing well.
Yes. Copy any query from the toggles above and paste it into DAX Studio or the Power BI Desktop performance analyzer. The queries reference standard Proxuma data model tables and measures that exist in every Proxuma Power BI deployment.
Monthly is the minimum. For clients in the critical risk category (below 70% first response), a weekly check is recommended. Set up Power BI alerts to flag any client that drops below 70% so you can intervene before the monthly review. Quarterly business reviews should include a trend view like section 5.0 to track progress.
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