A side-by-side comparison of automated RMM alert tickets and manually created tickets across volume, effort, and customer satisfaction. Data sourced from Datto RMM and Autotask PSA through Proxuma Power BI. RMM
A side-by-side comparison of automated RMM alert tickets and manually created tickets across volume, effort, and customer satisfaction. Data sourced from Datto RMM and Autotask PSA through Proxuma Power BI. RMM
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 desk managers, dispatch leads, and operations teams
How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning
A side-by-side comparison of automated RMM alert tickets and manually created tickets across volume, effort, and customer satisfaction. Data sourced from Datto RMM and Autotask PSA through Proxuma Power BI. RMM
Incidents make up the largest share of your ticket volume at 41.0%, followed by RMM alerts at 29.3%. Service requests account for 18.7% and change requests for 10.7%. Problem tickets are rare at just 167 total.
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[source_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'), "AvgResolutionHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]))Alert tickets require the least effort at 0.74 average billable hours, making them 18% faster to close than incidents (0.90 hrs) and 33% faster than service requests (1.10 hrs). Problem tickets sit at the other end of the spectrum at 1.43 hours, but with only 167 tickets they have minimal impact on total workload.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[ticket_type],
"TicketCount", COUNTROWS('BI_Autotask_Tickets'),
"AvgBillableHrs", AVERAGE('BI_Autotask_Tickets'[billable_hours])
)
ORDER BY [AvgBillableHrs] DESCAlert tickets show a 93.7% positive CSAT rate (74 of 79 responses), compared to 86.4% for incidents and 89.2% for service requests. The catch: only 0.4% of alert tickets generate a CSAT response, while 5.1% of incidents do. With just 79 total CSAT responses on 19,790 alert tickets, the sample is too small to draw firm conclusions.
Your Datto RMM generated 135,387 total alerts. Of those, 132,018 (97.5%) resolved automatically with an average auto-resolve time of just 5.45 minutes. Only 19,790 alerts turned into Autotask tickets. That means roughly 14.6% of all RMM alerts required a ticket, and the rest were handled by the system without human involvement.
EVALUATE
ROW(
"TotalAlerts", COUNTROWS('BI_Datto_Rmm_Alerts'),
"ResolvedAlerts", CALCULATE(COUNTROWS('BI_Datto_Rmm_Alerts'), 'BI_Datto_Rmm_Alerts'[resolved] = TRUE()),
"TotalSites", DISTINCTCOUNT('BI_Datto_Rmm_Alerts'[site_name]),
"AvgAutoResolve", AVERAGE('BI_Datto_Rmm_Alerts'[autoresolve_mins])
)
-- Alert Priority Distribution
EVALUATE
SUMMARIZECOLUMNS(
'BI_Datto_Rmm_Alerts'[priority],
"AlertCount", COUNTROWS('BI_Datto_Rmm_Alerts'),
"ResolvedCount", CALCULATE(COUNTROWS('BI_Datto_Rmm_Alerts'), 'BI_Datto_Rmm_Alerts'[resolved] = TRUE()),
"AvgAutoResolve", AVERAGE('BI_Datto_Rmm_Alerts'[autoresolve_mins])
)
ORDER BY [AlertCount] DESCWhile alert tickets account for 29.3% of ticket volume, they only consume 22.8% of total billable effort. Incidents dominate both volume (41.0%) and effort (38.7%). Service requests punch above their weight in effort, taking 18.7% of tickets but 21.6% of hours. This confirms that automation is pulling its weight: alert tickets generate volume without a proportional effort cost.
| Ticket Type | Tickets | % Volume | Avg Hours | Total Hours | % Effort |
|---|---|---|---|---|---|
| Incident | 27,664 | 41.0% | 0.90 | 24,898 | 38.7% |
| Alert (RMM) | 19,790 | 29.3% | 0.74 | 14,645 | 22.8% |
| Service Request | 12,653 | 18.7% | 1.10 | 13,918 | 21.6% |
| Change Request | 7,247 | 10.7% | 0.91 | 6,595 | 10.3% |
| Problem | 167 | 0.2% | 1.43 | 239 | 0.4% |
Alert tickets average 0.74 billable hours compared to 0.90 for incidents. Across 19,790 alert tickets, that is roughly 3,166 hours saved per year compared to handling them as standard incidents. On top of that, 97.5% of all RMM alerts resolve automatically in under six minutes, meaning only a fraction ever reach your service desk. The ROI on your Datto RMM investment is clearly visible in the data.
Only 79 out of 19,790 alert tickets received a CSAT response (0.4%). That makes it impossible to draw reliable conclusions about customer satisfaction with automated resolutions. The 93.7% positive rate looks good on paper, but with a sample this small, a handful of responses could shift it dramatically. If you want real CSAT data on alert tickets, you need to review whether SmileBack surveys are firing for this ticket type and whether the survey recipient makes sense for automated resolutions.
Service requests make up 18.7% of ticket volume but 21.6% of total effort. At 1.10 average hours, they take 49% longer than alert tickets and 22% longer than incidents. This suggests that service requests may include work that could be partially automated, pre-filled with templates, or broken into smaller tasks. Reviewing the top service request categories for automation opportunities could free up significant capacity.
With 97.5% of alerts auto-resolving, the remaining 2.5% deserve attention. Pull a list of unresolved alerts grouped by alert type and site. Look for patterns: are these the same monitors failing across multiple clients? Are they stale alerts from decommissioned devices? Cleaning up this tail will improve your resolve rate and reduce noise for the service desk.
A 0.4% CSAT response rate on alert tickets means you are flying blind on customer satisfaction for nearly a third of your ticket volume. Check whether SmileBack surveys are being sent for alert ticket closures and whether the survey goes to the right contact. Consider adding a brief resolution note to alert tickets so the customer understands what was fixed before they receive the survey.
Service requests average 1.10 hours, the highest of any volume ticket type. Break this down by service request category and look for types that could use self-service portals, pre-approved automation, or better documentation. Even reducing the average by 0.10 hours across 12,653 tickets saves over 1,265 hours per year.
The numbers tell a strong story: 135K alerts monitored, 97.5% auto-resolved in under six minutes, and the tickets that do get created cost 18% less effort than standard incidents. Package this into your QBR slides and sales proposals. Prospects and existing clients want proof that proactive monitoring actually reduces downtime and cost. This data is that proof.
Your current ratio is 14.6% (19,790 tickets from 135,387 alerts). Set this as a monthly KPI. If the ratio climbs, it means your alert thresholds may need tuning or you have new device types generating noisy alerts. If it drops, your automation policies are improving. Either way, the trend matters more than the absolute number.
Alert tickets are Autotask tickets with ticket type 5 (Alert). These are created automatically by Datto RMM when a monitoring alert triggers and is configured to create a ticket in Autotask. They differ from incidents (type 2), service requests (type 1), and change requests (type 4), which are created manually by technicians or through customer-facing portals.
Most alert tickets resolve without direct customer interaction. The customer may never know a ticket existed. SmileBack surveys are sent to the ticket contact upon closure, but for automated alert tickets, that contact may not be aware of the issue. This results in surveys going to people who did not experience the problem and do not feel compelled to respond.
Total hours are estimated by multiplying the average billable hours per ticket by the ticket count for each type. For example, 19,790 alert tickets at 0.74 average hours gives approximately 14,645 total hours. These are estimates based on averages and may differ slightly from a direct sum of all individual ticket hours.
Auto-resolve means the condition that triggered the alert cleared on its own without human intervention. For example, a disk space alert may fire when usage hits 90%, but if a scheduled cleanup runs and brings it below the threshold, the alert resolves automatically. In Datto RMM, the resolved field marks these. The average auto-resolve time of 5.45 minutes indicates most alerts are transient conditions.
Yes. Add the company name or site name column to the DAX queries to group results by client. This is particularly useful for identifying clients who generate a disproportionate number of alerts or have a low auto-resolve rate, which may indicate infrastructure problems worth addressing proactively.
The unlinked CSAT responses (7,688 total, 7,195 positive, 253 negative) are SmileBack reviews that could not be matched to a specific ticket type. This usually happens when the ticket was deleted, the ticket type was changed after the survey was sent, or the SmileBack-Autotask sync has gaps. These responses are still valid satisfaction data but cannot be attributed to a specific workflow.
Yes. Connect Proxuma Power BI to your Datto RMM and Autotask accounts, add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask the same question. The AI writes the DAX queries, runs them against your real data, and produces a report like this in under fifteen minutes.
Connect Proxuma Power BI to your PSA, RMM, and M365 environment, use an MCP-compatible AI to ask questions, and generate custom reports - in minutes, not days.
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