A single-client QBR covering ticket volume, SLA compliance, customer satisfaction, device health, and contract spend for Wall PLC in Q1 2026.
A single-client QBR covering ticket volume, SLA compliance, customer satisfaction, device health, and contract spend for Wall PLC in Q1 2026.
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: Account managers, MSP owners, and service delivery leads
How often: Monthly for client reviews, quarterly for QBRs, on-demand when client signals change
A single-client QBR covering ticket volume, SLA compliance, customer satisfaction, device health, and contract spend for Wall PLC in Q1 2026.
Top-level metrics for Wall PLC this quarter, pulled from Autotask PSA, Datto RMM, and SmileBack
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "ResolutionSLA", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1), "AvgResolution", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]), "CSATAvg", AVERAGE('BI_SmileBack_Reviews'[rating]), "CSATCount", COUNTROWS('BI_SmileBack_Reviews'))Wall PLC's Q1 2026 service delivery dashboard: where the account stands across every metric that matters for a QBR
| Metric | Wall PLC | Portfolio Avg | Status |
|---|---|---|---|
| Total Tickets | 2,376 | 67,521 total | 3.5% share |
| Hours Worked | 1,479 | 33,271 total | 4.4% share |
| Billable Hours | 1,849 | — | 125% of worked |
| SLA First Response Met | 73.6% | 52.9% | +20.7pp |
| SLA Resolution Met | 72.5% | 63.5% | +9.0pp |
| CSAT Score | 89.4% | 87.7% | Above avg |
| Revenue | €476,622 | — | 23 contracts |
| Devices Online | 114 / 320 | — | 64.4% offline |
| Unresolved RMM Alerts | 34 | — | Needs review |
Wall PLC is outperforming the portfolio average on both SLA metrics and customer satisfaction. First response compliance sits at 73.6%, which is 20.7 percentage points above the overall portfolio rate of 52.9%. Resolution SLA is also above average at 72.5%. The CSAT score of 89.4% confirms that the service experience matches the SLA numbers.
The concern is on the infrastructure side. Only 114 of 320 devices are reporting as online in Datto RMM, and 34 alerts remain unresolved. This does not necessarily mean 206 devices are down. It likely reflects stale agent installations, decommissioned hardware, or devices that have not checked in recently. But it needs cleanup because it obscures real issues.
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"Revenue", CALCULATE(SUM('BI_Autotask_Billing_Items'[total_amount])),
"Cost", CALCULATE(SUM('BI_Autotask_Billing_Items'[our_cost])),
"Contracts", CALCULATE(COUNTROWS('BI_Autotask_Contracts'))
)
ORDER BY [Revenue] DESCFirst response and resolution SLA compliance for Wall PLC vs. the portfolio average, based on 2,376 tickets this quarter
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"FRMet", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[first_response_met] + 0 = 1),
"ResMet", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[resolution_met] + 0 = 1),
"FRPct", DIVIDE(
CALCULATE(COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[first_response_met] + 0 = 1),
CALCULATE(COUNTROWS('BI_Autotask_Tickets'))),
"ResPct", DIVIDE(
CALCULATE(COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[resolution_met] + 0 = 1),
CALCULATE(COUNTROWS('BI_Autotask_Tickets')))
)
ORDER BY [Tickets] DESCHow Wall PLC's 2,376 tickets translate into worked hours and billable hours, with utilization context
| Metric | Value | Context |
|---|---|---|
| Total Tickets | 2,376 | Q1 2026 |
| Hours Worked | 1,479 | 0.62 hrs / ticket |
| Billable Hours | 1,849 | 0.78 hrs / ticket |
| Utilization Ratio | 125% | Billable > Worked |
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Companies'[company_name]),
"Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"WorkedHours", CALCULATE(
SUM('BI_Autotask_Tickets'[worked_hours])),
"BillableHours", CALCULATE(
SUM('BI_Autotask_Tickets'[billable_hours]))
)
ORDER BY [Tickets] DESCDatto RMM device status and unresolved alert count for Wall PLC's 320 managed devices
| Metric | Count | Status |
|---|---|---|
| Total Devices | 320 | Managed in RMM |
| Online | 114 | 35.6% |
| Offline | 206 | 64.4% |
| Unresolved RMM Alerts | 34 | Open |
EVALUATE
ADDCOLUMNS(
VALUES('BI_Datto_Rmm_Devices'[company_name]),
"TotalDevices", CALCULATE(COUNTROWS('BI_Datto_Rmm_Devices')),
"Online", CALCULATE(
COUNTROWS('BI_Datto_Rmm_Devices'),
'BI_Datto_Rmm_Devices'[status] = "online"),
"Offline", CALCULATE(
COUNTROWS('BI_Datto_Rmm_Devices'),
'BI_Datto_Rmm_Devices'[status] = "offline"),
"UnresolvedAlerts", CALCULATE(
COUNTROWS('BI_Datto_Rmm_Alerts'),
'BI_Datto_Rmm_Alerts'[resolved] = FALSE())
)
ORDER BY [TotalDevices] DESC206 of 320 devices are not checking in. This creates blind spots for patch management, antivirus status, and hardware health monitoring. Even if most of these are stale entries, the noise makes it harder to spot real outages. An RMM device audit should be scheduled before the next QBR.
Open alerts that sit unresolved accumulate over time and lose meaning. Technicians stop paying attention when the alert queue is noisy. Review these 34 alerts, resolve the ones that are no longer relevant, and escalate any that indicate real hardware or software problems.
First response SLA at 73.6% and resolution SLA at 72.5% both exceed the portfolio benchmarks of 52.9% and 63.5%. This client is getting faster responses and faster resolutions than most accounts in the book. The team is delivering well here.
The CSAT score of 89.4% is 1.7 percentage points above the portfolio average. Combined with the strong SLA numbers, this indicates consistent service quality. This is the kind of account where a case study or testimonial request would be well received.
Actions to take before the next quarterly review, ordered by priority
With 206 devices showing as offline, the RMM console for Wall PLC is unreliable as a monitoring tool. Schedule a device audit: cross-reference the RMM device list with the client's active asset inventory. Remove decommissioned machines, reinstall agents on devices that have gone silent, and flag any truly missing hardware. Target: get the offline rate below 20% by end of Q2.
Go through each unresolved alert. Categorize them as actionable (real issue that needs a fix), informational (can be auto-resolved with a policy change), or stale (relates to a device that no longer exists). Clearing the queue will restore trust in the alerting system and make new critical alerts visible immediately.
Wall PLC has 23 contracts on file, but only 13 are active. The remaining 10 may be expired, paused, or superseded. Confirm whether any of these should be renewed, consolidated, or formally closed. Clean contract data improves revenue forecasting accuracy.
Wall PLC has above-average SLA compliance and a CSAT of 89.4% across a meaningful ticket volume of 2,376. These are the metrics of a satisfied client. Ask their primary contact for a written testimonial or case study. Satisfied clients who generate high volume are the most credible references you can offer prospects.
This report pulls from three sources via Proxuma Power BI: Autotask PSA (tickets, hours, contracts, billing), Datto RMM (device status, alerts), and SmileBack (customer satisfaction ratings). The AI connects to Power BI via MCP, writes DAX queries against the live data model, and generates the report automatically.
SLA compliance is tracked in Autotask using the first_response_met and resolution_met fields. A value of 1 means the ticket met the SLA target defined in the contract. A value of 0 means it was breached. The percentages in this report are the count of tickets with met = 1 divided by total tickets for the client.
Billable hours can exceed worked hours when contracts use minimum billing increments (e.g., 15-minute or 30-minute minimums), when fixed-fee contract hours are billed at a predetermined rate, or when travel time is billed separately. In Wall PLC's case, 1,849 billable vs. 1,479 worked suggests contract billing rules are adding the difference.
SmileBack uses a 3-point scale: happy (5), neutral (3), and unhappy (1). The CSAT percentage shown here (89.4%) represents the average rating normalized to a percentage scale. A score of 89.4% means the weighted average of all responses is strongly positive, with most responses landing on the happy end of the scale.
Yes. Connect Proxuma Power BI to your Autotask, Datto RMM, and SmileBack accounts. Then ask the AI via MCP: "How are we performing for [client name] this quarter?" The AI writes the DAX queries, pulls the numbers, and generates a formatted QBR like this one in under fifteen minutes.
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