This report connects Autotask PSA ticket data (42,817 tickets across 90+ companies) with IT Glue documentation metrics (12,439 active documents, 8,214 configurations) to measure whether clients with better documentation coverage see higher first call resolution rates. Two data sources, one hypothesis: technicians resolve tickets faster when the answers are already written down.
This report connects Autotask PSA ticket data (42,817 tickets across 90+ companies) with IT Glue documentation metrics (12,439 active documents, 8,214 configurations) to measure whether clients with better documentation coverage see higher first call resolution rates. Two data sources, one hypothesis: technicians resolve tickets faster when the answers are already written down.
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
This report connects Autotask PSA ticket data (42,817 tickets across 90+ companies) with IT Glue documentation metrics (12,439 active documents, 8,214 configurations) to measure whether clients with better documentation coverage see higher first call resolution rates. Two data sources, one hypothesis: technicians resolve tickets faster when the answers are already written down.
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "ClosedByFirstResource", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[closed_by_first_resource]), "FirstDayResolution", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_day_resolution]))| Client | IT Glue Docs | Configurations | Docs per Config | Coverage |
|---|---|---|---|---|
| Client A | 1,847 | 923 | 2.0 | High |
| Client B | 1,204 | 687 | 1.8 | High |
| Client C | 892 | 534 | 1.7 | High |
| Client D | 634 | 412 | 1.5 | Medium |
| Client E | 287 | 356 | 0.8 | Medium |
| Client F | 143 | 289 | 0.5 | Low |
| Client G | 52 | 198 | 0.3 | Low |
| Client H | 18 | 145 | 0.1 | Low |
Doc Coverage by Client =
ADDCOLUMNS(
SUMMARIZE(
BI_ITGlue_Documents,
BI_ITGlue_Documents[organization_name]
),
"Total Docs",
COUNTROWS(BI_ITGlue_Documents),
"Total Configs",
CALCULATE(
COUNTROWS(BI_ITGlue_Configurations),
FILTER(
BI_ITGlue_Configurations,
BI_ITGlue_Configurations[organization_id]
= EARLIER(BI_ITGlue_Documents[organization_id])
)
),
"Docs Per Config",
DIVIDE(
COUNTROWS(BI_ITGlue_Documents),
CALCULATE(COUNTROWS(BI_ITGlue_Configurations)),
0
)
)| Client | Tickets | FCR Rate | Docs/Config | Trend |
|---|---|---|---|---|
| Client A | 6,412 | 82.1% | 2.0 | Strong |
| Client B | 4,893 | 78.6% | 1.8 | Strong |
| Client C | 3,721 | 76.3% | 1.7 | Strong |
| Client D | 5,108 | 67.4% | 1.5 | Average |
| Client E | 4,267 | 61.8% | 0.8 | Average |
| Client F | 3,456 | 52.3% | 0.5 | Weak |
| Client G | 2,891 | 47.9% | 0.3 | Weak |
| Client H | 2,134 | 41.2% | 0.1 | Weak |
FCR by Doc Density =
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[company_name]
),
"Total Tickets",
COUNTROWS(BI_Autotask_Tickets),
"FCR Count",
COUNTROWS(
FILTER(
BI_Autotask_Tickets,
BI_Autotask_Tickets[ticket_status] = "Complete"
&& BI_Autotask_Tickets[first_response_met] + 0 = 1
&& BI_Autotask_Tickets[resolution_met] + 0 = 1
&& DATEDIFF(
BI_Autotask_Tickets[create_date],
BI_Autotask_Tickets[resolve_date],
HOUR
) <= 4
)
),
"FCR Rate",
DIVIDE(
COUNTROWS(
FILTER(
BI_Autotask_Tickets,
BI_Autotask_Tickets[first_response_met] + 0 = 1
&& BI_Autotask_Tickets[resolution_met] + 0 = 1
&& DATEDIFF(
BI_Autotask_Tickets[create_date],
BI_Autotask_Tickets[resolve_date],
HOUR
) <= 4
)
),
COUNTROWS(BI_Autotask_Tickets),
0
)
)Client A (2.0 docs/config) hits 82.1% FCR while Client H (0.1 docs/config) manages only 41.2%. That is a 40.9 percentage point gap. The pattern holds across all eight clients in the analysis - every step down in documentation density corresponds to a measurable drop in first call resolution.
7,261 IT Glue documents have not been touched in over 90 days. Outdated SOPs and network diagrams give techs false confidence. Client E has 287 documents but a 61.8% FCR rate because much of that documentation was written during onboarding and never refreshed. The volume looks good on paper, but the content no longer reflects the live environment.
Clients A, B, and C resolve 78.6% of tickets on the first call versus the portfolio average of 68.4%. Applied to their combined 15,026 tickets, that 10.2% improvement translates to roughly 1,240 fewer escalation hours per year. At a blended tech rate of 85 EUR/hour, that is over 105,000 EUR in avoided cost.
1. Launch a documentation sprint for Client F, G, and H. These three clients have a combined 632 configurations and fewer than 213 documents between them. Their FCR rates are all below 53%. Start with the most common ticket categories for each client - password management SOPs, VPN setup guides, and printer configuration docs. Target 1.0 docs-per-config within 60 days. Even partial coverage will move the FCR needle.
2. Implement a 90-day documentation review cycle. Set a recurring task in Autotask for each client's primary tech to review and update IT Glue documents quarterly. Flag any document older than 90 days as "needs review" using IT Glue's built-in workflow. The 58.4% stale rate should drop below 30% within two quarters if this becomes a standing process.
3. Connect IT Glue articles to Autotask ticket categories. When a tech resolves a ticket on first call, capture which IT Glue document helped. This creates a feedback loop: popular articles get updated first, gaps in documentation become visible through unresolved ticket patterns, and management can see the ROI of documentation work in hard FCR numbers.
A ticket counts as first call resolved when it meets three conditions: the ticket status is Complete, both first_response_met and resolution_met equal 1 in BI_Autotask_Tickets, and the time between create_date and resolve_date is 4 hours or less. This excludes project tickets and only measures service requests.
This report shows correlation, not strict causation. Well-documented clients may also be better managed overall, with simpler environments or more engaged IT contacts. That said, the mechanism is plausible: techs who can look up a password, find a network diagram, or follow a runbook do not need to escalate. The 40-point FCR gap between the best and worst documented clients is too large to dismiss as coincidence.
Configurations in IT Glue represent managed assets - servers, workstations, network devices, printers, and cloud services. Each configuration has a type, status, and associated organization. The count comes from BI_ITGlue_Configurations where the status is active. Decommissioned or archived configurations are excluded.
Total document count is misleading because larger clients naturally have more documents. A company with 900 configurations and 900 documents is not well-documented - it has 1.0 docs per config. A smaller company with 200 configurations and 400 documents at 2.0 docs per config is in better shape. The ratio normalizes for client size and gives a fairer comparison across the portfolio.
At minimum, every 90 days. Password documents should update whenever credentials rotate. Network diagrams need updating after any infrastructure change. SOPs and runbooks should be reviewed quarterly even if nothing changed, to confirm they still apply. The 90-day threshold in this report is a practical starting point, not an ideal target.
Yes. The FCR rate per client is a strong QBR metric. Show the client their FCR rate next to a better-documented peer (anonymized). Explain that documentation investment reduces repeat calls and escalations. For clients with low documentation, position a documentation sprint as a co-investment: your team writes the docs, their IT contact reviews for accuracy. The cost savings from improved FCR typically pay for the documentation effort within one quarter.
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