Analyzing 67,521 tickets to identify self-service deflection opportunities across 264 clients.
Analyzing 67,521 tickets to identify self-service deflection opportunities across 264 clients.
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
Analyzing 67,521 tickets to identify self-service deflection opportunities across 264 clients.
Key metrics from the full ticket dataset, highlighting KB deflection potential.
EVALUATE ROW("Total", COUNTROWS('BI_Autotask_Tickets'), "FirstDayFix", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_day_resolution]), "AvgResHrs", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]))Each issue type scored by first-hour fix rate and average resolution time. High first-hour fix rates indicate repeatable resolutions, making them strong candidates for KB articles.
| Issue Type | Tickets | Avg Hours | First-Hour Fix | KB Eligible | Est. Deflection |
|---|---|---|---|---|---|
| General practice doctor | 15,835 | 1.8h | 18.1% | Yes | 4,750 |
| Community officer | 11,757 | 1.8h | 16.6% | Yes | 3,527 |
| Therapist, speech and language | 9,866 | 1.0h | 22.1% | Yes | 2,959 |
| Public librarian | 6,117 | 0.6h | 33.1% | Yes | 1,835 |
| Financial risk analyst | 4,662 | 2.0h | 13.8% | Yes | 1,398 |
| Radio broadcast assistant | 1,663 | 1.8h | 17.5% | Yes | 498 |
| Land/geomatics surveyor | 1,630 | 1.9h | 16.5% | Yes | 489 |
| Risk analyst | 1,197 | 0.8h | 33.0% | Yes | 359 |
| Prison officer | 1,113 | 1.7h | 15.7% | Yes | 333 |
| Chief Financial Officer | 1,040 | 2.1h | 12.0% | Yes | 312 |
| Designer, ceramics/pottery | 1,037 | 0.6h | 41.0% | Yes | 311 |
| Retail buyer | 646 | 4.8h | 6.5% | No | - |
EVALUATE
TOPN(12,
ADDCOLUMNS(
SUMMARIZE('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[issue_type_name]),
"Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"AvgHours", CALCULATE(
AVERAGE('BI_Autotask_Tickets'[estimated_hours])),
"FirstHourFix", CALCULATE(COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_hour_fix] = 1)))
), [Tickets], DESC)
ORDER BY [Tickets] DESCGranular view of the top sub-issue types by volume. Each row represents a potential KB article topic.
| Sub-Issue Type | Tickets | Avg Hours | First-Hour Fix Rate |
|---|---|---|---|
| Furniture conservator/restorer | 4,318 | 1.9h | 16.7% |
| Insurance claims handler | 3,204 | 1.9h | 15.9% |
| Teacher, secondary school | 3,338 | 2.1h | 12.9% |
| Scientist, biomedical | 2,734 | 1.8h | 16.8% |
| Surveyor, building control | 2,523 | 1.0h | 30.9% |
| Sport and exercise psychologist | 2,448 | 1.5h | 15.9% |
| Adult nurse | 2,402 | 1.9h | 15.8% |
| Educational psychologist | 2,056 | 1.8h | 16.5% |
| Chemist, analytical | 1,746 | 1.2h | 29.8% |
EVALUATE
TOPN(10,
ADDCOLUMNS(
SUMMARIZE('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[sub_issue_type_name]),
"Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"AvgHours", CALCULATE(
AVERAGE('BI_Autotask_Tickets'[estimated_hours])),
"FirstHourFix", CALCULATE(COUNTROWS(
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_hour_fix] = 1)))
), [Tickets], DESC)
ORDER BY [Tickets] DESCTop 10 clients by ticket volume with estimated KB deflection numbers. Clients with high volumes and high first-hour fix rates benefit most from self-service documentation.
| Client | Total Tickets | Avg Hours | First-Hour Fix | Est. KB Deflection |
|---|---|---|---|---|
| Anderson & Partners | 6,381 | 1.2h | 30.1% | 1,914 |
| Brooks Technology | 5,458 | 1.7h | 72.4% | 1,637 |
| Carter Medical Group | 5,290 | 1.9h | 65.9% | 1,587 |
| Davis Financial | 2,775 | 2.0h | 40.7% | 832 |
| Edwards Manufacturing | 2,376 | 1.4h | 76.0% | 712 |
| Foster Legal | 2,364 | 2.0h | 93.5% | 709 |
| Grant Holdings | 2,180 | 1.2h | 32.9% | 654 |
| Harrison Corp | 1,803 | 1.8h | 31.8% | 540 |
| Irving Solutions | 1,758 | 1.6h | 50.5% | 527 |
| Jensen Media | 1,728 | 1.7h | 39.1% | 518 |
How tickets distribute across service categories. Concentration in a few categories means targeted KB investment yields outsized results.
Month-by-month ticket volume for seasonal pattern detection. Spikes indicate periods where proactive KB articles would have the biggest deflection impact.
The data tells a clear story. Out of 67,521 tickets, roughly 82.8% fall into categories that are well-suited for knowledge base documentation. These are tickets with predictable resolution patterns: short average handle times, high first-hour fix rates, or both.
Applying a conservative 30% deflection rate (the industry average for well-maintained KB implementations) puts the potential ticket reduction at approximately 16,775 tickets. At an average of 9.0 estimated hours per ticket, that translates to significant capacity freed up for higher-value work.
The first-hour fix rate of 17.4% is particularly telling. Every ticket resolved within the first hour is a ticket where the technician already knew the answer. That knowledge is sitting in people's heads instead of in a searchable knowledge base. Moving it from tribal knowledge to documented procedures is the fastest path to ticket deflection.
Client-level analysis shows concentration: the top 10 clients generate a disproportionate share of total volume. Prioritizing KB articles for their most common issue types creates immediate, visible impact. Start with the three highest-volume clients and their top five issue types. That single action covers a meaningful percentage of deflectable tickets.
Ranked by estimated impact on ticket deflection.
These five categories alone account for the majority of KB-eligible tickets. Write step-by-step resolution guides, link them in your client portal, and track whether users access them before submitting tickets.
Your 17.4% first-hour fix rate means technicians already know these answers. Add a mandatory "resolution notes" field for first-hour tickets to build your KB content pipeline automatically.
High-volume clients have repeating patterns. Create client-facing FAQ documents that address their top 10 ticket topics. Share them during QBRs as a self-service resource.
You cannot measure deflection without tracking article views against ticket submissions. Enable article-level analytics, then compare monthly: are ticket volumes dropping for categories where KB articles exist?
Re-run this analysis each month. Track whether KB-eligible ticket percentages decline as you publish articles. The goal: bring the 82.8% KB-eligible rate down by 5-10 percentage points per quarter.
A ticket is considered KB-eligible when it has a high first-hour fix rate (above 10%) or a low average resolution time (under 2 hours). Both signals indicate a repeatable, well-understood resolution that can be documented as a self-service article.
Industry benchmarks for well-maintained IT knowledge bases range from 20% to 40% ticket deflection. This report uses a conservative 30% estimate. Your actual rate depends on article quality, client portal adoption, and how prominently you surface KB content.
Track two metrics side by side: KB article views per category, and ticket volume per category. After publishing an article, you should see article views increase while ticket volume for that specific issue type decreases over 30-60 days.
Start with your highest-volume clients. The top 10 clients in this dataset generate a large share of total tickets. Creating client-specific FAQ pages for these accounts and sharing them during QBRs drives immediate self-service adoption.
Yes. If your IT Glue data flows into the same data warehouse (via Proxuma or direct API sync), this report can cross-reference actual KB article categories against ticket issue types. That turns estimates into measured deflection rates.
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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