This report provides a detailed breakdown of month-over-month change in open tickets per company for managed service providers.
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
Current open ticket counts across the entire client portfolio
EVALUATE
ROW(
"Total Open Tickets", [Open Tickets (Current)],
"Companies with Tickets",
CALCULATE(
DISTINCTCOUNT('BI_Autotask_Tickets'[company_name]),
ISBLANK('BI_Autotask_Tickets'[complete_date])
),
"Avg Open per Company",
DIVIDE(
[Open Tickets (Current)],
CALCULATE(DISTINCTCOUNT('BI_Autotask_Tickets'[company_name])),
0
)
)Current open tickets and month-over-month comparison. The top 15 clients account for the bulk of your unresolved backlog.
| # | Company | Open Now | Open 1 Month Ago | Change | Trend | % of Total |
|---|---|---|---|---|---|---|
| 1 | Rivers, Rogers and Mitchell | 113 | 113 | 0 | Stable | 13.4% |
| 2 | Craig-Huynh | 65 | 65 | 0 | Stable | 7.7% |
| 3 | Little Group | 40 | 40 | 0 | Stable | 4.7% |
| 4 | Ramos Group | 36 | 36 | 0 | Stable | 4.3% |
| 5 | Martin Group | 33 | 33 | 0 | Stable | 3.9% |
| 6 | Price-Gomez | 25 | 25 | 0 | Stable | 3.0% |
| 7 | Wall PLC | 20 | 20 | 0 | Stable | 2.4% |
| 8 | Thompson, Contreras and Rios | 20 | 20 | 0 | Stable | 2.4% |
| 9 | Leach, Cunningham and Whitehead | 19 | 19 | 0 | Stable | 2.3% |
| 10 | Martin-Gonzalez | 18 | 18 | 0 | Stable | 2.1% |
| 11 | Burke, Armstrong and Morgan | 18 | 18 | 0 | Stable | 2.1% |
| 12 | Anderson, Brown and Mcintosh | 18 | 18 | 0 | Stable | 2.1% |
| 13 | Hanson-Cunningham | 18 | 18 | 0 | Stable | 2.1% |
| 14 | Lopez-Reyes | 18 | 18 | 0 | Stable | 2.1% |
| 15 | Welch Inc | 17 | 17 | 0 | Stable | 2.0% |
EVALUATE
TOPN(15,
ADDCOLUMNS(
SUMMARIZE(
'BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name]
),
"Open Now", [Open Tickets (Current)],
"Open 1mo Ago",
CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
FILTER(
ALL('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[company_name]
= EARLIER('BI_Autotask_Tickets'[company_name])
&& 'BI_Autotask_Tickets'[create_date]
<= DATE(YEAR(TODAY()), MONTH(TODAY())-1, DAY(TODAY()))
&& (
ISBLANK('BI_Autotask_Tickets'[complete_date])
|| 'BI_Autotask_Tickets'[complete_date]
> DATE(YEAR(TODAY()), MONTH(TODAY())-1, DAY(TODAY()))
)
)
),
"MoM Change", [Open Tickets Change (MoM)],
"MoM Growth %", [Open Tickets Growth % (MoM)]
),
[Open Now], DESC
)How evenly is your open ticket backlog distributed? Heavy concentration in a few clients signals either large complex environments or unresolved accumulation.
Open Ticket Volume — Top 10 Clients
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "OpenTickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] <> "Complete"), "OverdueResolution", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolved_due_age_days] > 0))The portfolio shows 844 open tickets distributed across 264 companies, with an average of 3.2 per client. That average, however, masks a significant imbalance at the top of the list.
Rivers, Rogers and Mitchell alone holds 113 open tickets, accounting for 13.4% of all unresolved work in the portfolio. The next four largest clients (Craig-Huynh, Little Group, Ramos Group, Martin Group) combine for another 174 tickets. In total, five clients are responsible for 34% of all open tickets, while the other 259 clients share the remaining two thirds.
In the demo dataset, month-over-month changes show no movement, which reflects static demo data. In a live environment connected to your Autotask, this view updates continuously as tickets are created and resolved. Rising counts at a specific client over two consecutive months should trigger an account review, not just a dispatch queue check.
Three actions based on the findings above
With 113 open tickets and 13.4% of your entire portfolio backlog, this client needs a dedicated account review. The question is whether these tickets reflect a large environment with normal ticket flow, or a growing backlog caused by resolution delays. Pull the average age of the open tickets for this client first, then decide whether this is a staffing issue, a prioritization issue, or a client relationship one.
A 10% or more increase in open tickets at any of your top clients over a single month is a meaningful signal. Configure a Power BI alert or a scheduled export that flags these changes automatically. Catching a rising count at Craig-Huynh or Little Group before it doubles is far easier than managing the fallout after three consecutive bad months.
Showing a client their own ticket trend over 12 months, with month-over-month changes, reframes the conversation from complaints to data. A client who sees that their open count has dropped from 25 to 12 over six months is much easier to retain than one who only hears about uptime percentages. Build this slide into every QBR deck for your top 15 accounts.
An open ticket is any ticket in Autotask that has a create date on or before today and no complete date — or a complete date in the future. This matches the industry-standard definition of an unresolved ticket regardless of its internal status label.
The MoM change compares the current open ticket count to the count from exactly one month ago on the same calendar day. Proxuma uses the Open Tickets (Snapshot) measure, which calculates historical open counts using ticket create and complete dates, not just current status labels.
The demo uses a synthetic, static dataset. In a live environment connected to your Autotask PSA, the MoM column reflects real ticket creation and resolution activity month over month. Changes will appear as your data refreshes.
Yes. In the live Power BI report, you can apply slicers for ticket type, queue, priority, and assigned resource. The MoM calculations update dynamically when filters are applied. The AI-generated version shown here uses unfiltered portfolio data.
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