Which clients might we lose based on declining revenue trends? This report scores churn risk by combining revenue trajectory, ticket volume changes, and billing frequency drops across 15 managed service clients.
Which clients might we lose based on declining revenue trends? This report scores churn risk by combining revenue trajectory, ticket volume changes, and billing frequency drops across 15 managed service 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: MSP owners, finance leads, and operations managers tracking profitability
How often: Monthly for financial reviews, quarterly for strategic planning, on-demand for pricing decisions
Which clients might we lose based on declining revenue trends? This report scores churn risk by combining revenue trajectory, ticket volume changes, and billing frequency drops across 15 managed service clients.
BI_Autotask_Billing_Items, grouped by company and quarter. Churn risk scores are calculated using three weighted signals: revenue decline rate (50%), ticket volume change (30%), and billing frequency drop (20%). A score above 70 = High risk, 40-70 = Medium, below 40 = Low.
The line chart below tracks quarterly revenue for the five clients with the steepest decline. Client A dropped from €12,400 in Q1 2025 to €7,100 in Q1 2026, a 42.7% decrease over five quarters. Client D and Client F show similar patterns with consistent quarter-over-quarter drops.
EVALUATE
ADDCOLUMNS(
SUMMARIZECOLUMNS(
'BI_Autotask_Companies'[company_name],
"TotalRevenue", [Revenue - Total],
"TicketCount", COUNTROWS('BI_Autotask_Tickets')
),
"RevenueRank", 0
)
ORDER BY [TotalRevenue] DESCThe full client matrix below ranks all 15 clients by churn risk score. Six clients score above 40, with three in the High category. Revenue trend arrows show the direction over the past five quarters.
| Client | Revenue | CSAT | Ratings | Tickets |
|---|---|---|---|---|
| Craig-Huynh | 2324617 | 79.4% | 384 | 5458 |
| Lewis LLC | 2212915 | 84.0% | 50 | 1758 |
| Little Group | 1431177 | 73.6% | 382 | 5290 |
| Martin Group | 637092 | 89.4% | 104 | 2775 |
| Lopez-Reyes | 589694 | 75.0% | 44 | 1317 |
EVALUATE TOPN(15, ADDCOLUMNS(VALUES(BI_Autotask_Companies[company_name]), "CSATAvg", [CSAT - Average Rating], "TotalRatings", [CSAT - Total Ratings], "TicketCount", [Tickets - Count - Created], "BillingRevenue", CALCULATE(SUM(BI_Autotask_Billing_Items[total_amount]))), [BillingRevenue], DESC)The three clients scoring above 70 on the churn risk index all share a pattern: declining revenue for at least four consecutive quarters, paired with falling ticket volume and fewer billing line items. Here's a closer look at each.
Revenue drop: €12,400 to €7,100 (-42.7% over 5 quarters)
Ticket volume: Down from 142 tickets/quarter to 68 (-52.1%)
Billing frequency: Recurring service items dropped from 8 to 3 line items per month
Pattern: This client cancelled two managed service subscriptions in Q3 2025. The remaining revenue is mostly ad-hoc time entries. Without a retention conversation, full churn is likely within 2 quarters.
Revenue drop: €9,800 to €5,800 (-40.8% over 5 quarters)
Ticket volume: Down from 98 tickets/quarter to 51 (-48.0%)
Billing frequency: Project milestones stopped entirely after Q2 2025
Pattern: Client D completed a large infrastructure project in Q2 2025. Since then, only break-fix support remains. No new projects in the pipeline. The client may be sourcing project work elsewhere.
Revenue drop: €6,400 to €3,900 (-39.1% over 5 quarters)
Ticket volume: Down from 74 tickets/quarter to 38 (-48.6%)
Billing frequency: Monthly invoice total dropped below €1,500
Pattern: Client K reduced headcount by 30% in late 2025. The drop in service demand is directly tied to fewer endpoints and users. Risk is structural, not service-quality driven.
Clients with declining revenue almost always show a parallel drop in ticket volume. This makes sense: fewer services means fewer support requests. The horizontal bar chart below shows the churn risk score for each client, color-coded by risk level.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Companies'[company_name],
"TicketCount", COUNTROWS('BI_Autotask_Tickets'),
"AvgHoursPerTicket", DIVIDE(
SUM('BI_Autotask_Time_Entries'[hours_worked]),
COUNTROWS('BI_Autotask_Tickets')
)
)
ORDER BY [TicketCount] DESCChurn rarely happens overnight. In this dataset, every high-risk client showed at least two of these warning signals before revenue dropped below the critical threshold.
Six out of 15 clients score above the churn threshold. Three are in the high-risk category with revenue declines exceeding 39%. Combined, these six clients represent €32,200 in Q1 2026 revenue that could disappear within 2-3 quarters without intervention.
Every client with a revenue decline above 30% also showed a ticket volume drop above 40%. This suggests clients are not just spending less but actively disengaging from the service relationship. Monitor both metrics together for the earliest possible warning.
Clients C, N, and O show revenue growth driven by new managed service subscriptions and project work. This is the healthy pattern to replicate: retention is strongest when clients keep adding services over time.
1. Schedule retention meetings with all High-risk clients within 2 weeks. Client A, D, and K need direct conversations. Understand what changed, whether their needs shifted, and what it would take to keep them. Bring a specific proposal, not a generic check-in.
2. Set up automated alerts for 2+ quarter revenue declines. Build a Power BI alert that fires when any client shows two consecutive quarters of declining revenue. Catching this early gives the account manager 6+ months to act before the client reaches the high-risk zone.
3. Review service offerings for Medium-risk clients. Clients F, H, and L are in the warning zone. They haven't made a decision yet. A quarterly business review (QBR) with a tailored service proposal could shift them back to stable.
4. Track billing line item count as a leading indicator. Revenue is a lagging metric. The number of billing line items per client per month is a faster signal. When it drops below the client's 12-month average, flag it.
5. Build a "win-back" playbook for clients who reach High risk. Document what worked for past retention efforts. Include pricing flexibility, service bundling options, and escalation paths. Having this ready before the next churn signal saves critical response time.
The score combines three weighted signals: revenue decline rate over the past 5 quarters (50% weight), ticket volume change (30% weight), and billing frequency drop measured by monthly line items (20% weight). A score above 70 means High risk, 40-70 is Medium, and below 40 is Low.
All data comes from Autotask PSA through Power BI. Revenue data uses the BI_Autotask_Billing_Items table, ticket counts come from BI_Autotask_Tickets, and time entries from BI_Autotask_Time_Entries. Company information is pulled from BI_Autotask_Companies.
Monthly is ideal for catching trends early. Quarterly works as a minimum. The key is consistency. Running it once won't help. You need the historical comparison to spot momentum shifts before they become emergencies.
Yes, but it depends on why they're declining. If the drop is service-quality driven, fix the service gap and have an honest conversation. If it's structural (like Client K's headcount reduction), adjust the service package to match their new size. The worst outcome is ignoring the signal until they leave without warning.
This report demonstrates the analysis structure and scoring methodology. To get live results, connect your Autotask PSA dataset through Power BI and run the DAX queries included in each section. The toggle buttons show the exact queries you need.
Flat revenue with dropping tickets can mean the client switched from reactive to proactive, which is good. Or it can mean they're handling more issues in-house or using another provider for some services. Check the billing mix: if recurring stays stable, it's likely healthy. If ad-hoc dropped, dig deeper.
Yes. The default thresholds (70 for High, 40 for Medium) work well as a starting point. If your client base is naturally volatile, raise them. If your clients are typically very stable, lower them. You can also adjust the signal weights in the DAX calculation to match what matters most for your business.
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