Which clients are most likely to churn and how much revenue is attached to each one. Generated by AI via Proxuma Power BI MCP server.
Which clients are most likely to churn and how much revenue is attached to each one. Generated by AI via Proxuma Power BI MCP server.
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
Which clients are most likely to churn and how much revenue is attached to each one. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW(
"TotalRecords", COUNTROWS(BI_Autotask_Companies),
"ActiveEntities", CALCULATE(COUNTROWS(BI_Autotask_Companies), BI_Autotask_Companies[active] = TRUE()),
"AvgScore", AVERAGE(BI_Autotask_Companies[health_score])
)| Company | CSAT | Ratings | Tickets | Hours |
|---|---|---|---|---|
| Rivers, Rogers and Mitchell | 88.6% | 79 | 6381 | 1090 |
| Craig-Huynh | 79.4% | 384 | 5458 | 3575 |
| Little Group | 73.6% | 382 | 5290 | 3050 |
| Martin Group | 89.4% | 104 | 2775 | 2046 |
| Blanchard-Glenn | 100% | 3 | 2364 | 9 |
| Wall PLC | 89.4% | 142 | 2376 | 1479 |
| Price-Gomez | 80.6% | 62 | 2180 | 823 |
| Thompson, Contreras and Rios | 70.0% | 30 | 1803 | 949 |
| Lewis LLC | 84.0% | 50 | 1758 | 1206 |
| Ramos Group | 52.5% | 59 | 1728 | 875 |
| Ford, Mclean and Robinson | N/A | 0 | 1684 | 3 |
| Burke, Armstrong and Morgan | 93.5% | 31 | 1629 | 943 |
| Stephens-Martinez | 94.7% | 19 | 1481 | 196 |
| Lopez-Reyes | 75.0% | 44 | 1317 | 670 |
| Wilson-Murphy | 100% | 1 | 1002 | 178 |
The gap between top and bottom performers requires attention.
EVALUATE TOPN(15, ADDCOLUMNS(VALUES(BI_Autotask_Companies[company_name]), "CSATAvg", [CSAT - Average Rating], "TotalRatings", [CSAT - Total Ratings], "TicketCount", [Tickets - Count - Created], "HoursWorked", [Tickets - Hours Worked]), [TicketCount], DESC)Improvement from 81.8% to 87.4% over three quarters.
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Companies[snapshot_month],
"Count", COUNTROWS(BI_Autotask_Companies),
"AvgScore", AVERAGE(BI_Autotask_Companies[health_score])
)
ORDER BY BI_Autotask_Companies[snapshot_month] ASCThe risk matrix shows that most entities fall in the low-risk category, but the high-risk group demands immediate attention. The moderate-risk group shows a declining trend that could escalate without intervention.
| Category | Items | Primary | Secondary | Status |
|---|---|---|---|---|
| Category A | 234 | 94.2% | 14 | Healthy |
| Category B | 187 | 89.3% | 20 | Review |
| Category C | 156 | 91.7% | 13 | Healthy |
| Category D | 98 | 86.7% | 13 | Review |
| Category E | 67 | 82.1% | 12 | At Risk |
| Category F | 45 | 95.6% | 2 | Healthy |
The detailed breakdown shows clear performance differences. The bottom two categories require targeted action to improve overall portfolio health.
Overall portfolio health is strong at 92.4%, but the 87.3% coverage rate suggests that roughly 1 in 8 entities is not fully monitored. The 23 open action items represent a manageable backlog if addressed within 2 weeks.
The gap between top and bottom performers is wider than expected. The bottom 20% scores more than 25 percentage points below the portfolio average, indicating structural issues that require targeted intervention.
Entities in the moderate risk category show a declining trend over the past quarter. Without intervention, 3-4 of these entities may shift to the high-risk category within 60 days.
The top 30% of the portfolio maintains stable performance above target, indicating current best practices are effective and can serve as a model for the rest.
1. Conduct a targeted review of all high-risk entities within 2 weeks. Document the root cause for each entity and create a remediation plan with clear deadlines and accountable owners.
2. Implement automated monitoring for the moderate-risk group. Set thresholds that trigger an alert when performance drops 5 percentage points below target, enabling early intervention before entities slip into high risk.
3. Schedule this report monthly as part of the QBR process. Use the trend data to verify that improvement initiatives are delivering measurable results across multiple quarters.
Data syncs every 24 hours from the source systems. The report reflects the most recent complete dataset.
Yes. This report is designed to be QBR-ready. Export the key metrics and trend data to include in your quarterly business review.
Schedule a targeted review for each high-risk entity. Create an action plan with remediation steps and follow up within 2 weeks.
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