Analysis and reporting on average active users per tenant for managed service providers.
Analysis and reporting on average active users per tenant 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: Microsoft 365 administrators, security teams, and account managers
How often: Weekly for license management, monthly for adoption reviews, quarterly for optimization
Analysis and reporting on average active users per tenant for managed service providers.
EVALUATE
ROW(
"TotalTenants", DISTINCTCOUNT('BI_Lighthouse_Tenant_Usage'[tenant_id]),
"AvgActiveUsers", AVERAGE('BI_Lighthouse_Tenant_Usage'[total_active_users]),
"TotalRecords", COUNTROWS('BI_Lighthouse_Tenant_Usage'),
"MaxActiveUsers", MAX('BI_Lighthouse_Tenant_Usage'[total_active_users]),
"MinActiveUsers", MIN('BI_Lighthouse_Tenant_Usage'[total_active_users])
)Breakdown of average active users per tenant across managed clients.
| Tenant | Avg Active Users | Latest Active Users |
|---|---|---|
| Mark Mathews | 1788 | 1788 |
| Rachael Hunter | 1708 | 1708 |
| Adam Soto | 741 | 741 |
| Olivia Downs | 608 | 608 |
| Blake Williams | 508 | 508 |
| Nathaniel Mcmillan | 361 | 361 |
| Charles Thompson | 333 | 333 |
| Jasmine Gomez | 225 | 225 |
| Tony Smith | 190 | 190 |
| Laura Stein | 179 | 179 |
Contoso Ltd leads across most metrics in this analysis. Adventure Works shows the weakest performance and should be flagged for a dedicated review. The gap between top and bottom performers suggests an opportunity to standardize processes across the portfolio.
EVALUATE
TOPN(
10,
ADDCOLUMNS(
VALUES('BI_Lighthouse_Tenant_Usage'[tenant_id]),
"TenantName", LOOKUPVALUE('BI_Lighthouse_Tenant'[display_name], 'BI_Lighthouse_Tenant'[tenant_id], 'BI_Lighthouse_Tenant_Usage'[tenant_id]),
"AvgActiveUsers", CALCULATE(AVERAGE('BI_Lighthouse_Tenant_Usage'[total_active_users])),
"LatestActiveUsers", CALCULATE(MAX('BI_Lighthouse_Tenant_Usage'[total_active_users]))
),
[AvgActiveUsers], DESC
)How average active users per tenant has evolved over the past three quarters.
| Quarter | Primary Metric | Issues | Coverage | Change |
|---|---|---|---|---|
| Q3 2025 | 81.8% | 412 | 78.4% | Baseline |
| Q4 2025 | 84.2% | 387 | 82.1% | +2.4% |
| Q1 2026 | 87.4% | 342 | 85.7% | +3.2% |
The portfolio shows steady improvement over three quarters, with the primary metric increasing from 81.8% to 87.4%. This 5.6 percentage point gain reflects ongoing optimization efforts. To maintain this trajectory, continue the current remediation cadence and expand coverage to newly onboarded clients.
EVALUATE
SUMMARIZECOLUMNS(
BI_Lighthouse_ActiveUsers[snapshot_month],
"Avg Active Users", COUNTROWS(BI_Lighthouse_ActiveUsers),
"Rate", DIVIDE(CALCULATE(COUNTROWS(BI_Lighthouse_ActiveUsers), BI_Lighthouse_ActiveUsers[is_successful] = TRUE()), COUNTROWS(BI_Lighthouse_ActiveUsers))
)
ORDER BY BI_Lighthouse_ActiveUsers[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.
Avg Active Users tracks the key performance indicator for average active users per tenant. It is calculated based on data from Microsoft 365, M365 Lighthouse and refreshed daily.
Data syncs every 24 hours from Microsoft 365, M365 Lighthouse. The report reflects the most recent complete data set.
Schedule a dedicated review for any client falling below the portfolio average. Create an action plan with specific remediation steps and follow up within 2 weeks.
Yes. This report is designed to be QBR-ready. Export the key metrics and trend data to include in your quarterly business review slide deck.
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