“Client Coverage per Technician”
Autotask PSA Datto RMM Datto Backup Microsoft 365 SmileBack HubSpot IT Glue All reports
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How many clients does each technician cover?

Client Coverage per Technician

Built from: Autotask PSA
How this report was made
1
Autotask PSA
Multiple data sources combined
2
Proxuma Power BI
Pre-built MSP semantic model, 50+ measures
3
AI via MCP
Claude or ChatGPT writes DAX queries, executes them, formats output
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This Report
KPIs, breakdowns, trends, recommendations
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Client Coverage per Technician

This report provides a detailed breakdown of client coverage per technician 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: Account managers, MSP owners, and service delivery leads

How often: Monthly for client reviews, quarterly for QBRs, on-demand when client signals change

Time saved
Cross-referencing client data from multiple tools manually takes hours. This report brings it together.
Client intelligence
See the full picture of each client across service, satisfaction, and commercial metrics.
Retention data
Early warning signals for at-risk clients, backed by actual data instead of gut feeling.
Report categoryClient Management
Data sourceAutotask PSA · Datto RMM · Datto Backup · Microsoft 365 · SmileBack · HubSpot · IT Glue
RefreshReal-time via Power BI
Generation timeUnder 15 minutes
AI requiredClaude, ChatGPT or Copilot
AudienceAccount managers, MSP owners
Where to find this in Proxuma
Power BI › Client Management › Client Coverage per Technician
What you can measure in this report
AI-Generated Power BI Report
Data source: Autotask PSA · Generated March 2026
NL version
Client Coverage per Technician
Unique clients served, tickets closed, and hours logged — ranked by client breadth
146
Broadest Reach
Daniel Daniels
16
Core Team
Techs with 100+ clients
4,803
Most Tickets
Tracy Fitzpatrick
2,136h
Deepest Hours
James Li (117 clients)
Core Team: Client Coverage Breakdown
The 16 technicians below each serve 100 or more unique clients — covering nearly every account in the portfolio. The spread is remarkably tight: just 38 clients separate the top from the bottom of this group.
Daniel Daniels
146
Gregory Horn
143
Tracy Fitzpatrick
143
Brandon Bishop
137
Jonathon Burton
137
John Mahoney
127
Ronald Smith
122
Paula Lewis MD
118
Rose Russell
117
James Li
117
David Collins
117
Mr. Craig Peck
115
Todd Sloan
115
Maxwell Reed
115
David Brown
109
Nathan Curtis
108
TechnicianClientsTicketsHours
Daniel Daniels2753,2201,418
Brandon Bishop2673,2751,362
Maxwell Reed2462,6132,050
Andrew Roberts2242,2971,888
Gregory Horn2102,0171,505
View DAX Query — Unique clients per technician 
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "ClientCount", DISTINCTCOUNT('BI_Autotask_Time_Entries'[company_name]), "TicketCount", DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id]), "TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked])), [ClientCount], DESC)
Work Profile: Four Ways Techs Cover Clients
Client count alone doesn't show how a tech works. Pairing it with ticket volume and hours reveals four distinct profiles across your team.

Broad Coverage

High client count, moderate tickets and hours. These techs touch most accounts with steady, distributed effort across the portfolio.

Daniel Daniels Jonathon Burton John Mahoney Ronald Smith Paula Lewis MD Rose Russell

Volume Handlers

High client count AND high ticket count. These techs are the front line — wide coverage, fast turnover, many short interactions.

Tracy Fitzpatrick (4,803 tickets) Brandon Bishop (3,275) Maxwell Reed (2,613)

Deep Specialists

Moderate client count, very high hours per client. Few tickets but long-running engagements — likely project or engineering work.

James Li (2,136h, 794 tickets) Kevin Allen (2,060h, 99 tickets) Dr. Amber Ayala DVM (2,400h)

Hybrid

High hours and moderate-to-high client count — carrying both breadth and depth. Often senior techs with mixed project and support loads.

Gregory Horn Maxwell Reed Andrew Roberts
Key Insights
What the data tells us about coverage, risk, and staffing patterns.

Coverage is remarkably even across the core team

The 16 techs serving 100+ clients span a range of just 38 — from 108 to 146. That's unusually tight for a team this size and suggests strong load-balancing practices or a routing setup that spreads tickets broadly.

Tracy Fitzpatrick: 33 tickets per client on average

With 4,803 tickets across 143 clients, Fitzpatrick averages more interactions per client than anyone on the team. That's either a sign of high client demand — or a routing imbalance worth investigating.

James Li logs 18 hours per client versus Tracy's 9

Both serve 117 clients. But Li logs 2,136 hours with only 794 tickets — roughly 2.7 hours per ticket. Fitzpatrick logs 1,290 hours across 4,803 tickets — about 16 minutes each. Two very different roles, same client coverage number.

Succession risk at the top of the breadth chart

Daniel Daniels, Gregory Horn, and Tracy Fitzpatrick each cover 143–146 clients. If any one of them leaves, that's a large portion of the client base that loses its primary technical contact. Cross-training depth matters here.

Frequently Asked Questions

What counts as a "unique client" in this report? +
A unique client is a distinct company name from Autotask PSA time entries. If a technician has logged time on any ticket, project task, or service call for a company — even once — that company counts as a unique client served.
Does this include project work or only support tickets? +
The query pulls from the BI_Autotask_Time_Entries table which includes all logged time — support tickets, service calls, project tasks, and internal work. Client coverage figures reflect total engagement, not just helpdesk activity.
Why do some techs have very few clients but many hours? +
Specialists like Kevin Allen (54 clients, 2,060 hours) and Dr. Amber Ayala DVM (46 clients, 2,400 hours) are concentrated on fewer accounts for longer periods. This is typical of engineers doing infrastructure projects, onboardings, or deep technical work rather than broad support coverage.
Can I filter this report by date range or client type? +
The report as published reflects all-time data from Autotask. In Power BI Desktop, you can add a date filter on BI_Autotask_Time_Entries[date_worked] to scope results to a specific period — for example, to see client coverage only in the last 12 months.

Related Reports

Most & Least Productive Technicians › Tech Performance Over Time › Billable Percentage per Tech › Weekly Hours vs Capacity › Average Time per Ticket › Management Performance Summary ›
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