Hospitality Case Study

A national hospitality company with more than 90 hotel properties across major U.S. business and leisure markets needed a better way to manage onsite IT support across its portfolio. In clustered city markets, full-time technicians were covering multiple hotels and spending too much paid time traveling between properties. That slowed response when urgent issues came up and made the support model expensive to scale. Cartennas replaced that travel-heavy structure with a more flexible onsite IT support model built around property-level dispatch, on-demand onsite response, and centralized coordination across clustered markets, reducing annual support spend from $1,423,000 to $583,430 and generating $839,570 in annual savings.

59% Reduction in annual onsite IT support and third-party support spend
$839,570 Annual savings after replacing a travel-heavy technician model
90+ Hotel properties across major U.S. markets
Centralized Dispatch coordination across clustered city markets

Key Outcomes

  • 59% reduction in annual onsite IT support and third-party support spend
  • $839,570 in annual savings
  • Annual support cost reduced from $1,423,000 to $583,430
  • Less paid technician time lost to travel between clustered properties
  • More flexible response to urgent onsite issues without expanding headcount
  • Centralized dispatch coordination across multiple hotel markets

Customer Snapshot

Industry Hospitality
Business Type Hotel ownership, investment, and management company
Portfolio Scale More than 90 hotel properties
Geographic Reach Major U.S. business and leisure markets
Representative Cluster Markets Boston, New York, Washington, DC, Atlanta, Dallas, Los Angeles, and San Diego
Cluster Density Example Boston 7, New York 7, Washington, DC 11, Atlanta 9, Dallas 8, Los Angeles 9, San Diego 5
Spend Before Cartennas $1.423 million annually
Previous Support Model Full-time onsite IT technicians covering property clusters, supplemented by third-party support

The Challenge

The company needed to support a large hotel portfolio spread across major U.S. markets, often with multiple properties in the same city. In those clustered markets, full-time technicians were assigned to groups of hotels and moved between properties as issues came up.

That structure looked workable on paper, but it created a costly operational problem. A meaningful share of paid technician time was being spent in transit instead of resolving issues onsite. If one hotel had an urgent issue, support could be delayed because the assigned technician was already at another property.

In hospitality, that kind of delay can affect front-desk systems, connectivity, point-of-sale devices, back-office hardware, guest-facing technology, and day-to-day property operations. Leadership needed a support model that could reduce wasted paid time, improve response to urgent onsite issues, support clustered city markets more efficiently, and avoid solving the problem by simply adding more headcount.

Why the Previous Model Was Not Working

Paid labor was being diluted by travel

The company was paying for skilled onsite IT support, but too much of that paid time was being consumed by technicians driving between hotels. Across a national portfolio, that became a structural cost issue.

Urgent issues had to wait on technician location

When one technician was responsible for multiple properties, response capability was often limited by routing and travel time rather than technical ability.

Cluster coverage was rigid

A technician assigned to several hotels cannot be in two places at once. When issues overlapped across properties, support availability became uneven even within the same market.

The default answer was more spend

As the portfolio grew, the old model naturally pushed leadership toward adding more full-time coverage or spending more on third-party support without fixing the underlying inefficiency.

The Cartennas Solution

Cartennas replaced the travel-heavy support structure with a more flexible onsite IT support model centered on on-demand dispatch, city-market coverage, property-level response, and centralized coordination through the Cartennas platform.

Instead of forcing service requests through one technician’s daily route, the company could request onsite support based on actual property need. Intake, triage, dispatch coordination, status visibility, and escalation could all be managed through one operating layer.

Property-level dispatch

Support no longer depended as heavily on whichever technician happened to be assigned to a cluster. The company could direct onsite help to the hotel that actually needed it.

Less non-productive movement

By reducing dependence on inter-property travel, more paid support time could be used for actual onsite work instead of movement between hotels.

Better handling of urgent issues

Urgent onsite problems no longer had to wait as often for one assigned technician to finish at another property and drive over.

Scalability without headcount expansion

The company improved support coverage across clustered markets without needing to solve the problem by adding more internal technicians.

How Cartennas Delivered

Coverage moved closer to actual demand

Instead of making one technician’s movement the center of the model, Cartennas let the company direct onsite IT support where property-level demand actually existed.

Urgent issues became easier to isolate and handle

When a property had an urgent issue, response no longer depended as heavily on whether the assigned technician was already across town at another hotel.

Support spend became more productive

The company’s support budget was no longer carrying the same level of travel waste. More of every support dollar could go toward actual onsite service activity.

Dispatch became centralized instead of fragmented

The Cartennas platform helped centralize intake, routing, status tracking, and escalation across the portfolio, making it easier to coordinate service activity across multiple clustered hotel markets.

Business Impact

The clearest result was financial. The company reduced annual onsite IT technician and third-party support spend from $1,423,000 to $583,430.

Intermediate calculation:
$1,423,000 - $583,430 = $839,570 in annual savings
$839,570 / $1,423,000 = 59% reduction

Lower annual support spend

This was not a marginal improvement. It was a structural reduction in support cost driven by a better operating model.

Better use of paid support time

Before Cartennas, a meaningful share of paid labor was being absorbed by travel between hotels. After the change, more paid support time could be used for productive onsite work.

Faster response for urgent onsite issues

By reducing dependence on one technician’s location and route, the company improved its ability to respond when urgent issues affected hotel operations, guest-facing systems, connectivity, or staff workflows.

More scalable support across clustered markets

Cartennas gave the company a more flexible alternative to expanding full-time staffing, making support across clustered city markets more scalable without repeating the same fixed-cost pattern.

What Changed

Before Cartennas, the company’s onsite IT support structure relied heavily on full-time technicians covering groups of hotels within each city market, with third-party support filling gaps. That created local coverage, but it also wasted paid time, reduced emergency flexibility, and made the model expensive to scale.

With Cartennas in place, the company moved to a more flexible onsite IT support structure built around actual property demand rather than technician movement. Instead of spending $1.423 million annually on a travel-heavy mix of full-time technicians and third-party support, the company reduced annual spend to $583,430 and saved $839,570 while improving flexibility, centralizing dispatch, and avoiding headcount expansion.

About the Customer

This customer is a national hospitality company operating more than 90 hotel properties across major U.S. business and leisure markets. Its clustered city footprint makes it a strong example of how inter-property technician travel can become costly, rigid, and difficult to scale across a large portfolio.