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Case Study Hospitality · Multi-Location

How a hotel group replaced 5 tools, saved 8 hrs/week, and cut software costs 60%

A multi-location hospitality operator with 35 staff across 3 properties was drowning in disconnected apps. In 30 days on FoxtInn, they got everything under one roof — and their GMs finally had time to manage.

60%

Software cost reduction

8 hrs

Saved per manager/week

30

Days to full deployment

5 → 1

Tools consolidated

Client Snapshot

Industry

Hotel / Hospitality

Scale

3 properties, 35 staff

Challenge

5 disconnected tools, scheduling chaos, missed maintenance

Solution

FoxtInn Pro, full deployment in 30 days

The problem: five tools that didn't talk to each other

Before FoxtInn, the operations team was running their three properties across five separate platforms: a scheduling app, a separate time clock, a maintenance ticketing system, WhatsApp groups for team communication, and a spreadsheet-based task tracker. The GM described it as "trying to run a business on sticky notes."

Every morning started the same way — pulling up five browser tabs, manually checking what happened overnight, chasing down status updates from department heads, and re-routing issues that had fallen through the cracks. A maintenance request logged in the ticketing app never made it to the morning briefing. A scheduling conflict that showed in one system wasn't visible in another. The team was reactive, and guests felt it.

"We were paying for five apps and still didn't have one clear picture of what was happening at our properties. I spent two hours every morning just getting up to speed."

— General Manager, 3-property hotel group

The switch: 30 days to full deployment

The team signed up for FoxtInn Pro and went live across all three properties within 30 days. The onboarding process involved migrating their existing staff roster, setting up department channels, and configuring the AI Virtual Manager with their key operational metrics.

Staff training took less than one shift per role. The mobile-first design meant frontline staff didn't need any classroom instruction — they were clocking in, receiving tasks, and logging maintenance issues within hours of getting their logins.

30-Day Implementation Timeline

1

Days 1–5: Setup & data migration

Staff roster imported, roles configured, department channels created. QR codes printed and placed at check-in, maintenance bays, and back-of-house stations.

2

Days 6–14: Parallel run

Team ran FoxtInn alongside existing tools. Issues logged in both systems. Within 10 days, staff were defaulting to FoxtInn unprompted.

3

Days 15–30: Full cutover

Old tools cancelled. All 35 staff fully on FoxtInn. WhatsApp groups archived. Morning briefings now run from the AI Virtual Manager dashboard.

Day 30+: Results measured

Software savings confirmed. Manager time recaptured. First month on record with zero missed maintenance SLAs.

The results

At 30 days, the operations team ran a review. The numbers were clearer than expected.

$230/mo saved

Software consolidation

5 tools at avg. $65/mo cancelled. FoxtInn Pro at $175/mo for 35 staff.

24 hrs/week

Manager time recaptured

3 GMs × 8 hrs/wk on admin tasks eliminated. Equivalent to a part-time hire.

0

Missed maintenance SLAs

Down from an average of 4–6 per month before FoxtInn.

1 day

Payback period

Monthly savings exceeded FoxtInn cost by Day 1 of going live.

"The morning briefing from the AI Virtual Manager changed how I start my day. I walk in knowing exactly what needs attention — instead of spending two hours figuring it out. That's the biggest shift."

— General Manager, 3-property hotel group

What's next for this operator

At 90 days, the team is looking at upgrading to the Advanced plan to unlock portfolio-level analytics across all three properties and gain access to the RevPAR and market pulse insights. They're also piloting the GuestLoop QR intake at one property for in-room service requests.

The GM put it simply: "We bought time back. That's the most valuable thing any software can do."

This case study is based on a composite of real operator feedback and industry benchmarks. Specific client details are anonymized. Individual results vary based on team size, operational complexity, and implementation approach.

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