Hotel Remodels
Company Size
1,000+
Product
- APT Test & Learn
Tech Stack
- Data Analytics
- Performance Measurement Tools
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Functions
- Business Operation
- Facility Management
Use Cases
- Predictive Maintenance
Services
- Data Science Services
- System Integration
About The Customer
An international hotel franchisor with thousands of hotels sought to enhance the aging properties of a core brand through a large-scale reinvention. The company operates globally and has a significant presence in the hospitality industry. With a vast portfolio of hotels, the company aims to maintain high standards and ensure customer satisfaction across all its properties. The company is committed to continuous improvement and innovation, leveraging data-driven approaches to optimize its operations and enhance guest experiences.
The Challenge
Management believed that a core brand underperformed, in part because of its outdated brand offerings. The hotel chain designed a hotel refurbishment program and began investing hundreds of thousands of dollars per hotel to update lobbies, guest rooms, and exterior signage. The brand’s management carefully tracked the performance of the initial set of “pilot” hotels but was unable to detect a clear impact on key performance metrics. Measured changes in RevPAR, occupancy, and customer satisfaction varied widely, even erratically, across the refurbished hotels. Management halted the remodel investment and reconsidered various options to address the brand’s underperformance.
The Solution
The company used APT’s Test & Learn solution to evaluate the precise impact of the capital investment on each hotel. Given the small number of hotels in the “pilot” program, it was critical to take an analytical approach that minimized the measurement error inherent in analyzing week-to-week changes in performance. APT’s refined measurements clearly identified incremental RevPAR and improvement in guest satisfaction. The confirmed increase in RevPAR made the capital investment attractive to hotel franchisees. APT’s analysis also confirmed that the remodel activity was not cannibalizing surrounding same-brand hotels, and that the incremental business was coming from nearby competitors. In addition, APT used guest data to identify the specific guest segments that the remodel program influenced the most. The company targeted these segments to enhance the program’s returns. Finally, APT identified the characteristics of hotels that were particularly well positioned to benefit from the remodel and therefore likely to generate above-average returns. The company used this analysis to determine which hotels should be prioritized for an earlier remodel, allowing all stakeholders to capture larger profit opportunities earlier in the remodel program.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Asset Management and Predictive Maintenance
The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.
