Driving Innovation and Embracing Change During a Pandemic with Pricing and Analytics Automation: The Case of ALH Hotels

Abstract

This case study focuses on revenue management (RM) and RM technology, as it details the implementation of an RM solution at ALH Hotels. The case study follows Brett, the National Accommodation Manager at ALH Hotels, and Murtaza, an RM consultant, as they appraised different RM tools based on ALH Hotels’ specific business requirements. Brett and Murtaza finally selected and implemented an RM solution to automate RM decision-making at ALH Hotels. The case study narrates a chronology of events that show the state of RM decision-making prior to the implementation, as well as the impact of the COVID-19 pandemic on revenue generation at ALH Hotels, and finally the consequences and results of the implementation of the RM solution.

Teaching Note

Case Summary

This case study follows the story of Brett, National Accommodation Manager for ALH Hotels and the Nightcap brand, and his approach to creating a revenue management (RM) strategy for the group. With the help of a consultant, Murtaza, they proceeded to select an automated RM tool for the business, called Pace Revenue. This case study was co-written with Brett and Murtaza, providing real-life examples and factual data of the past 2 years of the company. Moreover, this case study was written in a narrative way to allow students an insight into the decision-making and thought process of Brett and Murtaza.

Teaching and Learning Objectives

This case study aims for students to:

• 1. Reflect on the implementation of a revenue management strategy during a pandemic.

• 2. Discuss and evaluate the automation of revenue management decision-making.

• 3. Research and appraise different revenue management tools, based on specific business requirements.

The action verbs of the teaching objectives reflect a higher degree of learning, which is in line with the target audience, as developed below. The teaching objectives are focused on strategic RM, with an emphasis on automation and technology. This goes beyond the usual tactical approach to RM, focusing on demand forecasting or inventory management (Yeoman, 2016).

Target Audience

This case study is aimed for both undergraduate and postgraduate students. Revenue management is often found in both levels of studies in a hospitality degree. That said, this case study does focus on a more strategic approach to revenue management and the automation of decision-making, which undergraduate RM studies do not always include. Postgraduate RM studies do tend to have a strategic perspective embedded, which may contain a section on automation and RM technologies. Based on the learning objectives, this case study would fit undergraduate students in their final year or postgraduate students towards the middle of their degree.

Teaching Approach and Strategy

This case study can be provided to students as either class work or assessment, for either individual or groups of students. The case study can be delivered prior, during, or after class delivery, and a suggested lesson plan can be found below, highlighting what concepts must be delivered before the introduction of the case study to the students:

Case study introduction	Main theory/concept to be delivered beforehand
Prior to class delivery	RM foundations (demand forecasting, pricing and inventory management, distribution management)  RM KPI (occupancy, ADR, RevPAR, indexes) and internal/competitive analyses  Automated revenue management systems (Mahesh, 2015)
During class delivery	As above plus price optimisation (Vives et al., 2018)
After class delivery	As above plus technology and RM (Alrawadieh et al., 2021)

Theoretical Perspectives to Guide Students’ Discussion

This case study brings forward practical and factual elements of a real-life company and its approach to selecting an RM technology and automating decision-making. There is a clear focus on strategic pricing and the use of an RM tool to obtain real-time pricing decision-making. This and the steps taken by Brett and his team through the implementation of RM decision-making (manual at first and automated later) are also developed by Vives et al. (2018), who show a correlation between the adoption of technology, the real-time availability of data, pricing optimisation, and RM.

This correlation between technology and RM is further discussed by Alrawadieh et al. (2021), who review the impact of technology and big data on pricing strategies and RM results. Alrawadieh et al. further discuss how the adoption of technology and the use of different tools, such as price optimisers, rate shoppers, or reputation maximisation software, can complement automated RM systems and their results. This is the case with ALH Hotels and the implementation of Pace Revenue as an RM system and their results through the pandemic.

Individual or Group Work: In Class Discussion

This case study can be used either for individual or group work, as an in-class discussion. In either case, students would first work on their own or in a group focusing on the questions developed in the “Discussion and Questions” section. Then students would regroup to share answers and create a discussion. The activity should take overall 1–1.5 h, including reading the case study, further research, responding to the questions, and the discussion. If working in groups, a team leader could present the group findings to the class, to limit dispersion and background noise.

Assessment: Business Report or Group Presentation

This case study can also be used as an assessment, though it is advised to do so only as a group work. Lecturers can ask students to provide a business report, a group presentation, or both. The assessment can be based on the questions developed in the “Discussion and Questions” section, though lecturers can modify them slightly to allow the assessment to focus on most revenue management topics.

Additional Reading and Reference

• Vives, A., Jacob, M., & Payeras, M. (2018). Revenue management and price optimization techniques in the hotel sector: A critical literature review. Tourism Economics, 24(6), 720–752.