The theory-practice research gains from big data: Evidence from hospitality loyalty programs

Paulo Rita, Maria Teresa Borges Tiago, Joana Caetano

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
24 Downloads (Pure)

Abstract

Purpose
The hospitality industry values segmentation and loyalty programs (LPs), but there is limited research on new methods for segmenting loyalty program members, so managers often rely on conventional techniques. This study aims to use big data-driven segmentation methods to cluster customers and provide a new solution for customer segmentation in hotel LPs.

Design/methodology/approach
Using the k-means algorithm, this study examined 498,655 profiles of guests enrolled in a multinational hotel chain’s loyalty program. The objective was to cluster guests according to their consumption behavior and monetary value and compare data-driven segments based on brand preferences, demographic data and monetary value with loyalty program tiers.

Findings
This study shows that current tier-based LPs lack features to improve customer segmentation, and some high-tier members generate less revenue than low-tier members. Therefore, more attention should be given to truly valuable customers.

Practical implications
Hotels can segment LP members to develop targeted campaigns and uncover new insights. This will help to transform LPs to make them more valuable and profitable and use differentiated rewards and strategies.

Originality/value
As not all guests or hotel brands benefit equally from LPs, additional segmentation is required to suit varying guest behaviors. Hotel managers can use data mining techniques to develop more efficient and valuable LPs with personalized strategies and rewards.
Original languageEnglish
Pages (from-to)4486-4501
Number of pages16
JournalInternational Journal of Contemporary Hospitality Management
Volume35
Issue number12
Early online date1 May 2023
DOIs
Publication statusPublished - 8 Nov 2023

Keywords

  • Loyalty
  • Hotel loyalty programs
  • Customer segmentation
  • Clustering
  • k-means
  • Big data

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