Deploying a Location-Based Coupon Recommendation Service in Retail: Challenges and Lessons Learnt

被引:0
|
作者
Stavrou, Vasilis [1 ]
Griva, Anastasia [2 ]
Bardaki, Cleopatra [3 ]
机构
[1] Athens University of Economics and Business, ELTRUN, Athens,11362, Greece
[2] University of Galway, J.E. Cairnes School of Business & Economics, Lero - The SFI Research Centre for Software, Galway,H91 WN80, Ireland
[3] Harokopio University of Athens, Department of Informatics and Telematics, Athens,17778, Greece
来源
关键词
Sales;
D O I
10.1109/TTS.2024.3448506
中图分类号
学科分类号
摘要
Location-based services have been increasingly used to support various decisions in retail such as coupon recommendation, indoor advertisement, smart targeting, etc. However, effectively tracking user's position to provide extra value embeds several challenges ranging from technical and infrastructure to business, user acceptance and social. This paper presents a location-based coupon recommendation service deployed in a retail store, using Bluetooth Low Energy (BLE) Beacons technology to track the customers. We aim to share the challenges we encountered, and the factors that affected our system's quality and performance; and show how we handled the issues that arose. Our study moves beyond the technical challenges and discusses the business and user acceptance challenges, highlighting the role of the application context. This paper aspires to provide realistic and more holistic, not just technical, guidelines on prospective researchers and designers of location-based services for retail stores and other contexts. The outcome of the paper highlights new directions for existing challenges and introduces new ones related to beacon configuration, the choice of the unit of analysis, the definition of areas of interest, and acceptance by employees. © 2024 The Authors.
引用
收藏
页码:368 / 376
相关论文
共 50 条
  • [1] Mining Location-based Service Data for Feature Construction in Retail Store Recommendation
    Chen, Tsung-Yi
    Chen, Lyu-Cian
    Chen, Yuh-Min
    ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS, ICDM 2017, 2017, 10357 : 68 - 77
  • [2] Location-based service with context data for a restaurant recommendation
    Lee, Bae-Hee
    Kim, Heung-Nam
    Jung, Jin-Guk
    Jo, Geun-Sik
    DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, 2006, 4080 : 430 - 438
  • [3] Location-based collaborative filtering for web service recommendation
    Venkatachalaappaswamy M.
    Ramaraj V.
    Ravichandran S.
    Recent Patents on Computer Science, 2019, 12 (01): : 34 - 40
  • [4] Location-Based Service Using Ontology and Collaborative Recommendation
    Hu, Lantao
    Tong, Qiuli
    Du, Zhao
    Liu, Yongqi
    Tang, Yeming
    2014 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE, ELECTRONICS AND ELECTRICAL ENGINEERING (ISEEE), VOLS 1-3, 2014, : 652 - +
  • [5] A Secure Location-Based Coupon Redeeming System
    Prasad, J. Maruthi Nagendra
    Subramanyam, A.
    EMERGING TRENDS IN ELECTRICAL, COMMUNICATIONS AND INFORMATION TECHNOLOGIES, 2017, 394 : 19 - 25
  • [6] Location-based Hierarchical Matrix Factorization for Web Service Recommendation
    He, Pinjia
    Zhu, Jieming
    Zheng, Zibin
    Xu, Jianlong
    Lyu, Michael R.
    2014 IEEE 21ST INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2014), 2014, : 297 - 304
  • [7] Location-based deep factorization machine model for service recommendation
    Wang, Qingren
    Zhang, Min
    Zhang, Yiwen
    Zhong, Jinqin
    Sheng, Victor S.
    APPLIED INTELLIGENCE, 2022, 52 (09) : 9899 - 9918
  • [8] Location-based deep factorization machine model for service recommendation
    Qingren Wang
    Min Zhang
    Yiwen Zhang
    Jinqin Zhong
    Victor S. Sheng
    Applied Intelligence, 2022, 52 : 9899 - 9918
  • [9] Competitive location-based mobile coupon targeting strategy
    Luo, Meiling
    Li, Gang
    Chen, Xudong
    JOURNAL OF RETAILING AND CONSUMER SERVICES, 2021, 58
  • [10] Competition Strategies for Location-Based Mobile Coupon Promotion
    Xia, Pengcheng
    Li, Gang
    Cheng, T. C. E.
    Shen, Ao
    JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH, 2021, 16 (07): : 3248 - 3268