Toward customer hyper-personalization experience - A data-driven approach

被引:5
|
作者
Mendia, J. M. Valdez [1 ]
Flores-Cuautle, J. J. A. [2 ]
机构
[1] Customer Experience & Data Analyt, 360 Centric, Mexico City, DF, Mexico
[2] CONACYT Tecnol Nacl Mexico IT Orizaba, Postgrad Div, Orizaba, Veracruz, Mexico
来源
COGENT BUSINESS & MANAGEMENT | 2022年 / 9卷 / 01期
关键词
Big Data; Customer interaction; touchpoints; personalization; INNOVATION; CREATION; QUALITY;
D O I
10.1080/23311975.2022.2041384
中图分类号
F [经济];
学科分类号
02 ;
摘要
Today's omnichannel business models incorporate physical and digital touchpoints interacting with customers. A hyper-personalization strategy relies on the organization's capability to gather and transform customer data into personalized experiences; therefore, when a hyper-personalization organizational plan is put in place, it serves two main functions: to deliver personalized experiences and increase the number of customers receiving such experiences. For this to happen, four elements are required for a hyper-personalization strategy: data foundation, decisions, design, and distribution. While customer master data management relies on the correct identification of a customer, a real customer insight can only be achieved when three types of customer data are gathered: Identity, Contactability, and Traceability (I, C, T)- fulfilling the first element of a hyper-strategy. This article aims to identify the benefits in the total number of customers that can receive a hyper-personalization strategy when real-time touchpoints are linked to a customer Master Data Management that integrates the three types of customer data.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Data-Driven Robotic Tactile Grasping for Hyper-Personalization Line Pick-and-Place
    Xie, Zhen
    Chen, Josh Ye Seng
    Lim, Guo Wei
    Bai, Fengjun
    [J]. ACTUATORS, 2023, 12 (05)
  • [2] Hyper-Personalization as a Customer Relationship Management Tool in a SMART Organization
    Pukas, Anetta
    [J]. PROBLEMY ZARZADZANIA-MANAGEMENT ISSUES, 2022, 20 (03): : 95 - 108
  • [3] Assessing an on-site customer profiling and hyper-personalization system prototype based on a deep learning approach
    Micu, Adrian
    Capatina, Alexandru
    Cristea, Dragos Sebastian
    Munteanu, Dan
    Micu, Angela-Eliza
    Sarpe, Daniela Ancuta
    [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2022, 174
  • [4] A Dynamic Clustering Approach to Data-Driven Assortment Personalization
    Bernstein, Fernando
    Modaresi, Sajad
    Saure, Denis
    [J]. MANAGEMENT SCIENCE, 2019, 65 (05) : 2095 - 2115
  • [5] Data-driven mergers and personalization
    Chen, Zhijun
    Choe, Chongwoo
    Cong, Jiajia
    Matsushima, Noriaki
    [J]. RAND JOURNAL OF ECONOMICS, 2022, 53 (01): : 3 - 31
  • [6] Managing service flexibility in healthcare for improved customer experience: a data-driven approach
    Kumar, Pradeep
    [J]. JOURNAL OF STRATEGIC MARKETING, 2024, 32 (07) : 891 - 912
  • [7] A Data-Driven Customer Quality of Experience System for a Cellular Network
    Jung, Hyunglok
    Mo, Jeonghoon
    Park, Jungju
    [J]. MOBILE INFORMATION SYSTEMS, 2017, 2017
  • [8] A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
    Zhang, Tianyuan
    Moro, Sergio
    Ramos, Ricardo F.
    [J]. FUTURE INTERNET, 2022, 14 (03):
  • [9] Toward a community-driven approach to urban data-driven governance
    Bui, Matthew
    [J]. INTERNATIONAL COMMUNICATION GAZETTE, 2024,
  • [10] Customer future profitability assessment: A data-driven segmentation function approach
    Tian, Chunhua
    Ding, Wei
    Cao, Rongzeng
    Wang, Michelle
    [J]. DATA ENGINEERING ISSUES IN E-COMMERCE AND SERVICES, PROCEEDINGS, 2006, 4055 : 28 - 39