رویکرد CRM مبتنی بر آمارها از طریق سری زمانی بخش بندی RFM در داده های مقیاس بزرگ Statistics-based CRM approach via time series segmenting RFM on large scale data
- نوع فایل : کتاب
- زبان : انگلیسی
- ناشر : Elsevier
- چاپ و سال / کشور: 2018
توضیحات
رشته های مرتبط مدیریت
گرایش های مرتبط مدیریت کسب و کار، بازاریابی
مجله سیستم های مبتنی بر دانش – Knowledge-Based Systems
دانشگاه School of Computer Science – Beijing University of Posts and Telecommunications – China
منتشر شده در نشریه الزویر
کلمات کلیدی CRM، RFM، داده های مقیاس بزرگ، MCA، فاصله زمانی
گرایش های مرتبط مدیریت کسب و کار، بازاریابی
مجله سیستم های مبتنی بر دانش – Knowledge-Based Systems
دانشگاه School of Computer Science – Beijing University of Posts and Telecommunications – China
منتشر شده در نشریه الزویر
کلمات کلیدی CRM، RFM، داده های مقیاس بزرگ، MCA، فاصله زمانی
Description
1. Introduction The main task of customer relationship management (CRM) is to value and retain users by exploring the potential relationships among users and deriving innate values of their own characteristics [1], because the characteristics interact with these relation ships [2]. Characteristics are quantitative and qualitative ones; both are supposed to reflect different relationships [3]. Given the intense competition of telecommunication operators and rapid growth of telecom service data generated by smart phones, CRM for telecom service data has been a strategic initiative method for identifying high-net– worth clients and providing improved service [4]. During the course of valuing users by CRM, the potential relationships among users can be divided into the external entrance evident to researchers, and the internal entrance built by their innate characteristics. Data for innate entrance is more available; thus, we explore quantitative and qualitative characteristics of internality in detail. Typically, the RFM model explores quantitative characteristics and enriches the criteria for potential relationships in CRM, because customer value can be reflected by the most recent consumption as the recency, the frequency in normal consumption, and the monetary cost of consumers in the model [5, 6, 7, 8]. Given the magnitude and complexity of telecom service data, the trend of change with time has been considered for the RFM model [5].
