مشخصات مقاله | |
انتشار | مقاله سال 2016 |
تعداد صفحات مقاله انگلیسی | 22 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
منتشر شده در | نشریه الزویر |
نوع مقاله | ISI |
عنوان انگلیسی مقاله | Statistics-based CRM approach via time series segmenting RFM on large scale data |
ترجمه عنوان مقاله | رویکرد CRM مبتنی بر آمارها از طریق سری زمانی بخش بندی RFM در داده های مقیاس بزرگ |
فرمت مقاله انگلیسی | |
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 |
کلمات کلیدی | CRM، RFM، داده های مقیاس بزرگ، MCA، فاصله زمانی |
کد محصول | E5318 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
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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]. |