ارائه روشی برای بخش‌بندی پویای پایگاه مشتریان در سیستم هوش تجاری تطبیق‌پذیر

نوع مقاله: علمی-پژوهشی

نویسندگان

1 دانشکده مهندسی برق و کامپیوتر - دانشگاه آزاد قزوین

2 دانشکده مهندسی و علوم کامپیوتر - دانشگاه شهید بهشتی

3 دانشکده علوم‌اجتماعی و اقتصادی - دانشگاه الزهرا

چکیده

رکن اصلی هر سیستم هوشمند، توانایی آن در تطبیق با تغییرات محیطی است، بااین‌حال، توجه کافی به مسئله تطبیق‌پذیری در این سیستم‌ها نشده است. از این‌رو، هدف این مقاله ارائه روشی در سیستم هوش‌تجاری تطبیق‌پذیر است که به‌صورت پویا به بخش‌بندی مشتریان پرداخته و با نظارت بر رفتار خرید و همچنین تحلیل تازگی، تکرار و حجم پولی خرید هر یک از مشتریان، این بخش‌بندی را به‌روزرسانی می‌کند. در این پژوهش، روش‌های داده‌کاوی روی پیمانه تطبیق‌پذیری سیستم هوش تجاری تطبیق‌پذیر اعمال شده است تا بدین ترتیب مشتریان فعلی سازمان دسته‌بندی شده و در گذر زمان و با یادگیری از محیط، این دسته‌بندی بهبود یابد تا بتوان خدمات سفارشی‌شده به مشتریان ارائه داد این روش می‌تواند در کمتر از 0.5 ثانیه خوشه‌بندی اولیه را با توجه به تغییرات محیطی اصلاح کرده و از زمان‌های طولانی اجرای الگوریتم‌های خوشه‌بندی مرسوم (تقریباً در 22 درصد موارد) بکاهد. روش پیشنهادی با در نظرگرفتن بخش‌های مختلف مشتریان، مقادیر فعلی RFM آن‌ها و تغییراتی که با تکرار خرید در این مقادیر ایجاد می‌شود، خوشه‌بندی بهتر و جامع‌تری از مشتریان سازمان به دست می‌دهد که می‌تواند در بهبود عملکرد سیستم هوش تجاری تطبیق‌پذیر مفید واقع شود.

کلیدواژه‌ها


عنوان مقاله [English]

A Method for Dynamic Segmentation of Customer Base in an Adaptive Business Intelligence System

نویسندگان [English]

  • G. Ghanbaripanah 1
  • E. Nazemi 2
  • S. Rajaee Harandi 3
1 - Faculty of Electrical, Computer and IT Engineering, Islamic Azad University of Qazvin, Qazvin, Iran
2 Faculty of Science & Computer Eng., Shahid Beheshti University, Tehran, Iran
3 Faculty of Social Science and Economics, Alzahra University, Tehran, Iran
چکیده [English]

The core of every intelligent system is its ability to adapt to environmental changes, but there is not enough attention to the compatibility issue in these systems. Hence, the aim of this study is to provide a method in adaptive business intelligence system that dynamically segment customers and update this segmentation by monitoring purchasing behavior and analysis of "Recency", "Frequency" and "Monetary" of each one of them. In this research, data mining techniques have been applied on the adaptive modulus of the adaptive business intelligence system so that the current clients of the organization are classified and over time and with learning from the environment, this classification is improved to provide customized services to customers. This method can modify initial clustering with respect to environmental changes in less than 0.5 seconds and reduce the number of execution of conventional clustering algorithms (Approximately 22% of cases). The proposed method, considering different customer segments, their current RFM values and changes made by repeating purchases at these values, provides a better and more comprehensive clustering of the organization's customers that can be useful in improving the performance of an adaptive business intelligent system.

کلیدواژه‌ها [English]

  • Business Intelligence (BI)
  • Adaptive Business Intelligence
  • Decision Making
  • Self-adaptivity
  • Data Mining
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