مدل‎‌سازی پویای رویگردانی مشتری در صنعت بانکداری با استفاده از کاوش دنباله‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد مهندسی فناوری اطلاعات، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران.

2 استادیار، گروه مهندسی فناوری اطلاعات، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران

چکیده
شناخت دقیق رفتار مشتریان و پیش‌بینی روندهای آتی آن‌ها از اولویت‌های کلیدی در سازمان‌ها و به‌ویژه صنعت بانکداری است؛ زیرا حفظ مشتریان فعلی به‌مراتب کم‌هزینه‌تر از جذب مشتریان جدید است. یکی از مهم‌ترین چالش‌ها در این حوزه، شناسایی به‌موقع علائم رویگردانی مشتریان و پیش‌گیری از آن است، به‌ویژه در مواردی که نارضایتی از خدمات یا تغییرات رفتاری تدریجی منجر به قطع تعامل با سازمان می‌شود. در این پژوهش، با تمرکز بر مدل‌سازی پویای رویگردانی مشتریان، داده‌های مربوط به تراکنش‌ها و مانده‌حساب مشتریان طی یک دوره سه‌ساله از یکی از پنج بانک بزرگ کشور جمع‌آوری و تحلیل شده است. روش پیشنهادی مبتنی بر کاوش الگوهای دنباله‌ای است که به شناسایی توالی‌های رفتاری منجر به رویگردانی کمک می‌کند. الگوهای استخراج‌شده در دو دسته اصلی طبقه‌بندی شده‌اند: الگوهای غالب که وضعیت‌هایی با ریسک بالای رویگردانی یا وفاداری پایدار را نمایش می‌دهند، و الگوهای با اطمینان بالا و پشتیبان کم که ابزار مؤثری برای رصد مشتریان در آستانه رویگردانی فراهم می‌کنند. مزیت اصلی این روش، تولید قواعد تفسیرپذیر به‌صورت اگر-آنگاه است که به‌راحتی در محیط‌های واقعی قابل درک و پیاده‌سازی هستند. این چارچوب تحلیلی می‌تواند نقش مؤثری در طراحی مداخلات هدفمند در مدیریت ارتباط با مشتری ایفا کند.

کلیدواژه‌ها


عنوان مقاله English

Dynamic Customer Churn Modelling In Banking Industry Using Sequence Mining

نویسندگان English

Alireza Shebro 1
Elham Akhondzadeh Noughabi 2
1 1. MSc. in Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
2 Assistant Professor, Department of Information Technology Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
چکیده English

Accurately understanding customer behavior and predicting future trends are key priorities for organizations, particularly in the banking industry, where retaining existing customers is significantly more cost-effective than acquiring new ones. One of the major challenges in this context is the timely identification and prevention of customer churn, especially when dissatisfaction with services or gradual behavioral changes lead to disengagement. This study focuses on modeling dynamic customer churn by analyzing three years of transaction and account balance data from one of the top five banks in Iran. The proposed method employs sequence pattern mining to identify behavioral sequences that lead to churn. The extracted patterns are categorized into two main groups: dominant patterns that indicate high-risk churn situations or sustained loyalty, and high-confidence, low-support patterns that serve as effective tools for monitoring customers on the verge of churn. A key advantage of this method is its ability to generate interpretable if-then rules, which are easily understood and applied in real-world business environments. This analytical framework offers valuable insights for designing targeted interventions in customer relationship management.

کلیدواژه‌ها English

Customer Dynamics
Data Mining
Dynamic Customer Churn Modelling
Sequence Mining
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دوره 10، شماره 4
ویژه نامه مدیریت بازرگانی
زمستان 1404
صفحه 119-154