مدیریت نوآوری

مدیریت نوآوری

طراحی الگوی طبقه‌بندی مشتریان با تأکید بر عوامل ارتباط با مشتری در صنعت بانکداری

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استادیار، ریاضی، دانشگاه آزاد اسلامی واحد تهران غرب، تهران، ایران
2 گروه مهندسی صنایع دانشگاه صنعتی مالک اشتر- تهران-ایران
چکیده
هدف از این مقاله ارائه یک مدل طبقه‌بندی مشتریان بانک و ارائه راهکارهای حاصل از آن به‌منظور پرداختن به مقوله ارتباط با مشتری در یکی از بانک‌های ادغامی بانک سپه برای دو سال متوالی 1398 و 1399 می‌باشد.



این پژوهش از نوع کاربردی و دارای روش تحقیق آمیخته است. برای جمع‌آوری داده‌ها با بهره‌گیری از تکنیک داده‌کاوی، پیشینه پژوهش موردمطالعه قرار گرفت و فهرستی از عوامل مؤثر بر طبقه‌بندی مشتریان بانک شناسایی شد. در گام بعدی از طریق مصاحبه با خبرگان حوزه بانکی متغیرهای طبقه بندی در بانک مورد مطالعه بومی سازی و نهایی شد. جامعه آماری پژوهش حاضر، خبرگان حوزه صنعت بانکداری بودند که با استفاده از روش نمونه‌گیری هدفمند تعداد 15 نفر از خبرگان برای مصاحبه انتخاب شدند. برای خوشه‌بندی عوامل شناسایی‌شده از رویکرد CRISP-DM استفاده شد که در نهایت تعداد 11 متغیر برای طبقه‌بندی مشتریان انتخاب گردید. بعد از مشخص شدن متغیرهای طبقه بندی، در مرحله بعد اطلاعات 5500 مشتری بانک به‌منظور طبقه‌بندی آن‌ها جمع‌آوری و جهت تحلیل آن از تکنیک خوشه‌بندی k-means استفاده شد. خوشه بندی اولیه با 5 خوشه نشان داد که 5/98 درصد داده‌ها در یک خوشه و تنها 5/1 درصد داده ها در 4 خوشه دیگر قرار می گیرند. پس مشتریان این 4 خوشه حذف(به عنوان مشتریان پرت) و به خوشه بندی مجدد مشتریان(شامل 5417 مشتری) با 12 خوشه پرداخته می شود.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Designing a customer classification model with emphasis on customer relationship factors in the banking industry

نویسندگان English

Balal Karimi 1
Afshar Bazyar 2
1 Associate Prof., Faculty of Mathematic, Islamic Azad university, West Tehran Branches, Tehran, Iran
2 department of industrial engineering- malek ashtar university- tehran-iran
چکیده English

The purpose of this article is to present a classification model of bank customers and to present the resulting solutions in order to deal with the category of customer relations in one of Sepeh Bank's integrated banks for 2019 and 2020.



This research is of an applied type and has a mixed research method. To collect data using data mining techniques, the research background was studied and a list of factors affecting the classification of bank customers was identified. In the next step, through interviews with banking experts, the classification variables in the bank under study were localized and finalized. The statistical population of the present study was banking industry experts, who were selected for interviews using a purposive sampling method, 15 experts. The CRISP-DM approach was used to cluster the identified factors, and finally 11 variables were selected for customer classification. After the classification variables were determined, in the next stage, information from 5,500 bank customers was collected for classification and the k-means clustering technique was used to analyze it. Initial clustering with 5 clusters showed that 98.5 percent of the data falls into one cluster and only 1.5 percent of the data falls into the other 4 clusters. Therefore customers in these 4 clusters are eliminated (as outliers) and customers (including 5,417 customers) are re-clustered into 12 clusters.

کلیدواژه‌ها English

Customer relationship
Ccustomer classification
Data mining
K-means
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  • تاریخ دریافت 04 تیر 1403
  • تاریخ بازنگری 30 بهمن 1403
  • تاریخ پذیرش 24 فروردین 1404