Time series predict customer behavior analysis mobile banking
عنوان مقاله: Time series predict customer behavior analysis mobile banking
شناسه ملی مقاله: ICTI05_052
منتشر شده در پنجمین کنفرانس ملی فناوری های نوین در مهندسی برق و کامپیوتر در سال 1401
شناسه ملی مقاله: ICTI05_052
منتشر شده در پنجمین کنفرانس ملی فناوری های نوین در مهندسی برق و کامپیوتر در سال 1401
مشخصات نویسندگان مقاله:
Zahra Mamashli - Department of Management and Accounting University of Shargh-e Golestan Gonbad-e Kavus, Iran
خلاصه مقاله:
Zahra Mamashli - Department of Management and Accounting University of Shargh-e Golestan Gonbad-e Kavus, Iran
The purpose of this paper is to help businesses use datamining to analyze customers' dynamic behavior in mobilebanking and then drive augmented customer relationships overtime. The tool is used for customer relationships Traditionally,customer segmentation strategies are employed when dealingwith a large community of customers. The research recommendsan innovative methodology for forecasting segment-levelcustomer behavior. This method makes use of both theequipment and methods used by forecasters, such ascomputational intelligence methods and traditional time seriesforecasting methods. An assessment of the suitability of thesuggested method is conducted, as well as a case study based ondata from bank customers' mobile banking. The result of theARMA method in the RMSE evaluation model is ۰.۳۷ and theconfusion matrix has an accuracy of ۰.۸۹ for KNN (K = ۱, lag-۱:۴).
کلمات کلیدی: ARMA, ARIMA, KNN, SVMs, Customer behavior, Time series clustering, RFM model Introduction(Heading ۱)
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1545454/