Hybrid Model Of Structural Equation Modeling Pls And Rfm (Recency, Frequency And Monetary) Model To Improve Bank Average Balance

As a business players, entrepreneurs certainly need bank products and supports that provide fast and easy services with wide-spread network in Indonesia. In this study, Structural Equation Model (SEM) identify the transaction that influence the average balance. The objects of the RFM segmentation on the selected transaction is to understand customer segment score and build a marketing strategy for each segment with different levels of loyalty for the Financial result of higher Average Balance. The segmentation results found three driver categories, High Recency, Mid Recency and Low Recency category. High Recency is considered Active customer where campaign category can be cross/up-selling and promotional accordingly with their Frequency and Monetary category. Mid Recency category is considered Risky customer where campaign category can be retention program accordingly with their Frequency and Monetary. Last, Low Recency is considered already Churn customer where campaign category is to conduct reactivation.

Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors

Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analysis to understand the data and finally applies K-Means Clustering to the RFM Model which produces three clusters based on the Elbow model. In cluster 0 there are 2,436 customers, in cluster1 1,880 and finally in cluster2 there are 18 customers. RFM analysis can segment customers into homogeneous groups quickly with a minimum set of variables. Good analysis can increase the effectiveness and efficiency of marketing plans, thereby increasing profitability with minimum costs.

An LRFM Model to Analyze Outpatient Loyalty From a Medical Center in Taiwan

This research is intended to study the behaviors of outpatients in a medical center and constructs a set of data exploration procedures such that hospital management can deal with patient relationship management more effectively. This study adopts LRFM (length, recency, frequency, and monetary) model and cluster analysis, including self-organizing maps and K-means method, to categorize 321,908 outpatients of the medical center into 12 groups and then uses the multidimensional customer clustering philosophy to classify the outpatients. Outpatients can be categorized into five different types of groups, namely, core customer groups, potential customer groups, new customer groups, lost customer groups, and resource-consuming customer groups. In addition, seven types of outpatients based on five types of categories are identified. The similarities and differences of each group based on the patients’ characteristics are analyzed to give differentiation strategy advices for hospital management. Hospital management thus can design the optimal service strategies, provide the best care services, enhance hospital’s performance, and reduce the overall cost to establish quality relationships with outpatients.

Use of Optimal Control in Studying the Dynamical Behaviors of Fractional Financial Awareness Model

Around there, we new examination has been done on past investigations of perhaps the main numerical models that portray the worldwide monetary development and that is depicted as a non-straight fragmentary monetary model of mindfulness, where the investigations address the means following: One: The schematic of the model is proposed. Two: The sickness-free balance point (DFE) and the soundness of the harmony point are talked about. Three: The strength of the model is satisfying by drawing the Lyapunov examples. Fourth: The presence of consistently stable arrangements is examined. Five: The Caputo is portrayed as the fragmentary subsidiary. Six: Fragmentary ideal control for NFFMA is examined, by explaining the partial ideal control through drawing when control. Seven: We are utilizing the calculation, summed up Adams–Bashforth–Moulton technique (GABMP) to tackle the is utilized to take the goal of an NFFMA. At last, we show that GABMP is profoundly indistinguishable. The mathematical strategy utilized in this composition to address this model has not been used by any creator before that. Additionally, this model with partial subordinates characterized in this manner has not been concentrated before that. The strategies used are not difficult to impact, regardless of whether logical or mathematical, and give great results.MSC: 41A28, 65D05, 65H10, 65L20, 65P30, 65P40, 65Z05.

Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions

In Canada, financial advisors and dealers are required by provincial securities commissions and self-regulatory organizations—charged with direct regulation over investment dealers and mutual fund dealers—to respectively collect and maintain know your client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor’s guidance, make decisions on their investments that are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients covering the period from January 1st to August 12th 2019. We use a modified behavioral finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and unsupervised machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information—such as gender, residence region, and marital status—does not explain client behaviours, whereas eight variables for trade and transaction frequency and volume are most informative. Hence, our results should encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours.

PENERAPAN FREQUENCY,RECENCY, MONETERY MODEL DAN ALGORITMA K-MEAN PADA SISTEM PENGELOMPOKAN PELANGGAN (STUDI KASUS: PT. DITRA MANUNGGAL JAYA)

PT. Ditra Manunggal Jaya yang bergerak dalam bisni Sembilan kebutuhan pokok. Dalam proses bisnisnya PT Ditra Manunggal Jaya masih mengbungkan proses manual dengan transaksi dari pelanggan dari seluruh Indonesia, dengan permasalahan tersebut sangat tidak bisa perusahaan untuk memanajemen pelanggan. Sehingga dibutuhkan suatu sistem yang secara otomatis dapat memanjemen pelanggan. Frequency, recency and monetary model adalah suatu model yang sering diterapkan sebagai pemberian nilai ataupun bobot untuk pelanggan dari proses transaksi. Pemberian bobot yang di berikan akan dianalisa dan dikelompokkan oleh k-mean. Dari hasil analisa k mean diuji menggunakan purity dengan nilai total sebesar 0,441 . dengan pengujian dapadari fitur pada sistem disimpulkan bahwa sistem dapat dikatakan telah berjalan dengan baik, sehingga dapat membantu staff penjualan PT Ditra Manunggal Jaya dalam memberikan pelayanan yang bai bagi para pelanggannya.

Assessing Monetary Model of Exchange Rate Determination: Case of Indonesia

The main purpose of this paper is to assess the exchange rate determination in Indonesia after the Asian financial crisis. We use the Monetary Model to assess the prediction of the Indonesian Rupiah against the United States Dollar and other currencies of the largest trade partners of Indonesia. The models are the Flexible Price Monetary Model and the Sticky Price Monetary Model. We estimate short-run and long-run relationships using the Error Correction Model. The Monetary Model can explain partially the exchange rate variations, but the signs of money, income, and fiscal balance are not as expected. The causality may run from the exchange rate to money and price level.

Product Categorization for Social Marketing Applying the RFC Model and Data Science Techniques

Currently, it is the age of social market due to the growth of internet technologies. The marketers require the complete information of customer perspectives on products and services comparing with others. The RFM (recency, frequency, and monetary) model is a technique to measure a comparison of information, especially in traditional market analytics. Over the past decade, social market big data (SMBD), especially feedback, has been used to understand customer satisfaction. This paper proposes a new approach to classify the products from feedbacks, called the RFC (recency, frequency, and credit) model. The model focuses on the social market information and product categorization applying the natural language processing (NLP), opinion mining (OM), and data mining (DM) techniques.