INFLUENCE OF CONSUMERS KNOWLEDGE TO SHADAQAH PAYING DECISION AT BAITUL MAAL UNISBA

Abstract- Shadaqah has a very important role to every human when we leave this world, therefore moslem community build a foundation to take care of their shadaqah from Muzaki. Unisba as an Islamic University, also have organization to collect, manage, and allocate funding from shadaqah, named Baitul Maal of Unisba. There are 419 lecturers and employees in Unisba but there are only 27% from them who decide to become muzzaki at Baitul Maal of Unisba. On average, the Collected Fund at Baitul Maal of Unisba  per month  is only as many as Rp 3.000.000,-. Researchers  assumed that it caused by lack of consumers knowledge about Baitul Maal of Unisba programs. Consumers knowledge consists of attributes, benefit, and satisfaction to the products or services. A total of 30 questionnaires from offline surveys has been analysed using “several statistical analysis”, including multiple regression. Result of this research shows us that satisfaction to the products or services is the most dominant variable that influence lecturers and employees to become muzzzaki at Baitul Maal of Unisba

Telemarketing outcome prediction using an Ensemblebased machine learning technique

Business organisations often use telemarketing, which is a form of direct marketing strategy to reach a wide range of customers within a short time. However, such marketing strategies need to target an appropriate subset of customers to offer them products/services instead of contacting everyone as people often get annoyed and disengaged when they receive pre-emptive communication. Machine learning techniques can aid in this scenario to select customers who are likely to positively respond to a telemarketing campaign. Business organisations can use their CRM-based customer information and embed machine learning techniques in the data analysis process to develop an automated decisionmaking system, which can recommend the set of customers to be communicated. A few works in the literature have used machine learning techniques to predict the outcome of telemarketing, however, the majority of them used a single classifier algorithm or used only a balanced dataset. To address this issue, this article proposes an ensemble-based machine learning technique to predict the outcome of telemarking, which works well even with an imbalanced dataset and achieves 90.29% accuracy

CUSTOMER SEGMENTATION BY USING RFM MODEL AND CLUSTERING METHODS: A CASE STUDY IN RETAIL INDUSTRY

In today’s business environment companies should need better understanding on customers’ data. Detecting similarities and differences among customers, predicting their behaviors, proposing better options and opportunities to customers, etc. became very important for customer-company engagement. Segmenting customers according to their data became vital in this context. RFM (recency, frequency and monetary) values have been used for many years to identify which customers valuable for the company, which customers need promotional activities, etc. Data-mining tools and techniques commonly have been used by organizations and individuals to analysis their stored data. Clustering, which one of the tasks of data mining has been used to group people, objects, etc. In this paper we propose two different clustering models to segment 700032 customers by considering their RFM values. We suggest that the current customer segmentation which built by just considering customers’ expense is not sufficient. Hence, one of the models that recommended in this research is expected to provide better customer understanding, well-designed strategies, and more efficient decisions

Perspectives of business process ethics in data-driven marketing management

In today's digitalized world, people leave trails of data about themselves when operating online. While marketers are eagerly utilizing this data for marketing insights, many consumers have a growing fear of their personal data ending up in the wrong hands and losing their privacy. The aim of this study was to understand the role of ethics in current marketing based on consumer data and to examine how data could be used ethically in marketing. The empirical research was conducted by analyzing secondary data from a 2018 survey about the use of digital services in Europe. The main conclusions that can be drawn from study findings are that the need for ethics is even more apparent in marketing than ever before, because of the broad access to personal data. Giving the consumers the ability to opt-out from data use and data selling without it affecting the use of services can create an enormous competitive advantage for organizations in the future.© 2021 The Authors. Security and Privacy published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

KERANGKA PENGAMBILAN KEPUTUSAN UNTUK PEMASARAN PRESISI MENGGUNAKAN METODE RFM, ALGORITMA K-MEANS DAN DECISION TREE

Pemasaran presisi memberikan kemampuan pada perusahaan untuk menawarkan produk-produk yang dibuat secara khusus kepada pelanggan dan memberikan kemampuan kepada perusahaan untuk menarik minat pelanggan dengan pesan-pesan pemasaran yang dibuat secara khusus. Penelitian ini menyajikan kerangka kerja pengambilan keputusan baru menggunakan teknik data mining. Pertama, penelitian ini menyajikan model tren untuk memprediksi secara akurat kuantitas pasokan bulanan; kedua, menggunakan model RFM (Recency,Frequency, Monetary) untuk memilih atribut untuk mengelompokkan pelanggan ke dalam kelompok sesuai history transaksi belanjanya; ketiga, menggunakan Algoritma K-Means untuk membuat clustering pelanggan berdasarkan data RFM masing-masing pelanggan, keempat, menggunakan Decision Tree untuk mengidentifikasi nilai atribut penting untuk membedakan kelompok pelanggan yang berbeda; dan akhirnya, dari proses data mining yang peneliti lakukan menciptakan berbagai strategi penawaran yang menargetkan setiap cluster pelanggan. Data penjualan dari Syifamart di Subang Jawa Barat, dikumpulkan dan digunakan dalam studi kasus untuk menggambarkan bagaimana mengimplementasikan kerangka yang diusulkan. Dari penelitian yang telah dilakukan menunjukan bahwa proses data mining dari history transaksi penjualan 351,158 rows, dengan agregasi berdasarkan pelanggan menggunakan metode RFM dan diekstraksi dengan menggunakan algoritma clustering k-means membentuk 4 (empat) cluster optimal. Keempat (empat) cluster tersebut diklasifikasikan dengan menggunakan algoritma decision tree sehingga Syifamart dapat mengetahui mana pelanggan potensial dan mana pelanggan yang tidak potensial. Untuk ketersediaan pasokan stok, manajemen memprediksi kebutuhan persediaan produk dengan menggunakan metode tren dimana stok di bulan selanjutnya di prediksi dengan menggunkana history penjualan di bulan sebelumnya

Experiences of Public Agency Managers When Making Outsourcing Decisions

Managers in state transportation agencies in the United States must frequently choose between using the talents and abilities of in-house staff or outsourcing for road and bridge design projects. Budgetary crises have strongly affected funding for transportation infrastructure. Facing budgetary pressures to suppress costs, managers must frequently make the choice of outsourcing a project or performing it in-house. Yet, decision-making models for these decisions are inadequate. The purpose of this phenomenological study was to explore and describe the lived experiences of public agency managers when making decisions to outsource the core government functions such as road and bridge design projects. The research question was: What are the lived experiences of managers at the public agency when making decisions about whether to outsource core government functions such as road and bridge design projects? Participants were interviewed about their lived experiences at a state Department of Transportation with \u27make or buy\u27 decisions. Purposeful sampling was used to select 19 participants for the interviews and the collected data were coded and used a van Kaam approach for analysis. Five themes emerged as findings: acceptance of outsourcing, benefits versus problems, outsourcing propelled by staff limits, loss of control when a project is outsourced, and political pressure for and against outsourcing. These findings may be relevant for management personnel at U.S. public agencies. The implications for positive social change include improved cost, increased efficiency of use of time and talent of management personnel in state transportation agencies, and cost benefits for both management and public

Predictive Big Data Analytics for Supply Chain Demand Forecasting: Methods, Applications, and Research Opportunities

Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research