FRINK Linked Data Fragments server

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• W4382807866 abstract "Most businesses nowadays are designing their products and services with the customer in mind. Many businesses throughout the world are expected to shift from a product-centric to a customer-centric attitude. Customer relationships, experiences, and happiness are therefore crucial for any business's long-term existence, sustainability, and profitability in any industry, yet small businesses lack the resources and skills to succeed. An integrated decision-making framework is built in this present study, integrating diverse data mining methodologies from many fields. The primary goal of this research is to improve the application of predictive analytics in small and medium-sized organizations. The decision-making framework in the form of a Customer Relationship Management (CRM) tool for an online retail sector is the solution offered as part of this study. An integrated decision-making framework tool is built in the pretense of a predictive analytical CRM system, with seven core characteristics and more than thirty sub-activities. The seven features created as part of this study are Data Visualization and Analysis, Customer Segmentation, Customer Classification, Product Recommendation, Customer Linked Predictions, Sales Forecasting, and Forensic Analysis, since they are frequently requested by CRM tool users. It is built using a variety of data mining and machine learning methods. The tool is then made available as a real-time online application. This tool, which consists of a frontend and a backend application, is essentially designed to give users a complete picture of the data. Aside from that, there are several enhancements that can be made to this tool." @default.
• W4382807866 created "2023-07-02" @default.
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• W4382807866 date "2023-01-01" @default.
• W4382807866 modified "2023-09-25" @default.
• W4382807866 title "Development of an Integrated Customer Relationship Management Tool for Predictive Analytics in Supply Chain Management" @default.
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• W4382807866 doi "https://doi.org/10.1007/978-981-99-1019-9_30" @default.
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