Abstract
Numerous firms accumulate large quantities of data or transactions after importing information systems and services, which leads to troubles with data procedure. Firms also have demands to find customers’ information from large datasets and to understand how to develop marketing strategies accurately to adjust their operational methods. Therefore, this study proposed customer ranking combined Big Data process based on the RFM model (Recency, Frequency, Monetary) to develop a recommendation algorithm using an association rule, which finds greater recommendation to promote operational effects of firms. We adjust the weight of potential information to perform the customer ranking, which is conducted by using agglomerate hierarchical clustering. Finally, we present the recommendation by the association rule for each customer level.The datasets in this study use actual sales data; therefore, they are authentic and have been practically applied. The metrics of evaluation showed that the recommended system his study proposes is highly accurate.
Highlights
In recent years, consumption habits have changed drastically
Some researchers have performed methods of data mining, such as association-rules analysis and recommendation systems, in order to analyze the data collected by physical retailers
After separating the data in the output step, we evaluated on the basis of customer segmentation, classification rules, and association rules
Summary
Consumption habits have changed drastically. Take a minute to imagine the world we were in 15 years ago. According to an investigative report, firms have made information technology a key tool for business, and it has improved firms’ relationship with the customer (Bahrami, Ghorbani, & Arabzad, 2012). These improvements have sped up business processes by using information systems, such as point-of-sale machines and enterprise-resource-planning software. In the current study, we propose and evaluate a novel RFM-based model to connect high-loyalty customers with improved revenue streams on the basis of historical physical-store data. We would like to propose a novel model based on RFM model to segment the level of customers, and explore how to make choices better through analyzing data of physical store, to find the high loyalty customers and improve the revenues. We verify our promotion of novel model proposed on this study by using evaluation method
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