Vol 9 , Issue 1 , January - June 2020 | Pages: 37-42 | Research Paper
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Retailing has been planned to address spatial and transient gaps between the manufacturer and the end customer. The difficulty of monitoring retail sales however is exacerbated by the variety of consumers' geographical areas and the frequency of retail visits. In reality, this is a crucial knowledge for designing marketing relationships (CRM) strategies for consumers. Data mining is typically a way of extracting data from different backgrounds (also called data or knowledge discovery) and summing it up in valuable data information that could be used for the highest gain, minimization of expense, or both. Data mining is technically the practice of finding correlations or patterns in thousands of fields in large linked database networks. Data mining is conducted using proprietary software to convert and distinguish the relationships between data from various measures or perspectives. Although data extraction is a relatively new idea, it is not a science. Constant advancement in computer processing, computing and computer systems substantially improves the accuracy of the analysis and reduces costs. Transaction information in a retail data warehouse can be examined in a "customer-centric" or "visit-centric" retailing approach, which explores briefly the design and the growth of the sector and best practices. It analyzes the different aspects and problems that also need to be tackled by using data mining in retail transactions.
(CRM) strategies, Data mining, Challenges, Retailing, Retail transactions.