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- W3005454013 abstract "Recently, the use of accumulated data in the trade and financial sector has become increasingly important to improve the quality of services provided. For successful business, decision-making systems in retail should combine various marketing strategies with analysis of available data. For suppliers of goods and services and banks acquirers the forecast of the success of outlets and customer’s churn prediction are crucial, especially when price policy changes. We propose data-driven approach that evaluates clusters of the stores based on the success of each segment of suppliers and develop the model for assigning the optimal bank’s acquiring rate within the shopping center taking into account different customer’s loyalty rate accordingly to their success. In this study we analyze the data on consumer’s activity inside large shopping centers on the basis of the transactional dataset provided by our partner bank. We highlight the segments of outlets based on their location, category of goods and services, average monthly turnover and the number of transactions and apply the constructed model to recommend the optimal rate. The proposed approach can be applied in the retail sector by large suppliers of goods and services and banks." @default.
- W3005454013 created "2020-02-14" @default.
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- W3005454013 date "2019-11-01" @default.
- W3005454013 modified "2023-09-22" @default.
- W3005454013 title "Data-Driven Approach for Dynamic Pricing for Decision Making Systems in Marketing and Finance" @default.
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- W3005454013 doi "https://doi.org/10.23919/fruct48121.2019.8981517" @default.
- W3005454013 hasPublicationYear "2019" @default.
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