In today’s highly competitive retail industry,offline stores face increasing pressure on profitability.They hope to improve their ability in shelf management with the help of big data technology.For this,on-shelf ava...In today’s highly competitive retail industry,offline stores face increasing pressure on profitability.They hope to improve their ability in shelf management with the help of big data technology.For this,on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior.RFM(recency,frequency,andmonetary)patternmining is a powerful tool to evaluate the value of customer behavior.However,the existing RFM patternmining algorithms do not consider the quarterly nature of goods,resulting in unreasonable shelf availability and difficulty in profit-making.To solve this problem,we propose a quarterly RFM mining algorithmfor On-shelf products named OS-RFM.Our algorithmmines the high recency,high frequency,and high monetary patterns and considers the period of the on-shelf goods in quarterly units.We conducted experiments using two real datasets for numerical and graphical analysis to prove the algorithm’s effectiveness.Compared with the state-of-the-art RFM mining algorithm,our algorithm can identify more patterns and performs well in terms of precision,recall,and F1-score,with the recall rate nearing 100%.Also,the novel algorithm operates with significantly shorter running times and more stable memory usage than existing mining algorithms.Additionally,we analyze the sales trends of products in different quarters and seasonal variations.The analysis assists businesses in maintaining reasonable on-shelf availability and achieving greater profitability.展开更多
基金partially supported by the Foundation of State Key Laboratory of Public Big Data(No.PBD2022-01).
文摘In today’s highly competitive retail industry,offline stores face increasing pressure on profitability.They hope to improve their ability in shelf management with the help of big data technology.For this,on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior.RFM(recency,frequency,andmonetary)patternmining is a powerful tool to evaluate the value of customer behavior.However,the existing RFM patternmining algorithms do not consider the quarterly nature of goods,resulting in unreasonable shelf availability and difficulty in profit-making.To solve this problem,we propose a quarterly RFM mining algorithmfor On-shelf products named OS-RFM.Our algorithmmines the high recency,high frequency,and high monetary patterns and considers the period of the on-shelf goods in quarterly units.We conducted experiments using two real datasets for numerical and graphical analysis to prove the algorithm’s effectiveness.Compared with the state-of-the-art RFM mining algorithm,our algorithm can identify more patterns and performs well in terms of precision,recall,and F1-score,with the recall rate nearing 100%.Also,the novel algorithm operates with significantly shorter running times and more stable memory usage than existing mining algorithms.Additionally,we analyze the sales trends of products in different quarters and seasonal variations.The analysis assists businesses in maintaining reasonable on-shelf availability and achieving greater profitability.
