The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down.The general public has responded to call of the government to stay at home.Offline retail stores have been severely af...The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down.The general public has responded to call of the government to stay at home.Offline retail stores have been severely affected.Therefore,in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience,this study aims to utilize historical sales data for exploring,building sales prediction and recommendation models.A novel data science life-cycle and process model with Recency,Frequency,and Monetary(RFM)analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics.RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level.For the purchase prediction model,XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their.For the product recommendation model,the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes.Moreover,some suggestions are proposed for the marketing department according to the outcomes.Overall,the XGBoost model achieved better performance and better accuracy with F1-score around 0.789.The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness.Furthermore,it recommend specific products to new customers.This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.展开更多
With Internet changing the luxury business landscape,new players have emerged such as the Online Private Sales Retailers(OPSRs).These offer online buyers with a choice of limited-time sales to help companies get rid o...With Internet changing the luxury business landscape,new players have emerged such as the Online Private Sales Retailers(OPSRs).These offer online buyers with a choice of limited-time sales to help companies get rid of their overstocks.Luxury brands are no exception.No research has been conducted about how luxury consumers relate with such websites,hence this paper.In an exploratory fashion,interviews with luxury buyers who also buy online on OPSRs,are conducted to get insights on consumers’perceptions and luxury brand equity that selling through OPSRs may have.We find that appropriate product and brand help consumers forget that they are buying brands’unsold stocks,that transferring the luxury webmospheres would be positively perceived,that consumers from these websites are looking for benefits such as freedom of use and brand discovery,rather than personalized offers,that multiple discounts on several OPSRs may damage the luxury-perception of a brand,that the private sales members consider the service to be good enough for the demanded price,and that personalized invitations can help increase online consumers’feelings of desirability and exclusivity.The paper concludes with practical recommendations for both luxury companies and OPSRs.展开更多
Unlike consumers in the mall or supermarkets, online consumers are “intangible” and their purchasing behaviors are affected by multiple factors, including product pricing, promotion and discounts, quality of product...Unlike consumers in the mall or supermarkets, online consumers are “intangible” and their purchasing behaviors are affected by multiple factors, including product pricing, promotion and discounts, quality of products and brands, and the platforms where they search for the product. In this research, I study the relationship between product sales and consumer characteristics, the relationship between product sales and product qualities, demand curve analysis, and the search friction effect for different platforms. I utilized data from a randomized field experiment involving more than 400 thousand customers and 30 thousand products on JD.com, one of the world’s largest online retailing platforms. There are two focuses of the research: 1) how different consumer characteristics affect sales;2) how to set price and possible search friction for different channels. I find that JD plus membership, education level and age have no significant relationship with product sales, and higher user level leads to higher sales. Sales are highly skewed, with very high numbers of products sold making up only a small percentage of the total. Consumers living in more industrialized cities have more purchasing power. Women and singles lead to higher spending. Also, the better the product performs, the more it sells. Moderate pricing can increase product sales. Based on the research results of search volume in different channels, it is suggested that it is better to focus on app sales. By knowing the results, producers can adjust target consumers for different products and do target advertisements in order to maximize the sales. Also, an appropriate price for a product is also crucial to a seller. By the way, knowing the search friction of different channels can help producers to rearrange platform layout so that search friction can be reduced and more potential deals may be made.展开更多
网上商品销售与线下商品销售存在较大不同,为探索其消费模式,需要研究各影响因素对网上商品销量的作用机制。文章基于心理抗拒、贝勃定律、卢因人类行为理论等,对网络消费行为进行系统分析;综合运用分位数回归和门限回归方法,建立了门...网上商品销售与线下商品销售存在较大不同,为探索其消费模式,需要研究各影响因素对网上商品销量的作用机制。文章基于心理抗拒、贝勃定律、卢因人类行为理论等,对网络消费行为进行系统分析;综合运用分位数回归和门限回归方法,建立了门限分位数回归模型,揭示商品价格、商家信誉评分、商家信誉等级、保障标记数量、商品收藏人气、口碑数量和口碑分数等对销量的非线性异质影响。以受众广泛的i Pad air2网上销售为研究对象,实证结果表明:提高商家的信誉等级、增加口碑数量能使高销量商家的销量更高,而保障标记数量的增加对热销有阻碍作用;在非热门商品转向热销品的过程中,增加收藏人气、增加口碑数量和一定价格范围内的提价对低销量商家的销量有促进作用。展开更多
基金This research is funded by the School of Computer Sciences,and Division of Research&Innovation,Universiti Sains Malaysia,Short Term Grant(304/PKOMP/6315435)granted to Pantea Keikhosrokiani.
文摘The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down.The general public has responded to call of the government to stay at home.Offline retail stores have been severely affected.Therefore,in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience,this study aims to utilize historical sales data for exploring,building sales prediction and recommendation models.A novel data science life-cycle and process model with Recency,Frequency,and Monetary(RFM)analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics.RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level.For the purchase prediction model,XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their.For the product recommendation model,the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes.Moreover,some suggestions are proposed for the marketing department according to the outcomes.Overall,the XGBoost model achieved better performance and better accuracy with F1-score around 0.789.The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness.Furthermore,it recommend specific products to new customers.This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.
文摘With Internet changing the luxury business landscape,new players have emerged such as the Online Private Sales Retailers(OPSRs).These offer online buyers with a choice of limited-time sales to help companies get rid of their overstocks.Luxury brands are no exception.No research has been conducted about how luxury consumers relate with such websites,hence this paper.In an exploratory fashion,interviews with luxury buyers who also buy online on OPSRs,are conducted to get insights on consumers’perceptions and luxury brand equity that selling through OPSRs may have.We find that appropriate product and brand help consumers forget that they are buying brands’unsold stocks,that transferring the luxury webmospheres would be positively perceived,that consumers from these websites are looking for benefits such as freedom of use and brand discovery,rather than personalized offers,that multiple discounts on several OPSRs may damage the luxury-perception of a brand,that the private sales members consider the service to be good enough for the demanded price,and that personalized invitations can help increase online consumers’feelings of desirability and exclusivity.The paper concludes with practical recommendations for both luxury companies and OPSRs.
文摘Unlike consumers in the mall or supermarkets, online consumers are “intangible” and their purchasing behaviors are affected by multiple factors, including product pricing, promotion and discounts, quality of products and brands, and the platforms where they search for the product. In this research, I study the relationship between product sales and consumer characteristics, the relationship between product sales and product qualities, demand curve analysis, and the search friction effect for different platforms. I utilized data from a randomized field experiment involving more than 400 thousand customers and 30 thousand products on JD.com, one of the world’s largest online retailing platforms. There are two focuses of the research: 1) how different consumer characteristics affect sales;2) how to set price and possible search friction for different channels. I find that JD plus membership, education level and age have no significant relationship with product sales, and higher user level leads to higher sales. Sales are highly skewed, with very high numbers of products sold making up only a small percentage of the total. Consumers living in more industrialized cities have more purchasing power. Women and singles lead to higher spending. Also, the better the product performs, the more it sells. Moderate pricing can increase product sales. Based on the research results of search volume in different channels, it is suggested that it is better to focus on app sales. By knowing the results, producers can adjust target consumers for different products and do target advertisements in order to maximize the sales. Also, an appropriate price for a product is also crucial to a seller. By the way, knowing the search friction of different channels can help producers to rearrange platform layout so that search friction can be reduced and more potential deals may be made.
文摘网上商品销售与线下商品销售存在较大不同,为探索其消费模式,需要研究各影响因素对网上商品销量的作用机制。文章基于心理抗拒、贝勃定律、卢因人类行为理论等,对网络消费行为进行系统分析;综合运用分位数回归和门限回归方法,建立了门限分位数回归模型,揭示商品价格、商家信誉评分、商家信誉等级、保障标记数量、商品收藏人气、口碑数量和口碑分数等对销量的非线性异质影响。以受众广泛的i Pad air2网上销售为研究对象,实证结果表明:提高商家的信誉等级、增加口碑数量能使高销量商家的销量更高,而保障标记数量的增加对热销有阻碍作用;在非热门商品转向热销品的过程中,增加收藏人气、增加口碑数量和一定价格范围内的提价对低销量商家的销量有促进作用。
