Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing

This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms.

Impact of Mobile Access to the Internet on Sales Completion Time in E-Commerce

Supported by a new generation of mobile devices, e-commerce is now in the process of being converted into m-commerce. While the traditional fixed PC access to the Internet continues to be important, the mobile access appears to attract more people because of its flexibility. The purpose of this paper is to develop and analyze a mathematical model for capturing how e-commerce performance would be affected by the mobile access to the Internet, where the original paper by Sumita and Yoshii （2010） is extended for better reality. The traditional e-commerce via the fixed PC access is compared with m-commerce which accommodates both the fixed PC access and the mobile access. The distribution of the number of products purchased by time t and the distribution of the time required for selling K products are derived explicitly. Numerical examples are given for illustrating behavioral differences between m-commerce consumers and traditional e-commerce consumers.

Impact of Internet Advertisement and Its Features on E-Commerce Retail Sales: Evidence from Europe

The stimulus to carry out this research is to investigate the relationship between internet advertisement and its features on the total E-commerce sales of the top five countries of Europe. The units of analysis are the individuals of UK, France, Italy, Germany and Netherland. Secondary data are collected from the reports of [1] (ADEX, 2010) and [2] (Eurostats, 2011). To empirically determine the relationship between independent variable and dependent variable in the European context, the study uses various statistical techniques, including OLS regression and correlation analysis techniques. The empirical findings indicate that the Internet advertisement features of search advertisement and classified advertisement have positive significant relationship with the E-commerce sales in Europe. The empirical findings indicate negative significant relationship of display advertisement with the E-commerce sales in Europe. However, this variable is also justified with the help of literature. Findings also demonstrate that search advertisement has strong positive relationship and it generates positive influence for the E-commerce sales as compared to the classified advertisement and display advertisement. Firms and marketers which are investing in online advertisement will find these results useful as they can get better sales and can use these features of online advertisement in order to maximize the sales of their products and services.

Research on Online Reviews Impact on Commodity Sales in B2C E-commerce

The objective of this paper was to analyze the impact of online reviews on sales. Based on dual path model of commodity sales, an online reviews impact on the relationship between various factors, and then the theoretical hypothesis of each factor has been put forward in the model. As it is intuitive and strongly supported empirically, data including Chinese texts captured from Tmall.com was utilized, and then analyzed by SPSS and ROST CM6. Our empirical study on the reviews of Tmall.com indicated that the hypotheses are verified.

Research on Online Reviews Impact on Commodity Sales in B2C E-commerce

The objective of this paper was to analyze the impact of online reviews on sales. Based on dual path model of commodity sales, an online reviews impact on the relationship between various factors, and then the theoretical hypothesis of each factor has been put forward in the model. As it is intuitive and strongly supported empirically, data including Chinese texts captured from Tmall. com was utilized, and then analyzed by SPSS and ROST CM6. Our empirical study on the reviews of TmalLcom indicated that the hypotheses are verified.

Sales Prediction and Product Recommendation Model Through User Behavior Analytics

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.

Research and Modelling on the E-commerce Consumer Behavior based on Intelligent Recommendation System and Machine Learning

In this paper, we conduct research on the E-commerce consumer behavior based on the intelligent recommendation system andmachine learning. Closely associated with consumer network information search of a problem is that the consumer’s information demand ascan be thought of consumer’s information demand is leading to trigger the power of consumer network information search behavior, whenconsumer is willing to buy goods, in a certain task under the infl uence of factors, environmental factors, individual factors, consumers and thetask object interaction to form the demand of consumer cognition. Under this basis, this paper proposes the new idea on the related issues thatwill solve the related challenges.

Selling Luxury Brands in Online Private Sales Multibrand Retailers--An Exploratory Study of Consumers’Perspective

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.

A Data-Driven Research of Sales and Purchases on JD.com Platform

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.

The Impact of Digital Payment on Consumer Behavior in China

To continue China’s economic growth,plenty of research has been conducted to find the relationship between advancing payment methods and thriving consumption.Currently,digital payment is the latest and most mature way in China.Instead of being a single application for online transactions,digital payment represents a complete online shopping environment.To generally review the impact of digital payment on consumer behavior in China,literature containing research on consumers from different age groups and gender will be collected and concluded.This research takes women and undergraduate students as the critical research object because people ubiquitously consider these two groups of people as the primary consumers in the e-commerce market.After arranging information from the research about three stages that cover the shopping intention by digital payment,consumer psychology,and consumer behavior,this review concludes that economic growth is potentially attained by digital payment in a secure online environment,with the business target at profit maximization at the same time keeping consumers rationale in a normal range,especially for woman and undergraduate students.

基于消费者行为的汽车销售策略研究

近年来,我国经济社会步入转型发展期,汽车企业面临着严峻挑战。企业营销活动是任何一个行业发展的基础。新的形势下,消费者行为对我国汽车销售和整体汽车行业的发展影响更加显著。纵观现今我国的汽车销售策略,其中仍存在一部分待改进和探索的地方,尤其忽视了消费者行为对于销售的重要影响。文章从消费者角度出发,首先说明了我国汽车消费者行为的主要类型和影响因素,阐述了我国汽车销售策略存在的不足,并针对性提出相关的汽车销售策略建议,以对我国汽车行业整体发展提供帮助。

Coordination of e-commerce supply chain when e-commerce platform providing sales service and extended warranty service

With the rapid development of cybereconomy and the enhancement of sales service,many e-commerce platforms provide extended warranty service to alleviate consumers from worries about product quality when consumers purchasing products online.This study examines the decisions and coordination of the e-commerce supply chain where the e-commerce platform dominates and provides sales service and extended warranty service.The findings show that the centralised decision-making model is an ideal model because of the low sales price,high service level and high extended warranty price.With the increase of the consumers’sensitivity coefficient to the extended warranty service price,sales price increases but extended warranty service price and service level decrease,the retailer’s profit and the platform’s profit decrease.The proposed contract of‘revenue sharing joint commission’can realise the coordination of ecommerce supply chain.

消费者购前信息搜索行为对制造商平台销售模式选择的影响

为研究消费者行为对制造商平台销售模式选择的影响,构建了制造商平台销售模式选择模型,将消费者购前信息搜索行为引入模型。研究发现,若不考虑消费者购前信息搜索行为,制造商只在平台抽成比例较小或较大时选择代销模式,其他时候选择分销模式;考虑消费者购前信息搜索行为后,制造商选择分销模式的区域扩大;进一步,消费者的搜索行为使制造商在同一销售模式下的利润增加。同时,搜索概率的增加会使分销模式下供应链成员的批发和零售价格下降,而使代销模式下的价格呈非单调变化。

大学生旅游囤货行为特征和发展分析

近年来,疫情的爆发造成了旅游业市场的停滞,困境之中由携程所提出的预售旅游成为旅游业的一个突破口,为疫情防控期间停摆的旅游市场提供了一个良好的缓冲期,同时也为人们带来了一场可以提前预约的未来旅行。预售旅游的出现让旅游产品成为可囤积的日常消费品,囤货旅游行为逐渐成为重要的旅游消费方式,其中预售旅游受众人群呈现高度年轻化趋势,随着大学生逐渐成为旅游业的消费主体,研究大学生囤货旅游的态度评价及行为特征对预售旅游及旅游业的发展十分重要。文章基于问卷调查数据,分析了大学生囤旅游行为的基本特征和选择偏好,从价格、时间、消费欲望和心理激励四个方面详细剖析了大学生囤旅游的行为动机,并提出相应建议。

存量房交易中客户“跳单”行为法律问题刍议

在存量房交易中,房产中介承担着桥梁的作用,为出卖人与买受人提供了解、比较选择房屋信息及其后续交易的平台与途径,解决了双方信息不畅、交流不对称的难题,有效提高了存量房交易数量,促进了房地产经济的繁荣。而在房屋交易过程中,委托人“跳单”现象的时有发生,不仅损害了房产中介的合法权益,违背诚实信用原则,还会影响房地产中介行业进一步发展的积极性,房产中介为此也常常诉至法院。但是,因各地司法实践的经验及各地实际情况的参差,导致各地法院对“跳单”行为的认定标准略有不同,处理结果也不尽一致。为此,分析“跳单”行为产生的原因、“跳单”行为的法律认定以及“跳单”发生时中介人的报酬请求权的确认,期为裁判实务提供一种思路。

基于门限分位数回归的网上商品销量影响因素探析

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。

“真实销售行为”的动态选择与经济后果

本文在理解企业一般"真实销售竞争"行为基础上深入考察盈余管理动机下"真实销售操控"行为的经济后果。结果发现:一般企业的"真实销售竞争"行为会受到企业市场竞争压力的影响。非微利公司采取更多"真实销售竞争"行为会对公司下一期"经营业绩增长变化"产生正面影响。具有盈余管理动机的微利公司运用过度"真实销售操控"所带来的负面影响会抵消其大部分正面影响;但这并不意味着微利公司的"真实销售操控"行为一定会对下一期"经营业绩增长变化"产生负面影响。研究还发现,公司运用更多"激进收入确认"会对下一期"经营业绩增长变化"产生负面影响。

生鲜农产品“卖难买贵”原因解析及纾困策略——基于中间商购销行为的视角

近年来,生鲜农产品“卖难买贵”事件频发,农户和市民双双受损,解析其中缘由、探寻纾困之策对破除农产品上行困境、增加农民收入、改善民生有着重要的现实意义。以批发市场主导模式下生鲜农产品产业链条为主线,剖析生鲜农产品各环节交易价格形成的过程,从中间商购销行为、经济动机角度解释生鲜农产品“卖难”与“买贵”同现的内在机理。研究表明,在农产品滞销情况下,由于露天市场的整治、电商对农产品的渗透率不高以及合作社发展不成熟等原因,农产品更多依赖于农产品批发商进城销售,然而农产品过剩时的批发商会采取谨慎的风险规避行为,减少收购量、抬高批发价格的这种行为动机加剧了滞销事态,成为农产品“卖难”的重要推手。而“最后一公里”存在的各项加价及零售摊贩赚取的用于维系生计的经营利润是终端零售价格大幅下降的重要阻力,最终形成“卖难”与“买贵”并存的窘境。可见,生鲜农产品流通中无论是批发商的避险减量收购,还是零售商的高幅加价售卖,其行为决策都符合理性经济人的逻辑,是利益权衡之下的无奈选择。实际上,中间商购销行为出现的偏颇只是引起“卖难买贵”的一隅,从深层次观察,“卖难买贵”呈现出的农产品供给脆弱性是多种问题倒逼下的结果。解决生鲜农产品“卖难买贵”难题,应搭建生鲜农产品市场信息平台,强化农民专业合作社的产销对接功能,促进农产品批发主体的规范化、规模化发展,构建公益性农产品流通体系,完善农产品市场供应应急预案等。

关于消费者对促销的反应行为研究

不同类型的促销工具在导致消费者产生特定反应行为方面具有差异性。针对消费者品牌转换行为最有效的促销工具是价格折扣和样品赠送;针对消费者购买加速行为最有效的促销工具是价格折扣与购物赠礼;针对消费者增加购买量最有效的促销工具是价格折扣;针对消费者产品试用行为最有效的促销工具是价格折扣、样品赠送、购物赠礼;购物抽奖相对来说促销效果最差。营销者应根据企业目标针对性地选择最合适的促销工具。

社会资本多维视角与农户销售渠道选择——基于微观调研数据的实证

基于陕、甘、鲁、豫苹果主产区微观农户数据,运用多元选择模型,以外地客商渠道作为参照,从社区社会资本、社会网络、社会信任、社会参与、社会声望多维视角探究社会资本影响农户销售渠道选择的机理。结果表明:社区社会资本越丰富,农户选择公司渠道、合作社渠道及自销的概率越小;社会网络越丰富,农户选择外地客商渠道的概率越大;社会信任程度越高,农户选择公司渠道及自销渠道的概率越大;社会声望越高农户选择公司渠道、合作社渠道及自销渠道的概率越大;社会参与程度越大,农户选择公司渠道、合作社渠道及自销渠道的概率越大。此外,户主文化程度、风险认知程度、是否为社员、家庭收入及销售地点等对农户销售渠道选择具有显著影响。