Online purchase intentions: A multi-channel store image perspective

The advantages of the bricks-and-clicks retail format in the battle for the online customer has been widely discussed but empirical research on it has been limited. We applied a multi-channel store image perspective to assess its influence on online purchase intentions. Drawing on a sample of 630 customers of a large music retail store in the Netherlands, the results demonstrated that offline and online store perceptions directly influenced online purchase intention. In addition, our findings confirmed that offline store impressions were used as references for their online store counterparts. Synergy and reference effects are discussed.     

Amazon.com recommendations: item-to-item collaborative filtering

Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.     

Which is more important in Internet shopping,perceived price or trust?

Price and trust are considered to be two important factors that influence customer purchasing decisions in Internet shopping. This paper examines the relative influence they have on online purchasing decisions for both potential and repeat customers. The knowledge of their relative impacts and changes in their relative roles over customer transaction experience is useful in developing customized sales strategies to target different groups of customers. The results of this study revealed that perceived trust exerted a stronger effect than perceived price on purchase intentions for both potential and repeat customers of an online store. The results also revealed that perceived price exerted a stronger influence on purchase decisions of repeat customers as compared to that of potential customers. Perceived trust exerted a stronger influence on purchase decisions of potential customers as compared to that of repeat customers.     

Personalization of Supermarket Product Recommendations

We describe a personalized recommender system designed to suggest new products to supermarket shoppers. The recommender functions in a pervasive computing environment, namely, a remote shopping system in which supermarket customers use Personal Digital Assistants (PDAs) to compose and transmit their orders to the store, which assembles them for subsequent pickup. The recommender is meant to provide an alternative source of new ideas for customers who now visit the store less frequently. Recommendations are generated by matching products to customers based on the expected appeal of the product and the previous spending of the customer. Associations mining in the product domain is used to determine relationships among product classes for use in characterizing the appeal of individual products. Clustering in the customer domain is used to identify groups of shoppers with similar spending histories. Cluster-specific lists of popular products are then used as input to the matching process. The recommender is currently being used in a pilot program with several hundred customers. Analysis of results to date have shown a 1.8% boost in program revenue as a result of purchases made directly from the list of recommended products. A substantial fraction of the accepted recommendations are from product classes new to the customer, indicating a degree of willingness to expand beyond present purchase patterns in response to reasonable suggestions.     

Adopting television as a new channel for e-commerce. The influence of interactive technologies on consumer behavior

The aim of this research is twofold. Firstly, it analyzes how the two main features of Internet Protocol Television (IPTV), interactivity and personalization, influence both customers’ perceived performance and involvement with a web-based information service. Secondly, it studies whether personalization and interactivity improve customer purchase intentions of this service through IPTV. We developed a 2×2 between-subjects factorial design and applied MANOVA analyses. Findings verify that interactivity and personalization foster customer involvement with the service, perceived performance of IPTV and purchase intentions. Moreover, interactivity promotes the effect of personalization on perceived performance and customer involvement. This research concludes that customers’ involvement and purchase behavior are not derived exclusively from their relationship with the firm, but also from connections established in the channel with other customers. Customers also appreciate participating in the service provision process, so firms should promote personalization activities during the purchase in order to improve customers’ performance evaluation.     

LTV model in consultant sector. Case study: mental health clinic

Since the early 1980s, customer relationship management (CRM) has been important in the new competitive business environment. Today, due to development of competitive factors in the business, the enterprise's need to create and retain effective relations with customers has been highlighted more and more. With the aim of customer scoring applications, the most profitable customers can be identified. In this paper, we categorized customers by three types of values for the clinic by using logistic regression as a data-mining technique, and calculated the customer defection and future purchase probability in a mental health clinic of the university of Tehran. Model verification and validation (using lift chart) was done and customer segmentation and analysis presented with proper marketing strategies.     

LTV model in consultant sector. Case study: mental health clinic

Since the early 1980s, customer relationship management (CRM) has been important in the new competitive business environment. Today, due to development of competitive factors in the business, the enterprise's need to create and retain effective relations with customers has been highlighted more and more. With the aim of customer scoring applications, the most profitable customers can be identified. In this paper, we categorized customers by three types of values for the clinic by using logistic regression as a data-mining technique, and calculated the customer defection and future purchase probability in a mental health clinic of the university of Tehran. Model verification and validation (using lift chart) was done and customer segmentation and analysis presented with proper marketing strategies.     

Developing recommender systems with the consideration of product profitability for sellers

In electronic commerce web sites, recommender systems are popularly being employed to help customers in selecting suitable products to meet their personal needs. These systems learn about user preferences over time and automatically suggest products that fit the learned model of user preferences. Traditionally, recommendations are provided to customers depending on purchase probability and customers’ preferences, without considering the profitability factor for sellers. This study attempts to integrate the profitability factor into the traditional recommender systems. Based on this consideration, we propose two profitability-based recommender systems called CPPRS (Convenience plus Profitability Perspective Recommender System) and HPRS (Hybrid Perspective Recommender System). Moreover, comparisons between our proposed systems (considering both purchase probability and profitability) and traditional systems (emphasizing an individual’s preference) are made to clarify the advantages and disadvantages of these systems in terms of recommendation accuracy and/or profit from cross-selling. The experimental results show that the proposed HPRS can increase profit from cross-selling without losing recommendation accuracy.     

Analyzing customer behavior from shopping path data using operation edit distance

Radio frequency identification (RFID) technology has been successfully applied to gather customers’ shopping habits from their motion paths and other behavioral data. The customers’ behavioral data can be used for marketing purposes, such as improving the store layout or optimizing targeted promotions to specific customers. Some data mining techniques, such as clustering algorithms can be used to discover customers’ hidden behaviors from their shopping paths. However, shopping path data has peculiar challenges, including variable length, sequential data, and the need for a special distance measure. Due to these challenges, traditional clustering algorithms cannot be applied to shopping path data. In this paper, we analyze customer behavior from their shopping path data by using a clustering algorithm. We propose a new distance measure for shopping path data, called the Operation edit distance, to solve the aforementioned problems. The proposed distance method enables the RFID customer shopping path data to be processed effectively using clustering algorithms. We have collected a real-world shopping path data from a retail store and applied our method to the dataset. The proposed method effectively determined customers’ shopping patterns from the data.     

基于随机森林模型的电信运营商外呼推荐系统

在电信运营商领域,外呼推荐是一种重要的推荐产品和服务的途径。实现了一种基于运营商大数据的自动外呼推荐系统,该系统能够挖掘用户的行为特征并且使用机器学习的方法预测用户对于被推荐产品的接受可能性。传统推荐系统使用的模型算法为矩阵分解、大规模稀疏特征分类、神经网络等。采用随机森林算法的主要原因是随机森林具有并行化程度高、训练速度快、生成的决策树可解释等诸多优点,适合于基于电信业数据的推荐系统。该外呼推荐系统基于Hadoop、Impala和Spark等大数据处理平台及工具,使用随机森林分类器作为核心算法,将用户最近的行为特征回归为接受外呼推荐产品的可能性。在线测试表明使用该系统与当前部署的人工随机外呼相比,能够提升约41%的用户接受率;同时,根据模型算法输出特征的重要性,进一步给出了两类用户的特征分析。     

How purchase intention consummates purchase behaviour: the stochastic nature of product valuation in electronic commerce

This study challenges the conventional assumption that online customers with high purchase intention routinely move to the purchase stage. To this end, the process of how online customers form purchase intention and behaviour is examined. On the basis of product value distribution (PVD), we propose that the hypothetically expected product value (i.e. PVD average) determines purchase intention, whereas the actual probability of attaining the expected product value (i.e. PVD variance) moderates purchase behaviour. This proposal indicates that the expected product value has significance only when repeated purchase is assumed given that most consumers do not repeatedly purchase the same product in reality. Thus, the actual probability of attaining the expected product value more critically affects customer behaviour than does its expected value. The effectiveness of the research model is verified by conducting a survey on 300 online mall shoppers in Korea. The results confirm model effectiveness.     

A comparison of purchase decision calculus between potential and repeat customers of an online store

Potential and repeat customers of an online store possess different amount of information and use different criteria for making purchase decisions. Internet vendors should therefore adopt different sales strategies for creating initial sales and generating repeat sales. Yet little is known about the differences in online purchase decision making between the two customer groups. This study examines the differences between potential and repeat customers based on mental accounting theory and information processing theory. We found that value perception (of transactions made with the online vendor) as an overall judgment for decision making is more strongly influenced by the non-monetary (perceived risk) factor than by the monetary factor (perceived price) for potential customers, whereas it is more strongly influenced by the monetary factor than by the non-monetary factor for repeat customers. The findings of our study would help Internet vendors develop customized strategies for creating initial sales and repeat sales.     

Mining changes in customer buying behavior for collaborative recommendations

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences.In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.     

Hybrid recommendations for mobile commerce based on mobile phone features

Mobile data communications have evolved as the number of third generation (3G) subscribers has increased. The evolution has triggered an increase in the use of mobile devices, such as mobile phones, to conduct mobile commerce and mobile shopping on the mobile web. There are fewer products to browse on the mobile web; hence, one‐to‐one marketing with product recommendations is important. Typical collaborative filtering (CF) recommendation systems make recommendations to potential customers based on the purchase behaviour of customers with similar preferences. However, this method may suffer from the so‐called sparsity problem, which means there may not be sufficient similar users because the user‐item rating matrix is sparse. In mobile shopping environments, the features of users' mobile phones provide different functionalities for using mobile services; thus, the features may be used to identify users with similar purchase behaviour. In this paper, we propose a mobile phone feature (MPF)‐based hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users' characteristics and then cluster users into groups with similar interests. The hybrid method combines the MPF‐based method and a preference‐based method that uses association rule mining to extract recommendation rules from user groups and make recommendations. Our experiment results show that the proposed hybrid method performs better than other recommendation methods.     

An empirical study on Web-based services and customer loyalty

E-business success is tied to the ability to foster customer loyalty. Businesses that deliver superior value derived from excellent services and quality products are likely to win customer loyalty. This paper examines Web-based services and the effects of three sets of factors: pre-purchase, transaction-related, and post-purchase services on customer loyalty (measured as repeat purchase intention from a given Web-based store) in a business-to-consumer environment. Based on the study's results, pre-purchase services that support search and evaluation of products replete in e-commerce systems have limited effect on customer loyalty. Among transaction-related services, transparency of the billing mechanism positively impacts customer loyalty. Customers shun any hidden costs associated with product acquisition. Post-purchase services consisting of support of order tracking, on-time delivery, and customer support positively influence customer loyalty. These findings imply that Web-based stores need to pay more attention to post-purchase services in their strategy to retain customers. This is what will keep customers satisfied and willing to continue the relationship with a company over the long term.     

Discovering patterns of online purchasing behaviour and a new‐product‐launch strategy

Spurred by rapid development of computers and Internet technology, online shopping is gradually overtaking in‐store shopping, because of advantages such as convenience, more choice of products or services etc. Online stores must devote a great deal of time and resources to locating and attracting new customers. Growing a customer base requires first understanding customers and then providing the products or services they need, thus encouraging customers to purchase more. This paper develops a system to analyse customers’ purchasing behaviour and track shifts in their interests. Customers’ purchasing behaviour is measured using proposed standard product loyalty status and standard brand loyalty status. Using these metrics, together with the preference map established for each customer, a marketing specialist can easily locate potential customers to target when a company launches a new product. The new‐product‐launch strategy proposed in this paper can be used to create a list of potential customers for a product being launched under a variety of conditions. A prototype system has been built to test the feasibility of the proposed new‐product‐launch strategy. The result shows almost 40% of potential customers respond to the recommendation positively.     

Understanding the product information inference process in electronic word-of-mouth: An objectivity–subjectivity dichotomy perspective

We examined the actions of a customer when inferring product information from electronic word-of-mouth (eWOM) material at a website. We developed a customer purchase intention model and simulated various eWOM levels within this, adopting an objectivity–subjectivity dichotomy, and considering quality and preference as the major antecedents of customer purchase intention. We inferred the information that the customers had obtained from the eWOM by categorizing the customers’ responses. The eWOM was parameterized using mean and variance; products that were categorized into quality and preference goods. We considered four cases in which customers infer different product information and exhibit different reactions. Items for quality and preference goods were developed by using a card-sorting method. An experimental survey was conducted on 121 Korean Internet shopping mall users. The hypotheses were partially supported using a Partial Least Squares path comparison method. Overall, our study should provide guidance to firms in their managing eWOM systems by identifying how customers react to them at various levels.     

Designing an electronic commerce interface: attention and product memory as elicited by web design

This paper describes a study conducted to understand, in part, the effects of web interface features (image size, fidelity, and motion) on responses such as attention, and memory. The increasing proliferation of B2C web sites and their attempts to enhance the experience of customers shopping on-line has made the work of Reeves and Nass on the psychological responses elicited by interactions with media relevant to the electronic commerce domain. This study is an attempt to validate the claims of Reeves and Nass and extend their theory to web-based media. We have conducted a laboratory experiment to test the influence of three web design features—image size, fidelity (clarity of an image), and motion—for an experimental electronic commerce website. Subjects were instructed to search for information on the web, and given attention and memory tasks that were then used to measure the impact of these three web design features. Results indicated that, at the early stages of a subject’s interaction with a web site: (1) higher visual fidelity images on a web interface lead to greater user attention to the product examined than lower visual fidelity images; (2) motion on a dynamic web interface demands greater user attention than a static web interface; and (3) an interface with higher fidelity and motion leads to greater attention span in comparison to one associated with only one feature manipulated. In addition, compared to smaller images, larger images on a web interface enhance user memory performance for images. In terms of practical applications, the study indicates that interface features, such as fidelity and motion, which are instrumental in keeping customers at one’s web site longer, are important and may lead to an eventual purchase. Second, it is becoming evident that a key role of the web site is not only to lead to the purchase of a company’s product over the web, but also to lead customers to visit one’s physical store, and eventually to an “off-site” purchase. The results of this study show that size is an important variable that influences customers to remember the image aspects of a product, and this might lead to a higher likelihood of off-line buying. Overall, this study confirms the relevance of Reeves and Nass’ studies in the area of human–media interaction. Also, it sheds new light on the application of their work to the electronic commerce context. It also contributes knowledge to the research community with a relatively new paradigm of studying interface and human–computer interaction.     

Product recommendation with latent review topics

Online customer reviews complement information from product and service providers. While the latter is directly from the source of the product and/or service, the former is generally from users of these products and/or services. Clearly, these two information sets are generated from different perspectives with possibly different sets of intentions. For a prospective customer, both these perspectives together provide a complementary set of information and support their purchase decisions. Given the different perspective and incentive structure, the information from these two source sets tends to be necessarily biased, clearly with the high probability of negative information omission from that provided by the product/service providers. Moreover, customers oftentimes face information overload during their attempts at deciphering existing online customer reviews. We attempt to alleviate this through mining hidden information in online customer reviews. We use a variant of the Latent Dirichlet Allocation (LDA) model and clustering to generate equivalent options that the customer could then use in their purchase decisions. We illustrate this using online hotel review data.     

基于客户喜好的双向个性化推荐算法

为了解决现有推荐算法仅考虑同类产品间单向推荐所缺乏的灵活性,提升产品的销量及用户的购物体验,提出一种基于客户喜好的双向个性化推荐算法,不仅可以为客户精准推荐产品,还可以为商家推荐潜在客户.首先,基于产品购买网络中客户及其邻居的购买信息,扩展客户购买信息;其次设计客户产品喜好权重计算办法,分析客户的购买喜好,并在客户喜好的指导下为客户提供个性化的产品推荐;最后,基于商家提供的样本客户,挖掘与样本客户相似的客户构成社区,为商家提供潜在客户推荐以及精准客户维护.在真实数据集上的实验验证了算法的有效性.该算法从客户和商家两个维度出发实现了产品与客户的双向推荐,为个性化推荐领域的研究提供有益的帮助.