Mining purchasing decision rules from service encounter data of retail chain stores

In this explorative research, we aim to find the most important service experience variables that determine customer purchasing decision and the clerks’ influence on customers’ purchases. This study was conducted as a case study of a children’s apparel company, denoted Company L, which has 243 retail stores. Company L has implemented Point of Sale (POS) systems in its retail stores, and would like to know what functions could be added to induce storefront employees to deliver better customer service. We, therefore, focus on observing the services provided by storefront employees and their reflection on a customer’s purchasing decision in a retail store. The study generated decision trees via Weka, a data mining open source software platform, to analyze multiple data sources to (1) understand what makes a good service experience for a customer, (2) get explicit knowledge from service encounter information, and (3) externalize the tacit knowledge of storefront service experiences. These findings can be used to improve Company L’s POS system to guide storefront employees to learn from trained decision rules. Moreover, the company can internalize service experience knowledge by aggregating learned rules from the company’s retail stores.     

Mining the change of customer behavior in fuzzy time-interval sequential patterns

Comprehending changes of customer behavior is an essential problem that must be faced for survival in a fast-changing business environment. Particularly in the management of electronic commerce (EC), many companies have developed on-line shopping stores to serve customers and immediately collect buying logs in databases. This trend has led to the development of data-mining applications. Fuzzy time-interval sequential pattern mining is one type of serviceable data-mining technique that discovers customer behavioral patterns over time. To take a shopping example, (Bread, Short, Milk, Long, Jam), means that Bread is bought before Milk in a Short period, and Jam is bought after Milk in a Long period, where Short and Long are predetermined linguistic terms given by managers. This information shown in this example reveals more general and concise knowledge for managers, allowing them to make quick-response decisions, especially in business. However, no studies, to our knowledge, have yet to address the issue of changes in fuzzy time-interval sequential patterns. The fuzzy time-interval sequential pattern, (Bread, Short, Milk, Long, Jam), became available in last year; however, is not a trend this year, and has been substituted by (Bread, Short, Yogurt, Short, Jam). Without updating this knowledge, managers might map out inappropriate marketing plans for products or services and dated inventory strategies with respect to time-intervals. To deal with this problem, we propose a novel change mining model, MineFuzzChange, to detect the change in fuzzy time-interval sequential patterns. Using a brick-and-mortar transactional dataset collected from a retail chain in Taiwan and a B2C EC dataset, experiments are carried out to evaluate the proposed model. We empirically demonstrate how the model helps managers to understand the changing behaviors of their customers and to formulate timely marketing and inventory strategies.     

A Dominance-based Rough Set Approach to customer behavior in the airline market

Market segmentation is a crucial activity in the present business environment. Data mining is a useful tool for identifying customer behavior patterns in large amounts of data. This information can then be used to help with decision-making in areas such as the airline market. In this study, we use the Dominance-based Rough Set Approach (DRSA) to provide a set of rules for determining customer attitudes and loyalties, which can help managers develop strategies to acquire new customers and retain highly valued ones. A set of rules is derived from a large sample of international airline customers, and its predictive ability is evaluated. The results, as compared with those of multiple discriminate analyses, are very encouraging. They prove the usefulness of the proposed method in predicting the behavior of airline customers. This study demonstrates that the DRSA model helps to identify customers, determine their characteristics, and facilitate the development of a marketing strategy.     

A computerized quality function deployment approach for retail services

Product and service quality can only be effectively improved when the most important needs of customers are satisfied. Quality Function Deployment (QFD) is an approach used to guide R&D, manufacturing, and management toward the development of products and services that satisfy the needs of consumers. The QFD operations are performed by way of a diagram called the House of Quality (HOQ). The HOQ contains information about the customers' needs (what), mechanisms to address these needs (how), and the criterion for deciding which “what” is the most important and which “how” provides the greatest customer satisfaction. A less familiar application of QFD is for the improvement of retail services. When QFD is applied to retail services, a computerized HOQ approach becomes integral to the process for providing continuous, iterative quality improvement. The objective of this research is to develop a formal QFD methodology for the retail industry and to build a computerized retail QFD system. The system provides a HOQ architecture for specifying and analyzing the customers' needs, deriving improvement strategies, and formalizing records of progress. Furthermore, two ranking methods that apply customer satisfaction theory are used to assist managers improve retail services. This system provides an integrated workbench for building retail HOQs and designing retail service strategies.     

Discovering recency,frequency, and monetary (RFM) sequential patterns from customers’ purchasing data

In response to the thriving development in electronic commerce (EC), many on-line retailers have developed Web-based information systems to handle enormous amounts of transactions on the Internet. These systems can automatically capture data on the browsing histories and purchasing records of individual customers. This capability has motivated the development of data-mining applications. Sequential pattern mining (SPM) is a useful data-mining method to discover customers’ purchasing patterns over time. We incorporate the recency, frequency, and monetary (RFM) concept presented in the marketing literature to define the RFM sequential pattern and develop a novel algorithm for generating all RFM sequential patterns from customers’ purchasing data. Using the algorithm, we propose a pattern segmentation framework to generate valuable information on customer purchasing behavior for managerial decision-making. Extensive experiments are carried out, using synthetic datasets and a transactional dataset collected by a retail chain in Taiwan, to evaluate the proposed algorithm and empirically demonstrate the benefits of using RFM sequential patterns in analyzing customers’ purchasing data.     

Mining shopping behavior in the Taiwan luxury products market

The rapid growth of Taiwan’s economy has been accompanied by the country’s developing market for luxury products. To successfully establish the new market demand chain for the luxury industry in Taiwan, it is essential to understand customer preferences. Thus, this study uses an association rules approach and clustering analysis for data mining to mine knowledge among luxury product-buying customers in Taiwan. The results of knowledge extraction from data mining, illustrated as knowledge patterns, rules and knowledge maps, are used to make recommendations for future developments in the luxury products industry.     

Mining linguistic browsing patterns in the world wide web

 World-wide-web applications have grown very rapidly and have made a significant impact on computer systems. Among them, web browsing for useful information may be most commonly seen. Due to its tremendous amounts of use, efficient and effective web retrieval has thus become a very important research topic in this field. Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for a certain purpose. In this paper, we use the data mining techniques to discover relevant browsing behavior from log data in web servers, thus being able to help make rules for retrieval of web pages. The browsing time of a customer on each web page is used to analyze the retrieval behavior. Since the data collected are numeric, fuzzy concepts are used to process them and to form linguistic terms. A sophisticated web-mining algorithm is thus proposed to find relevant browsing behavior from the linguistic data. Each page uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the pages. Computational time can thus be greatly reduced. The patterns mined out thus exhibit the browsing behavior and can be used to provide some appropriate suggestions to web-server managers.     

Consumption universes based supermarket layout through association rule mining and multidimensional scaling

The success of retail business is influenced by its fast response and its ability in understanding consumers’ behaviors. Analysis of transaction data is the key for taking advantage of these new opportunities, which enables supermarkets to understand and predict customer behavior, has become a crucial technique for effective decision-making and strategy formation. We propose a methodological framework for the use of the knowledge discovery process and its visualization to improve store layout. This study examines the layout strategy in relation to supermarket retail stores and assists managers in developing better layout for supermarkets. We use the buying association measure to create a category correlation matrix and we apply the multidimensional scale technique to display the set of products in the store space. This is a new approach to supermarket layout from industrial categories to consumption universes that is consumer-oriented store layout approach through a data mining approach. This framework is useful for both academia and retail industry. For industry professionals, it may be used to guide development of successful layout. Retailers can utilize the proposed model to dynamically improve their in-store conversion rate. As the empirical study, a practical application proceeded for Migros Turk, a leading Turkish retailing company.     

One step ahead: using predictive inference to select service providers in loosely coupled electronic markets

The rapid growth of service-oriented electronic markets implies a common belief among managers that participation in these dynamic, loosely coupled markets can yield many benefits for their firms. At present, however, very little is known about organizational behavior in these nascent markets. By utilizing a sophisticated simulation of a loosely coupled interorganizational service market, this paper demonstrates that customer organizations seeking to purchase and consume services in such markets can benefit from the application of predictive inference in the provider selection process. Specifically, it is shown that a customer organization employing a simple predictive method to select service providers can, in the aggregate, achieve notably superior outcomes in terms of price, quality of service received, and several other metrics when compared to competitors who act opportunistically in selecting their business partners. The implications of these findings for managers and researchers are presented and discussed in the context of the rising popularity of loosely coupled electronic markets.     

数据挖掘中聚类分析的研究

聚类分析是由若干个模式组成的,它在数据挖掘中的地位越来越重要。文中阐述了数据挖掘中聚类分析的概念、方法及应用,并通过引用一个用客户交易数据统计出每个客户的交易情况的例子,根据客户行为进行聚类。通过数据挖掘聚类分析,可以及时了解经营状况、资金情况、利润情况、客户群分布等重要的信息。对客户状态、交易行为、自然属性和其他信息进行综合分析,细分客户群,确定核心客户。采用不同的聚类方法,对于相同的记录集合可能有不同的划分结果对其进行关联分析,可为协助各种有效的方案,开展针对性的服务。     

Understanding Internet service switching behaviour based on the stage model

As customer switching is the major concern in the competitive Internet industry, many studies have sought to identify the determinants that cause customers to switch in order to build effective customer retention strategies. However, they were found to be insufficient for explaining the determinants and processes related to service switching. To fill this gap, this study attempts to provide a theoretical mechanism explaining customer service switching behaviours. More specifically, this study examines three hypotheses that may help ISPs develop appropriate marketing and business strategies. Survey data collected from 151 ISP customers in Australia were analysed to test the hypotheses. The results identify four stages of customers switching behaviours and suggest that motivational variables for switching behaviours differ across stages. This study provides a stepping-stone for analysing the staged model in the service-switching context and will help managers enhance their customer retention capability, and thus improve their organizational performance.     

The Cyclic Model Analysis on Sequential Patterns

Sequential pattern mining has been used to predict various aspects of customer buying behavior for a long time. Discovered sequence reveals the chronological relation between items and provides valuable information to aid in developing marketing strategies. Nevertheless, we can hardly know whether the buying is cyclic and how long the interval between the two consecutive items in the sequential pattern is. To solve this problem, in this paper, data mining skills and the fundamentals of statistics are combined to develop a set of algorithms to unearth the cyclic properties of discovered sequential patterns. The algorithms, coupled with the sequential pattern mining process, constitute a thorough scheme to analyze and predict likely consumer behavior. The proposed algorithms are implemented and applied to test against real data collected from a consumer goods company. The experimental results illustrate how the model can be used to predict likely purchases within a certain time frame. Consequently, marketing professionals can execute campaigns to favorably impact customers' behaviors.     

Multi-period optimization with loss-averse customer behavior: Joint pricing and inventory decisions with stochastic demand

To maximize a firm's profit over a finite planning horizon, we develop a dynamic optimization model by considering loss aversion when making pricing and inventory decisions. We estimate customer demand through a choice model, which incorporates reference price, utility function and customer loss aversion. Our model forms the core of the expert system for decision support. Through a sequence of Bellman equations, we find that the firm's profit is a concave function of price and inventory, and we solve the model optimally. The profit is positively correlated with the reference price, and the price and inventory decisions are non-monotonic functions of loss aversion intensity. Our results shed new light on pricing and inventory management with customer behavior in a multi-period system. Through various theorem developments, we are able to identify the optimal inventory level and the corresponding price. Numerical examples are provided to illustrate and validate the model and to derive managerial insights. To show the potential significance, we demonstrate how a dynamic programming model yields good results with customer loss aversion under realistic customer behavior assumptions. Our system can improve the efficiency of decision making and provide better customer service.     

Identifying interesting visitors through Web log classification

Web site owners have trouble identifying customer purchasing patterns from their Web logs because the two aren't directly related. Thus, organizations must understand their customers' behavior, preferences, and future needs. This imperative leads many companies to develop a great many e-service systems for data collection and analysis. Web mining is a popular technique for analyzing visitor activities in e-service systems. It mainly includes Web text mining, Web structure mining and Web log mining. Our Web log mining approach classifies a particular site's visitors into different groups on the basis of their purchase interest.     

A text analytics framework for automated communication pattern analysis

Human-coding reliant conversation analysis methods are ineffective when analyzing large volumes of data. In this paper, we propose a text analytics framework for automated conversation pattern analysis. This framework first extracts speech acts (i.e., activities) from conversation logs, and then analyzes their flow through frequent pattern mining algorithms to reveal insightful communication patterns. Using a real-world data set collected from a customer service center, we demonstrate the usefulness of the framework for identifying patterns that are associated with service quality outcomes. Our work has implications for the design of communication policies and systems for customer service management.     

The classification and strategic management of services in e-commerce: Development of service taxonomy based on customer perception

Previous studies have failed to take into account of the service sector, which accounts for a large portion of e-commerce transactions these days. To overcome the limitation, this research focuses on online service offers and attempts to develop their taxonomy. For the purpose, online services were identified from Korean Portal Sites and classified by 11 variables representing customer perceptions about service characteristics in the e-commerce context. Data for the analysis were obtained from a survey and the data-mining techniques and statistical processes including factor analysis, clustering and ANOVA were used. As a result, online services were classified into six groups – mass, professional, intellectual, credit, supporting and facility services and the distinctive features of each group were examined and strategies for marketing and operations recommended. By understanding the unique characteristics of each service group, managers can implement more suitable strategies, foster more positive attitudes towards online transactions, and finally increase online buying intentions.     

序列模式挖掘在电子商务个性化服务中的应用

分析了电子商务发展面临的问题和个性化服务的特点,提出了Web使用挖掘技术在电子商务个性化服务中的应用方法,论述了基于Web挖掘的个性化服务研究,详细阐述了其挖掘过程,最后讨论了使用序列模式和分类相结合的技术得以实现个性化服务的方法。利用这些算法得到的个性化信息可以准确把握用户兴趣模式并对Web信息资源的组织方式进行有效更新,从而提高网络信息服务效率,为用户提供“一对一”的具备自适应性的智能个性化服务。     

How does perceived retail service innovativeness affect retail patronage intentions?

This study aims to conceptualize the effects of perceived retailer service innovativeness (PRSI), perceived service advantage, customer emotional satisfaction, and attitudes to retail patronage intentions. The proposed model is examined by structural equation modelling (SEM) using 1,386 samples from three retail formats in Taiwan. Results suggest that PRSI can be used as a strategic tool to create competitive advantage and customer satisfaction. PRSI is a critical antecedent factor that affects store patronage intentions through material‐based and experience‐based routes. This study affirms that a retailer's ability to offer service innovations, especially free in‐store services, may not immediately increase the financial performance of firms; however, it can change a consumer's attitude towards the store by becoming a critical determinant of success of the retailing system. The research model can be utilized by other retail studies, especially in this era of increased competition when innovation has become a critical strategic tool for differentiation.     

How the Kano model contributes to Kansei engineering in services

Recent studies show that products and services hold great appeal if they are attractively designed to elicit emotional feelings from customers. Kansei engineering (KE) has good potential to provide a competitive advantage to those able to read and translate customer affect and emotion in actual product and services. This study introduces an integrative framework of the Kano model and KE, applied to services. The Kano model was used and inserted into KE to exhibit the relationship between service attribute performance and customer emotional response. Essentially, the Kano model categorises service attribute quality into three major groups (must-be [M], one-dimensional [O] and attractive [A]). The findings of a case study that involved 100 tourists who stayed in luxury 4- and 5-star hotels are presented. As a practical matter, this research provides insight on which service attributes deserve more attention with regard to their significant impact on customer emotional needs. STATEMENT OF RELEVANCE: Apart from cognitive evaluation, emotions and hedonism play a big role in service encounters. Through a focus on delighting qualities of service attributes, this research enables service providers and managers to establish the extent to which they prioritise their improvement efforts and to always satisfy their customer emotions beyond expectation.     

Web数据挖掘在企业CRM中的应用

CRM的本质在于通过WWW的渠道,在营销、销售、服务和支持四个方面与客户建立良好的关系,从而提高企业收益。在电子商务中,提高客户忠诚度保持住客户,实现交叉销售等成为电子商务成败的一个关键问题。而Web数据挖掘能在电子商务中更好地运作CRM,建立良好客户关系的一种解决方法。该文研究了Web数据挖掘技术在CRM中的应用。