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A Predictive Modeling of Retail Satisfaction
Abstract
Data mining allows managers to make more knowledgeable decisions by predicting future trends and behaviors. One of the most widely used areas of data mining for the retail industry is in marketing. There are also at least seven methods of analysis or statistical techniques that are commonly used in data mining. It is obvious that the approach to the data mining is the key determinant of the statistical technique to be used. Predictive modeling uses variety of techniques such as linear regression, logistic regression, and their extensions can be used to identify patterns, which can be used to predict the future. This research specifically focuses on the application of multiple regression technique a data mining tool in Indian retail industries to predict the retail satisfaction using store attributes as independent variables.
... Other interesting topic is clustering and usage of e.g. K-means clustering on profiles recognized by PSO algorithm usage based on predictive model (Hemalatha, 2012;Hussain & Liatsis, 2009). Clustering could recognize similar profiles and consolidate similar profiles together. ...
Chapter introduce usage of particle swarm optimization algorithm and explained methodology, as a tool for discovering customer profiles based on previously developed Bayesian network (BN). Bayesian network usage is common known method for risk modelling although BN's are not pure statistical predictive models (like neural networks or logistic regression, for example) because their structure could also depend on expert knowledge. Bayesian network structure could be trained using algorithm but, from perspective of businesses requirements model efficiency and overall performance, it is recommended that domain expert modify Bayesian network structure using expert knowledge and experience. Chapter will also explain methodology of using particle swarm optimization algorithm as a tool for finding most riskiness profiles based on previously developed Bayesian network. Presented methodology has significant practical value in all phases of decision support in business environment (especially for complex environments).
... Other interesting topic is clustering and usage of e.g. K-means clustering on profiles recognized by PSO algorithm usage based on predictive model (Hemalatha, 2012;Hussain & Liatsis, 2009). Clustering could recognize similar profiles and consolidate similar profiles together. ...
Chapter introduce usage of particle swarm optimization algorithm and explained methodology, as a tool for discovering customer profiles based on previously developed Bayesian network (BN). Bayesian network usage is common known method for risk modelling although BN's are not pure statistical predictive models (like neural networks or logistic regression, for example) because their structure could also depend on expert knowledge. Bayesian network structure could be trained using algorithm but, from perspective of businesses requirements model efficiency and overall performance, it is recommended that domain expert modify Bayesian network structure using expert knowledge and experience. Chapter will also explain methodology of using particle swarm optimization algorithm as a tool for finding most riskiness profiles based on previously developed Bayesian network. Presented methodology has significant practical value in all phases of decision support in business environment (especially for complex environments).
This chapter represents the business case in the telecommunication company called Veza, in domain of churn prediction and churn mitigation. The churn project was divided into few stages. Due to limited budget and cost optimization, stage one was concentrated on prospective customer value calculation model based on fuzzy expert system. This helps Veza company to find most valuable telecom subscribers. It also helped company to better understand subscriber portfolio structure. Developed fuzzy expert system also helped Veza Company in detection of soft churn. Stage two is profiling and customer segmentation based on time series analysis which provided potential predictors for predictive churn model. The central stage was concentrated on developing traditional predictive churn model based on logistic regression. This calculated probability that subscribers will make churn in next few months. The final stage was dedicated to SNA (Social Network Analysis) model development which found out the most valuable customers from the perspective of existing subscriber network. This model gave the answer that subscribers have the greatest influence on other subscribers in a way what is dangerous if they leave Veza Company because they will motivate other subscribers to do the same thing. All three stages made complete churn detection/mitigation solution which take into consideration past behaviour of subscribers, their prospective value, and their strength of influence on other subscribers. This project helped Veza Company to decrease churn rate and it gave directions for better understanding customer needs and behaviour which were the base for new product development.
This chapter represents the business case in the telecommunication company called Veza, in domain of churn prediction and churn mitigation. The churn project was divided into few stages. Due to limited budget and cost optimization, stage one was concentrated on prospective customer value calculation model based on fuzzy expert system. This helps Veza company to find most valuable telecom subscribers. It also helped company to better understand subscriber portfolio structure. Developed fuzzy expert system also helped Veza company in detection of soft churn. Stage two is profiling and customer segmentation based on time series analysis which provided potential predictors for predictive churn model. The central stage was concentrated on developing traditional predictive churn model based on logistic regression. This calculated probability that subscribers will make churn in next few months. The final stage was dedicated to SNA (Social Network Analysis) model development which found out the most valuable customers from the perspective of existing subscriber network. This model gave the answer that subscribers have the greatest influence on other subscribers in a way what is dangerous if they leave Veza company because they will motivate other subscribers to do the same thing. All three stages made complete churn detection/mitigation solution which take into consideration past behaviour of subscribers, their prospective value, and their strength of influence on other subscribers. This project helped Veza company to decrease churn rate and it gave directions for better understanding customer needs and behaviour which were the base for new product development.
Developed predictive models, especially models based on probabilistic concept, regarding numerous potential combinatory states can be very complex. That complexity can cause uncertainty about which factors should have which values to achieve optimal value of output. An example of that problem is developed with a Bayesian network with numerous potential states and their interaction when we would like to find optimal value of nodes for achieving maximum probability on specific output node. This chapter shows a novel concept based on usage of the particle swarm optimization algorithm for finding optimal values within developed probabilistic models.
This chapter will introduce methodology how to use analytical potential of multimedia contents like YouTube, Bing Videos or Vimeo for discovering behavioral consumer characteristics. Chapter will also show how to consolidate unstructured text data sources from blogs and Twitter with revealed knowledge from multimedia contents for better understanding consumer habits and needs. For this purposes Social Network Analysis will be used as well as text mining techniques on different internet data sources. Presented methodology has practical value where information about customer behavior, preferences and changes in preferences during different time periods is valuable information for campaign planning, campaign management and new product development. Presented methodology also captures different techniques for data crawling from different internet resources, as well as analytical consolidation of revealed results which aim is better understanding of client behavior.
Chapter explains efficient ways of dealing with business problems of analyzing market environment and market trends under complex circumstances using heterogeneous data source. Under the assumption that used data can be expressed as time series, widely applicable multi variate model is explained together with case study in textile retail. This Chapter includes an overview of research conducted with a brief explanation of approaches and models available today. A widely applicable multi-variate decision support model is presented with advantages, limitations, and several variations for development. The explanation is based on textile retail case study with model wide range of possible applications in perspective. Complex business environment issues are simulated with explanation of several important global trends in textile retail in past seasons. Non-traditional approaches are revised as tools for a better understanding of modern market trends as well as references in relevant literature. A widely applicable multi-variate decision support model and its usage is presented through built stages and simulated. Model concept is based on specific time series transformation method in combination with Bayesian logic and Bayesian network as final business logic layer with front end interface built with open source Bayesian network tool. Explained case study provides one of the most challenging issue in textile retail: market trends seasonal/weather dependence. Separate outcomes for different scenario analysis approaches are presented on real life data from a textile retail chain located in Zagreb, Croatia. Chapter ends with a discussion about similar research's, wide applicability of presented model with references for future research.
In this study an attempt is made to develop a generic instrument that could be used to measure customer satisfaction with the controllable elements of the in-store shopping experience. By closely following the most contemporary guidelines for scale development, and involving 11 063 respondents in four different surveys, the authors emerge with a 22-item instrument to measure satisfaction with the in-store shopping experience. The evidence of the psychometric properties of the proposed ISE instrument offered here is compelling in terms of its uni-dimensionality, with-in-method convergent validity, cross-validation of dimensions in a cross-validation sample, reliability of the instrument, its discriminant validity and its nomological validity.
Total retail experience is, for the purposes of this study, defined as all the elements that encourage or inhibit consumers during their contact with a retailer. This article reports on the influence of three dimensions of consumers’ total retail experience on their satisfaction levels. The items used to measure the theoretical model demonstrate sufficient reliability and discriminant validity. The theoretical model is then tested using a structural equation modeling approach. Although not a test of a comprehensive model of the total retail experience, the study does shows that the personal interaction between retail employee and customer on the one hand, and physical cues on the other hand, impact strongly on customer satisfaction.
Organised Retail in India has seen remarkable growth in the last decade. In addition to the blossoming economy, one of the major drivers of this strong growth trajectory can be attributed to the rapid mall development in the country. This study, based on interviews with practicing retailers, mall managers and secondary data, tries to understand the analytical and financial steps that are undertaken before setting up a mall, sources of financing a mall and the sources of revenue and expenditure. On the basis of the findings, some suggestions are also put forth to enable a better relationship between tenant and mall management, and to ease the growing pains currently faced by malls in India.
Research has shown that an increase in perceived crowding in a retail store (created from either human or spatial density) can decrease the level of satisfaction that shoppers have with the store. The three studies reported here examine the retail crowding-satisfaction relationship to deter- mine the extent to which it is a simple, direct relationship. Specifically, we consider the possibil- ity that the crowding-satisfaction relationship is mediated by emotional reactions that are expe- rienced while shopping. In addition, moderating variables such as prior expectations of crowding, tolerance for crowding, and store type are examined for their influence on the crowd- ing-satisfaction relationship. The results of two field studies indicate that whereas emotions only partially mediate the relationship, the decrease in shopping satisfaction due to crowding is moderated by expectations of crowding and personal tolerance for crowding. A laboratory ex- periment replicated the field studies and shows, in addition, that although ceiling and floor ef - fects may be present, the relationship between perceived crowding and shopping satisfaction appears to vary by store type.
Customer loyalty is an important strategic objective for all managers. Research has investigated the relationship between customer satisfaction and loyalty in various contexts. However, these predominantly cross-sectional studies have focused on customer retention as the primary measure of loyalty. There has been little investigation into the impact on share of wallet. Using data from the Canadian banking industry, this research aims to (1) provide the first longitudinal examination of the impact of changes in customer satisfaction on changes in share of wallet and (2) determine the moderating effects of customer age, income, education, expertise, and length of relationship. Data from 4319 households using 12,249 observations over a five-year period indicate a positive relationship between changes in satisfaction and share of wallet. In particular, the initial satisfaction level and the conditional percentile of change in satisfaction significantly correspond to changes in share of wallet. Two variables, income and length of the relationship, negatively moderate this relationship. Other demographic and situational characteristics have no impact.
Two studies were conducted to examine whether shopping values are affected by perceived retail crowding, and whether shopping values mediate the relationship between perceived retail crowding and shopping satisfaction. Results of the first study show that perceived retail crowding affects shopping values, albeit not very strongly. However, the effects appear to be moderated by factors such as personal tolerance for crowding, time spent shopping, shopping intention, and whether a purchase was made. Study 2 indicates that the impact of perceived crowding on shopping value is mediated by emotions experienced by the shopper. The emotions and shopping value reactions, in turn, mediate the effect of spatial crowding on shopping satisfaction. Interestingly, the results show that when these mediating variables are accounted for, human crowding positively affects shopping satisfaction. These findings provide support for the inverted U explanation in the general crowding literature, and suggest new avenues of future research in the context of retail crowding.
Data mining is a new technology that helps businesses to predict future trends and behaviours, allowing them to make proactive, knowledge-driven decisions. When data mining tools and techniques are applied on the data warehouse based on customer records, they search for the hidden patterns and trends. These can be further used to improve customer understanding and acquisition. Customer Relationship Management (CRM) systems are adopted by the organisations in order to achieve success in the business and also to formulate business strategies, which can be formulated based on the predictions given by the data mining tools. Basically three major areas of data mining research are identified: implementation of CRM systems, evaluation criteria for data mining software and CRM systems and methods to improve data quality for data mining. The paper is concluded with a proposed integrated model for the CRM systems evaluation and implementation. This paper focuses on these areas, where there is need for more explorations, and will provide a framework for analysis of the data mining research for CRM systems.
Sharing of data among multiple organisations is required in many situations. The shared data may contain sensitive information about individuals which if shared may lead to privacy breach. Thus, maintaining the individual privacy is a great challenge. In order to overcome the challenges involved in data mining, when data needs to be shared, privacy preserving data mining (PPDM) has evolved as a solution. The objective of PPDM is to have the interesting knowledge mined from the data at the same time to maintain the individual privacy. This paper addresses the problem of PPDM by transforming the attributes to fuzzy attributes. Thus, the individual privacy is also maintained, as one cannot predict the exact value, at the same time, better accuracy of mining results is achieved. ID3 and Naive Bayes classification algorithms over three different datasets are used in the experiments to show the effectiveness of the approach.
Computational and digital advancements with the advent of relationship marketing have changed the land signs of business. Digital revolution led to generation and collection of data in companies and extracting knowledge from this data through knowledge discovery in databases (KDD) process. KDD involves many steps, of which an important step is data mining. Data mining is a process of extracting patterns in data through statistical and other techniques and algorithms. In business, firms are shifting their marketing approach from mass marketing to relationship based marketing leading to an era of customer relationship management (CRM). CRM requires sustainable long term relationship with customers and allocation of resources to maintain these relationships. Customer lifetime value (CLV) is a metric to justify resource allocation by segregating customers on the basis of their contribution to the company. In this paper we review applications of statistical and data mining techniques for predicting CLV and its parameters. The applications of techniques such as logistic regression, decision trees, artificial neural networks, genetic algorithms, fuzzy logic and support vector machines are covered. In the end, a case study is presented to estimate few CLV parameters for a direct marketing company.
this paper we discuss the technology and enterprise-adoption trends in the area of business analytics. The key consumer of these analytics is the business user, a person whose job is not directly related to analytics per-se (e.g., a merchandiser, marketer, salesperson), but who typically must use analytical tools to improve the results of a business process along one or more dimensions (e.g., profit, time to market). Fortunately, data mining , analytic applications, and business intelligence systems are now being better integrated with transactional systems creating a closed loop between operations and analyses that allows data to be analyzed faster and the analysis results to be quickly reflected in business actions. Mined information is being deployed to a broader business audience, which is taking advantage of business analytics in everyday activities. Analytics are now regularly used in multiple areas, including sales, marketing, supply chain optimization, and fraud detection [4, 5]
It has been well documented in the marketing literature that customer satisfaction is critical to any businesses' success. However, it is far less clear as on how marketers comprehend customer differences in customer satisfaction evaluations, and leverage such understanding in forming their marketing strategies. Only recently have researchers begun to explore the notion of individual or segment differences in the formation of overall satisfaction judgments.
To extend the exploration of unobserved customer heterogeneity in customer satisfaction studies with multiple attributes, we propose a latent structure multidimensional scaling (MDS) model to visually depict unobserved customer heterogeneity with respect to the theoretical components of customer satisfaction judgments. Our model is developed on the basis of the well-established expectancy–disconfirmation theory of customer satisfaction. We describe the proposed MDS model and discuss the technical aspects of the model structure and maximum likelihood estimation.
e-Tail, or internet-based retailing, has emerged as an innovative channel for retailers to reach target consumers in the comfort of their homes. e-Tailing has exhibited an upward trend across the world in recent years, although it is more prevalent in certain regions such as Europe and North America. With varying levels of internet penetration, telecom infrastructure, the business and legal environment, e-tailing is at varying stages of maturity in countries across the globe. In order to objectively compare the actual preparedness of various countries to exploit their potential for e-tail, an analytical model was built by using principal component analysis. This model returns a numeric value, coined as the e-Tail Readiness Index. A set of countries were subsequently ranked on the basis of this index. As expected, while the more developed countries of Europe and North America occupied the top slots, the emerging economies of eastern Europe and Latin America occupied the middle slots. Surprisingly, India and China figured right at the bottom of the heap.
This study empirically examines the impact of situational, consumer, store and product characteristic variables on consumers' behavioural reactions in out-of-stock situations in retail. A survey method for data collection was used with a sample of 1,207 retail customers in India's unorganised retail sector across five product categories in Varanasi, India. Results showed that 11 out of 13 independent variables had a significant impact on at least one of the three behavioural responses considered. As the data was collected only for five product categories and for unorganised retail setting, findings are not generalisable beyond these limitations. Empirical research like this helps to develop an understanding about the consumer behaviour which differs widely across settings. It is one of the first studies in an unorganised retail in India. Study provides crucial insights to the retailers by identifying independent variables that must be considered while designing their operations and strategies to fight out-of-stock. Implications for the retailers and the future research are discussed.
This study examines the relationships that exist between consumers' perceptions of a retail environment and their emotions, satisfaction and behavioural intentions with respect to that shopping setting. A model of these relationships is developed and then tested in two distinct retail settings—shopping centres and traditional retailing areas. The results show that, in general, positive perceptions of a retail environment have a positive influence on positive emotions, on repatronage intentions, and on the desire to remain longer in the shopping area in both retail settings. However, some interesting differences emerge between shopping centres and traditional retailing areas: (i) the internal environment has a stronger effect on emotions in shopping centres than in traditional retailing areas; and (ii) the internal environment has a negative effect in shopping centres on the disposition to pay more.
In the past, very little research has been carried out into the differing roles played by various quality attributes on customer satisfaction. In addition, the vast majority of scholars consider the relationship between quality attributes and customer satisfaction to be linear. However, from the point of view of prospect theory, this relationship may be non-linear. This study begins by reviewing past research into quality attributes and customer satisfaction. An investigative study was then carried out on hypermarket customers and it was found that the relationship between most quality attributes and customer satisfaction is asymmetrical and non-linear. It was also discovered that the value function curve of prospect theory can only be used on certain one-dimensional quality attributes. The attractive quality, must-be quality, indifferent quality, and reverse quality attributes as well as some of the one-dimensional quality attributes cannot be described by the value function curve. Based on the differences in the way they affect customer satisfaction, the 43 hypermarket quality attributes were categorized in accordance with the Kano model and are presented here as a guide to relevant persons in drawing up their marketing strategies. Some implications for management are discussed and some suggestions for further research are also given.
Purpose
To examine the relative roles played by cognition and emotion in the development of customer satisfaction in a retail setting.
Design/methodology/approach
An experimental design was employed. Study participants were exposed one of two expectation scenarios depicting past experience with an unnamed retail store, with one developed to build high expectations and one developed to create low expectations. Study participants were then exposed to one of two performance scenarios depicting a new experience with the unnamed retail store, with one depicting a successful pleasurable experience and one depicting a performance failure. Participants were also asked to complete scales measuring their cognitive evaluation of the perceived performance and their emotional reaction to the perceived performance.
Findings
Both cognitive evaluation and emotional reaction were found to explain the level of satisfaction experienced in a retail setting. As opposed to a service setting, however, cognitive evaluation was found to be more important than emotional reaction in explaining customer satisfaction. When the individual treatment levels were examined, anger/delight and shame were observed to be significant only for the third treatment level (high expectations/poor performance).
Practical implications
The findings suggest that retailers whose customers possess high expectations will need to place explicit attention on their customers' emotions. If a performance is deemed as negative, not only will the negative performance affect level of satisfaction, but also the negative emotions associated with the poor performance will also likely affect level of satisfaction.
Originality/value
The paper examines the role of emotion in developing customer satisfaction in a retail setting.
This article first examines how data warehousing provides data in a format especially designed to support strategic management decisions. Then it explores major data mining techniques as they relate to market segments. The coupling of massive data bases contained in data warehouses with the automated analytic capabilities of data mining software packages enables the detection of previously unknown trends or patterns that may identify new market segments. Although much of the data mining activity has not been related to market segmentation, the new knowledge generated from data mining provides powerful support for market segmentation decisions. Being able to evaluate, adjust, and develop the best product-market is critical. The authors present a basic model and show how data mining can be used for identifying new, more viable and better market segments.
Despite the proliferation of theoretical developments in the area of western consumer behaviour, to date the likelihood of Asian variants to existing consumer behavioural models has not been explored in any detail. Based on prior research in the field of retail attribute testing this empirical study investigates the influence of supermarket store attributes on Chinese (Macau) customer satisfaction moderated by the biographical factors of age and income. The results indicate that the store attributes of distance and speed of check out were significant for age, and that delivery service and price reductions were significant by income. Total independent variable rankings indicated store location, price and product variety were perceived by Chinese respondents as being the most important store attributes influencing satisfaction.
Interviewed 206 female customers of a large department store concerning their satisfaction with products and services. A statistical model was then developed to predict retail satisfaction. It is suggested that this model is the best possible representation of the psychological process underlying consumer satisfaction. (26 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Cet article examine les rapports entre la satisfaction de la clientèle et les contraintes relevant des situations techniques et sociales. A cette fin, une enquête de terrain fut réalisée auprès d’un échantillon de 57 managers et 835 clients d’organisations de service. On a recensé les contraintes sociales et techniques vécues par les managers. La satisfaction des clients a également été mesurée avec plusieurs aspects des services. En général, l’absence de situation contraignante dans les organisations de service était en relation positive avec la satisfaction de la clientèle. Les résultats montrent aussi que la seule contribution des contraintes techniques à la satisfaction des clients était supérieure à celle des contraintes sociales. Nous signalons des implications managériales et des orientations pour les futures recherches portant sur les relations entre les contraintes et la satisfaction des clients.
This paper aims to test the links of social and technical situational constraints to customer satisfaction with services. To this end, a field survey study was conducted using a sample of 57 managers and 835 customers of service organisations. We studied social and technical constraints perceived by managers. Customer satisfaction with several service attributes was also measured. In general, a lack of situational constraints in service organisations was positively associated with customer satisfaction. The results also showed that the unique contribution of technical constraints to customer satisfaction was greater than that of social constraints. We point out managerial implications and future directions for research on constraints–customer satisfaction relationships.
Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ranging from traditional statistical methods to new data mining approaches. In this study the performance of three data mining methods in detecting network intrusion is examined. An experimental design (3times2x2) is created to evaluate the impact of three data mining methods, two data representation formats, and two data proportion schemes on the classification accuracy of intrusion detection systems. The results indicate that data mining methods and data proportion have a significant impact on classification accuracy. Within data mining methods, rough sets provide better accuracy, followed by neural networks and inductive learning. Balanced data proportion performs better than unbalanced data proportion. There are no major differences in performance between binary and integer data representation.
In today's competitive environment, rapid advances in economic globalization and information technology have forced many organizations to anticipate and respond to increasing volatility and competitive pressures. Numerous researches have focused on developing techniques on the analysis, design and management of whole logistics chains in particular in the domain of data mining, data analysis and data classification. While the data mining techniques and computational intelligence techniques are good independently for solving specific tasks, they can be synergized through the formation of an integrated and unified model, which can take advantage of the goods and offset the flaws of the two techniques. In this paper, an intelligent information infrastructure, which is characterized by its ability to encompass a rich collection of knowledge representation formalisms for dealing with a logistics information flow problem, is presented. Such intelligent information infrastructure includes a two-step strategy that embraces the combination of online analytical processing and neural networks to support knowledge discovery. In addition, extensible markup language is used to support the overall infrastructure, in order to facilitate the seamless data interchange within an enterprise.
Retailing in India is growing at the rate of about 18-20 per cent per annum. As part of its strategy to gradually open it up to foreign competition, the government is also in the process of increasing foreign direct investment in the retail sector. At the same time, the urban consumer is becoming more discerning and demanding as far as the lifestyle is concerned. Urban Indian household income and purchasing power are also on the rise. Under such circumstances, the success of organized retailing in India mainly depends on delivery of services through quality improvements. In service organizations, customer-perceived service quality is considered as one of the key determinants of business performance. So far, in the Indian context, there is a dearth of tested instruments which can measure customer-perceived service quality of a retail store. At the same time, instruments developed in other countries have not been tested for their applicability in the Indian retail industry.
It is in this context that this paper reports on the application of Dabholkar, Thorpe and Rentz's (1996) retail service quality scale in measuring the gap between the customers' expectations and their perceptions about the service quality of retail stores in India. Statistical analyses were performed to test the dimensionality of service quality and to examine the reliability of the scale. Finally, the analysis of the gap scores was used to suggest relevant improvements in the retail store service quality.
The results indicate the following: Although the instrument was found to be quite reliable, the gap scores did not merge into five dimensions of service quality as proposed by the scale developers; rather, the gap scores roughly merged into nine dimensions. The instrument and the five dimensions of service quality may need considerable restructuring. A few statements which showed considerable reliability problems should be restated or substituted by more relevant statements. The instrument may not be applicable to the retail sector in India without further restructuring. Further research is necessary to understand retail store service quality in India.
The analysis of the gap scores indicates that the highest perceived service gap lies in the policies of the retail stores, particularly, parking facilities provided by them. Apart from this, all other statements also show a negative gap implying the need for considerable improvements in retail service quality. Therefore, as organized retailing develops in India, retail stores in India will have to improve the quality of their services significantly in order to compete successfully in the global marketplace.
Data mining is proving to be a valuable tool, by identifying potentially useful information from the large amounts of data collected, and enabling an organization to gain an advantage over its competitors. This article defines what data mining is and covers the operations of data mining as have been classified throughout literature. It then focuses on the important uses of data mining, which include but are not limited to marketing, risk management, fraud detection, and customer acquisition and retention. Examples of how the banking and retail industries have been effectively utilizing data mining in these areas are provided.
y illustrative of the tremendous potential of KDD technology. 1.1 Risk Management and Targeted Marketing Insurance and direct-mail retail are examples of businesses that rely on effective data analysis in order to make profitable business decisions. For example, insurers must be able to accurately assess the risks posed by policyholders in order to set insurance premiums at competitive levels. Overcharging low-risk policyholders would motivate such policyholders to seek lower premiums elsewhere. Undercharging high-risk policyholders would attract more high-risk policyholders because of the lower premiums. In both cases, costs would increase and profits would decrease. Effective data analysis leading to the creation of accurate predictive models is essential in order to address these issues. In the case of direct-mail targeted marketing, retailers must be able to identify subsets of the population that are likely to respond to promotions in order to offset mailing and printing costs.
The capacity of digital data storage worldwide has doubled every nine months for at least a decade as of August 1, 2002. This increase in the speed of digital data storage is one of the reasons for the increasing importance and rapid growth in the field of data mining. The aggressive rate of growth of disk storage and the gap between Moore's Law and Storage Law growth trends represents a very interesting pattern in the state of technology evolution. Our ability to capture and store data has far outpaced our ability to process and utilize it. This growing challenge has produced a phenomenon we call the data tombs, or data stores that are effectively write-only. Data mining is defined as the identification of interesting structure in data. Structure designates patterns, statistical or predictive models of the data, and relationships among parts of the data. Each of these terms-patterns, models, and relationships has a concrete definition in the context of data mining. Data mining is primarily concerned with making it easy, convenient and practical to explore very large databases for organizations and users with lots of data but without years of training as data analysts.
Managing the growing problem of computer fraud within organisations has led researchers and practitioners to emphasise the need to take into account the 'social' aspects of information security. In addition, wider organisational issues such as lack of communication have been associated with computer fraud. In trying to minimise 'opportunities' for computer fraud, this paper argues that managers' awareness and knowledge of how an organisation functions can significantly affect the effectiveness of management information security. This is because managers can send 'cues' to other employees, which influence how the latter perceive and abide by information security and other policies and procedures in their daily activities. Such perception and application are examples of tacit knowledge development and use. In developing the argument, this paper uses the case of Barings Bank together with Mintzberg's five functions of an organisation. Thus it contributes in effective management of information security from a knowledge management perspective.
From 1978 to 2004, China's GDP growth rate has been 9.4% annually. In 2005, China's GDP growth rate was 9.9%, which at $2,278 billion currently is ranked number four in the world. This paper discusses the significance of transforming research findings in data mining and knowledge management (DMKM) to support China's economic development as well as the trend of DMKM applications in China. It first reviews development of DTKM in international communities and industries, which includes academic activities and business applications tools. Second, this paper outlines the significance and needs of DTKM in China's economic development, as to promote further DMKM research, innovatively produce new business intelligence tools, and effectively adopt existing commercial software packages. Finally, the paper observes some problems and challenges of using DMKM to support China's economy.
Consumer responses to clearance sales, both in terms of consumer satisfaction with the decision process and in terms of subsequent store choice behavior, are explored in the paper through four controlled experiments conducted involving clearance sales in a consumer choice and decision satisfaction context. The results suggest that consumer response to clearance sales is driven to a large extent by two factors: the effect of a clearance sale on the available options of goods and the degree of store loyalty. Response to a clearance sale was found to be a function of two primary forces-the degree to which a consumer was personally committed to the discount sales alternative, and changes in the difficulty of making a decision due to limitation of buying options. Overall, the discussion of results of the four studies presented in the paper demonstrates that consumer response to clearance sales, both in terms of decision satisfaction levels and observed store-loyalty behavior, are strongly influenced by the variables of price sensitivity, attractiveness of products, store-loyalty and perceived value on available brands.
Suggestions to enhance the cyber store customers satisfaction.
- K.Kijoo