Implications of customer value perceptions for the design of electricity efficiency services in times of smart metering

Smart meters are the backbone of modern electricity metering and an important enabler of reaching energy efficiency targets. The implementation of new metering infrastructure is, however, making little progress and is often focused on technical aspects only. Additionally, existing smart metering information systems do not yet exploit the possibilities to optimally support customers in their electricity savings activities. Knowing customer preferences is absolutely essential for the effectiveness of energy efficiency measures and, as a consequence, for realizing the economic value of smart metering technology. The presented research contributes to the field by identifying customer value perceptions concerning new smart meter services in the retail electricity market in Switzerland. Founded on a choice-based conjoint analysis with a data sample of more than 1500 respondents from three Swiss regions, five customer segments with different preferences are identified. With the exception of the comfort-oriented customer segment, the other four segments are comprised of customers who are willing (1) to pay for smart meter services and (2) to change their behavior to save electricity. Based on the identified customer value perceptions, implications for the design of smart meter-based energy efficiency services are elaborated.     

Cloud computing adoption: an empirical study of customer preferences among start-up companies

Cloud computing represents a paradigm shift to utmost scalable and flexible IT services. However, research related to preferences of certain customers concerning cloud services is scarce. Especially start-up companies with their limited capacities to implement and operate IT infrastructure and their great demand for scalable and affordable IT resources are predestined as customers of cloud based services. In this study, we apply a multi-method approach to investigate customer preferences among start-up companies. Based on a literature review and a market analysis of cloud service models, we propose a set of cloud provider characteristics. These properties were examined among 108 start-up companies and analyzed in three steps using factor analysis to define customer preferences, cluster analysis to identify customer segments and discriminant analysis to validate the identified clusters. The results show that start-ups can be basically divided in five clusters each with certain requirements on cloud provider characteristics.     

Mining affective needs of automotive industry customers for building a mass-customization recommender system

Mass customization systems aim to receive customer preferences in order to facilitate personalization of products and services. Current online configuration systems are unable to efficiently identify real customer affective needs because they offer an excess variety of products that usually confuse customers. On the other hand, mining affective customer needs may result in recommender systems, which can enhance existing configuration systems by recommending initial configurations according to customer affective needs. This paper introduces a mass customization recommender system that exploits data mining techniques on automotive industry customer data aiming at revealing associations between user affective needs and the design parameters of automotive products. One key novelty of the presented approach is that it deploys the Citarasa engineering, a methodology that focuses on the provision of the appropriate characterizations on customer data in order to associate them with customer affective needs. Based on the application of classification techniques we build a recommendation engine, which is evaluated in terms of user satisfaction, tool’s effectiveness, usefulness and reliability among other parameters.     

Determining optimal levels of engineering characteristics in quality function deployment under multi-segment market

Customers often have various requirements and preferences on a product. A product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. In this paper, a methodology which mainly involves a market survey, fuzzy clustering, quality function deployment (QFD) and fuzzy optimization, is proposed to achieve the optimal target settings of engineering characteristics (ECs) of a new product under a multi-segment market. An integrated optimization model for partitioned market segments based on QFD technology is established to maximize the overall customer satisfaction (OCS) for the market considering the weights of importance of different segments. The weights of importance of market segments and development costs in the model are expressed as triangular fuzzy numbers in order to describe the imprecision caused by human subjective judgement. The solving approach for the fuzzy optimization model is provided. Finally, a case study is provided for illustrating the proposed methodology.     

Customer loyalty programs to sustain consumer fidelity in mobile telecommunication market

Companies realized the importance of well-managing their relationships with their customers. Customer Relationship Management (CRM) allows companies to manage their marketing strategies and deliver specific services to clients with different values. The mobile telecommunication market is a very competitive market where the customers are tended to move from one company to another easily. Mobile telecommunication companies should carry on specific programs and services to their customers in order to keep them satisfied and thus ensure their fidelity with the company. In this article our objective is to provide companies a model that facilitates to decide what kind of customer loyalty programs they should address to their clients from different segments. In order to do that we present a fuzzy based Hungarian method that allow assigning different loyalty programs to customers with different characteristics.     

Leading the Internal Audit Function

Abstract

Customers are increasingly viewing the product and standard service-line offerings from suppliers as commodities. As a result any competitive edge from their offerings is neutralized. To compete suppliers must provide differentiated services to different types of customer segments. However, this means suppliers need to know how profitable, currently and potentially, their customers and prospects are in order to know which types of customers to retain, grow, win-back and acquire as well as how much to optimally spend doing these for each type of customer segment. Most accounting systems fall short providing this information below the product gross profit margin line. What is needed is a profit and loss income report for each customer segment, and ideally for each customer.     

FCM-based customer expectation-driven service dispatch system

Maintaining long-term customer loyalty has been an important issue in the service industry. Although customer satisfaction can be enhanced with better service quality, delivering appropriate services to customers poses challenges to service providers, particularly in real-time and resource-limited dynamic service contexts. However, customer expectation management has been regarded as an effective way for helping service providers achieve high customer satisfaction in the real world that is nevertheless less real-time and dynamic. This study designs a FCM-based customer expectation-driven service dispatch system to empower providers with the capability to deal effectively with the problem of delivering right services to right customers in right contexts. Our evaluation results show that service providers can make appropriate decisions on service dispatch for customers by effectively managing customer expectations and arranging their contextual limited resources and time via the proposed service dispatch system. Meanwhile, customers can receive suitable service and obtain high satisfaction when appropriate services are provided. Accordingly, a high-performance ecosystem can be established by both service providers and customers who co-create value in the dynamic service contexts.     

A hybrid approach for personalized service staff recommendation

In this study, we established a novel set of service procedures that epitomize the human-centered spirit of service. By using self-organizing maps and collaborative filtering recommendation, we developed a mechanism that links the two service procedures of selecting service staff members and how customers decide tip amounts based on perceived value. Through the proposed mechanism, the recommender system could effectively predict customer preferences regarding service staff members and assign suitable members for delivering services. In addition, this study integrated the service experiences of previous customers with local tipping cultures for calculating recommended tip amounts for the reference of customers. Under this mechanism, the customer-centered spirit can be completely integrated into service procedures for effectively enhancing customer satisfaction, increasing the job satisfaction of employees, and producing a virtuous cycle of service quality improvement.     

Improving Personalization Solutions through Optimal Segmentation of Customer Bases

On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and personalized products and services to them. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying distance-based clustering algorithms in the space of these statistics. In this paper, we present a direct grouping-based approach to computing customer segments that groups customers not based on computed statistics, but in terms of optimally combining transactional data of several customers to build a data mining model of customer behavior for each group. Then, building customer segments becomes a combinatorial optimization problem of finding the best partitioning of the customer base into disjoint groups. This paper shows that finding an optimal customer partition is NP-hard, proposes several suboptimal direct grouping segmentation methods, and empirically compares them among themselves, traditional statistics-based hierarchical and affinity propagation-based segmentation, and one-to-one methods across multiple experimental conditions. It is shown that the best direct grouping method significantly dominates the statistics-based and one-to-one approaches across most of the experimental conditions, while still being computationally tractable. It is also shown that the distribution of the sizes of customer segments generated by the best direct grouping method follows a power law distribution and that microsegmentation provides the best approach to personalization.     

Unleashing the Power of mCRM: Investigating Antecedents of Mobile CRM Values from Managers’ Viewpoint

Mobile technology together with Internet-based electronic commerce has transformed the way businesses operate. Mobile Customer Relationship Management (mCRM) combines mobility and CRM (customer relationship management) to enable organizations to increase their business performance by delivering better products/services to their customers. Incorporating location-based information into CRM systems has created new values and business opportunities. Using location information, for example, businesses can offer more personalized and real-time location-based services (LBS) to their customers. In this study, we investigate how the IT infrastructure and LBS support of mCRM, as technical resource capabilities, and mCRM knowledge and education, as human resource capabilities, can enhance business performance of the organization by improving mCRM’s quality dimensions. We propose a research model that integrates the resource-based view (RBV) of a firm and DeLone and McLean IS success model to investigate the effects of mCRM quality dimensions on business performance. To validate the proposed research model, we collected a set of empirical data from managers in a wide range of organizations that use mCRM in South Korea. The results indicate that the IT infrastructure of an organization as a technical resource, along with mCRM users’ knowledge, as a human resource capability can significantly influence managers’ perceptions of quality along various dimensions of mCRM: customer data quality, system quality, and service quality. Consequently, improving these quality dimensions will significantly influence the performance of an organization in terms of financial performance, productivity, and customer satisfaction. The present study would help both academic and professionals to understand and improve business performance by employing the appropriate resources that can harness the full potential of mCRM.     

Conjoint analysis for IPTV service

Given the upcoming introduction of IPTV service in Korea, it is necessary to develop business models and marketing strategies to improve customer satisfaction and succeed in market competition. We use conjoint analysis to estimate customer preferences and the relative importance of service factors. Based on results from total customers’ and clustered customers’ service preferences, we propose marketing strategies for service providers.     

Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling

Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction–loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction–loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes.     

Dynamic micro-targeting: fitness-based approach to predicting individual preferences

It is crucial to segment customers intelligently in order to offer more targeted and personalized products and services. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying clustering algorithms. Recent research proposed a direct grouping-based approach that combines customers into segments by optimally combining transactional data of several customers and building a data mining model of customer behavior for each group. This paper proposes a new micro-targeting method that builds predictive models of customer behavior not on the segments of customers but rather on the customer-product groups. This micro-targeting method is more general than the previously considered direct grouping method. We empirically show that it outperforms the direct grouping and statistics-based segmentation methods across multiple experimental conditions and that it generates predominately small-sized segments, thus providing additional support for the micro-targeting approach to personalization.

Extracting knowledge of customers’ preferences in massively multiplayer online role playing games

Due to fierce competition in game markets, to identify customers’ true needs is one of the crucial factors in online game industry. Traditionally, game producers heavily rely on game testers, who are primarily responsible for analyzing computer games, finding software defects and being a part of quality control process, to achieve this goal. But, it is not often reliable. To ensure the investment can be returned, game producers need an effective approach to discover frequently shifted customer preferences in time. Recently, Kano model and data mining techniques have been successfully applied to recognize customers’ preferences and implement customer relationship management tasks, respectively. However, in traditional Kano analysis, only basically statistical analysis techniques are used, and they are insufficient to provide advanced knowledge to enterprisers. Therefore, in order to discover the relationship between/among quality elements in Kano model and to extract knowledge related to customer preferences, this study proposes a knowledge acquisition scheme that integrates several data mining techniques including association rule discovery, decision tree, and self-organizing map neural network, into traditional Kano model. An actual case of customer satisfaction survey regarding massively multiplayer online role playing game has been provided to demonstrate the effectiveness of our proposed scheme.     

A strategic approach to customer satisfaction in the telecommunication service market

As is the case in most other service industries, customer satisfaction is of paramount importance in the telecommunications industry. owever, customer satisfaction management in the telecommunication industry is difficult because of the diversity of services and customer segments that exist. This diversity makes it implausible to have a uniform customer satisfaction questionnaire that can be administered to all the different service/customer segment combination. Therefore, we first carried out a segmentation study to identify key customer segments for Korea Telecom, and then focused on one service/customer group in developing the questionnaire. The questionnaire was developed using the SERVPERF approach to measuring service satisfaction. We then developed a decision support system to manage and analyze the customer satisfaction data. The system consists of data input, tracking ability, and statistical modeling capabilities. The computer software was designed as an “open” decision support system that was intended to be used by a wide and general audience within Korea Telecom to review and make active use of customer satisfaction data.     

Web页面和客户群体的模糊聚类算法

web日志挖掘在电子商务和个性化web等方面有着广泛的应用.文章介绍了一种web页面和客户群体的模糊聚类算法.在该算法中,首先根据客户对Web站点的浏览情况分别建立Web页面和客户的模糊集,在此基础上根据Max—Min模糊相似性度量规则构造相应的模糊相似矩阵,然后根据模糊相似矩阵直接进行聚类.实验结果表明该算法是有效的.     

User preference mining techniques for personalized applications

Advanced personalized e-applications require comprehensive knowledge about their users’ likes and dislikes in order to provide individual product recommendations, personal customer advice, and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. In this paper we present preference mining techniques for detecting strict partial order preferences in user log data. Real-life e-applications like online shops or financial services usually have large log data sets containing the transactions of their customers. Since the preference miner uses sophisticated SQL operations to execute all data intensive operations on database layer, our algorithms scale well even for such large log data sets. With preference mining personalized e-applications can gain valuable knowledge about their customers’ preferences, which can be applied for personalized product recommendations, individual customer service, or one-to-one marketing.     

Service interaction design: A Hawk-Dove game based approach to managing customer expectations for oligopoly service providers

In the “experience economy”, effectively delivering memorable and exciting customer experiences has become a key issue for service providers. Service experience delivery involves service encounters through which interactions between service providers and customers can be shaped into interactive artifacts managing customer expectations and dynamically delivering suitable services. Service interaction design aims to optimize customer interactions with services to match customer expectations and yield satisfactory service experiences. On the other hand, service providers typically make profits and cost the priority, despite knowing that high service quality can maximize satisfaction, particularly in markets served by an oligopoly, resulting in customers only accepting existing limited-value services. Hence, the oligopoly market can be regarded as a value-bounded context. Additionally, understanding customer expectations regarding a wide range of interactions is crucial to service providers selecting and designing services that match customer expectations. Therefore, this paper presents a service interaction design mechanism to help oligopoly service providers systematically and effectively manage customer expectations in dynamic interactions, even in value-bounded contexts. The proposed mechanism models this service interaction design problem as a series of Hawk-Dove games that approach an evolutionary stable state. The evaluation results suggest that oligopoly service providers should change their mindsets and design service interactions to manage customer expectations associated with service delivery, not only to ensure high satisfaction and profit but also to engage customers in co-creating value.     

A PSO-based intelligent service dispatching mechanism for customer expectation management

In the era of experience economy, service providers have to provide customers with high quality service experience in order to attract more customers and achieve higher customer satisfaction. Managing customer expectation is a critical approach for service providers to consider. Although customer expectation has been discussed across different research disciplines, to our knowledge, there is still no systematical and feasible way to apply customer expectation management into real environments. This study attempts to establish an intelligent service dispatching mechanism by using particle swarm optimization for customer expectation management. This mechanism can help service providers design and deliver satisfactory service experience to customers. In order to evaluate the effectiveness and robustness of this mechanism, this study employs micro- and macro-simulation experiments to confer and analyze its performance. The simulation results show service providers can gain benefit and raise customer satisfaction by managing customer expectation during service experience delivery. Meanwhile, customers can also receive memorable experiences and have positive responses to service providers and other customers. Consequently, a high performance ecosystem within service providers and customers can be formed.