Application of the European Customer Satisfaction Index to Postal Services. Structural Equation Models versus Partial Least Squares

Customer satisfaction and retention are key issues for organizations in today’s competitive market place. As such, much research and revenue has been invested in developing accurate ways of assessing consumer satisfaction at both the macro (national) and micro (organizational) level, facilitating comparisons in performance both within and between industries. Since the instigation of the national customer satisfaction indices (CSI), partial least squares (PLS) has been used to estimate the CSI models in preference to structural equation models (SEM) because they do not rely on strict assumptions about the data. However, this choice was based upon some misconceptions about the use of SEM’s and does not take into consideration more recent advances in SEM, including estimation methods that are robust to non-normality and missing data. In this paper, both SEM and PLS approaches were compared by evaluating perceptions of the Isle of Man Post Office Products and Customer service using a CSI format. The new robust SEM procedures were found to be advantageous over PLS. Product quality was found to be the only driver of customer satisfaction, while image and satisfaction were the only predictors of loyalty, thus arguing for the specificity of postal services.European Customer Satisfaction Index; ECSI; Structural Equation Models; Robust Statistics; Missing Data; Maximum Likelihood

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Examining the Customer Equity of Retail Clothing Stores in a Bahawalpur Context: Nishat Linen & Sapphire

The Customer Equity framework was developed to answering the necessities and fulfil the lack of a model that could make marketing activities accountable and measurable for the firm by offering the missing link that connects marketing actions with the Customer spending actions. The purpose of this study is to apply the Customer Equity framework from the Customer perspective including the different Customer Equity drivers. For this purpose the primary data was collected by questionnaire from the customers of district of Bahawalpur. The data was tested for chi square test and frequency test. The results showed that there is significant relationship among all the variables. The results will be beneficial for managers and policy makers as well for allied industries. Keywords: Customer equity, Brand equity, Value equit

A hybrid model for migrating customer segmentation with missing attributes

Due to missing attributes in an enterprise's database, migrating customer segmentation results from external dataset to enterprise database in difficult. In this paper, a hybrid model, called HMCS model, is presented. This model artificially generates values of missing attributes based on external dataset and populates them to enterprise database. Based on this model, an application in a telecom application is reported. Application indicates the presented model can produce acceptable segmentation results on the enterprise dataset which is with missing attributes. © 2013 IEEE

Products and prototypes: What’s the difference?

Prototypes are intended to demonstrate or test an idea. Commercial Off-The-Shelf products are intended for ongoing profitable sales. Their quality requirements are different: the former should be as cheap as possible whilst meeting the need for an adequate Proof-of-Concept or Demonstrator; the latter should be fit-for-purpose, cost-effective and an attractive, reliable solution to real world needs. Selling a prototype as a product risks customer dissatisfaction, com-plaints, legal challenges and reputation damage. Often the proto¬type has to be re-written to meet product quality-level expectations. This paper reviews the quality properties required of a product ready for delivery. This follows the ISO/IEC 25010 Quality Model, then adds important missing elements that lie “behind the scenes” in customer support, product management, legal aspects and defensive programming. It draws on a lifetime’s experience working on software products, products containing software and Software as a Service, providing facilities to end users

Stand der Literatur zur operativen Steuerung von Dienstleistungsprozessen

Services are characterised by the integration of customers while the service is produced. This integration leads to interruptions in the processing of a customer order until the customer provides the missing input. Since customer behaviour can be planned to a certain extent only challenges in planning an efficient delivery of a service process arise. This holds especially true for operational control as it has to correct deviances in the short-term. Thus, the following research question occurs: How can service processes be controlled efficiently taking customer integration into account? The aim of this working paper is to conduct a comprehensive literature review with regard to the research question. The results show that the majority of approaches are originate from manufacturing dealing with processes mainly conducted by machines and having stock-keeping possibilities. These manufacturing processes and the approaches typically do not deal with the complex influence of customer integration on operational control as in the case of service processes. It is concluded that a sufficient answer to control service processes is missing so far and thus potential research areas are addressed. --operational control,services,literature review

Perlindungan Hukum terhadap Nasabah Korban Kejahatan Penggandaan Kartu Atm pada Bank Swasta Nasional di Denpasar

Responsibility of the bank to refund customers money that became victims of ATMcard duplication is the bank have responsibility to refund the customer money, so that theloss of customer funds was caused by his own negligence, then the bank is not haveresponsibility to refund losses suffered by customers. Legal efforts taken by the bank torefund customers money that became victims of ATM card duplication such as :Clarification of customer complaints by checking the data to determine the customersaccount transactions that cause a reduction in the customers account balance, thendeliver customer transactions was conducted customer; Checking customer transactionsallegedly clumsy, one of which is checking the CCTV at the cash machine withdrawalsare not recognized by the customer, checking and known whether the transaction isconcluded that the transaction is correct or odd transactions; Returns missing customerfunds, if it is concluded that the clients do not make transactions recorded in thecustomers account, and making a crime report in the ATM card duplication police to dothe investigation against the perpetrator

The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values

The evolution of big data analytics through machine learning and artificial intelligence techniques has caused organizations in a wide range of sectors including health, manufacturing, e-commerce, governance, and social welfare to realize the value of massive volumes of data accumulating on web-based repositories daily. This has led to the adoption of data-driven decision models; for example, through sentiment analysis in marketing where produces leverage customer feedback and reviews to develop customer-oriented products. However, the data generated in real-world activities is subject to errors resulting from inaccurate measurements or fault input devices, which may result in the loss of some values. Missing attribute/variable values make data unsuitable for decision analytics due to noises and inconsistencies that create bias. The objective of this paper was to explore the problem of missing data and develop an advanced imputation model based on Machine Learning and implemented on K-Nearest Neighbor (KNN) algorithm in R programming language as an approach to handle missing values. The methodology used in this paper relied on the applying advanced machine learning algorithms with high-level accuracy in pattern detection and predictive analytics on the existing imputation techniques, which handle missing values by random replacement or deletion..  According to the results, advanced imputation technique based on machine learning models replaced missing values from a dataset with 89.5% accuracy. The experimental results showed that pre-processing by imputation delivers high-level performance efficiency in handling missing data values. These findings are consistent with the key idea of paper, which is to explore alternative imputation techniques for handling missing values to improve the accuracy and reliability of decision insights extracted from datasets