878 resultados para customer analytics


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Indian economy is witnessing stellar growth over the last few years. There have been rapid developments in infrastructural and business front during the growth period.Internet adoption among Indians has been increasing over the last one decade.Indian banks have also risen to the occasion by offering new channels of delivery to their customers.Internet banking is one such new channel which has become available to Indian customers.Customer acceptance for internet banking has been good so far.In this study the researcher tried to conduct a qualitative and quantitative investigation of internet banking customer acceptance among Indians. The researcher tried to identify important factors that affect customer's behavioral intention for internet banking .The researcher also proposes a research model which has extended from Technology Acceptance Model for predicting internet banking acceptance.The findings of the study would be useful for Indian banks in planning and upgrading their internet banking service.Banks could increase internet banking adoption by making their customer awareness about the usefulness of the service.It is seen that from the study that the variable perceived usefulness has a positive influence on internet banking use,therefore internet banking acceptance would increase when customers find it more usefulness.Banks should plan their marketing campaigns taking into consideration this factor.Proper marketing communications which would increase consumer awareness would result in better acceptance of internet banking.The variable perceived ease of use had a positive influence on internet banking use.That means customers would increase internet banking usage when they find it easier to use.Banks should therefore try to develop their internet banking site and interface easier to use.Banks could also consider providing practical training sessions for customers at their branches on usage of internet banking interface.

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In this thesis we have introduced and studied the notion of self interruption of service by customers. Service interruption in queueing systems have been extensively discussed in literature (see, Krishnamoorthy, Pramod and Chakravarthy [38]) for the most recent survey. So far all work reported deal with cases in which service interruptions are generated by sources other than customers. However, there are situations where interruptions are due to the customers rather than the system. Such situations are especially arise at doctors clinic, banks, reservation counter etc. Our attempt is to quantify a few of such problems. Systematically we have proceed from single server queue (in Chapter 2) to multi-server queues (Chapter 3). In Chapte 4, we have studied a very general multiserver queueing model with service interruption and protection of service phases. We also introduced customer interruption in a retrial setup (in Chapter 5). All models (from Chapter 2 to Chapter 4) that were analyzed involve 'non-preemptive priority' for interrupted customers where as in the model discussed in Chapter 5 interruption of service by customers is not encouraged. So the interrupted customers cannot access the server as long as there are primary customers in the system. In Chapter 5 we have obtained an explicit expression for the stability condition of the system. In all models analyzed in this thesis, we have assumed that no more than one interruption is allowed for a customer while in service. Since the models are not analytically tractable, a large number of numerical illustrations were given in each chapter it illustrate the working of the systems. We can extend the models discussed in this thesis to several directions. For example some of the models can be analyzed with both server induced and customer induced interruptions the results for which are not available till date. Another possible extension of work is to the case where there is no bound on the number of interruptions a customer is permitted to have before service completion. More complex is the case where a customer is permitted to have a nite number (K ≥ 2) of We can extend the models discussed in this thesis to several directions.

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As of 1999. the state of Kerala has 3210 offices of scheduled commercial banks (SCBS). In all, there are 48 commercial banks operating in Kerala, which includes PSBs, OPBs, NPBS. FBs, and Gramin Banks. The urban areas give a complete picture of the competition in the present day banking scenario with the presence of all bank groups. Semi-urban areas of Kerala have 2196 and urban areas have 593 as on March 1995.“ The study focuses on the selected segments ofthe urban customers in Kerala which is capable of giving the finer aspects of variation in customer behaviour in the purchase of banking products and services. Considering the exhaustive nature of such an exercise, all the districts in the state have not been brought under the purview of the study. Instead. three districts with largest volume of business in terms of deposits, advances, and number of offices have been short listed as representative regions for a focused study. The study focuses on the retail customer segment and their perceptions on the various products or services offered to them. Non Resident Indians (NRIs), and Traders and Small—ScaIe Industries segments have also been included in the study with a view to obtain a comparative picture with respect to perception on customer satisfaction and service quality dimensions and bank choice behaviour. The research is hence confined to customer behaviour and the implications for possible strategies for segmentation within the retail segment customers

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Recognizing that high satisfaction leads to high customer loyalty, companies today are aiming for total customer satisfaction. This article explains relative impact of product quality, service quality and contextual experience on customer perceived value and intention to shop in the future. The data has been collected using a questionnaire from 205 customers of a national retailer chain. The relative importance of product quality, service quality and contextual experience on customer perceived value and thus on customer preference and future intentions was measured using multiple regression. Also, the contribution of perceived value to preference and thus on future buying intention was also measured. Structural Equation Model (SEM) using Amos 4 was used to find the overall fitness of the model. It was found that product quality, service quality and contextual experience have a major influence on customer perceived value

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Earlier studies on measurement of customer satisfaction are based on either transaction specific or overall approaches. The transaction specific approach evaluates customer satisfaction with single components in the whole purchase process but the overall satisfaction was based on all the encounters or experiences to the customer throughout the purchase process. Consumers will comment on particular events of their purchase process when asked about transaction-specific satisfaction and they will comment their overall impression and general experiences in overall satisfaction (Bitner & Hubbert 1994) Through a critical review on the literature, it has been identified a new approaches to customer satisfaction, say, cumulative approaches that can be more useful than overall and transaction specific approaches for strategic decision making (Fornell et al 1996). The cumulative approach to customer satisfaction doesn’t study earlier due to the difficulty in operationalization of the concept. But the influencers of customer satisfaction are context specific and the prevailing models doesn’t give the sources of variations in the satisfaction, the importance of cumulative approaches to customer satisfaction has emerges that lights to a new research. The current study has focused to explore the influencers of overall customer satisfaction to form individual elements that can be used to identify the cumulative customer satisfaction.

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Zusammenfassung Mobile Telekommunikationstechnologien verändern den Alltag, ihre Benutzer und die Geschäftswelt. Im Zuge der Mobilität haben die Nutzer von mobilen Übertragungstechnologien ein hohes Kommunikationsbedürfnis in jeglicher Situation entwickelt: Sie wollen überall und jederzeit kommunizieren und informiert sein. Dies ist auch darauf zurückzuführen, dass ein Wandel der Individualisierung – von der Person zur Situation – stattgefunden hat. Im Rahmen der Untersuchung gehen wir auf diese entscheidenden Veränderung ein und analysieren die Potenziale des Kontextmarketing im mobilen Customer Relationship Management anhand der Erringung von Wettbewerbsvorteilen durch Situationsfaktoren. Daneben zeigen wir mögliche Geschäftsmodelle und Wertschöpfungsketten auf. Abgerundet wird die Arbeit durch die Darstellung möglicher personenbezogener, technischer und rechtlicher Restriktionen.

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Die langfristige Sicherung bestehender (profitabler) Kundenbeziehungen erweist sich für Unternehmen zunehmend als eine wichtige und zugleich immer schwieriger zu bewältigende Herausforderung. Vor dem Hintergrund hoher Kosten für die Neukundengewinnung und sinkender Kundenloyalität auf gesättigten, wettbewerbsintensiven und transparenten Märkten – verbunden mit tendenziell steigenden Abwanderungsraten – rücken die Früherkennung und Prävention von Kundenabwanderungen sowie die Kundenrückgewinnung verstärkt in den Fokus. Der Aufwand für derartige Anstrengungen muss in einem sinnvollen Verhältnis zum Ertrag stehen. Letztlich wird also für den Komplex „Kundenabwanderung“ ein ergebnisgesteuertes Gesamtsystem der Früherkennung, Prävention und Rückgewinnung benötigt. An dieser Stelle setzt das Customer Recovery Controlling an. Auf Basis des kontributionsorientierten Controllingansatzes wird ein ganzheitliches Controllingsystem für das Customer Recovery Management entwickelt. Dabei werden die führungsunterstützenden Controllingprinzipien der Entscheidungsfundierung, -reflexion und Koordinationsentlastung einschließlich zentraler Controllinginstrumente in den Gesamtzusammenhang des Customer Recovery Managementprozesses gestellt. Es wird aufgezeigt, dass mit einem professionellen Customer Recovery Controlling große Nutzenpotenziale verbunden sind, die sich auf der Customer Recovery Managementebene (z.B. verbesserte Entscheidungsqualität, höhere Präventions- bzw. Rückgewinnungsraten) wie auch auf der Ebene der Gesamtunternehmung (z.B. Sicherung bzw. Erhöhung des Kundenstammwertes) auswirken. Die Erfolgsmodellierung zählt zu den wesentlichen Aufgaben des Controlling. Diesbezüglich bedarf es eines mehrdimensionalen Controllinginstruments, das neben Ergebnisindikatoren auch Leistungstreiber berücksichtigt: die Customer Recovery Scorecard. Ihre Perspektiven – Finanz-, Kunden-, Prozess-, Potenzial- und Wettbewerbsperspektive – sichern eine ganzheitliche Betrachtung der strategisch relevanten Erfolgsfaktoren und darüber hinaus gewährleisten die Kennzahlen eine systematische Planung, Steuerung und Kontrolle des Customer Recovery Management Erfolgs. Für die Erfolgsgrößen werden kausale Abhängigkeiten in Form von Ursache-Wirkungs-Beziehungen innerhalb und zwischen den Perspektiven erfasst (Strategy Maps), wodurch gewissermaßen eine Modellierung der Wertschöpfungskette im Customer Recovery Management erfolgt. Unsere durchgeführte Studie zum Status Quo des Customer Recovery Controlling in der deutschen (groß-)unternehmerischen Dienstleistungspraxis hat gezeigt, dass der präventive Umgang mit Kundenabwanderung zukünftig an Bedeutung gewinnen wird. Obwohl die Mehrheit der befragten Unternehmen über ein organisatorisch verankertes Controlling verfügt, sind bezüglich des allgemeinen Controllingentwicklungsstandes inkl. des Instrumenteneinsatzes Defizite zu konstatieren. In Bezug auf Letzteres hat sich herausgestellt, dass rein ökonomische Aspekte eine dominante Stellung einnehmen; Finanzkennzahlen werden gegenüber den Markt-, Prozess und Potenzialkennzahlen zum einen häufiger eingesetzt und zum anderen auch in ihrer Bedeutung höher eingeschätzt. Darüber hinaus ist der Einsatz von Kennzahlensystemen im Customer Recovery Management noch nicht weit verbreitet und auch hier ist ein finanzwirtschaftlicher Fokus festzustellen. Der Erfolg von Customer Recovery Maßnahmen wird zu einem großen Ausmaß durch die Nutzung des Synergiepotenzials von Customer Recovery Management (Führung vom Markt bzw. Kunden her) und Controlling (Führung vom Erfolg her) determiniert.

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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

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In this lecture for a second year interdisciplinary course (part of the curriculum innovation programme) We explore the scope of social media analytics and look at two aspects in depth: Analysing for influence (looking at factors such as network structure, propagation of content and interaction), and analysing for trust (looking at different methods including policy, provenance and reputation - both local and global). The lecture notes include a number of short videos, which cannot be included here for copy-write reasons.

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Wednesday 26th March 2014 Speaker(s): Dr Trung Dong Huynh Organiser: Dr Tim Chown Time: 26/03/2014 11:00-11:50 Location: B32/3077 File size: 349Mb Abstract Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. In this talk, I will present an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. I will also talk about CollabMap (www.collabmap.org), an online crowdsourcing mapping application, and show how we applied the approach above to the trust classification of data generated by the crowd, achieving an accuracy over 95%.

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Resources from the Singapore Summer School 2014 hosted by NUS. ws-summerschool.comp.nus.edu.sg

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Real-time geoparsing of social media streams (e.g. Twitter, YouTube, Instagram, Flickr, FourSquare) is providing a new 'virtual sensor' capability to end users such as emergency response agencies (e.g. Tsunami early warning centres, Civil protection authorities) and news agencies (e.g. Deutsche Welle, BBC News). Challenges in this area include scaling up natural language processing (NLP) and information retrieval (IR) approaches to handle real-time traffic volumes, reducing false positives, creating real-time infographic displays useful for effective decision support and providing support for trust and credibility analysis using geosemantics. I will present in this seminar on-going work by the IT Innovation Centre over the last 4 years (TRIDEC and REVEAL FP7 projects) in building such systems, and highlights our research towards improving trustworthy and credible of crisis map displays and real-time analytics for trending topics and influential social networks during major news worthy events.

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Abstract: Big Data has been characterised as a great economic opportunity and a massive threat to privacy. Both may be correct: the same technology can indeed be used in ways that are highly beneficial and those that are ethically intolerable, maybe even simultaneously. Using examples of how Big Data might be used in education - normally referred to as "learning analytics" - the seminar will discuss possible ethical and legal frameworks for Big Data, and how these might guide the development of technologies, processes and policies that can deliver the benefits of Big Data without the nightmares. Speaker Biography: Andrew Cormack is Chief Regulatory Adviser, Jisc Technologies. He joined the company in 1999 as head of the JANET-CERT and EuroCERT incident response teams. In his current role he concentrates on the security, policy and regulatory issues around the network and services that Janet provides to its customer universities and colleges. Previously he worked for Cardiff University running web and email services, and for NERC's Shipboard Computer Group. He has degrees in Mathematics, Humanities and Law.