Auflistung nach Schlagwort "Customer relationship management"
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- ZeitschriftenartikelCURIE: Towards an Ontology and Enterprise Architecture of a CRM Conceptual Model(Business & Information Systems Engineering: Vol. 64, No. 5, 2022) Fernández-Cejas, Miguel; Pérez-González, Carlos J.; Roda-GarcÃa, José L.; Colebrook, MarcosCompanies face the challenge of managing customer relationships (CRM) in a context marked by a drastic digital transformation and unbridled evolution of consumer behavior, exacerbated by the COVID-19 pandemic. The customer is more demanding, has access to the global market and interacts with companies through multiple digital channels, such as email, social networks, mobile apps or instant messaging. In this situation, the success of a CRM implementation highly depends on information technology and the applications used. To harmonize this new business context with the development of information systems (IS), a suitable CRM ontology and enterprise architecture (EA) is needed. While an ontology-based conceptual model provides a unifying framework, aids sharing and reusing knowledge, and facilitates communication within a domain, an EA-based model unequivocally describes, analyzes, and visualizes how an organization should operate from the perspective of business, application, and technology. The purpose of the paper is the proposal of CURIE-O, a CRM OntoUML UFO-based ontology, together with CURIE-EA, a CRM ArchiMate-based EA to serve business managers and IS specialists an updated unifying framework of reference in the CRM domain as well as a highly efficient tool to support application development and maintenance in this changing and increasingly digital context. Modeling has proven to be an essential element to achieve high-performance information systems. In order to apply the ontology and the EA proposed here, the authors developed a CRM task management application prototype that was implemented as a case study in a consulting company. The methodology followed was design science research (DSR), in order to design and validate the artifacts. Within the DSR framework, other complementary research methods have been used, in particular literature research, interviews and focus groups carried out with several hotel chains in Tenerife (Canary Islands). The main existing CRM models in the scientific literature have also been analyzed together with the leading CRM market solutions.
- ZeitschriftenartikelIntelligent Business Processes in CRM - Exemplified by Complaint Management(Business & Information Systems Engineering: Vol. 60, No. 4, 2018) Zaby, Christopher; Wilde, Klaus D.Customer relationship management (CRM) is becoming a critical source of competitive advantage for businesses today. However, many CRM business processes are deficient and inflexible. For example, many customers are dissatisfied with complaint management. Still, companies seldom systematically adapt the complaint management process. In theory, operational and analytical CRM form a closed loop: analytical CRM uses business intelligence (BI) tools to analyze operational data and the knowledge gained is used for continual optimization of operations. One special approach in establishing this loop is to continually support decision points in operational processes with knowledge from BI. In this way, the use of BI becomes an integral part of business processes, which are then referred to as intelligent business processes. However, in CRM not much is known about this approach. Based on an extensive review of the literature, the study explores the state of theory and practice in the field of intelligent business processes in CRM, with special attention to complaint management because of its considerable importance and application potential. In particular, the conceptual framework of intelligent business processes in CRM is depicted and two implementation options are identified: embedded intelligence and business rules. Focusing on complaint management, evidence on intelligent business processes is systematically documented, weak points are identified, and a research agenda for the shift to more intelligent processes is presented.
- ZeitschriftenartikelMarketing Automation(Business & Information Systems Engineering: Vol. 57, No. 2, 2015) Heimbach, Irina; Kostyra, Daniel S.; Hinz, Oliver
- ZeitschriftenartikelNext-Purchase Prediction Using Projections of Discounted Purchasing Sequences(Business & Information Systems Engineering: Vol. 60, No. 2, 2018) Shapoval, Katerina; Setzer, ThomasA primary task of customer relationship management (CRM) is the transformation of customer data into business value related to customer binding and development, for instance, by offering additional products that meet customers’ needs. A customer’s purchasing history (or sequence) is a promising feature to better anticipate customer needs, such as the next purchase intention. To operationalize this feature, sequences need to be aggregated before applying supervised prediction. That is because numerous sequences might exist with little support (number of observations) per unique sequence, discouraging inferences from past observations at the individual sequence level. In this paper the authors propose mechanisms to aggregate sequences to generalized purchasing types. The mechanisms group sequences according to their similarity but allow for giving higher weights to more recent purchases. The observed conversion rate per purchasing type can then be used to predict a customer’s probability of a next purchase and target the customers most prone to purchasing a particular product. The bias–variance trade-off when applying the models to target customers with respect to the lift criterion are discussed. The mechanisms are tested on empirical data in the realm of cross-selling campaigns. Results show that the expected bias–variance behavior well predicts the lift achieved with the mechanisms. Results also show a superior performance of the proposed methods compared to commonly used segmentation-based approaches, different similarity measures, and popular class predictors. While the authors tested the approaches for CRM campaigns, their parameterization can be adjusted to operationalize sequential features of high cardinality also in other domains or business functions.
- ZeitschriftenartikelUnterstützung kundenbezogener Entscheidungsprobleme(Wirtschaftsinformatik: Vol. 52, No. 2, 2010) Lessmann, Stefan; Voß, StefanDie Klassifikation repräsentiert ein wichtiges Instrument zur Unterstützung kundenbezogener Planungs- und Entscheidungsprobleme. Hierzu zählen z. B. die Prognose von Abwanderungswahrscheinlichkeiten im Vertragskundengeschäft oder die Abgrenzung einer geeigneten Zielgruppe für Marketingkampagnen. Während die Entwicklung neuer Klassifikationsverfahren ein populäres Forschungsfeld repräsentiert, werden entsprechende Neuerungen in der betrieblichen Praxis bisher nur selten eingesetzt. Diese Divergenz zwischen wissenschaftlichen und praktischen Interessen lässt sich z. T. dadurch erklären, dass das Potenzial moderner Klassifikationsverfahren in diesem Anwendungskontext noch nicht hinreichend geklärt ist. Die vorliegende Arbeit möchte einen Beitrag zur Schließung dieser Erkenntnislücke liefern. Hierzu wird eine empirische Studie durchgeführt, in deren Rahmen eine große Zahl etablierter wie neuer Klassifikationsverfahren verglichen wird. Eine Bewertung erfolgt anhand der Kosten bzw. Erträge, welche sich aus dem Einsatz einer bestimmten Methode in einer konkreten Entscheidungssituation ergeben. Die Untersuchung zeigt, dass eine stärkere Berücksichtigung moderner Methoden durchaus empfohlen werden kann und diese unter verschiedenen Bedingungen einen ökonomischen Mehrwert bieten.AbstractClassification analysis is an important tool to support decision making in customer-centric applications like, e.g., proactively identifying churners or selecting responsive customers for direct-marketing campaigns. Whereas the development of novel algorithms is a popular avenue for research, corresponding advancements are rarely adopted in corporate practice. This lack of diffusion may be explained by a high degree of uncertainty regarding the superiority of novel classifiers over well established counterparts in customer-centric settings. To overcome this obstacle, an empirical study is undertaken to assess the ability of several novel as well as traditional classifiers to form accurate predictions and effectively support decision making. The results provide strong evidence for the appropriateness of novel methods and indicate that they offer economic benefits under a variety of conditions. Therefore, an increase in use of respective procedures can be recommended.