219,826 research outputs found
Digital marketing actions that achieve a better attraction and loyalty of users: an analytical study
Currently, the digital economy contributes decisively to an increase in competitiveness, especially as a digital transformation involves migrating to new technological models where digital marketing is a key part of growth and user loyalty strategies. Internet and Digital Marketing have become important factors in campaigns, which attract and retain Internet users. This study aims to identify the main ways in which users can be gained and retained by using Digital Marketing. The Delphi method with in-depth interviews was the methodology used in this study. The results of the research show the most important actions for achieving user recruitment and loyalty with Digital Marketing from the opinions of consulted experts. The limitations of this study are those related to the number of experts included in the study, and the number of research papers consulted in the literature review. The literature review and the results of this research are used to propose new solid research with a consolidated critical methodology. This research deals with a new approach that will optimize web technologies for the evolution of user trends, and therefore, will be of academic and professional use for marketing managers and web solution developers. The conclusions of the investigation show the key factors, discarding others that do not affect the optimization of conversions in B2C businesses such as the duration of the session and the rebound percentage. Likewise, the results of the research identify the specific actions that must be carried out to attract and retain users in B2C companies that use the Digital Marketing ecosystem on the Internet. The requirements for companies that wish to implement a model to optimize conversions using the current digital economy are also shown.info:eu-repo/semantics/publishedVersio
Structural Equation Modeling and simultaneous clustering through the Partial Least Squares algorithm
The identification of different homogeneous groups of observations and their
appropriate analysis in PLS-SEM has become a critical issue in many appli-
cation fields. Usually, both SEM and PLS-SEM assume the homogeneity of all
units on which the model is estimated, and approaches of segmentation present
in literature, consist in estimating separate models for each segments of
statistical units, which have been obtained either by assigning the units to
segments a priori defined. However, these approaches are not fully accept- able
because no causal structure among the variables is postulated. In other words,
a modeling approach should be used, where the obtained clusters are homogeneous
with respect to the structural causal relationships. In this paper, a new
methodology for simultaneous non-hierarchical clus- tering and PLS-SEM is
proposed. This methodology is motivated by the fact that the sequential
approach of applying first SEM or PLS-SEM and second the clustering algorithm
such as K-means on the latent scores of the SEM/PLS-SEM may fail to find the
correct clustering structure existing in the data. A simulation study and an
application on real data are included to evaluate the performance of the
proposed methodology
Academic quality measurement: A multivariate approach
This paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. The methodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data sets where we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combined with Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a set of dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduate programme. Among the most important conclusion we say the methodology presented in this work has the following advantages: Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student, showing the key factors for improving quality. Student weights can be related to student characteristics to make market segmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can be determined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process or SWOT analysis).Quality Measurement, Postgraduate Programme, Nonparametric Model.
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Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior
This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings
Incorporating service quality tools into Kansei Engineering in services: A case study of Indonesian tourists
Due to market dynamics and challenges, it is imperative for companies to put their concern on strategic marketing orientation. In facts, products and services of similar quality are ubiquitous in todayâs global market. Basically, functionality and usability alone are no longer prominent success factors in product and service innovation because customers today concern themselves more on satisfying their emotions than merely their cognition. Kansei Engineering (KE) has shown its superiority in investigating and modelling customer emotion (âKanseiâ in Japanese) for product development. In dealing with customer needs, service quality tools such as quality function deployment (QFD) and the Kano model, have been applied extensively. But none have been able to incorporate and model customerâs emotional needs. Some attention has been given to investigate this but, thus far, there is no formal methodology that can account for customer emotional needs in service design. To fill this niche, this study proposed an integrative framework of KE incorporating the Kano model and QFD applied to services. This study extended the work by Hartono and Tan (2011) and Hartono et al. (2012) and presented a survey on luxury hotel services involving more than a hundred Indonesian tourists as the subject of study. Luxury hotels are reported to have greater strength of emotion than any other hotel segment. This work confirmed that emotion is to be more important than cognition in impacting overall customer satisfaction. Practically, it gives insight on which service attributes deserve more attention with regard to their impact on customer emotion. Indonesian tourists shared a common response to the Kansei word âelegantâ which correlates with their common cultural dimension of âpower distanceâ. Performing a Kansei evaluation to understanding cultural backgrounds may yield valuable insights for international tourist marketing strategies and companiesâ business sustainability
Defining the gap between research and practice in public relations programme evaluation - towards a new research agenda
The current situation in public relations programme evaluation is neatly summarized by McCoy who commented that 'probably the most common buzzwords in public relations in the last ten years have been evaluation and accountability' (McCoy 2005, 3). This paper examines the academic and practitioner-based literature and research on programme evaluation and it detects different priorities and approaches that may partly explain why the debate on acceptable and agreed evaluation methods continues. It analyses those differences and proposes a research agenda to bridge the gap and move the debate forward
Ensemble of Example-Dependent Cost-Sensitive Decision Trees
Several real-world classification problems are example-dependent
cost-sensitive in nature, where the costs due to misclassification vary between
examples and not only within classes. However, standard classification methods
do not take these costs into account, and assume a constant cost of
misclassification errors. In previous works, some methods that take into
account the financial costs into the training of different algorithms have been
proposed, with the example-dependent cost-sensitive decision tree algorithm
being the one that gives the highest savings. In this paper we propose a new
framework of ensembles of example-dependent cost-sensitive decision-trees. The
framework consists in creating different example-dependent cost-sensitive
decision trees on random subsamples of the training set, and then combining
them using three different combination approaches. Moreover, we propose two new
cost-sensitive combination approaches; cost-sensitive weighted voting and
cost-sensitive stacking, the latter being based on the cost-sensitive logistic
regression method. Finally, using five different databases, from four
real-world applications: credit card fraud detection, churn modeling, credit
scoring and direct marketing, we evaluate the proposed method against
state-of-the-art example-dependent cost-sensitive techniques, namely,
cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision
trees. The results show that the proposed algorithms have better results for
all databases, in the sense of higher savings.Comment: 13 pages, 6 figures, Submitted for possible publicatio
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