Data analysis across various media: Data fusion, direct marketing, clickstream data and social media

Breur, Tom

doi:10.1057/dddmp.2011.32

Cited by 18 publications

(10 citation statements)

References 8 publications

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“…This extended means is enabled by better knowledge of customer, the ability to create new customer-focused products and services, enabled where appropriate through value-added partnerships with other service providers. The findings reveal that owner-managers are more comfortable with shorter term planning cycles and this model is designed to enable more agile marketing (Breur, 2011;Chaffey, 2010). However, the lack of knowledge and, in some cases, confidence, of owner-managers is a barrier to achieving a higher level of agility and gives credence to the caution sounded by Valos et al namely that marketers need more 'sophisticated campaign measurement systems ' (2010, p. 369).…”

Section: Practical Implications Of the Studymentioning

confidence: 90%

Marketing technology for adoption by small business

Alford

Page

2015

The Service Industries Journal

107

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?? 2015 The Author(s). Published by Taylor & Francis. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Available online at: http://www.tandfonline.com/doi/full/10.1080/02642069.2015.1062884The adoption of technology for marketing is essential for the survival of small businesses and yet little is understood about owner-manager practice in this area. This paper aims to address that gap through a qualitative study of 24 owner-managed small businesses operating in the visitor economy. It found that there was a strong appetite for the adoption of technology for marketing and a clear recognition of its opportunities particularly related to how it could create a stronger market orientation and more agile marketing, adhering to the principles of effectual reasoning. However, the ability to take advantage of these opportunities was constrained by a lack of knowledge and in particular an inability to measure the return on investment. While the wider implications of the study are limited by the niche sample, a planning model for the adoption of technology for marketing is presented which can be tested through future research

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Section: Practical Implications Of the Studymentioning

confidence: 90%

Marketing technology for adoption by small business

Alford

Page

2015

The Service Industries Journal

107

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“…Currently, research is focused on using machine learning methods, including the deep neural network, unsupervised data fusion and hybrid models to fuse wide variety of data sources. Typically, data fusion is implemented on the edge computation platform [ 62 ], fog computation platform [ 63 ], cloud computation platform [ 64 , 65 ] or a hybrid computation platform where the processing is realized at both edge or cloud [ 66 ]. However, by using the method described in Section 4.3 , various data fusion algorithms can be distributed at sinks or gateways, and the results can be sent to the knowledge plane, where new knowledge can be further extracted by using the knowledge in the knowledge plane.…”

Section: Perspectives Of Knowledge-driven Sdn For Iotmentioning

confidence: 99%

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

Ding

et al. 2020

Sensors

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The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

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“…In the literature, various methodologies have been proposed to accumulate heterogeneous information from diverse sources. Breur (Breur 2011) discussed data fusion as one out of four useful data analysis streams available to researchers. Bello-Orgaz et al (Bello-Orgaz, Jung, and Camacho 2016) summarized the challenges in data fusion: (i) obtaining more reliable methods for the fusing the multiple features of multimedia objects for social media applications, (ii) studying the dynamics of individual and group behaviour, (iii) characterizing the patterns of information diffusion, and (iv) identifying influential individuals in social networks.…”

Section: Data Fusionmentioning

confidence: 99%

Affective design using machine learning: a survey and its prospect of conjoining big data

Chan

Kwong

Clark

et al. 2018

International Journal of Computer Integrated Manufacturing

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Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today's competitive markets. Consumers not only need to be solely satisfied with the functional requirements of a product, and they are also concerned with the affective needs and aesthetic appreciation of the product. A product with good affective design excites consumer emotional feelings so as to buy the product. However, affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This article presents a survey of commonly used machine learning approaches for affective design when two data streams namely traditional survey data and modern big data are used. A classification of machine learning technologies is first provided which is developed using traditional survey data for affective design. The limitations and advantages of each machine learning technology are also discussed and we summarize the uses of machine learning technologies for affective design. This review article is useful for those who use machine learning technologies for affective design. The limitations of using traditional survey data are then discussed which is time consuming to collect and cannot fully cover all the affective domains for product development. Nowadays, big data related to affective design can be captured from social media. The prospects and challenges in using big data are discussed so as to enhance affective design, in which very limited research has so far been attempted. This article provides guidelines for researchers who are interested in exploring big data and machine learning technologies for affective design.

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Data analysis across various media: Data fusion, direct marketing, clickstream data and social media

Cited by 18 publications

References 8 publications

Marketing technology for adoption by small business

Marketing technology for adoption by small business

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives

Affective design using machine learning: a survey and its prospect of conjoining big data

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