Spatial Clustering Based on Analysis of Big Data in Digital Marketing

2019 ◽  
pp. 335-347 ◽  
Author(s):  
Anton Ivaschenko ◽  
Anastasia Stolbova ◽  
Oleg Golovnin
Keyword(s):  
Big Data ◽  
Spatial Clustering ◽  
Digital Marketing

The Role of Big Data in Digital Marketing

2021 ◽  
pp. 16-33
Author(s):  
Neslihan Cavlak ◽  
Ruziye Cop
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Big Data Analytics ◽  
Digital Marketing ◽  
Business Success ◽  
Sustainable Business ◽  
Marketing Environment ◽  
Digital Environments ◽  
Digital Channels

Consumers perform their activities through digital channels more often as a result of technological advancements where those advancements also allow marketers to reach excessive information about consumers, store them, and use them whenever and however they consider necessary. These big data provide businesses to understand the unmet demands and expectations of consumers and achieve a sustainable business success. Despite the importance of big data analytics for marketing of businesses, research on this issue is scarce. In order to contribute the literature, the purpose of this chapter is to reveal the importance of big data in the digital marketing environment. In line with this purpose, a comprehensive literature review including the definition, components, sources of big data, and the role of big data in digital environments and the examples of businesses using big data is undertaken.

A Closer Look at Direct Carrier Billing

2020 ◽  
Vol 10 (4) ◽  
pp. 18-40
Author(s):  
Lorena Herrera López
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Marketing Channels ◽  
Digital Marketing ◽  
Mobile Service ◽  
Web Analytics ◽  
Network Operator ◽  
Over The Top

The impulse to digitalization by telecom operators requires the commercialization of over-the-top services (OTT) based on the fine understanding and prediction of customer behaviour through pattern recognition involving big data, resulting in an essential part of web analytics and digital marketing. The objective of this research is to analyse factors influencing the purchase and use of a mobile game commercialized by a mobile network operator (MNO), through different digital marketing channels and using direct carrier billing (DCB) as payment channel. The novelty contribution of this study is twofold. Firstly, it assesses determinants related to the purchase and use of a mobile service through the analysis of variables identified in the scientific literature's review. In addition, it also incorporates a set of variables based on data retrieved from big data analytics. Secondly, this research analyses the willingness of consumers to pay through DCB.

scholarly journals Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion

2020 ◽  
Vol 12 (16) ◽  
pp. 2513 ◽  
Author(s):  
Qiwei Ma ◽  
Zhaoya Gong ◽  
Jing Kang ◽  
Ran Tao ◽  
Anrong Dang
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Spatial Clustering ◽  
Landscape Fragmentation ◽  
Analytical Framework ◽  
Urban Land Use ◽  
Urban Systems ◽  
Landsat 8 ◽  
Clustering Methods

Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.

scholarly journals A Discussion on How to Apply Smart Data for Precision Marketing in the Global Economy: Asian Context

2021 ◽  
Vol 1 (2) ◽  
pp. 70-84
Author(s):  
Sandy Zhu
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Global Economy ◽  
Digital Marketing ◽  
Core Concepts ◽  
Artificial Intelligence Technology ◽  
Smart Data ◽  
Asian Context ◽  
The Right ◽  
Full Swing

The aim of the research is to provide support for the application of smart data, precision marketing, and business analysis and in so doing, it is aimed to contribute to the further sustainable development of the economy. At present, intelligent technologies such as artificial intelligence and big data are developing in full swing, and various application scenarios are gradually being launched. Smart data is a new sort of database in combination with artificial intelligence and big data technology, which makes artificial intelligence technology and big data the core concepts and the foundation of digital smart data. With smart data, companies could apply precision marketing to better reach their target consumers, push notifications at the right time, advertise the products and services consumers are interested in, and establish personalised marketing communication with each consumer in order to increase marketing efficiency. Undoubtedly, precision marketing has become the top priority in the development of the digital marketing industry, and it is becoming increasingly popular. The paper is based on this perspective and starts with an overview of smart data. The definition and development status of smart data are first reviewed, followed by an analysis of the application of smart data technology and precision marketing in digital marketing.

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