Digital Marketing and E-Tailing Technological Innovations

2020 ◽  
pp. 37-65
Author(s):  
Alan D. Smith
Keyword(s):  
Data Mining ◽  
Quality Assurance ◽  
Big Data ◽  
Information Retrieval ◽  
Customer Service ◽  
Middle Level ◽  
Purchase Decision ◽  
Product Reviews ◽  
Privacy Concerns ◽  
Web Presence

In an age of public mistrust of the most basic institutions, businesses are not exempted. Essentially all e-tailers want to deliver personalized and real-time communications to customers that are tailored to their interests and preferences, and are based on big data mining that customers will value over privacy concerns. This is an era in which e-commerce retailers continue to dominate the marketplace and it is integral that consumers are able to trust the manufacturers, retailers, and the service/product reviews that they read online. Such trust is particularly important if their ultimate purchase decision is a successful one. A survey of middle-level managers was analyzed to identity the basic elements: e-personalization, namely online purchasing behaviors, personalized communications, information-retrieval services, degree of personal web presence, quality assurance of customer service, and the promotion of customization services. These elements were found to be conceptually and statistically related to retailer benefits of increased buying and customer loyalty.

Data Mining Problems Classification and Techniques

2020 ◽  
pp. 70-93
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Retrieval ◽  
Data Analytics ◽  
Complex Data ◽  
Healthcare Industry ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
Hidden Knowledge

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

Cloud Computing and Big Data

2014 ◽  
pp. 118-132
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Association Rules ◽  
Big Data Analysis ◽  
Center Problem ◽  
Privacy Concerns ◽  
Numerical Examples ◽  
Big Data Mining

This chapter aims at exploring the intersection of cloud computing with big data. The big data analysis, mining, and privacy concerns are discussed. First, this chapter deals with the software framework, MapReduce™ that is commonly used for performing Big Data Analysis in the clouds. In addition, some of the most used techniques for performing Big Data Mining are detailed. For instance, Clustering, Co-Clustering, and Association Rules are described in detail. In particular, the k-center problem is described while with reference to the association rules beyond the basic definitions, the Apriori Algorithm is outlined and illustrated by some numerical examples. These techniques are also described with reference to their versions based on MapReduce. Finally, the description of some real applications conclude the chapter.

Exploring the Inherent Growth of e-Tailing via e-Personalization and Technological Innovations

2017 ◽  
Vol 8 (1) ◽  
pp. 19-46 ◽  
Author(s):  
Alan D. Smith
Keyword(s):  
Customer Service ◽  
Customer Loyalty ◽  
Multivariate Analyses ◽  
Technological Innovations ◽  
Interaction Data ◽  
Value Proposition ◽  
Privacy Concerns ◽  
Web Presence ◽  
Big Data Mining ◽  
Reduction Techniques

It is the hope of essentially all e-tailers to deliver personalized and real-time communications to customers that are tailored to their interests, preferences, based on big data mining that customers will value over privacy concerns. The technology (e.g., interaction data from segmentation marketing, transactions data, and sophisticated analytics) should optimize the customer's journey through the array of brands via unique identifiers from customer's profiles that provide enrichment, not just enlargement, of each brand's value proposition. These interactions can vary from simple transactional e-mails to conversations with product experts and recommended selections based on previous purchases from a variety of websites. Through appropriate multivariate analyses and data-reduction techniques, the basic elements e-personalization, namely online purchasing behaviors, personalized communications, information retrieval services, degree of personal web presence, quality assurance of customer service, and the promotion of customization services, were found to be conceptually and statistically related to retailer benefits of e-personalization (e.g., increased buying and creates customer loyalty).

Data Mining Problems Classification and Techniques

2018 ◽  
Vol 3 (1) ◽  
pp. 38-57 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Information Retrieval ◽  
Data Analytics ◽  
Complex Data ◽  
Healthcare Industry ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Big Data Technologies ◽  
Hidden Knowledge

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

scholarly journals BIG DATA TELECOMMUNICATION COMPANY TOOLS TO ENHANCE DECISION-MAKING EFFICIENCY IN COMPLEX ECONOMIC SYSTEMS

2021 ◽  
Author(s):  
Nataliia Geseleva ◽  
Anastasiia Yaroslavtseva
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Customer Service ◽  
Mobile Communication ◽  
Telecommunications Industry ◽  
World Market ◽  
Global Trends ◽  
The World ◽  
Very Large Datasets ◽  
Big Data Technologies

The paper examines the telecommunications industry, its development and impact on economic growth in countries including Ukraine. The characteristics of mobile communication, as a segment of the telecommunications industry that is most actively progressing, both in the world as a whole and in Ukraine, are given. It’s examined a current state of the Ukrainian mobile communication market. Its importance for the national economy is reviewed. The Ukrainian mobile market has been studied; the changes that have taken place in recent years in the direction of global trends in the field of communications. Development trends that encourage mobile operators to develop their own platforms, introduce new products and services are considered. Examples of current developments and services of operators such as virtual mobile automatic telephone exchange, Big Data Scoring, Vodafone Analytics and others are given. The article pays special attention to Big Data processing and analysis technologies. Big data is defined as very large datasets that can be analyzed computationally to reveal patterns, trends, and associations – especially in connection with human behavior and interactions. A big data revolution has arrived with the growth of the Internet, wireless networks, smartphones, social media and other technology. These features of Big Data are the ability to use Data Mining. Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. Data mining depends on effective data collection, warehousing, and computer processing. Data mining processes are used to build machine learning models that power applications including search engine technology and website recommendation programs. Also describes how Big Data affects the retail industry, namely helping to optimize merchandising tactics, personalize customer service, increase advertising effectiveness, target offline shoppers (remarketing) and expand cross-selling. Also in the field of telecommunications, Big Data helps providers to automate and optimize the provision of their services. Thus, the introduction of Big Data technologies will allow Ukraine to become a more competitive country on the world market.

Beneficial E-Personalization and Related Technological Innovations Applied to E-Tailing

2014 ◽  
pp. 20-46
Author(s):  
Alan D. Smith
Keyword(s):  
Quality Assurance ◽  
Customer Service ◽  
Competitive Advantages ◽  
Chain Store ◽  
Business Professionals ◽  
Web Presence ◽  
Goods And Services ◽  
Global Recession ◽  
Reduction Techniques ◽  
Online Retailers

The numerous advancements in electronic-personalization communication have generated both benefits and challenges as online retailers try to regain competitive advantages in the current global recession. A literature review of personalization strategies was used to generate a survey instrument to examine the important characteristics of such programs from business professionals. Through appropriate multivariate analyses and data-reduction techniques, the basic elements e-personalization, namely online purchasing behaviors, personalized communications, information retrieval services, degree of personal Web presence, quality assurance of customer service, and the promotion of customization services, were found to be conceptually and statistically related to retailer benefits of e-personalization (increased buying and creates customer loyalty) from the viewpoint of managers for a large goods and services chain store headquartered in Pittsburgh, Pennsylvania.

Beneficial E-Personalization and Related Technological Innovations Applied to E-Tailing

2017 ◽  
pp. 1572-1599
Author(s):  
Alan D. Smith
Keyword(s):  
Quality Assurance ◽  
Customer Service ◽  
Competitive Advantages ◽  
Chain Store ◽  
Business Professionals ◽  
Web Presence ◽  
Goods And Services ◽  
Global Recession ◽  
Reduction Techniques ◽  
Online Retailers

The numerous advancements in electronic-personalization communication have generated both benefits and challenges as online retailers try to regain competitive advantages in the current global recession. A literature review of personalization strategies was used to generate a survey instrument to examine the important characteristics of such programs from business professionals. Through appropriate multivariate analyses and data-reduction techniques, the basic elements e-personalization, namely online purchasing behaviors, personalized communications, information retrieval services, degree of personal Web presence, quality assurance of customer service, and the promotion of customization services, were found to be conceptually and statistically related to retailer benefits of e-personalization (increased buying and creates customer loyalty) from the viewpoint of managers for a large goods and services chain store headquartered in Pittsburgh, Pennsylvania.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

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