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Future Internet
Special Issues
Digital Marketing and App-based Marketing
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Digital Marketing and App-based Marketing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 12934

Special Issue Editor

Dr. Naser Valaei

E-Mail Website
Guest Editor
Liverpool Business School, Liverpool John Moores University, Liverpool L3 5UX, UK
Interests: digital marketing; marketing strategy; app-based marketing; creativity and innovation; improvisation

Special Issue Information

Dear Colleagues,

Companies have been augmenting omnichannel strategies in order to interact with customers in efficient ways. This is due to hypercompetition and the tendency to develop better customer relationships. The internet technology has changed companies’ strategic plan in formulating new channels and improving their channel strategy. As a channel strategy, using mobile applications has gained popularity over recent years and it has become an indispensable part of many users’ experience. The advent of app development environments such as Apple app store and Google play store has created new channels for firms which bypass traditional physical stores and even online stores. New technologies, namely Web 2.0 and Web 3.0, are changing marketing functions in several ways and new concepts such as marketing 2.0 and marketing 3.0 are being introduced while creating challenges in consumers’ decision-making process. However, it is critical for companies to incorporate app-based marketing in their digital marketing strategy in order to deliver a seamless and consistent customer experience. Therefore, the aim of this Special Issue is to fill the gap in linking app-based marketing to overall digital marketing strategy.  

Dr. Naser Valaei
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SEO (Search Engine Optimization) and analytics
  • App-based marketing based on SEO and analytics
  • Digital marketing
  • Digital tools
  • Omni-channel marketing
  • Multi-channel marketing
  • Mobile apps
  • App marketing strategy

Published Papers (1 paper)

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Research

20 pages, 1172 KiB  
Article
Using Machine Learning for Web Page Classification in Search Engine Optimization
by Goran Matošević, Jasminka Dobša and Dunja Mladenić
Future Internet 2021, 13(1), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13010009 - 02 Jan 2021
Cited by 28 | Viewed by 12205
Abstract
This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built [...] Read more.
This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research. Full article
(This article belongs to the Special Issue Digital Marketing and App-based Marketing)
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