Search Results (3)

This research investigates the application of search engine optimization (SEO) in developing the digital image of rural tourism businesses in the Terras de Trás-os-Montes region of Portugal. With digital marketing becoming increasingly important for businesses to stay competitive, SEO has become a vital tool for developing online recognition, qualified traffic acquisition, and enhancement of conversion rates. The research performs an SEO analysis of 21 rural tourism websites by applying the Ubersuggest tool, analyzing such key indicators as on-page SEO scores, organic traffic, keyword ranking, backlinks, and technical performance. The results identify wide SEO performance discrepancies, with some sites registering excellent practices and others with critical errors that impair the sites’ online recognizability. In particular, low word count, absent meta description, and loading speed issues are very much present. The research emphasizes the need for effective SEO methods, such as on-page maintenance, content creation, and link building, to advance search engine ranking and end-user experience. Moreover, the study emphasizes the necessity for rural tourism businesses to evolve and adapt to current SEO trends, i.e., voice search optimization and local SEO, in the changing digital business environment. The results provide recommendations for rural tourism businesses to develop their digital marketing activities and make progress online. Full article

In the present-day digital landscape, websites have increasingly relied on digital marketing practices, notably search engine optimization (SEO), as a vital component in promoting sustainable growth. The traffic a website receives directly determines its development and success. As such, website owners frequently engage the services of SEO experts to enhance their website’s visibility and increase traffic. These specialists employ premium SEO audit tools that crawl the website’s source code to identify structural changes necessary to comply with specific ranking criteria, commonly called SEO factors. Working collaboratively with developers, SEO specialists implement technical changes to the source code and await the results. The cost of purchasing premium SEO audit tools or hiring an SEO specialist typically ranges in the thousands of dollars per year. Against this backdrop, this research endeavors to provide an open-source Python-based Machine Learning SEO software tool to the general public, catering to the needs of both website owners and SEO specialists. The tool analyzes the top-ranking websites for a given search term, assessing their on-page and off-page SEO strategies, and provides recommendations to enhance a website’s performance to surpass its competition. The tool yields remarkable results, boosting average daily organic traffic from 10 to 143 visitors. Full article

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