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.

Considering the evolution of search engine optimization (SEO) methods, it is crucial to generate and update relevant keywords continuously based on trends in a dynamic method to maintain the relevance and visibility of a web page, which enhances user engagement. To automate the process of continuous SEO, this paper proposes a method called AutoTrendyKeywords to leverage large language models (LLMs) to automate the constant generation of SEO keywords and the selection of trending keywords based on changing data. Unlike the manual selection method, the new system automatically uses real-time trend data from sources such as Google Trends. This unsupervised approach ensures up-to-date relevance of the page and can ensure satisfactory traffic for pages in unpopular languages when the web is dominated by content in popular languages. Additionally, the system offers transparency to understand the reason behind each keyword selected. The method streamlines the keyword update process for digital content creators and marketers by eliminating the need for manual intervention, thereby constantly ensuring visibility across search results. The system has wide-ranging societal benefits, such as facilitating access to desired information and promoting diverse languages. The system successfully generated keywords using an LLM, selected the most trending keywords, generated a title and description of the page for SEO, and integrated the keywords and the description into HTML code. The system generated URL paths that are short and contain the trending keywords.

This study examines the influence of digital marketing on brand awareness within the multinational Fast-Moving Consumer Goods (FMCG) sector, focusing on how various digital strategies contribute to overall marketing effectiveness. Utilizing a sample of 277 respondents, the research employs descriptive statistics, correlation, and regression analyses to explore the relationships between digital marketing strategies, brand awareness outcomes, and perceived digital marketing effectiveness. The findings reveal that digital marketing strategies significantly enhance brand awareness and overall marketing effectiveness, underscoring the pivotal role of activities such as social media engagement, content marketing, and search engine optimization. Specifically, the regression analysis shows that digital marketing strategies have a positive and statistically significant impact on overall effectiveness, indicating that companies investing in these areas are better positioned to achieve higher levels of brand recognition and marketing success. However, the study also uncovers a complex relationship between brand awareness outcomes and overall effectiveness, where increased brand awareness does not directly correlate with enhanced digital marketing effectiveness. This suggests that while brand awareness is critical, its influence on marketing success is nuanced and may involve diminishing returns or additional mediating factors. The regression model explains a modest portion of the variance in marketing effectiveness, highlighting the need for a holistic evaluation approach that incorporates a broader set of performance metrics and contextual factors. The high reliability of the measures used affirms the robustness of the findings, which emphasize the importance of a strategic and integrated approach to digital marketing in the FMCG sector. Companies must continuously adapt and optimize their digital marketing strategies to align with dynamic market conditions and achieve sustainable success.

The retail industry's shift from traditional to online platforms has heightened the need for effective search engine optimization (SEO) strategies to improve digital marketing outcomes. SEO involves optimizing a website's technical and content features to increase its visibility in search engine results, potentially driving traffic, enhancing brand awareness, and boosting revenue. This systematic review aims to evaluate the impact of various SEO tactics on small and medium-sized enterprises (SMEs) in the retail sector, focusing on key performance metrics such as website traffic, search engine rankings, conversion rates, and overall business growth. A comprehensive search was conducted across major academic databases, including Google Scholar, Scopus, and Web of Science, resulting in 102 studies published between 2014 and 2024. The methodological approach utilized both narrative synthesis and quantitative analysis to evaluate the quality and findings of the selected studies, following PRISMA2020 guidelines. The analysis shows that effective SEO practices, such as robust content marketing, technical SEO enhancements, and precise keyword optimization, lead to significant improvements in digital performance metrics. Specifically, up to a 30% increase in website traffic was observed, and keyword optimization improved click-through rates by 20% to 40%. The impact on conversion rates was more variable, with improvements ranging from 10% to 25%, influenced by factors like SEO quality and market conditions. Furthermore, integrating SEO with digital marketing strategies within e-business models enhanced operational efficiency by up to 15% and increased revenue growth by 25%. However, biases in the literature, such as the selective reporting of positive outcomes, may affect the overall reliability of these findings. While SEO strategies effectively drive website traffic and improve search engine rankings, their direct influence on conversion rates is less consistent. A balanced approach, combining SEO practices with strategic IT alignment, is crucial for sustained business success in the retail industry. Future research should address the variability in conversion outcomes and investigate the conditions under which SEO tactics are most effective.