Search Engine Results Optimization for Supply Chain SMEs Through Digital Content Management and Fuzzy Cognitive Models

Abstract

Throughout crises and financial prosperity, the role and influence of small and medium supply chain enterprises (SMEs) have been solid and key factors for the economic growth of global markets. Hence, a great need arises for digital promotion to keep up with recent technological advancements. Such an aim could be achieved by SMEs in the supply chain by optimizing the results of their websites in search engines and utilizing the digital content (DC) of their web pages. This research is focused on the optimization of supply chain SMEs’ search engine results from the efficient management of their DC. The authors collected big data from the DC of five supply chain SMEs’ websites and performed statistical analysis (correlation and linear regression analysis), followed by six fuzzy cognitive mapping simulation scenarios. Supply chain SMEs’ customers enter their website and are found capable of producing DC metrics, originating from their interaction with the webpage, which is valuable for supply chain SMEs since they impact the performance of their search engine results. The outcomes of this study indicate that specific DC metrics, related to website customers’ activity, can accurately simulate and predict the course of supply chain SMEs’ digital marketing performance KPIs (global rank, organic, and paid traffic). It has been discerned that supply chain SMEs could enhance their search engine results, by increasing traffic originating from social sources, organic keywords, and the costs required for paid advertising campaigns. Accordingly, the increased number of pages opened by website visitors also indicates enhanced search engine results.

Received: 22 September 2023 | Revised: 22 November 2023 | Accepted: 29 November 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.