The Impact of Web Analytics Tools on the Performance of Small and Medium Enterprises

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Volume: 13 | Issue: 5 | Pages: 11753-11762 | October 2023 | https://doi.org/10.48084/etasr.6261

Abstract

In the era of data analytics, the use of web analytics tools is widely spread among Small and Medium Enterprises (SMEs), especially in the e-commerce sector, to analyze web usage data and measure website performance due to the several features it provides. Most web analytics studies were conducted based on marketing rather than a managerial point of view. Therefore, there is a necessity to study the impact from a managerial perspective by exploring the impact on financial and non-financial performance and the decision-making process. This study assessed the impact of web analytics tools on the overall performance of SMEs, targeting Saudi SMEs and considering the lack of web analytics studies in this sector in Saudi Arabia. The sample involved eight Saudi SMEs who participated by answering interview questions, which were analyzed using thematic analysis. The findings demonstrated four main themes: the current web analytics tools used by Saudi SMEs, the motivations behind their use, and their impact on SMEs' performance and decision-making. The results showed that these tools positively affect the overall performance of SMEs. Google Analytics (GA) was the tool most used by the sample. Seven motives were discovered that encourage SMEs to use web analytics tools. This study also addressed the common Key Performance Indicators (KPIs) used by the participants. Concerning decision-making, it was found that web analytics tools affected and supported SMEs' decisions. Furthermore, this study evaluated the role of Zid, the Saudi e-commerce platform for data analytics and performance measurement.

Keywords:

data analytics, SMEs, web analytics tools, KPIs, google Analytics, e-commerce

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How to Cite

[1]
A. M. Almatrafi and Z. H. Alharbi, “The Impact of Web Analytics Tools on the Performance of Small and Medium Enterprises”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 5, pp. 11753–11762, Oct. 2023.

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