There has been a notable shift in how businesses improve customer service to ensure customer satisfaction from traditional questionnaires, interviews, and focus groups. This is necessitated by fast-changing user likes. As such, Artificial Intelligence approaches that take advantage of online content can be handy for fast-changing market trends. In Namibia, where over 60% of the population is active on the Internet, data is readily available on social media platforms such as Meta or X, thus making this approach suitable. For this study, three service-oriented companies with active users on their Meta pages were chosen as a case study. A customer review analysis model was built from data that was scrapped online using sentiment analysis. Our results show that Namibia’s service-oriented industry can improve its ability to provide high-quality services to customers by examining online customer reviews. Besides the approach being cheaper than traditional means, web scrapping and sentiment analysis approaches are faster and continually improve since data is readily accessible without active user engagement. Insights gained also support future decision-making essential in achieving organizational goals and objectives. Lastly, the steps used in data extracting and cleaning applied in this study are reproducible, making them a necessary reference for other data scientists. A repository of the Python code used in data cleaning is also provided.