Billions of people everyday use Social Media (SM), such as Facebook and Twitter, to express their opinions and experiences with brands. Companies are highly interested in understanding such SM brand-related content. Consequently, many studies have been conducted and many applications have been developed to analyse this content. For analysis purposes, the main SM metrics used include volume and sentiment. Interestingly, however, brand equity theory proposes different metrics for assessing brand reputation. These include brand image, brand satisfaction and purchase intention (henceforth referred to as marketing metrics). The objective of this paper is to explore the feasibility of applying marketing metrics in Twitter brand-related content. For this purpose, we collect, study and analyse tweets that mention two brands, namely IKEA and Gatorade. The manual analysis suggests that a significant amount of brand tweets is related to brand image, brand satisfaction and purchase intention. We thereafter design an algorithm that classifies tweets into relevant categories to enable automatic marketing metrics computation. We implement the algorithm using statistical learning approaches and prove that its classification accuracy is good. We anticipate that this article will motivate other studies as well as applications' designers in adopting marketing theories when evaluating brand reputation through SM content.