In two-sided marketplaces, items compete for attention from users since attention translates to revenue for suppliers. Item exposure is an indication of the amount of attention that items receive from users in a ranking, and can be influenced by factors like position bias. Recent work suggests that another phenomenon related to inter-item dependencies may also affect item exposure, viz. outlier items in the ranking. Hence, a deeper understanding of outlier items is crucial to determining an item’s exposure distribution. In this work, we study the impact of different presentational e-commerce features on users’ perception of outlierness of an item in a search result page. Informed by visual search literature, we design a set of crowdsourcing tasks where we compare the observability of three main features, viz. price, star rating, and discount tag. We find that various factors affect item outlierness, namely, visual complexity (e.g., shape, color), discriminative item features, and value range. In particular, we observe that a distinctive visual feature such as a colored discount tag can attract users’ attention much easier than a high price difference, simply because of visual characteristics that are easier to spot. Moreover, value range plays an important role where features with a known range of values (e.g., star rating) are easier to detect when deviating from the rest compared to features with an unknown value range (e.g., price).