Online product reviews have become an efficient source to gather consumer needs, instead of going through the laborintensive surveys. The contribution of the paper is to relate the content of online reviews to a product's sales rank, that implicitly reflects the needs and motivation behind what drives customers to purchase the product. In particular, the review content includes product features stated in the review, together with the sentiment expressed towards the feature. Part-of-speech tagging is used to extract the features and sentiment from the reviews. The extracted data from reviews and price then subsequently become independent variables in the regression model, while sales rank is the dependent variable. An experiment is run for the wearable technology products to illustrate the methodology and interpret the results. In general, the features in reviews that are related to sales rank significantly are button, calorie tracker, design, time functions, and waterproof abilities. Moreover, the products are further stratified based on price average. In the cluster of the most expensive items, the sales rank is found to be not significantly related to price.