Web stores have become a primary way to distribute products and services. In order to successfully sell such goods, the User Experience (UX) is nowadays increasingly important. TheUXencompasses all aspects of the end user’s interaction with a company, its services and its products. Its quality can have a very tangible impact on the business and is crucial in terms of achieving Key Performance Indicators (KPIs) such as reached clientele, sales and conversion rate. However, evaluating the UX of web stores is still an expensive and often neglected practice. While large companies are able to dedicate resources to investigate and improve the UX of their web store and their products, Small and Medium-sized Enterprises (SMEs) lack the corresponding resources. We conducted interviews with SMEs as part of this work to explore the extent to which this statement is true and the extent to which the lack of resources affects the evaluation. Most SMEs clearly agreed that high expenses, lack of basic UX and technical knowledge as well as the restricted number of human resources are the main impediments for assessing UX. This indicates that greater attention should be paid to this topic and demonstrates the significance and utility of our research, as we aim for an automated solution that overcomes these deficiencies. Existing research already considers some solutions to automatically detect the usability or UX smells in web pages. UX smells highlight potential UX problems that can lead to poor user experience. However, the focus has mostly been on usability and not on UX. Online stores were also not supported in the previous works, as only normal websites were considered. Moreover, most existing approaches do not specify UX smells and thus do not provide explanation and details about them. In order to compensate for these discrepancies and provide a useful basis for increasing the competitiveness of SMEs compared to large enterprises, we developed a tool-based approach to automatically analyze and detect UX smells. The usage of this tool does not require much familiarization. Within the scope of this work, we have defined a catalog of UX smells on the basis of a literature review and interviews with UI/UX experts. This catalog consists of the categories Visual Presentation, User Interaction, Lack of Transparency, Performance and Navigation. People who are interested in UI/UX but do not have deep technical understanding would benefit from this knowledge base. In fact, it helps to get and understand essential information in the UX field with no prior knowledge required. We further introduced the UX Smell Detector (UXSD), the tool we implemented that automatically detects UX smells with a minimalist setup. Its functionality consists of three consecutive steps. First, user interaction data with online stores using the analytics software platform Matomo is collected. Afterwards, the data is processed and aggregated before UX smells are automatically recognized. In order to test the effectiveness of our tool UXSD, we set up a test store with artificially planted smells and then collected usage data from test users. Based on the usage data, we calculated precision and recall performance metrics for each smell. Most of the smells that were embedded in the pages visited by users could be reliably detected. The precision values substantiate this claim, ranging from from 0.72 to 1.0, with the value 1.0 occurring 3 out of 5 times. However, our tool does not recognize the smells that are either present on non-visited pages or have not been triggered. This is reflected in the recall values. Apart from one outlier with a recall value of 0.09, all recall values are in the range of 0.61 and 1.0. If the outlier is not considered, the mean of the recall values of the other smells is around 0.76. Calculating the harmonic mean of precision and recall, the F1-Score, all values are between 0.72 and 0.85 with a single outlier of 0.16, as mentioned previously. The above described values and contributions demonstrate that our work provides a fundamental basis for detecting UX smells in real-world online stores in an operational and effective manner. We provided future work approaches on how to expand our work. This includes for instance the use of Machine Learning (ML) algorithms to detect UX smells by analyzing conspicuous user behavior, dashboards for intuitive visualization and WebDrivers to automatically search for UX smells that are not triggered by user behavior.