Optimizing Business Sales and Improving User Experience by using Intelligent User Interface

Abstract

– This research explores the impact on the user experience when the users, that is, the people in business, are exposed to an improved version of an intelligent user interface of the review management software. Machine learning algorithms, such as Lexicon-based sentimental analysis and NRC Emotion recognition, are employed to assist the proposed review management software, Review Dock. To provide additional assistance, a Content-based Recommendation system is integrated. More than 17,000 Amazon reviews are used to generate the results. To improve the satisfaction level of the already created prototype, three iterations of usability testing were conducted on nine participants. The findings show that by following the Web Content Accessibility Guidelines (WCAG) standards, an average satisfaction score of 2.49 out of 5 on the first iteration is significantly improved to 4.9 on the last iteration. Furthermore, the polarity categorization is similar across most evaluations, which are accomplished on previously unseen data sets. However, the results also reveal that the designs will only perform well for a small-medium industry. This research attempts to fill the limitations in the literature with respect to user experience. Regardless of the tools offered, the issue for businesses in utilizing an available solution that diminishes the engaging experience remains unchanged. As a result, a new solution should solve the limits, which will directly affect the company's sales. The research question states what steps the review management software may take to reduce the overly convoluted user interface? Therefore, proposing a solution called Review Dock will provide a plethora of responses and entirely focus on customer happiness by providing a comprehensive overview of how to enhance a product's sales.