Optimizing Online Shopping Experience: The Role of AI Chatbots and ‘Like’ Features in Enhancing User E-commerce Engagement and Purchasing Decisions
Abstract
In the midst of fierce e-commerce competition, user experience (UX) has become a crucial factor for success. However, users often face challenges such as slow responses to product inquiries and a lack of personalization, which can hinder purchasing decisions. This research aims to analyze and prove how optimizing UX through the implementation of an AI Chatbot and a ‘like’ feature can significantly increase user engagement and encourage purchasing decisions. The main focus is to design, test, and validate the effectiveness of these two interactive features as a solution to the identified problems. Using a Design Thinking approach, this study began with qualitative data collection through interviews to validate user needs. Based on the initial findings, an initial prototype (version A) and an optimized prototype (version B) were developed. Subsequently, the A/B testing method was used to quantitatively compare the two prototype versions to measure the increase in user acceptance after the optimization process. The results of the initial data analysis show that users perceive the ‘like’ feature as highly influential (80%) and the chatbot as positively influential (60%) on their purchasing decisions. The results of the A/B testing then confirmed that the optimized prototype (version B) received significantly higher ratings, especially in the ‘Very Good’ category, compared to the initial version, which validates the success of the design optimization process. In conclusion, the implementation of an AI Chatbot and a ‘like’ feature, designed through an iterative process, proved effective in creating a more efficient, personal, and satisfying shopping experience.
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