Conversational Design as Choice Architecture for AI Chatbot Experience in Digital Marketing Communication
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Abstract
The competition in the online business landscape has intensified. Businesses increasingly utilize chatbots in order to enhance their service efficiency. However, communication through chatbots still has gaps, limiting their ability to persuade or close sales effectively. This research studied behavioral design strategies to improve the efficacy of chatbot interactions. By compiling keywords and phrases, the study designed chatbot response strategies using choice architecture strategies to appropriately guide product information inquiries. The results showed that choice architecture strategies improve 89.39% in purchase interest compared to standard NLP-generated responses without any optimization. The different choice architecture strategies had varying impacts on customer purchasing behavior and chatbot user experience. The strategies ranked from most to least effective in driving purchase interest were: Framing effects, Loss aversion, Nudging, Default options, and Social proof. When tested in a simulated chatbot, the responses optimized with choice architecture strategies significantly reduced the number of messages and conversational duration. However, some responses also caused user discomfort and led to conversation termination, especially when there was excessive attempt to steer the user towards the massages.
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