Effectiveness of Artificial Intelligence (AI) Chatbots in Improving Customer Satisfaction in E-Commerce in Rwanda

Abstract

Purpose: The aim of the study was to assess the effectiveness of artificial intelligence (AI) chatbots in improving customer satisfaction in e-commerce in Rwanda.

Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.

Findings: The study demonstrated that artificial intelligence (AI) chatbots significantly enhance customer satisfaction in e-commerce settings. These chatbots provide instant, round-the-clock customer support, addressing queries and resolving issues promptly, which contributes to higher customer satisfaction levels. By leveraging natural language processing (NLP) and machine learning algorithms, AI chatbots can understand and respond to customer inquiries accurately and contextually, mimicking human-like interactions. This efficiency reduces waiting times and improves the overall customer experience. Moreover, AI chatbots can personalize interactions by analyzing customer data and preferences, offering tailored product recommendations and promotions, which further boosts customer satisfaction and engagement. They also help in streamlining the purchase process by guiding customers through product selection, checkout procedures, and even handling post-purchase inquiries, leading to a smoother and more enjoyable shopping experience. In addition to enhancing customer satisfaction, AI chatbots provide valuable insights for businesses by collecting and analyzing customer feedback and behavior. This data can be used to refine marketing strategies, improve products, and optimize the overall customer service process. Consequently, the implementation of AI chatbots in e-commerce not only fosters a more satisfied customer base but also drives operational efficiency and business growth.

Implications to Theory, Practice and Policy: Technology acceptance model (TAM), service quality (SERVQUAL) model and expectation-confirmation theory may be used to anchor future studies on assessing the effectiveness of artificial intelligence (AI) chatbots in improving customer satisfaction in e-commerce in Rwanda. E-commerce platforms should invest in continuous training and updates for their AI chatbots to handle a wider range of queries and provide more personalized interactions. Policymakers should develop and enforce standards that ensure transparency in AI chatbot interactions. Customers should be clearly informed when they are interacting with AI and provided with options to escalate to human support if needed.