Chatbots have been considered as one of the leading technologies in the e-commerce domain as virtual shopping assistants able to guide customers throughout the entire shopping experience automatically. However, this technology is currently registering a high failure rate, and several users are still skeptical of its effectiveness. One of the reasons for this low performance lies in the fact that chatbots have a poor human-likeness that negatively influences customer perception of the technology. Secondly, the low level of personalization does not allow for tailored services based on customers’ needs and requirements. Previous studies discovered that it is possible to attach specific personality traits to chatbots in order to increase their human-likeness. Besides, other researchers have discovered the importance of service personalization as a distinctive requirement for successful e-commerce businesses. Depending on consumers’ decision-making behaviour, scientists have found that different services lead to different levels of customer satisfaction. The present study explored the two sides of Human-Computer Interaction with the final aim of understanding how to better align chatbot personality with human decision-making personality. Two different chatbot personalities (neutral and extravert) were created and randomly assigned to different users. During the experiment, participants had to complete a real chatbot conversation focused on dress shoes and were asked to conduct a post-interaction questionnaire to assess their satisfaction. Results showed that it is possible to effectively attach an extravert personality into a chatbot conversation through the use of language, emoticons and GIFs. Moreover, participants with different personal decision-making behaviour and gender registered different levels of customer satisfaction. Finally, the chatbot neutral personality registered different level of customer satisfaction depending on gender and personal decision-making behaviour. These findings support the literature by analyzing the complementary relationship between chatbot and human personalities. Future studies could use these findings to develop chatbot experiences that better fit with customer needs and requirements.