The research aims to provide companies knowledge of (1) why customers use the chat feature, (2) who – the agent or the bot – is more similar (in content) to the customer and (3) whether and how this similarity impacts the customer’s engagement during the chat.
I conducted three analyses, each of which uses machine learning.
Analysis 1 reveals that customers prefer chatting with an agent (vs. the bot) when they seek detailed or sensitive information. Analysis 2 demonstrates that relative to the bot, the agent is more similar (in content) to the customer. Analysis 3 uses guided latent Dirichlet allocation and gradient boosting (XGBoost) to show that matching the customer on the dominant topic boosts customer engagement during the chat.
The findings help academics know why customers choose an agent versus a bot and whether this choice helps or hurts their engagement.
The findings help retail managers design better chat features and chatbots, thus improving customer engagement.
I am aware of no research in marketing or business that has provided evidence on customers’ choice of agent versus bot and the engagement consequences of this choice.
