Not only do they help in saving money, but also bring down product costs and customer hassles
According to research jointly conducted by Temple University, U.S., Sichuan University, China and Fudan University, China, chatbots which use AI for simulating human conversation through voice commands or text chats, incur almost zero marginal costs. The findings provide useful insights for chatbot applications in targeting customers using conversational commerce.
Xueming Luo, professor and Charles Gilliland Distinguished Chair at Temple University, said, “Chatbots offer enhanced technological benefits, reduced customer hassle costs and increased consumer welfare (offering the product at a lower cost because bots save money on labor). This data empowers marketers to target certain customer segments to cultivate customer trust in chatbots.”
Many industry outlets like American Eagle Outfitters and Domino’s Pizza use chatbots, as well as online services like Amazon and eBay. Machines have the advantage that they never get frustrated or tired like humans, and save money for consumers. This prevents machines to have “bad days”.
Effective but more work needed
For the study, about 6,000 customers were selected from a financial services company. They were then randomly assigned to either humans or chatbots, with the disclosure of the identity – bots or humans, varied from not telling the consumer at all, telling them at the beginning or after the conversation or revealing the true identity after they’d purchased something.
“Our findings show that when customers don’t know about the use of artificial intelligence (AI) chatbots, then they are four times more effective at selling products than inexperienced workers, but when customers know the conversational partner is not a human, they are curt and purchase less because they think the bot is less knowledgeable and less empathetic,”
The negative feedback seemed to be driven by human perception against machines, despite the objective competence of AI chatbots. Fortunately, such negative impact can be mitigated by a late disclosure timing strategy and customer prior AI experience.