iQuanti: Are customers ready for the truth that the friendly customer support agent on a company’s website is really a chatbot? Maybe not. A recent study from Göttingen University found that “chatbot disclosure has a negative indirect effect on customer retention through mitigated trust for services with high criticality.” Although chatbots are in wide use and provide material benefits for consumers such as 24/7 support, customers may still be suspicious of whether automated responses can get them the help they need. These findings, and others like them, are making some companies reconsider how they are going about implementing chatbot technology into their customer service - can they continue to “pretend” chatbots are real? Can customers handle the truth about chatbots? How do conversational AI chatbots change the game? And how should businesses integrate AI and chatbots into their workflows?  The truth about chatbots A Genesys survey from 2019 found that 73% of respondents were open to dealing with a voice/chatbot. When issues were more complex, only 21% felt comfortable dealing with bots. That said, nearly 70% report having positive interactions with customer service bots when they require support, and more than 20% say they can “almost always” resolve their issue through a bot without having to escalate to a customer service rep, while nearly 50% say they can do this “more than half of the time.”  While customers have daily AI-enabled interactions with organizations, trust remains to be an area for improvement. Customers want more human-like interactions, but still prefer to speak to a live representative most of the time. More than 70% of organizations are actively trying to make their AI interactions more human-like, but this raises the question of whether chatbots should “pretend” to be real, or if it should be clearly disclosed the bot is indeed a bot.  What is conversational AI? Conversational AI uses:

Natural language processing (NLP) Automatic speech recognition (ASR) Advanced dialog management Other machine learning 

This technology allows these chatbots to interact with consumers with conversations that feel less static than the call-and-response triggers of a traditional chatbot. With conversational AI, chatbots are capable of understanding topics, recalling data about a specific user between conversations, and learning from continuing interactions with customers, similarly to a real customer support agent. With the rise of conversational AI and its integration with business technology, the lines get even more blurred and confusing whether a customer is talking to a real person or a bot.  How businesses should use conversational AI How transparent should retailers be about letting customers know they are talking with either a live or virtual customer service agent?  The bottom line is AI and chatbot technology is here to stay, and more companies than ever are integrating it into their day-to-day customer service functionality. The key is to do so in a way that allows for transparency.  The other key is to provide a way to get to a live person quickly and easily if the chatbot isn’t resolving the issue. Doing so can help avoid frustration and dissatisfaction, and help customers feel empowered. And because conversational AI chatbots can collect and store data about the customer through their interaction before reaching a customer service agent, they can then quickly and easily send this completed information to the human expert, saving time and providing maximum support to both the customer and the representative. Additionally, by disclosing the bot is a bot, customers are often more positive and accepting if their issue is not resolved or the outcome is not what they need. It’s easier for the user to understand the cause of the error, and chatbots are more likely to be forgiven.  Because conversational AI can learn and adjust in real time, and uses NLP and ASR to communicate, chatbots are smarter and more life-like than ever. However, until this technology is widely and more often than not used, it’s important to continue to err on the side of caution and be as transparent and efficient as possible. Source: iQuanti, Inc.