AI has been revolutionizing the face of customer service globally- more so during the pandemic- with AI-powered chatbots and other virtual agents taking the center stage. An increasing need to offer streamlined end-to-end customer experience is the primary reason why more and more firms are aggressively investing in modern technology to improve their customer support. However, traditional ways of providing customer service- which was solely based on humans- proved to be tedious both from the employee as well as from the customers’ perspectives.
While customers (especially the millennials and gen-z users) were tired of pressing buttons to avail themselves different kinds of services, service reps also considered that answering the same questions repeatedly was monotonous. This is why most organizations in recent times have decided to switch to virtual agents, which use AI, ML and other tools to frame and deliver customized responses to different types of customer requests and queries.
However, total dependence on such virtual agents is not feasible yet, and recent surveys have revealed that most customers are not quite happy with their overall experience with bots.
There are two major problems that customers face when dealing with chatbots:
1.Before making critical decisions (for example, buying a high-involvement product), customers often seek answers to complex questions from the brands, which usually involve several follow-up questions. This category of questions- which take longer periods for resolution based on the level of complexity or amount of information involved- are not handled well by bots.
The virtual agents either give up and redirect the customer to human service reps or display links that the customers can wade through to resolve their queries by themselves, something that they might have already tried. Hence, it can be inferred that bots do not have the capability to identify tasks that customers have already performed via other channels on the website, and this is one of the reasons why bots might fail in handling complex customer issues.
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2. Another area where virtual customer service agents are not performing adequately is in understanding human emotions accurately. We cannot ignore the fact that even the most sophisticated AI tools will not be able to replicate the complex range of human emotions. With the recent integration of sentiment analysis, companies have been able to solve this issue up to a certain extent. Conversational chatbots backed by sentiment analysis technology can determine the emotions and tonalities that are hidden behind a customer’s message (voice or text), and accordingly frame and deliver the right responses.
Nevertheless, sentiments are highly subjective in nature and vary from person to person, which often reduces the accuracy of sentiment analysis. Emotions like irony, sarcasm, humour etc. cannot be comprehended by this tool, which results in higher detachment of the customer from the organization and creates a negative impression about the brand. Case in point: Indigo’s (unintentionally) hilarious response to a dissatisfied customer’s tweet which was laden with sarcasm. (For the uninitiated, you can read about the fiasco here).
Problems like these necessitate the need for employing a combination of human and virtual customer service agents by organizations. While most of them would prefer talking to chatbots to get their issues resolved fast, customers also need to know that there are human agents available if their queries are too complex for the chatbots to resolve. There should be a logical escalation from bots to human agents. Handoff should be timed in a manner so that it occurs as soon as the bot fails to resolve the customer’s issue the second time.
The human service rep should also get a summary of all the tasks that the user has already performed when it was conversing with the chatbot so that he is updated and takes off from there. This will avoid repetition and thereby save time. Such practice will reassure the customers that the company or the brand actually cares for them. An example of an efficient chatbot would be one whose problem-solving capability is clearly specified to the customer before he starts using it. If the chatbot makes it clear to the customer right in the beginning that in case it is unable to resolve his/her issue, he/she would automatically be redirected to human service reps, it will be able to earn the customer’s trust. Therefore, through proper expectation setting, the customer is less likely to get disappointed.
Customer support is the section over which a company has more control in framing a positive brand perception in the minds of the customers. By designing efficient bots and by training service reps to communicate with users in a manner that perfectly aligns with the overall brand value proposition, the company will be able to retain happy customers and convince them to keep using its services.