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AI and the Evolution of Customer Experience Management (CEM)
About 10 years ago, all business communications were done through email. Then, with the emergence of social channels such as Facebook and Twitter, it became progressively essential for customer experience strategy to involve support teams to monitor these channels. In the last two years, businesses have seen an increasing preference for live chat messaging like Whatsapp and Facebook Messenger. It can be asserted from this timeline that the first and foremost necessity when assessing customer experience is to identify the channel the customer prefers to interact on.
Nowadays customers interact on a multichannel level; to provide a seamless customer service across all channels, businesses should be prepared to maintain the context when interacting with a particular customer across various channels. In other words, the organization should be aware of the full conversational history between the customer and the enterprise. Bots or automated conversations are quite helpful in this kind of scenario. It gives the customer an opportunity to resolve the issue instantaneously through self-service, as the context has already been ascertained.
Statistics suggest that customers prefer the instantaneous option of self-service with the assistance of a bot unless human interaction is necessary for resolution. When the customer does require agent intervention, the transfer is seamless and effortless when compared to legacy IVR system which was a time-consuming and frustrating process.
"Answer Bot gives the customer an opportunity to resolve the issue instantaneously through self-service, reducing friction for the customer and allowing agents to spend more time on interactions where a human is needed "
When interacting with bots, customers are completely aware that these are automated responses, as these conversations do not impersonate a human interaction. Still, the customer values the faster resolution time and the knowledge of the context, which is possible only through Answer Bot by Zendesk.
It is true that modern forms of natural language processing (NLP) and machine learning (ML) require extensive datasets. We are able to achieve this requirement through our platform that hosts numerous interactions between customers and bots or customers and agents. These interactions generate a massive amount of data. In addition to that, the service department is involved in a lot of repetitive conversations, answering the same questions for different customers. This results in a rich, labeled interaction that can be used to train deep learning models. Even human interactions generate a series of paired question and answers which can be used to train these models.
Therefore, when queries are made they are clustered under similar topics, so that Answer Bot can analyze corresponding articles or answers and send them to the customers. The objective is to effectively utilize all the questions we are collecting from the vendor’s platform through a multichannel approach. This way we can train a model that is generalized and has a wide knowledge base.
It is evident from a technological and algorithm perspective that application of AI is still at the stage of infancy, especially in terms of unstructured data or employing deep learning. That being stated, application of machine learning is already enhancing customer experience through automation. Take the instance of Answer Bot. It has reduced workloads for agents while altering their role within the organization. With the time saved due to automation, these agents are handling channels like live chat that require real-time staffing and interaction with the customer. Through these interactions, these agents can distill important customer insights, capturing the voice of a customer that was lost among the millions.
Therefore, an agent transforms into a valuable resource for the enterprise. We are still trying to figure out a manner to utilize these algorithms to augment the daily functioning of our knowledge workers or agents. The biggest challenge is identifying the right user experiences by balancing the predictions generated by these models and the existing knowledge of an agent. The objective is to create an improved customer experience through models that predict more accurately, which in turn enables our employees to work more efficiently.