Customer Service AI Use Cases- How Businesses Can Deliver Real Value

TechSee describes customer service AI use cases and how they hold potential to improve customer support

Artificial Intelligence was the talk of the last decade. The hype was generated by successive breakthroughs in multiple industries. The customer service industry, in particular, pinned high hopes on AI, expecting that upon its introduction to the market, smart machines would truly transform the customer experience. This article discusses the initial disappointment of AI use and more recently, several successful customer service AI use cases that businesses are implementing today.

  

AI customer service holds promising potential 

In 2017, all eyes were on chatbots and virtual agents – the assumption being that they would take over a significant part of the customer interaction and deliver real value in communicating with customers. The industry – as well as scientists, engineers, journalists and eager venture capitalists – were mostly disappointed when the results did not deliver on the expectations built up by the hype. 

However, while AI has not yet become the answer to all our customer service challenges, the technology is moving forward at a rapid pace. While it may still take some time, AI is without a doubt making immense progress and is poised to transform business over the next decade. 

AI and its enabling methodologies, such as Machine Learning, Deep Learning and NLP, are the focus of major investments and research. They are continuously evolving and dramatically improving, along with computing power, data collection, storage and architecture, together providing a stronger backbone and massive potential for the future of artificial intelligence in customer service.

In addition, IoT growth plays a significant role in the practical advancement of AI. Smart machines will benefit from more context and data to learn from and reference in their interactions with humans. This will enable greater accuracy and deliver more value to enhancing the customer experience.

Building successful artificial intelligence in customer service is an ongoing process, and is similar in some ways to the onboarding of a new employee. As there will be a learning curve, you have to start simple, familiarize him with the business, the customers, and the domain, and slowly give him more responsibilities until he can move forward independently. 

Examples of AI in customer service

That said, we have identified three areas of focus where AI has made significant strides in automating the customer support domain and is already providing value to enterprises. These areas represent excellent starting points for introducing AI into your organization and building the intelligent platform of tomorrow. 

  • Bots and agents work together towards better CX

The interconnection of humans with technology can be utilized to provide effective decision support during the agent-customer interaction. The agent and machine collaborate together, with the agent’s performance enhanced by the computer’s ability to provide faster resolutions. The bot learns from the agent’s feedback and improves the automated responses over time. 

This model is especially effective when the contact center is required to handle large call volumes or highly complex calls. This decision-support tool is expected to reduce agent training time and streamline the entire support process, resulting in a more satisfying customer experience.

  • Prediction of customer behavior

While AI is not yet able to communicate back and forth with customers, as humans can, there is one area where AI has already surpassed human capabilities: predicting behavior. AI captures every nuance of human behavior, whether through chat or voice, and is able to extract customer insights from these structured and unstructured data sets. Comparing data from multiple sources to past patterns can predict sales conversions, customer lifetime value, churn and more.

In cases of technical support, AI can predict the best path to achieve a satisfactory resolution based on a combination of issue type and customer behavior. 

  • Virtual agents help to answer customer inquiries

A major driver for the chatbot disappointment in 2017 was the inflated expectations. Based on the hype, companies incorporated chatbot solutions, and expected them to perform on par with human agents within a very short time frame. 

While these expectations were not realistic, chatbots as a customer service solution should not be discarded. Chatbots solutions are continuously learning and will continue to improve. They have already been proven effective at handling specific tasks, and over time, will become more proficient at handling the complex tasks as well. Already in 2020, more than 70% of businesses say chatbots make it easier for resolving customers’ issues. 

For example, using these bots to automate answers to basic customer questions has been shown to decrease the average agent handle time (AHT) by 10% or more. Companies can also capture the data from the decision-tree based interaction, for later use by a live agent. Clearly, chatbots have the potential to deliver great value.

Implementing customer service AI use cases 

Putting aside any immediate expectations for Terminators or Star Wars droids, the customer service industry is clearly experiencing the beginning of a shift towards artificial intelligence reliant systems. 

It is evident from just several AI customer service use cases that we can look forward to a future where AI platforms will power call centers and drive the customer experience to new heights, in collaboration with human agents. 

To build smart decision-making machines, we must capitalize on the significant progress that has already been made, continue to invest in innovation, take steps to digitize data and identify customer service AI use cases where even narrow usage of AI in its current form can deliver value to the business. In other words, put the hype and sensationalism aside, and take concrete steps toward long-term success by laying the groundwork for incorporating artificial intelligence in customer service.

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