• Written by Sonali Subhadarsini, Product Manager

A customer service shift supervisor checks the queue of incoming tickets, trying to allocate them to the right agent, while also keeping an eye on the knowledge base updates that have been pending for a while, and this is where the process of automating customer service often starts, with a focus on the steps around a decision rather than the decision itself, which is where AI process automation for customer service can be particularly useful.

Mapping the current process

Before automating any part of the customer service process, it is necessary to map out the current workflow, including all the steps that are taken, from the initial ticket submission to the final resolution, and this involves understanding the roles of different agents, such as the category manager, who is responsible for maintaining the knowledge base, and the shift supervisor, who oversees the allocation of tickets, and how they interact with each other and with the systems they use, such as the ticketing system and the product database.

This mapping process can be time-consuming, but it is essential to identify the areas where automation can have the most impact, and where it can help to streamline the process, reduce delays, and improve the overall efficiency of the customer service operation, and it is also important to involve the agents who are actually working on the front line, as they have a deep understanding of the process and can provide valuable insights into where the pain points are and how they can be addressed.

The handoffs that create delay

One of the main causes of delay in the customer service process is the handoff between different agents or teams, such as when a ticket is escalated from a junior agent to a senior agent, or when a customer issue requires input from a specialist team, such as the product development team, and these handoffs can be time-consuming and prone to errors, as information is lost or miscommunicated, and the customer is left waiting for a resolution.

By automating the steps around these handoffs, such as by using AI-powered workflow management tools, it is possible to reduce the delay and improve the efficiency of the process, and this can have a significant impact on the customer experience, as customers are able to get their issues resolved more quickly and easily, and are less likely to have to repeat themselves or provide the same information multiple times.

The customer service data this depends on

The automation of the customer service process depends on access to a wide range of data, including ticket history, knowledge base articles, product and order records, and escalation history, and this data must be accurate, up-to-date, and easily accessible, in order for the automation tools to be effective, and for the agents to be able to make informed decisions about how to resolve customer issues.

For example, a category manager may use the knowledge base to research a customer issue and provide a solution, while a shift supervisor may use the ticket history to identify patterns and trends in customer complaints, and to allocate resources accordingly, and the product development team may use the product and order records to identify potential issues with a product and to develop solutions, and all of this data must be integrated and accessible, in order for the automation tools to be effective.

What it changes about the process

One of the key benefits of AI process automation for customer service is that it can help to prevent the same issue from being answered from scratch each time, by providing agents with access to a knowledge base of previous solutions and outcomes, and by automating the steps involved in resolving common issues, and this can help to reduce the time and effort required to resolve customer issues, and to improve the consistency and quality of the solutions provided.

Additionally, AI process automation can help to identify the root causes of customer issues, and to feed this information back to the product development team, so that they can develop solutions to prevent the issues from occurring in the first place, and this can help to reduce the number of customer complaints and to improve the overall quality of the products and services provided, and for more information on how to implement AI process automation, you can visit our operations page.

Measured with key metrics

The effectiveness of AI process automation for customer service can be measured using a range of key metrics, including first contact resolution, time to answer, and repeat contact rate, and these metrics can provide valuable insights into the efficiency and effectiveness of the customer service process, and can help to identify areas where further automation and improvement are needed.

For example, a reduction in time to answer can indicate that the automation tools are helping to speed up the resolution of customer issues, while an increase in first contact resolution can indicate that the automation tools are helping to provide more effective solutions, and a reduction in repeat contact rate can indicate that the automation tools are helping to prevent the same issues from occurring multiple times, and for more information on how to get started with AI process automation, you can contact us.

When it is not the right choice

While AI process automation can be a powerful tool for improving the efficiency and effectiveness of the customer service process, it is not always the right choice, and there are some situations where it may not be suitable, such as where the issues are highly complex or require a high degree of human judgment and empathy, and in these cases, it may be more effective to rely on human agents, who can provide a more personal and nuanced response to customer issues.

In these situations, it is often better to focus on providing training and support to the human agents, rather than trying to automate the process, and this can help to ensure that customers receive the high-quality, personalized service that they need, and that the customer service operation is able to provide effective solutions to complex issues, and this requires a careful evaluation of the specific needs and requirements of the customer service operation, and a consideration of the potential benefits and limitations of AI process automation.