• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, a customer service team leader is likely to be reviewing the weekend's ticket backlog, trying to understand where the team's time is being spent and how they can reduce the number of unresolved issues. The team leader may be considering implementing a new AI-powered tool, such as AsscherAi, to help the team answer customer queries more efficiently. However, the team leader may be unsure about how to measure the effectiveness of such a tool, and whether the time saved by the team will actually be redeployed to more valuable tasks.

The difference between time saved and time redeployed

The concept of time saved is often misunderstood, as it is not always clear whether the time saved is actually being redeployed to more valuable tasks. For example, a customer service representative may be able to answer a customer's query more quickly using AsscherAi, but if the representative is not given any additional tasks or responsibilities, the time saved may simply result in the representative being underutilized. On the other hand, if the time saved is redeployed to more valuable tasks, such as resolving complex customer issues or providing proactive support, the benefits of the AI-powered tool can be more fully realized.

In order to understand whether time is being redeployed, it is necessary to look beyond simple metrics such as time saved, and to consider how the team's workload and responsibilities are being managed. This may involve working with the team leader and other stakeholders to identify opportunities for the team to take on more valuable tasks, and to ensure that the time saved is being used effectively.

Why self-reported time savings overstate the effect

Self-reported time savings are often unreliable, as they are based on the subjective estimates of team members or managers. These estimates may be influenced by a range of factors, including the desire to demonstrate the effectiveness of a new tool or initiative, or to justify the investment of time and resources. As a result, self-reported time savings may overstate the actual benefits of the tool, and may not accurately reflect the impact on the team's productivity.

A more reliable approach is to use objective metrics, such as those provided by AsscherAi, to measure the actual time saved and the impact on the team's workload. This can help to provide a more accurate understanding of the benefits of the tool, and to identify opportunities for further improvement.

The customer service data this depends on

In order to provide effective support for customer service teams, AI-powered tools such as AsscherAi rely on access to a range of customer service data, including ticket history, knowledge base articles, product and order records, and escalation history. This data is used to provide answers to customer queries, and to help the team to resolve issues more efficiently. For example, a customer service representative may use AsscherAi to quickly access information about a customer's previous interactions with the company, or to find answers to common questions.

The quality and completeness of this data are critical to the effectiveness of the tool, as incomplete or inaccurate data can lead to incorrect or incomplete answers. As a result, it is essential to ensure that the data is well-maintained and up-to-date, and that the tool is configured to use the data effectively.

For more information on how to manage and maintain customer service data, please visit our operations page.

What it changes about the customer service process

The implementation of an AI-powered tool such as AsscherAi can have a significant impact on the customer service process, particularly in terms of the way that issues are resolved and the way that knowledge is shared. For example, the tool can help to ensure that the same issue is not answered from scratch each time it arises, by providing access to previous answers and resolutions. This can help to reduce the time and effort required to resolve issues, and to improve the consistency and quality of the support provided.

The tool can also help to identify root causes of issues, and to feed this information back to the product development team. This can help to improve the overall quality of the product, and to reduce the number of issues that arise in the first place. For example, a category manager may use AsscherAi to analyze customer feedback and identify areas for improvement, and to work with the product development team to implement changes.

Measured with key performance indicators

The effectiveness of an AI-powered tool such as AsscherAi can be measured using a range of key performance indicators, including first contact resolution, time to answer, and repeat contact rate. These metrics provide a clear and objective measure of the tool's impact on the customer service process, and can help to identify opportunities for further improvement. For example, a shift supervisor may use these metrics to monitor the performance of the customer service team, and to identify areas where the team may need additional training or support.

By using these metrics to measure the effectiveness of the tool, it is possible to get a clear understanding of the benefits and to identify opportunities for further improvement. For more information on how to get in touch with our team, please visit our contact us page.

When it is the wrong choice

While AI-powered tools such as AsscherAi can be highly effective in supporting customer service teams, there are some situations where they may not be the best choice. For example, in situations where the customer service process is highly complex or nuanced, a more traditional approach may be more effective. Additionally, in situations where the customer service team is very small or lacks the necessary skills and expertise, the implementation of an AI-powered tool may not be the most effective use of resources.

In these situations, it may be more effective to focus on providing additional training and support to the customer service team, or to consider alternative approaches such as outsourcing or automation. By carefully considering the needs and requirements of the customer service team, it is possible to determine whether an AI-powered tool such as AsscherAi is the right choice, and to ensure that the tool is implemented in a way that maximizes its benefits.