• Written by Muhammed Abdulla NC, CEO

How can we tell if a new customer service process is actually working, when we can't see what would have happened if we hadn't made the change? This is a question I've heard asked many times in meetings, and it's a hard one to answer because the counterfactual - what would have happened without the change - is invisible. We can't run two parallel universes, one with the new process and one without, to compare the outcomes. But we can observe some leading measures that give us clues about whether the new process is on track.

Leading measures you can actually observe

One approach is to look at leading measures that we can actually observe, such as the time it takes to answer customer queries, or the number of queries that are resolved on the first contact. These measures give us an early indication of whether the new process is having the desired effect. For example, a customer service team leader checks the first contact resolution rate for the new process and compares it to the rate for the old process. If the new rate is higher, it's a good sign that the new process is working.

A shift supervisor can also check the time to answer customer queries and see if it's decreasing over time. This is a leading measure because it indicates whether the new process is reducing the time it takes to resolve customer issues. By observing these leading measures, we can get a sense of whether the new process is on track, even if we can't see the counterfactual.

Why aggregate outcome metrics move for unrelated reasons

Aggregate outcome metrics, such as customer satisfaction or net promoter score, can be influenced by many factors unrelated to the new customer service process. For instance, a change in the overall market or a new product launch can affect customer satisfaction, making it difficult to attribute any changes to the new process. Most people get wrong that these metrics are a reliable way to measure the effectiveness of a new process, but they are wrong because these metrics are too broad and can be influenced by many factors.

Furthermore, aggregate outcome metrics often lag behind the implementation of a new process, making it difficult to determine whether the process is having the desired effect. By the time the metrics show a change, it may be too late to make adjustments to the process. This is why it's essential to focus on leading measures that can give us an early indication of whether the new process is working.

The customer service data this depends on

The ability to measure the effectiveness of a new customer service process depends on having access to relevant customer service data, such as ticket history, knowledge base, product and order records, and escalation history. This data provides context and helps to identify patterns and trends that can inform process improvements. For example, a category manager can compare two suppliers based on their ticket history and escalation rates to determine which one is performing better.

By analyzing this data, customer service teams can identify areas for improvement and make data-driven decisions to optimize their processes. This data can also be used to train AI models to predict customer behavior and provide personalized support. With the right data, customer service teams can create a more efficient and effective support process that meets the needs of their customers.

What it changes about

The implementation of a new customer service process can change the way issues are handled, reducing the likelihood of the same issue being answered from scratch each time. By documenting knowledge and solutions, customer service teams can create a knowledge base that can be used to resolve similar issues in the future. This approach also helps to identify root causes of issues and feed them back to the product development team, reducing the likelihood of the same issue occurring again.

This is a significant change from the traditional approach, where each issue is handled in isolation, and the knowledge and solutions are not retained. By taking a more proactive and data-driven approach, customer service teams can create a more efficient and effective support process that benefits both the customers and the organization. If you have any questions about how to implement this approach, you can contact us for more information.

Measured with

The effectiveness of a new customer service process can be measured using metrics such as first contact resolution, time to answer, and repeat contact rate. These metrics provide a clear indication of whether the process is working as intended and can be used to identify areas for improvement. By tracking these metrics, customer service teams can make data-driven decisions to optimize their processes and improve the overall customer experience.

For example, a customer service team can track the first contact resolution rate and adjust their process to improve it. They can also track the time to answer and make adjustments to reduce it. By using these metrics, customer service teams can create a more efficient and effective support process that meets the needs of their customers.

What this does not do

This approach to measuring the effectiveness of a new customer service process does not provide a complete picture of the organization's overall performance. It is focused specifically on the customer service process and does not take into account other factors that may be influencing customer satisfaction or loyalty. Therefore, it should be used in conjunction with other metrics and approaches to get a more comprehensive understanding of the organization's performance.