Capturi AI Assistant: A co-pilot that makes it easier to improve customer experiences

Lack of quick and easy access to knowledge is a drag on customer satisfaction levels

To successfully manage the job as a customer service agent, it requires an extensive amount of background knowledge, the ability to navigate multiple systems, and the capacity to understand customer behavior and needs.

Research from Capturi shows that, on average, agents lack knowledge in about 12% of all customer conversations. However, in knowledge-intensive departments and situations, this number can rise as high as 35%.

Lack of knowledge occurs in conversations where the agent is unable to immediately answer a customer’s question. This may lead them to search for information in one of their systems or to ask a colleague for help. If you do not have an updated knowledge base or if the knowledge base is difficult to navigate, searching for information can be a time-consuming process.

Knowledge gaps and ineffective knowledge searches can lead to longer calls, lower customer satisfaction levels, and, potentially, the loss of customers. To avoid this, efficiency and automation have become critical focus areas for customer service departments who want to stand out and excel in the field.

How knowledge is created and managed today

The approach that customer service departments use to create, manage, and find knowledge articles varies from company to company and often depends on the size of the organization.

While larger companies often have dedicated knowledge experts to update articles and create new content, smaller companies may not always have the resources to do so, and often end up asking colleagues for advice leaving customers waiting on the line.

For companies that do have a dedicated knowledge expert, the process of creating and maintaining articles can be both long and complex.

First, the experts will have to find relevant information. They will then have to ask experienced agents for input and advice to ensure that the information they provide is correct and suits the company’s best practice techniques. Once they have ensured that each piece of content is of top quality, they are ready to publish the article.

However, such manually updated knowledge databases are often costly to create and require continuous maintenance. In addition, the ongoing process of quality assurance often turns out to be time-consuming and complex.

How you should manage knowledge

To avoid outdated material and facilitate easy access to relevant information, you can benefit from centering your knowledge database around customer conversation data. This way, you ensure that all information is updated based on your customer interactions and feedback while also minimizing the time wasted on collecting information manually.

This data-centered approach makes it easier to establish a knowledge management process, particularly for companies that have not had the resources or budget to actively create or maintain a knowledge database before.

At the same time, it will also simplify knowledge management for companies with a dedicated quality assurance team, as they can now maintain even more knowledge articles for the same amount of time and effort. In short, their role is transformed from that of an investigative journalist into more of an editor role, where ensuring quality and facilitating even better experiences become their main tasks. 

Once you decide to utilize the data from your customer conversations, you can benefit from advancements in AI technology. By collecting and categorizing data from your customer conversations, this technology will automatically update existing knowledge articles and suggest topics for new ones based on spikes in conversation topics.

These automatically created knowledge articles consist of a series of questions and answers related to specific topics, which makes it even easier for agents to provide answers that are both accurate and efficient.