Improving Effectiveness at a Call Center with Machine Learning

Víctor González    19 August, 2019

Understanding our clients and giving the best response to meeting their needs in the shortest amount of time possible is key to improving satisfaction and engagement with the company.

The problem begins when the number of clients is high and we are receiving hundreds or thousands of messages per day. In this situation, we have two problems to solve. First, we have to prioritize which messages we are going to respond first and, second, we have to understand what it is that they are saying. It is clear that some messages will be more important or more urgent than others and that it will not always be easy to prioritize. To make things easier, we can use Machine Learning techniques to help with this task, which can do part of the work for us.

In this post, we are going to focus on the case of a Call Center for B2B clients of a Telco operator. Part of the communication and management with the clients of the Call Center is done by e-mail. By analyzing these e-mails, it is possible to extract some metrics that can help us to understand the needs of the clients who communicate in this way. This analysis is mainly done through Natural Language Processing (NLP) techniques. In fact, we did the analysis on two levels:

  • Operational level – This level is more basic and implies a level of sending and answering e-mails. This is done simply by analyzing the subject lines of the e-mails.
  • Content level – In other words, processing the content of the messages from the clients in order to automatically classify them according to their content. We can understand this to be rules of filtering that we program for our e-mail clients, but much more advanced.

The analysis on the operational level allows us to obtain simple metrics such as how many e-mails are received by the Call Center, from which clients, what times the most e-mails are received, how long it takes to respond to each e-mail, etc.

But it is also possible to carry out a more complex analysis, extracting data from entire conversations: analyzing e-mails from one conversation (an e-mail thread), we can discover how long we take in solving a client’s problem, how many people from the Call Center were involved or which departments were involved.

The content level analysis is more complex as it requires a more sophisticated pre-processing of the data, but it allows us to extract much more interesting insights. E-mail cleaning includes extracting the body of the messages, cleaning them (here the challenge is to eliminate signatures or responses to other e-mails) and using Natural Language Processing (NLP) techniques that allow us to transform the content into characteristics that can be understood by the automatic learning algorithm that we want to use. This step usually includes eliminating stop-words (words without meaning such as prepositions or articles), creating “bags of words” or “n-grams” and vectorizing them with algorithms such as TF-IDF.

Once the data is prepared, we can feed an algorithm that will learn to automatically categorize the e-mails. We approach this problem as one of classification as the operator provides us with the categories in which they want the e-mails classified. Thus, we end up with eight categories (activations, line modifications, technical incidents or cancellations among others) that we manually tag together with the data for training.

Although this may seem like a laborious process, interesting results can be obtained very quickly. For example, with a sample of just 100 tagged e-mails, it was already possible to distinguish perfectly some of the categories suggested by the client.

As follows, we detail some of the KPIs that we obtained from the Call Center:

  • Average response time by the operators
  • Incident resolution time (that is, the time between the first and the last e-mail in the chain of e-mails)
  • Size of the e-mail inbox and list of incidents to be resolved in each moment. That is to say, the number of e-mails to be answered and incidents to be resolved in each moment (we can obtain the sizes of these waiting lists by hour or day of the week), which allows us to know if we are on top of incidents.
  • As we process the content of the e-mails, we can categorize them automatically and with these categories, we can automatically assign categories to the correct person to answer them.
  • Additionally, knowing these e-mail categories, it is possible to have metrics for each category in advance. For example, volumes and response times per category.

In this post, we have seen which metrics we can obtain from an analysis of e-mails from the clients of a call center. By analyzing the subject lines and content of the e-mails, it is possible to obtain metrics that provide us with KPIs of the operational and dimensioning capacity of the Call Center as well as data that helps us to profile clients and their needs.

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