Sentiment analysis in the call center: Hear the Voice of the Customer.
Nearly every corporation has a call center. Ask an executive if his/her organization has a call center and the odds are good that the executive will say yes. Then you ask the executive whether or not he/she knows what is going on in the call center. The executive assures you that the executive knows what is going on in the call center. Then you ask the executive what really is going on in the call center. The executive says the corporation is getting 6,000 calls a day and the average length of the call is 4 ½ minutes. Now knowing the number of calls and knowing how long the calls have lasted is one interesting measurement of the activity going through the call center. But this kind of information does not tell you anything about the content of what is going on in the call center.
What would you like to know?
The kind of information you would like to find out about the call center is information about –
- What are customers complaining about?
- What are customers/prospects asking questions about?
- Do customers/ prospects want to buy something?
- Do customers need more information about operating equipment?
- Are customers having installation problems?
- Are customers interested in further options associated with equipment?
- And so forth.
The call center information that is valuable to the corporation is not how much the call center is being used but what is the content of the conversations that are occurring in the call center. When you ask the executive if he/she knows the content of what is being discussed in the call center, the answer comes back – “you can’t know that kind of information”.
But in today’s world you CAN know that kind of information. Today there is textual disambiguation (or Textual ETL) and with textual disambiguation it is absolutely possible to know precisely what is being said in the call center. In order to see just how Textual ETL creates the opportunity for the corporation to start to use the information found in the call center for better decision making, consider the following example.
In a day’s time the telephone company will get thousands of phone calls in their call center. The calls are about the many aspects of the day to day operation of the telephone company. In addition the telephone company also services television programming.
There is no way that an individual can read the messages and assimilate what is being said by the customer. There simply are too many messages.
So Textual ETL is used in order to read the textual information of what has transpired in the call center and to assimilate that information. The conversation information is read by Textual ETL and converted into a data base. Once the text has been converted into a data base, the data base can be read and analyzed.
Once the text is read and converted into a data base, the data base can be read and fed into a visualization tool. The visualization tool can be used to create a dashboard. The following figure shows a dashboard that can be created. (Note: The dashboard seen here was created by Boulder Insight.) The dashboard shows the activities that are occurring inside the call center.
The Relational Database
The result of the processing of the text by Textual ETL is a relational data base. While the creation of a simple relational data base is hardly new, to the organization struggling with text the ability to create a relational data base represents a significant milestone. The significance of the ability to turn text into a relational data base is this. Once the text is turned into a data base –
- There is no limit to the number of documents that can be read and analyzed
- Analysis can be done by standard analytical software.
While there are many aspects of the data found in the data base that are important, one of the unique and most important features of the data base is the identification of the context found in the call center.
Stated differently, while text is important, if you are going to be analyzing text, you need to be analyzing the context of text as well. And context of text is a standard feature of the data base produced by Textual ETL.
As an example of the value of context, suppose two men are on a street corner and a young lady passes by. One of the gentlemen says to the other – “She’s hot”. Now what is the meaning of “she’s hot”? One interpretation is that the lady is attractive and the gentleman would like to have a date with the lady.
Another interpretation is that it is Houston, Texas on a July day where the temperature is 98 degrees and the humidity is 100%. The lady is covered with sweat. She’s physically hot.
Or the gentlemen could be doctors and one doctor has just taken the lady’s temperature and she has a condition that has driven her temperature to 103 degrees. She has a bad temperature.
From an analytical standpoint, it is crucial to capture context as well as text. Textual ETL then is valuable in unlocking the secrets of the call center.