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The Missing Sentiment Analysis From Contact Center Data

Sentiment Analysis is also called as Opinion Mining. It can be referred to as identifying, analyzing and understanding the emotions expressed in conversation. The conversation can be written or oral. This basically aims to find out the attitude (towards product, service, processes, individuals of an organization, etc.) of the person with whom conversation is going on. The attitude can be positive, negative, or neutral. The data which is analyzed generally is surveyed results, feedbacks, complaints, reviews, recorded telephonic conversations, etc.

Sentiment Analysis is part of speech analytics. It helps in gaining insights from a huge range of public. It is mainly used in contact centers/call centers. The application of sentiment analysis has a very broad perspective. With this, a wide range of data can be analyzed, which in turn helps an organization in many ways. The ways in which it helps are as follows:

  • It can help in ascertaining the opinion of the public – find out immediately the reaction of the public and take action accordingly on a real-time basis.
  • It can also help in knowing the emotions of the public about competitors.
  • It can enable to plan for the future and make strategies for improvising the products, services, and processes of the organization.
  • It can help in designing training programs – required as per the service level achieved.
  • It will help in providing enhanced customer experience and thus will result in good customer relationship management.
  • It helps in finding out the relation between the operational efficiency of the company and the sentiments of the customers.
  • It helps in finding out the sources of customer dissatisfaction and helps in removing them.

Earlier, in traditional times, call centers used to select few calls randomly and perform analysis on that. But, there were huge chances that those randomly selected calls did not provide accurate results for analysis as full interactions were not being captured. So, there were chances that important is being missed while ransom selection. In the modern era, the sentiment analysis technique replaced this traditional system. The broadly automated centers replaced traditional setup. Here, all interactions are being taken and considered while doing analysis. This takes into consideration the broader picture and helps a company in finding out the shortcomings.

Though, Sentiment Analysis has various benefits but that does not mean it is the perfect solution for a successful business. This is because it is not completely perfect in itself. It has its own limitations and flaws.

As the volume and complexity of interactions are increasing, so does the problems with sentiment analysis. The human language is not easy to understand. Different people have different writing styles. This makes the data complex. It is not easy for a machine to understand such complex data which include various grammatical errors, spelling mistakes, the effect of cultural differences in writing, use of slang, jargons, etc. Here, comes the weakness of sentiment analysis. Also, sometimes it happens that a person uses positive language with negative tone for providing negative feedback (sarcasm), which is difficult for a machine to understand. Due to this, it is happening that key data is being missed by the analysis. This results in inaccurate results and an unclear picture of the situation. It also provides faulty results in a situation where multiple sentiments are being expressed in single conversation.

It is also happening that no sentiment is being expressed in a conversation but if any negative word is used like bad, etc. results are ambiguous.

To overcome the weakness of sentiment analysis, the use of machine language is being introduced to sentiment analysis. But this is also not completely flawless. This is because the machine lacks contextual understanding (cannot understand sarcasm) thus bringing a limitation to sentiment analysis. Although with the passage of time, a dictionary of the machine is expanding. This will enable the sentiment analysis more accurate thereby enable a company to provide better customer loyalty. Along with machine learning, natural language will also play an important role in sentiment analysis. Thus, it can be said a combination of both – machine language and natural language will make sentiment analysis perfect.

Sentiment analysis basically helps in finding out what a customer is feeling about the company/product/service. So, till the time sentiment analysis (using customer interaction data received from call center) is not able to completely decode the feelings of the customers in an accurate manner, it can be combined with other measures so to properly understand the feelings and emotions of the customers. This will solve the problem of the company in the ways listed below:

  • Getting rid of inaccurate data
  • Help in analyzing emotions in a better manner which will help in further enhancing customer service.

There are various methods which can be followed:

  • Customer positivity index: Measurement of the level of positivity of a customer.
  • Social shout index: In this, it is found what a customer is saying about the company/product/service on a social media platform. This data is analyzed to understand the feelings of a customer.
  • Making use of heat map: Heat map is the visual representation of data, wherein data is represented using different colors. A quick look at heat map enables to gain insight on data. A component can be divided into various factors and represented using a heat map to understand the whole picture in lesser time.

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