Text Analytics & NLP For Call Centres: An In-depth Look

Call centres are usually the point of contact between the customers and the selling brand’s company wherein customer concerns for on-going & post-purchase support. Call centres are where conversations with vital intelligence about the inner workings of the company take place, all the way from a commendable action to a drawback that needs to be rectified. In order to work with the intelligence that is gathered through various conversations, text analytics solutions can help in discovering, extracting and analysing valuable insights at a big scale. This is done through opinion mining, or sentiment analysis- as the intelligence is deeply personal and dynamic to individual customer uses.

The intelligence collected is also termed business intelligence, and is highly unstructured in nature. The kind of data collected can range from birth years to bank account numbers and owing to this is where text analytics comes in handy. The above processes mentioned of sentiment analysis and opinion mining about the business is done through the function of text analytics. Businesses are able to track out a new direction to ascertain whether the customers are talking about their product. Speech analytics work in a similar function except on spoken words instead of textual datal.

• Text analytics help to make certain the customers are happy, as it is necessary for businesses to measure and improve customer satisfaction with a feedback system.
• Tracking agent performances and prospective customers to driver better sales by identifying up-selling and cross-selling opportunities is made easy wherein algorithms track the same.
• In order to retain customer base, it is necessary to understand the difference performances of various service areas. This is done by improving call centre agent scripts in adherence to what customers have a positive feedback to using sentiment analysis.

Descriptive Analytics :

When data is gathered from unstructured text and are given a logical structure, themes and phrases can be usually grouped together in order to create rough understanding of the overall user sentiments, purchasing habits and more over a period of time.

Predictive analytics :

This is used to help businesses specifically forecast future events that might affect the productivity of the company. An example of the same would be to see how many agents are needed on site at a call centre based on the number of open tickets and time taken to close each one until then.

Post-interaction and real-time analysis :

It is paramount to note that every text conversation, one should be able to do a detailed analysis of the information collected about the top intentions of contact, quality assurance and intent or purpose of the customers. This is done with post-interaction analysis whereas having alert during real-time events when politeness or empathy is in question helps conduct a real-time analysis.

The ability of a text analytics software or a machine to be able to identify words or phrases in a language and then turn them into a format that machines can understand is referred to as speech recognition. The use of speech-to-text in call centres has increased in recent years due to its efficiency in time and cost. Some ways it can help are:

• They can encourage conversations that are natural enough but also satisfy a high degree of the customer’s needs.
• It can collect dynamic data like addresses and identification details with quite the ease.
• It can enrich the customer experience by bringing down operational costs and crucial time in logging down data manually.
• Producing bigger volumes of transcription can be done with the software’s capabilities wherein the diction is intact and constant throughout.
• It is easier for callers to input a variety of information including the purpose of call, account digits, and names with the voice recognition technology.

Also Read: How Voice-to-Text and NLP-based solutions can drive efficiency into Telehealthcare

• Sentiment: Sentiment analysis solutions is used to answer how the customer react to certain words of phrases in response to queries. It expresses sentiments of anger, joy, anxiety, suspicion and much more. These are an important point in understanding where the customer comes from and what their intention with the call is. The directness of NLP in sentiment analysis makes it so that there is no need for a separate post-call analysis and that the company is able to infer the vibe of the customer first-hand.
• Entity Recognition: There is a need of identifying whether the particular information can be actionable by tying them to services, products or other identifiable entities. A reviewer who is looking to discover more about just one specific entity such as a competitor or an event, they can do the same to specify where entities to track the sentiment of.
• Themes: The general essence of a word, document of a number of documents is what is called themes. They are pulled automatically from texts based on patterns of conversations and in reference to similar and contextual words.
• Categorization: Unlike themes, categories are to be determined ahead of time so as to sort the content into buckets where there is similar and relevant information for businesses. For example, a school establishment would be interested in categories such as park, classrooms, laboratories & staff rooms.

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