Speech and text analysis (aka speech and text mining) have become a decisive part of delivering business intelligence to provide enhanced customer satisfaction. Talking in the context of customer experience, even though, text and speech analytics seem to be different from each other, they have many attributes that bring both together. The ultimate objective of both analytics is to direct towards finding meaningful information that identifies words and patterns and detect topics of interests, emotions, stress, etc of the user.

How should companies be thinking about speech analysis?

Previously recorded calls, voice feedbacks, audio surveys, or live customer calls can be put into analyzing speech. The analysis can be done either in real-time during customer calls or on recorded audios. Real-time analysis can be used to help customer executive with scripted answers for customers based on earlier calls on similar problems, or to escalate the situation based on customer’s tone. Post recording analytics are done using transcription mechanisms such a speech-to-text engines.

When speech analytics mechanisms are used effectively, exceptional insights can be acquired, like identifying the reason to call, mentioned products or services, mood of the customer, extracting and distinguishing feedback into compliments, complaints and areas that need improvement, customers’ wants, needs, expectations and so on. And then these data enables the business to take actions for an improved customer experience, to analyze the business impact of the previously undertook marketing and sales strategies and sharpen their operational performance.

How should companies be thinking about text analysis?

Anything and everything related to customer interactions can be recorded for text mining. Social Media posts, comments, email, online reviews, survey results and any other written feedback, all can be perceived as insights on customers. Text analytics involves deciphering meaning from unstructured texts using algorithms for natural language processing and text mining.

Natural Language Processing have a wide variety of uses that holds significant importance in text analysis. One important feature is the sentiment analysis, which is used to extract the positivity or negativity from the text. Categorization, another feature, is used to group the texts into different buckets based on unique features. Several others include Answering questions used for chatbots, Text Summarization, and so on.

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