![]() Natural Language Processing have a wide variety of uses that holds significant importance in text analysis. Text analytics involves deciphering meaning from unstructured texts using algorithms for natural language processing and text mining. Social Media posts, comments, email, online reviews, survey results and any other written feedback, all can be perceived as insights on customers. How should companies be thinking about text analysis?Īnything and everything related to customer interactions can be recorded for text mining. 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. 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. Post recording analytics are done using transcription mechanisms such a speech-to-text engines. 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. The analysis can be done either in real-time during customer calls or on recorded audios. Previously recorded calls, voice feedbacks, audio surveys, or live customer calls can be put into analyzing speech. How should companies be thinking about speech analysis? 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. 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. Speech and text analysis (aka speech and text mining) have become a decisive part of delivering business intelligence to provide enhanced customer satisfaction.
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