Growing emphasis on user experience in businesses has made it essential to understand customer sentiments at a deeper level than ever before. Increasingly, brands are being judged by the overall experience delivered, beyond their functional differentiation and individual benefits offered. Though customer conversations in consumer forums are rich sources of how brands are being judged, it is challenging to extract specific information related to business entities, due to the number of topics being discussed, and the wide variety in the languages, and contexts in which these comments are posted.
Aspect extraction enables extraction of interesting and non-trivial patterns or knowledge from unstructured text documents. It is now possible to break down a single review or conversation into different components, and assign a sentiment value to each of these aspects, helping marketers understand which part of the business, and particularly which entity of the particular business is causing customer experience to be perceived in a negative or positive light.
Studying such trends over a period of time helps brands determine the underlying conditions that give rise to the reasons for the problem/phenomenon. In the event of a drop in business, aspect extraction can help business understand the drivers of positive and negative sentiments (like drivers affecting customer perception of the food in the case below). In line with business philosophy, aspect extraction provides meaningful insights that let decision makers identify that specific part of the business which needs to be changed, rather than approach the whole aspect/process as something that requires change.
Given below is an example of how a customer review was used to identify business entities of a boutique hotel, so as to improve customer acquisition for the upcoming seasonal holidays:
Dedicated to delivering the most relevant insights, at Course5 we evaluate aspects from business relevance, in addition to the aspects produced by Unstructured Text Analytics.
With innate capabilities to handle up to a million rows of data a day, UTAP (Our in-house Unstructured Text Analytics Platform) provides businesses an opportunity to understand customer experience unlike ever before by breaking down the business to understand purchase drivers, reasons for negative user experience etc.
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