Text Analytics – Understanding the Voice of Consumers
Over the decade’s Hospitality Industry wings expand to the new horizon due to the widespread usage of mobiles which allows customers to plan the vacation & visualize the ambiance at their fingertips. Social Media, Blogging & Reviews are the new age connectors among the Millennials, where they post their experiences. Text analytics helps to draw insights from the unstructured data.
In 2014, the Trip Barometer survey, which was conducted by TripAdvisor, suggested that online reviews influenced about 95% of the travelers in the United States. Another independent study backed by TripAdvisor found that more than 80% of the travelers spent the time to read as many as 6 to 12 reviews before finalizing their hotel bookings. Similarly, another survey indicated that for 29% of consumers, positive online reviews are the most important factor in their booking decision. The key factor for the prosperity of the Hotel is service, online reviews & experience, using the information technology organizations are capturing the data to develop the latest techniques using data analytics to survive the competition.
Decoding Online Reviews Through Analytics
In today’s information-saturated world, it’s a challenge for Hotels to keep on top of all the tweets, emails, feedback, and reviews that come up every day, and of late most of which found to be unstructured comments which can’t be analyzed easily using the traditional methods. Text Analytics – is a process of turning unstructured text – available in the form of tweets, comments, reviews, etc. – into structured data to develop actionable managerial insights to enhance their operations.
Text mining is also referred to as text analytics, is the process of deriving high -quality information from text. High-quality information is typically derived through the devising of patterns and trends through statistical pattern learning. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output.
Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via the application of natural language processing (NLP) and analytical methods.
The Way Forward
Text analytics opens the doors to valuable insights about customers’ characteristics and purchase patterns that may have otherwise remained largely untapped.