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Data-Powered Opinion Mining Is The Next Big Thing For Customer Satisfaction

Data-Powered Opinion Mining Is The Next Big Thing For Customer Satisfaction

data opinion mining ai nlp

data opinion mining ai nlp

Data mining is taking turns in the industry like anything, but have you ever heard of Opinion Mining? Leveraging customer opinion as quantifiable data is a concept of future to a layman but with Natural Language Processing, the world can finally process and completely absorb customer feedback.

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Often data is associated with quantity-based statistics with numbers and metrics floating around, however, with natural language processing (NLP), qualitative factors like customer feedback can be processed and used as quantifiable data. For example, if a specific mobile phone models witness a higher number of sales in a given year, the manufacturers tend to incorporate features of that mobile phone to increase the sales of other models where they somehow miss to make upgrades properly basis the customer feedback. 

Although quantitative statistics play a major role, customer service analysts and marketing directors know that qualitative factors like customer satisfaction represent a huge, unstructured data set that can’t easily be represented by a numerical value. Companies have been analyzing the number of negative online reviews a business receives but these numbers do not give insights around why the business is receiving negative reviews and how the company can improve to make these negative experiences into positive ones.

Majority of valuable information from customer opinions and attitudes lies in the language the customers use to describe their experiences and share their feedback. Therefore, language constitutes a crucial data set that companies have access to through archived online comments and mentions, recorded phone calls. Even though customer opinion represents essential data, businesses often lack effective ways to extract meaningful insights from this kind of unstructured data.

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But worry not, for AI is always here to the rescue! By leveraging natural language processing (NLP) with AI, companies can convert thousands of online comments and mentions and phone calls into measurable and quantifiable data sets to improve their branding practices and customer satisfaction.

Processing Natural Language with AI

As Artificial Intelligence (AI) and Internet of Things (IoT) becomes integral to our processes of collating and analyzing crucial business data, it is important to consider how to get voice assistants and search tools to understand the way we use language naturally and convert these insights into valuable business insights metrics.

NLP is turning out to be a vital tool for the growing need for concise data sets derived from language. Within this particular domain of Artificial Intelligence, machines are learning how to make processes better and aptly respond to human speech through NLP. Speech recognition, responding to questions, and sentiment analysis are tools of NLP that generate an opportunity for artificial intelligence to become adept at analyzing, interpreting and responding to voice commands and typed queries.

Applications of NLP to Business Operations

The biggest demonstration of Artificial Intelligence’s use of NLP to recognize speech and even respond in a natural way was presented in the 2018 I/O event where Google demonstrated its assistant’s ability to successfully collate meaningful data and book appointments through two automated phones calls with real humans. As the machine learning capabilities of AI become more comprehensive and in-depth, brand directors, market research analysts, and digital marketing specialists can expect to gain valuable insights that are scalable and rapidly produced from these NLP advancements in AI.

Online product reviews, social media mentions, and other forms of customer feedback can be scanned using NLP algorithms in a process referred to as opinion mining, Opinion mining is used to rapidly identify which words and phrases indicate a negative response versus a positive or neutral response. The result? Unstructured, language-based data now can be turned into valuable marketing insights regarding customer experience or public opinion about a particular business brand.   

NLP also can be used by businesses internally to expedite language-based operations. During the hiring process, larger companies can make use of NLP when reviewing thousands of applications submitted to a job post. NLP algorithms can conduct an initial filtering process that scans applications and resumes for keywords and phrases that are relevant to the job descriptions in order to minimize the time HR representatives must spend on selecting the top applicants to move forward within the hiring process.

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