Top 5 Business Use Cases of NLP and Text Mining

by July 21, 2019 0 comments

Text Mining

Artificial intelligence technology for organizations is an inexorably prominent topic and everything except unavoidable for most of the organizations. It has the ability to automate support, improve client experiences, and analyze outputs and feedback.

While executing AI innovation may sound threatening, it doesn’t need to be. Natural language preparing (NLP) is a type of AI that is simple and easy to use. It can likewise complete a ton to help impel your business forward.

Further, the coolest aspect regarding text analysis is, it’s all over! Regardless of industry, organizations and people want to settle on better-informed business decisions based off identifiable and quantifiable knowledge. With progressions in Text Analysis, organizations would now be able to mine text to insights and improve their service or offering to flourish in their market. Let’s look at some of the use cases of NLP and text analysis which has helped companies to improve their products and services.

 

Search Autocomplete

Search autocomplete is another kind of NLP that numerous individuals use consistently and have nearly generally expected when looking for something. This is thanks to enormous part to pioneers like Google, who have been utilizing the feature in their search engine for quite a long time. The element is similarly as supportive on company sites.

Salesforce coordinated the feature into their own search engine. Clients keen on getting familiar with a topic or function of Salesforce’s product may know one keyword, however, perhaps not the full term. Search autocomplete will enable them to find the right data and answer their inquiries quicker. This enables to chop down on the probability that they’ll end up uninvolved and explore far from the site.

 

Financial Trading

Similarly, as with sports trading, having a knowledge into what’s going on at a local level can be entirely significant to a financial trader. Domain explicit sentiment analysis/classification can add genuine value here. A similar manner by which fans have their own unmistakable vocab based on the game, so too do traders in specific markets. Intent recognition and Spoken Language Understanding services for identifying user intents (for example “purchase”, “sell”, and so forth) from short articulations can help dealers in choosing what to trade, how much and how rapidly.

 

Creditworthiness Assessment

By utilizing NLP, banks in developing nations would now be able to evaluate the creditworthiness of customers with practically zero financial records. Regardless of whether these customers have never utilized credit, the majority of them despite everything use cell phones, browse on the web and take part in different exercises that leave a lot of digital impressions. NLP algorithms analyze geolocation information, social media activity, browsing behavior to infer bits of knowledge into their habits, peer systems, and quality of their relationships. By assessing a lot of customer related factors, the software creates a credit score exceptionally prescient of client’s further activity. Access to client information is only allowed on assent and the information can never be transferred to outsiders.

One of the instances of creditworthiness evaluation tools dependent on NLP and text mining is the Lenddo application created by a Singapore-based organization LenddoEFL. Lenddo has built up its patented innovation based on 4 years of actual online lending background that included collection, analysis and processing of billions of information points. In 2015, Lenddo opened its API for outsiders, for example, banks, lending organizations, utilities and credit card organizations worldwide to diminish risk, increase portfolio estimate, improve customer service, and verify candidates.

 

TV Advertising & Audience Analysis

TV programs or live broadcast events are probably the most discussed topics on Twitter. Advertisers and TV makers can both profit by utilizing Text Analytics in two particular ways. If producers can get a comprehension of how their group of spectators ‘feels’ about specific characters, settings, storylines, highlighted music and so on, they can make changes in a bid to appease their viewers and consequently increase the crowd size and viewers ratings. Advertisers can delve into social media networking platform streams to analyze the viability of product placement and commercials broadcasted during the breaks. For instance, the TV character ‘Cersei’ from Game of Thrones is turning into a style symbol among fans, who consistently Tweet about her most recent gown. High street retailers that need to exploit this pattern could come up with a line of Queen of Westeros’ style attire and adjust their commercials to shows like Game of Thrones. Text Analytics could likewise be utilized by TV Executives hoping to offer to advertisers. For instance, a TV organization could mine viewers tweets and discussion activity to profile their group of spectators all the more precisely. So rather than just pitching the size of their group of spectators to publicists, they could wow them by recognizing their gender, area, age and so forth and their emotions towards specific products.

 

Sentiment Analysis

With regards to modifying sales and marketing strategy, sentiment analysis helps gauge how clients feel about your brand. This innovation, otherwise called opinion mining, originates from social media analysis and is fit for analyzing news and blogs allocating a value to the content (negative, positive or neutral). A Switzerland-based organization Sentifi utilizes NLP to discover influencers and characterize its key brand advocates. The present NLP algorithms go as far as recognizing emotions, for example, glad, irritated, grumpy, miserable. Obviously, with exact tools like these marketers currently, have everything necessary to create significant strategies and settle on informed decisions.

One of the main European retailers was hoping to harness the power of NLP and text mining for customer feedback sentiment analysis and employed a committed AI development team in Ukraine to come up with an answer. By including this sentiment analysis tool, the organization proposed to expand customer loyalty, drive business changes, and accomplish a considerable return on sales and marketing investments. 8allocate, the organization that facilitated the retailer’s devoted software team, performed data purifying and munging as the essential task and connected words tokenization alongside a Natural Language Toolkit that was utilized to characterize equivalent words, semantics, and the general tone of voice of feedback. Subsequently, the team executed the business rationale based on language characteristics, abbreviations, collocations, and vernacular articulations, and finished a comprehensive semantic analysis.

This arrangement helped the retailer streamline and overhaul their marketing and sales strategy, which brought about 30% income increment within a year of deployment.

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