Organisations invest heavily in business intelligence and analytics solutions that aim to extract “business” value from data. Traditionally BI solutions focus mainly on structured data or numbers. But what about the mountains of data that isn’t predefined or stored in an easily reference-able database?

This unstructured data makes up the majority of “data” that businesses deal with on a daily basis; documents, emails, tweets, comments, form submissions, reviews. There is a wealth of “business” value hidden in text, however, to analyze this type of data takes a particularly sophisticated type of technology.

Problem:

Valuable business insights hidden in text.

Solution:

Uncover patterns and insight with Text Analytics.

Are you listening to your customers?

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Use Cases

Social Listening

  • Don’t just listen, understand the voice of your customers.
  • Meet them where they congregate and converse and engage with them appropriately.
  • Go beyond keyword, username and #hashtag tracking to get a deeper understanding of their opinions and needs.
  • Analyze overall and aspect level sentiment, to figure out what in particular they refer to in tweets, comments and reviews.
  • Disambiguate words and focus on context, easily determine if a reference of apple is in relation to the brand or the fruit.

Customer Support

  • Mine tweets, comments, emails and form submissions in real time to stay on top of customer enquiries.
  • Intercept customer concerns and frustrations before they become an issue.
  • Arm you customer service team with the tools and data they need to provide the best support possible.
  • Efficiently route customer queries based on language, entities or sentiment.
  • Evaluate your customer support functions based on real-time and historical data.
   

Brand Development

  • Listen to and engage in social conversations about your brand, products or service in tweets, comments, reviews by mining text from social channels.
  • Get real customer insights in a natural environment outside of a structured survey.
  • Determine what people love and hate about your brand.
  • Analyze the overall sentiment towards your brand and how it is affected over-time with product launches, events and company announcements.

Features

The features listed below outline the API functions that are most commonly used in Business Intelligence use cases.

Sentiment Analysis

Identify positive/negative sentiment within tweets, reviews and comments. Deal with negative tweets in real time with immediate action.

Entity Extraction

Route enquiries to the necessary departments by extracting entities (people, locations, organizations) or values (URLS, emails, phone numbers, currency amounts and percentages) and keywords mentioned in emails, tweets and comments.

Concept Extraction

Identify intent with word sense disambiguation to improve accuracy and block out noise. (does apple refer to the fruit or the company)

Aspect-level Sentiment Analysis

Automatically and accurately identify what aspects of a tweet, comment or review are positive, negative or neutral.

Batch Processing

Save time by automatically analyzing large volumes of tweets, emails, reviews.

Language Detection

Efficiently route customer enquiries to relevant agents or departments by identifying what language they are written in.