Picking the Right Text Analytics Product: A 5-Step Guide

Text analytics promises to unlock a world of insights even from unstructured data such as text, images, audio, and video files, hitherto not available to businesses. This means that businesses can actually listen to their customers’ chatter on social media and gather insights from their reviews and feedback. It can help them spot frauds.

It can help e-marketplaces with product classification. It can help to improve product design, devise focused marketing strategies, increase operational efficiency, and much more.

Recommended: What Text Analytics Tells Us about a Customer’s e-commerce Shopping Experience

While the list of possibilities is long, the success rate is not as high. According to a Gartner study, 80% of AI projects will be unable to scale in 2020 and by 2022, only 20% will deliver business outcomes.

One of the key reasons for this failure could be not selecting the right text analytics tool that can meet business goals and scale up.

A Buyer’s Guide for choosing a Text Analytics Solution

When scouting the market for the right text analytics solution, the 5 points to keep in mind include:

  1. Customization: Each organization has a different business goal and a different set of data mix to work with. Most advanced platforms use a variety of methods such as machine learning, natural language processing, business rules and topic identification to analyze data. However, these tools tend to have a fixed, black-box model approach which may generate results fast but may be ineffective in working with small data sets. Being able to see the combination of algorithms and modify them to suit your specific and unique needs is necessary for you to benefit from the tool. 
  2. Accuracy of Sentiment Analysis: Human beings communicate in complex ways. Words in themselves may sometimes mean the exact opposite in a particular context. Sarcasm is a tool that conveys much but requires reading between the lines to understand it. Emojis and exclamations contribute to the meaning. So tools that merely group words to identify the sentiment as positive or negative can be widely off the mark and be misleading. Training the tool with enough data sets to be able to accurately assess the tone becomes very important for the tool to be successful. A solution such as teX.ai from Indium Software is built on a strong foundation of semantics where the tone and the other components of communication are also factored in to arrive at the meaning accurately.
  3. Use of Metadata: Aiding the semantics capabilities of any good tool like teX.ai is metadata that is often ignored by many tools. This can enhance the understanding of the sentiment better and get clarity in the face of ambiguity.
  4. Multi-Lingual Support: This is the age of globalization where businesses can reach out to international markets. The Internet supports people to express themselves in the language they are most comfortable with and this makes it essential for businesses to be able to tap into chatter in those languages. Text analytics focused on English alone is no longer enough and the tool should be just as proficient in the semantics of that language to be able to unearth hidden meanings.
  5. Dashboards and Visualization: Are you getting only basic charts or does the tool empower you to customize reports for a better understanding of the results is a clincher. Tools with better analytics models and enhanced dashboards along with multiple visualization options can help you get a better view from your slicking and dicing of data.

Indium Software’s teX.ai is a comprehensive tool with several visualization options, an intuitive user interface, customizable algorithms providing semantics-based sentiment analysis with multi-linguistic support that can fit the text analytics needs of organizations of any size.

Relevant read: Text Analytics of Social Media Comments Using Sentiment Analysis

Are You Ready for Text Analytics?

While the tool capabilities are very important for the success of the text analytics project, it is also essential to assess your internal preparedness to derive greater success from your text analytics initiative.

  • Having the Right Data sets: Your organization must have enough documentation with textual data and of the right kind to get meaningful insights.
  • The Right Team: While the text analytics software can help with analytics, your team should have the capability to benefit from the data to gather actionable insights.
  • Company-wide Buy-In: Any analytics initiative can provide holistic insights only with an enterprise-wide commitment to implement the changes needed to enhance customer delight.
  • Speed of Implementation: The speed of transformation as well as implementing the changes based on insights will have an impact on the success of the project.

Indium – End-to-End Solution Provider

teX.ai is a SaaS product solution from Indium Software, a technology solutions company. Incepted in 1999, Indium is a ISO 27001 certified company with 1000+ team members, servicing 350+ clients across several domains. It provides customer-centric, high quality technology solutions that deliver business value for Fortune 500 and Global Enterprises.

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The teX.ai solution and the experienced team with cross-domain expertise can empower you to leverage your unstructured data for insights that can accelerate growth.  teX.ai helps produce structured data, metadata & insights by extracting data from text, summarizing information and classifying content.

If you wish to implement a scalable text analytics project to transform your business, contact us now.