Text analytics, also known as text mining, defines the process of semi-automatically collecting, aggregating, segregating, and analyzing large volumes of unstructured data. By using statistical tech and industry expertise applications to extract valuable business outcomes, text analytics becomes a prominent tool for natural language processing. With the increasing use of text analytics in business, the text analytics market is expected to reach 15 billion dollars by 2026 and it is increasing @16% every year.
The application of text analytics in businesses range from research to development, patent recognition, competitor intelligence, developing market mix strategy, social media mining, and sentiment analysis at various customer touchpoints.
After the data explosion, there is a gradual increase in data generated from social, web, news, emails, surveys, and customer loyalty programs in large volumes. And 80% of this available data is unstructured, where the information is in the form of image, text or audio formats. And these datasets are hard to read by a machine or system while performing various computational and forecasting tasks.
Traditionally extracting real-time data insights from the available unstructured datasets is a time-consuming and complex task. With appropriate text analytical tools, structured processes, and well-organized planning businesses can drive value outputs from these unstructured data lakes.
Why do companies fail at harnessing unstructured data?
Text mining and natural language processing have been prominent tools used by many businesses in analyzing unstructured data. The companies that invested due to market pressures and forgot to develop internal-business architecture couldn’t take full advantage of it.
Before deploying analytical tools to harness the huge volumes of unstructured business data, companies should study internal systems and their capabilities. Every business should question and develop strategies before deploying new systems for harnessing large chunks of data.
The examination of internal talent and available list of resources, recruiting experts or senior management, and other minor decisions that can affect the deployment of new systems should be called off.
What kind of business values can be driven from unstructured data?
To decode the meaning, intent, context, and sentiments hidden in unstructured data, through statistical and machine learning models, many businesses leverage NLP, text analytics, and artificial neural networks. The industrial unstructured data sets can be distinguished into-
1. Business insights: The industries including healthcare, BSFI, energy & utility, manufacturing, retail, and other end-user industries, today, are leveraging text analytics to drive valuable insights. The insights-driven from unstructured data helps in better business operations through advanced business intelligence, risk, and fraud management systems. The benefits include-
- Developing new strategies for advancing business operations, by understanding competitors, market opportunities, internal infrastructure, customer analysis, optimized product development strategies, and loopholes or fallback in product or services offered by a business.
- These data insights help in understanding the risks in future investments, based on historical data and techniques for adopting fraud-free payment transfer systems.
- Sentiment analysis helps in understanding the churn ratio of internal and external public connected with business
- 2. Market insights: The availability of large volumes of digital and physical data provides various insights. Understanding and cleaning of data for deriving the right market insights that maximize business growth opportunities is a complex task. The text mining and data mining tools help in market analysis for any product or service. The text analytics provides an advantage in understanding the market environment through market intelligence and competitor intelligence systems. Hence a business can study real-time market fluctuations, product or service positioning in the targeted market, target audience impressions, future market trends, and identifying potential markets for better market segmentation.
- 3. Customer insights: Understanding customer needs and expectations is the key to resolve today’s customer demands. The unstructured data lakes can provide insights on customer preferences, tastes, future needs, and wants. Hence these insights help in understanding data facts like :
- 1. Loyalty towards a particular product or service.
- 2. How often a customer switches between brands and competitive products?
- 3. How often a customer visits for a service or purchase?
- 5. The chance of customers opting for competitor products or services?
Businesses by integrating this information can have a clear understanding of customer churn ratio, identifying the target audience, building strategies for selecting mediums that help to reach maximum numbers of the target groups. The text analytics provides an advantage over other forms of analytics in analyzing unstructured customer data. Hence a business can perform sentiment analytics on data recorded in the form of audio, video, or text that helps in a better understanding of their customers. The advantage driven strategy helps in analyzing reasons for unsuccessful conversions and churn ratio analysis.
Unstructured text analytics usecases for business:
- Analyzing field data extracting insights from doctor’s patient notes, legal document verification, vendor agreements, and contracts.
- Chatbots in customer support for enhancing customer interactions and better optimization of business operations.
- Improving search relevance, advanced automated filter for personalized customer search, targeted response, and personalized results based on customer queries.
The large volumes of business data are unstructured and traditional statistical tools to harness these data sets used by companies are outdated and the insights-driven may have faulty predictive outcomes. Using text analytical tools helps in analyzing, organizing, systematizing, and driving valuable insights with more accuracy that helps in various stages of business strategy development and for optimized business operations to drive maximized revenues.
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