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Text Mining Predictive Methods: Examples

Text mining predictive methods help organizations enhance the value of unstructured information by deploying insight from text analysis in software applications and business processes.

Once textual information is transformed into a set of structured data using text mining (or text analytics) it can be combined with traditional data mining algorithms to generate new insight for sentiment analysis and predictive analytics.

The importance of predicting

Whether it is marketing and competitive intelligence, customer relationship management, social media monitoring, operational risk mitigation or threat discovery, big data is a key element for understanding where you are and where you’re going.

Text mining predictive methods support organizations in staying competitive. It helps them improve the ability to quickly react to customer feedback, market changes, competitive landscape evolutions, etc. This is precisely why enterprises should embed text analytics and predictive analytics into their business processes.

Why implementing text mining predictive methods: three examples

Organizations can increase their predicitive abilities by implementing text analytics and predictive analytics in many business application and for a range of business goals, including:

Competitive advantage: By monitoring information streams, acquiring relevant data and combining it with other business data (sales data, client records, product results, customer spending, etc.), organizations can achieve a better understanding of customer habits, capture emerging trends and predict future behaviors in order to promote specific products and services or mitigate problems.

Customer support: Text mining predictive methods can be implemented to align marketing campaigns with the consumer’s purchase journey and to improve customer support by leveraging new insight during the entire customer life cycle.

Strategic decision making: Tracking results, measuring and leveraging performance metrics through a combination of quantitative business intelligence analysis combined with an in-depth text analytics in real-time: these are key assets to inform critical decisions, improve decision making processes and capitalize on emerging trends and opportunities before others do.


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