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The Necessity of Sentiment Analysis in Market Research
Market Research involves gathering information about consumers’ preferences and needs and about competitors. It is a critical part of a business’s success strategy and is essential for maintaining its competitiveness.
Traditional market research techniques include interviews, customer surveys, and focus groups. In today’s digital age, a much larger wealth of opinion data can be found in online forums, social media platforms, call center transcripts, and support communities.
However, this data presents two challenges. First, most of this data is unstructured, which makes it difficult to analyze for actionable insights. Second, the data volumes are staggering and keep growing exponentially. In fact, according to the International Data Corporation (IDC), digital data will grow at a compound annual growth rate of 42% through 2020.
Large volumes of unstructured data make manual analysis time consuming, costly, and simply impractical. They call for automated solutions that can turn the unstructured data into structured data suitable for analysis. This is what Text Analytics, a growing field whose market is forecast to be worth up to $6 billion by 2020, is all about.
Sentiment Analysis for Market Research
Sentiment Analysis is a subfield of Text Analytics devoted to identifying and categorizing sentiments and opinions expressed in text. The text is analyzed to determine the writer’s attitude, likes, dislikes, and complaints regarding a particular product, topic, or service.
Sentiment Analysis software is critical for market research because it can help to identify market trends, potential problems of product features or ads, and what customers wish they had. Once this data has been identified and categorized, organizations can use it to create informed business strategies and to power innovation.
NetOwl’s Sentiment Analysis Software
For optimal market research results, companies need software that is capable of identifying more than just positive and negative sentiments in unstructured data. NetOwl’s entity-based and aspect-based Sentiment Analysis is able to recognize the specific entities (e.g., people, companies, products) and aspects (e.g., price, size, management) that customers are expressing their sentiments about and can distinguish a variety of sentiments. For instance, it can distinguish negative sentiments regarding a product feature vs. a threat to boycott a company’s brand.
This advanced sentiment recognition enables companies to act accordingly and prioritize specific responses. What’s more, NetOwl’s software operates in real-time, enabling your organization to obtain the efficient and accurate market research it needs to stay competitive in today’s digital world.
The manual analysis of large volumes of unstructured data is practically impossible. Consider utilizing NetOwl’s Text Analytics products today to unlock the valuable insights in unstructured data.