In the podcast, hear how you can use text analytics for both quantitative and qualitative consumer experience research.

Text analytics is the computer-based interpretation of textual and unstructured data. At its most basic, textual analysis counts instances of words in a document and compares their frequency with how often those words could be expected to appear based on chance alone.

Words and associated phrases that occur most frequently can indicate central themes or concepts in the data sets. Because words can be tagged as positive, negative or neutral, the tone of a document or open-ended survey response can be analyzed for sentiment.

Quantitative Consumer Experience Research

Blue Kite Insight recently fielded a quantitative study for one of our clients in the travel and hospitality industry to measure customers’ behaviors and attitudes toward different aspects of their recent travel experiences. We received over 50,000 responses to open-ended questions!

The manual process we use to code transcripts from qualitative, in-depth interviews doesn’t scale well for large, quantitative datasets so we employ text analytics to do some of the heavy lifting.

One of our strategic partners is Ascribe, a leading international provider of innovative text analytics software and solutions. For this podcast episode, we interviewed Ascribe’s Vice President Gary Zucker about the growing adoption of text analytics among researchers.

Gary is a no-nonsense kind of guy, and his comments about the uses of technology for consumer insights reflect years of professional experience working with researchers in large corporations as well as smaller, independent research agencies.

Incorporating text analytics in Blue Kite Insight’s consumer experience research process has helped us expedite the delivery of actionable insights. For clients, keeping research budgets in check is a legitimate concern; the ability to highlight concepts and sentiments in large sets of open-ended survey data at an accelerated pace benefits agencies and clients alike.