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Sensory Emotion Algorithm

When our Small Data is Collated into Big Data

2017-06

In the digital era, we hear so much about “Big Data” and the innovative tools that effectively process it. But what about the “small data,” which is proven to be better at predicting behavior? Are we slowly forgoing it for the ease of quick, quantified and time effective tools? Thanks to today’s text analytics technology, analyzing the “small data” e.g. the open-ended questions of the quantitative studies, or even the qualitative studies themselves, are increasingly less time-consuming and labor-intensive. This very thought-provoking article discusses how the line between Qualitative and Quantitative Research is blurring due to text analytics and data mining tools and challenges us to think about the data in terms of only its quantity, large or small. We could not agree more with the article.

At SENSANALYSIS, we help our clients create products and brands consumers love. Our philosophy is to understand consumers’ holistic sensory experience and the emotional engagement that comes with it. Yes, we love the breadth and depth of our small data. The unique sensory research that we have systematically collected over 30+ years of consumers’ natural language (covering descriptions of scent, tastes, textures, and emotions) is small data. This small data is now collated into Big Data—which will be the foundation for many B2B and B2C tools when it comes to sensory emotions. Our first tool is our proprietary Sensory Emotion Algorithm, with which we can process rich consumer language from qualitative, quantitative or web-based information in a matter of hours. We have the proven ability to enhance flat quantitative numeric results and provide fast turnaround for qualitative text-heavy studies while maintaining the emotional insights elicited from consumer’s natural language through our sensorial lens.

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