We know that 70% of our communication is non-verbal, while verbal communication adds another 7%. Yet, interpreting non-verbal communication by humans is a time-consuming and highly subjective process. For this presentation, we show how machine learning is making qualitative concept testing more efficient, more scalable, and more objective. We demonstrate how the latest cloud computing and machine learning technologies of emotion analytics and text mining were applied in the process of qualitative in-depth interviewing. The combination of the two methods help observe nuanced consumer responses that never before were capable of being observed and compared by humans.
Marketers have always known the role that emotions play in consumers' product purchase process. Today, in addition to injecting emotions into new products through advertising, marketers are building emotions into their product designs. Researchers have recognized the importance of measuring emotions embedded in products but the traditional metrics of rating scales fail to reflect the true picture. Various neuropsychological technologies have emerged but none is easily scalable or easy to interpret. Voice analytics, which comprises linguistic and acoustic components, provides a highly viable new solution to marketers' needs. This paper illustrates the potential for mass adoption of voice analytics with a pilot study recently completed in India, illustrating how the analysis of consumers' spoken responses brings a level of insight that traditional rating scales cannot provide alone.