February 23, 2023
The galvanic skin response (GSR) refers to changes in sweat gland activity that are reflective of our emotional arousal. Typically. an electrode is applied to the fingers to measure skin conductance, and the higher the skin conductance, the higher the arousal. GSR is becoming an increasingly popular and exciting ‘biometric’ in market research as it can uncover the consumers’ subconscious, physiological arousal towards a particular product, brand or advertisement.
Our level of emotional arousal changes in response to the environment we’re in – if something is scary, threatening, joyful, or otherwise emotionally relevant, our bodily processes trigger automatically; the heart beats faster, the pulse rises, and our hands become sweaty. Sweat, even in small quantities, acts as a conductor of electricity. Both positive (“happy” or “joyful”) and negative (“threatening” or “saddening”) stimuli can result in an increase in arousal and an increase in skin conductance. The GSR signal is, therefore, not representative of the type of emotion but its intensity.
As skin conductance is not under conscious control, GSR is seen as a reliable, objective indicator of our emotional state, and emotion is a well-established driver of consumer decision-making. In market research, respondents are strapped to GSR sensors whilst exposed to various stimuli. Key metrics such as the number of peaks and the peak amplitudes allow researchers to compare the arousal levels of various products, brands or marketing materials. The results can inform product development, packaging development, copy and brand messaging, website and advert testing.
GSR is not designed as a standalone market research tool due to its broad measure of emotional arousal. It is therefore important to use it in combination with other techniques, such as facial expression analysis or self-reported survey data, to delve deeper into the type of response.
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Data science is the practice of extracting meaningful insights from large and complex datasets using statistical analysis, programming, and machine learning. In market research, it helps identify patterns, predict behaviours, and support data-driven decision-making across customer segmentation, trend analysis, pricing strategies, and campaign optimisation.
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