Perspectives on capturing and understanding consumer emotions
You’ve almost certainly seen one of those television ads. Sarah McLachlan’s “Angel” is playing while images of cats and dogs scroll by. Just a few seconds into the ad and you feel your heartstrings being tugged. Undoubtedly, you have been exposed to an emotional appeal. Are you feeling sad? Angry? Guilty? More importantly, will these feelings lead you to donate to the ASPCA?
Emotions have an air of mystery. It is often implied, if not directly stated, that behavior based on emotion is “hot,” irrational, illogical and that people cannot explain or articulate how they feel. This situation poses a bit of a challenge for marketers and researchers. It is difficult to use consumers’ emotions to inform actions if consumers can’t tell you what they’re feeling and are behaving irrationally. Fortunately, much of these beliefs about emotions are myths. It is indeed possible to get people to articulate their feelings, and there are meaningful, discernable patterns in the experience of emotions. By applying a scientific approach to emotion, it’s possible to get an understanding of how emotions operate, how to measure them and how to influence consumer behavior by taking them into consideration.
So tell me. What’s an emotion?
An emotion is, at its most basic level, a feeling. Yet, a wide variety of experiences reflect emotion. You watch a television ad with families of all shapes and stripes and you get a warm, fuzzy feeling as you smile and sink into the couch. You’re crossing the street, and a car is coming toward you seemingly at 100 mph in a 25 mph zone. Your heart races, you hop onto the curb, stare down the long-gone driver and burn up inside. Tomorrow you leave for a European vacation with some of your oldest friends; you’re jittery, images of your trip are on repeat in your mind and you can’t concentrate enough to get through the rest of your work day.
While all of these experiences reflect emotions – content, furious, excited – they differ in important ways. One of the benefits of applying behavioral science to market research is the ability to apply scientific methods of organization and categorization to the problem of understanding complex phenomena like emotions. Here are some ways we can systematically characterize emotions.
1. Emotions vary in valence.
This is perhaps the most straightforward aspect of emotions. Emotions can be categorized along a continuum from positive to negative.
2. Emotions vary in levels of arousal.
Emotions exist on a continuum from low arousal (e.g., boredom) to high arousal (e.g., rage); the higher the emotion-based arousal, the more energy is involved. When more energy is involved, you are more likely to be moved to action.
3. Emotions vary in levels of consciousness.
At the less conscious level, emotions occur quickly and automatically and may include involuntary behaviors and physiological responses. At a more conscious level, emotions involve realization and interpretation of less conscious reactions. One experience may involve emotions along multiple points throughout the consciousness continuum. For example, when a car is speeding toward you, your initial and less conscious reaction may include a general feeling of negativity and your heart rate increasing. And you may also have a more conscious emotional reaction, the feeling of anger, as you realize that you were nearly hit.
But how do you measure emotion?
Given that emotions vary in consciousness, valence and arousal, what does this mean for the measurement of emotion? As with other psychological processes, how we measure constructs should match the process by which they occur. At the most basic level, this suggests that, to capture the full range of emotion, we need to measure some automatic and not entirely conscious reactions as well as reactions that are more reflective and conscious.
Measuring less conscious emotions
The most well-known measure of the automatic, less conscious aspect of emotion is facial coding. Facial coding is based largely on research by Paul Ekman which identified facial expressions consistent with six basic emotions (i.e., happiness, fear, sadness, anger, surprise, disgust). These six basic emotions are thought to be biologically rooted and as such, the facial expressions associated with them are universally expressed and understood. There is some debate about the universality of these expressions and whether these emotions are consistently expressed in these ways. (Lisa Feldman Barrett, a psychologist at Northeastern University, has written extensively about this issue. See, for example, Feldman Barrett, 2006.) Despite the debate, experts agree that emotion is, to some extent, conveyed in facial expressions. Although there may be instances where we are happy but do not smile or where a smile is an indicator of embarrassment rather than happiness, facial expression and thus facial coding is a useful tool for gathering some information about a person’s emotional state.
An important caveat with facial coding is that it gets at basic aspects of emotion and is best at capturing differences in valence. That is, facial coding can distinguish between anger and happiness but by itself is not sufficient to distinguish between complex emotions like inspiration and satisfaction. Furthermore, in the real world, facial expressions of emotion are often subtle and fleeting. (This is especially true in research, where the stimuli respondents see are generally not completely novel or life-threatening and thus do not garner extreme reactions, resulting in a narrow range of emotional responses.) Thus, facial coding is ideal for capturing basic emotions as expressed via facial muscles and requires either a person with expertise in coding human faces or an automated coding system.
Though less conscious aspects of emotion can be captured with other types of physical measurements such as psychophysiology (e.g., heart rate, skin conductance) and neuroimaging, this type of research has not yet been able to clearly and consistently distinguish between different emotions and instead most likely captures changes in arousal. For example, when a respondent’s heart rate increases, we can’t be sure if that means they are feeling excitement, love or even fear, as increased heart rate has been attributed to all three of these emotions (and others!). While physiological measures like heart rate, galvanic skin response and facial coding provide some insight into the emotional responses of respondents, they are best utilized when included among other more conscious measures of emotion.
Measuring more conscious emotions
The more conscious aspect of emotion is a subjective experience that is contingent on interpretation, evaluation, labeling and revision. We perceive our own emotions similarly to the way we decide whether we like a new acquaintance. That is, we take into consideration the general feeling of good vs. bad, what we know about what different emotions look like, when they are likely to occur and the context in which they are likely to occur and we deduce the appropriate emotion. In essence, we “observe” our emotion, much as we might observe emotion in someone else in a nonconscious and undeliberate manner.
In the example above of avoiding a near miss, the emotion of relief comes from our appraisal of the holistic negative feeling we experience from almost being run over, our thoughts about avoiding a potentially terrible alternative outcome and a natural reduction in our alerted physiological responses like heart rate and pupil dilation. The reflection and cognition required to merge and synthesize the different emotion inputs can happen instantly or can happen over an extended period of time.
Thus, if we are to measure emotions like excited, nervous, relaxed and stressed, we need a measure that requires individuals to directly report on their emotion state after some level of cognitive processing and reflection has occurred. At LRW, our approach to measuring and understanding conscious aspects of emotion is to use a framework that captures a range of conscious emotions in space defined by the dimensions of valence (positive or negative) and arousal (high, moderate and low). For example, excited is categorized as a positive valence and high arousal.
Emotions have long been thought to be a “black box,” requiring special skills to interpret the irrational and illogical behavior that results from emotions. Using tools that assess both the less conscious and more conscious aspects of emotion provide a deeper understanding of how emotions influence behavior in consumers. Less conscious measures like facial coding and psychophysiological indices provide a view into basic emotions like anger, happiness and sadness, while a more conscious measure of emotion relies on self-report to address more nuanced emotions like inspired and secure. Paired together, each type of tool compensates for the other’s shortcomings and sheds light on the emotions underlying behaviors in consumers.
Emotions have been a core psychological research area for hundreds of years, alongside cognitions and behaviors. By using a holistic approach to emotion data collection, we can make sure we get an accurate read on an individual’s emotional response to a stimulus, thereby enabling us to make better predictions about future behavior.