[Illustration from Du, S. and Martinez, A.M. (2015). Compound facial expressions of emotion: from basic research to clinical applications. Dialogues in Clinical Neuroscience – Vol 17 . No. 4.]
These days, hundreds, if not thousands, of marketers are interested in exploring emotions. They understand that some form of affect — whether it’s physical sensations, emotions, feelings, moods, or some other — drives all behavior, including consumer behavior.
The more astute realize several things about emotions:
- Consumer decisions that lead to consumer behavior are not “rational or emotional,” they’re always emotional (synonymous here with “affective”), sometimes with rationality included (in this case, rationality refers to ‘thinking or deliberation,’ not necessarily logic).
- Emotions operate in large part nonconsciously (a.k.a., implicitly or via System 1) to influence consumer behavior.
- Emotion is often a common-vernacular term. But more specifically, emotion is multi-faceted. This leads to many distinctions that are important to understand when studying and leveraging emotion to influence consumer behavior.
This third point — the multi-faceted nature of emotion, particularly “discreteness” — is the subject of this article.
The figure below illustrates key facets of emotion. A quick study shows that they move from general to specific.
First, we constantly have a general level of “core affect.” As Russell (2009) puts it, “core affect is a simple, non-reflective neurophysiological state of feeling.”
Next, when an “emotionally competent” stimulus occurs (i.e., one that elicits a particular emotional response beyond core affect), emotional responses are classified along a set of dimensions, most commonly three. Emotional responses have positive or negative valence, they have a certain degree of intensity or arousal, and they create some type of action tendency like approach or avoidance, fight or flight. Again, these characteristics are at the dimensional level, still not very specific.
Getting more specific, emotions are discrete. Specific types of emotions occur based on the situation or environment. Primary discrete emotions, of which there are many taxonomies, are relatively few in number and loosely conceived as emotions that are universal and, to many emotions experts, have recognizable neurophysiological signatures (although there is much debate between proponents of emotions as “natural kinds” vs. “conceptual or constructed acts”). Listed above are Ekman’s seven basic or primary emotions, commonly referred to in the emotion literature.
An important point not depicted in the illustration above is that emotions and feelings operate in large part nonconsciously (a.k.a., implicitly or via System 1). In other words, we aren’t always aware of how we’re “feeling” about stimuli, or how it may be affecting our behavior. (For more information on this dynamic, see our article about Emotional Dynamics.)
Beyond basic or primary emotions are discrete secondary feelings. ‘Feelings’ often refers to cognitive interpretations of emotional body states. Many emotions experts (e.g., Antonio Damasio and Joseph LeDoux) recognize that as we cognitively appraise our bodily reactions to emotionally competent stimuli, the resulting interpretation, which depends on situational factors, becomes much more emotionally discrete. For instance, an aroused body state can be appraised (and ‘felt’) as anger when one’s goals are blocked or fear when there is a threat to well-being.
Discrete secondary feelings have the power to influence consumer behavior beyond their fundamental dimensions or higher-order primary discrete emotions.
Key to this article, discrete secondary feelings have the power to influence consumer behavior beyond their fundamental dimensions or higher-order primary discrete emotions. In other words, as consumers “construct” their feelings based on situationally-based cognitive appraisals, their “conclusions” about how they’re discretely feeling can lead to different behavior than feelings with the same valence, arousal level, action tendency, or primary emotion “parent.” Here are several citations and examples:
- Shiota et al. (in press) say that [based on empirical studies] “it is no longer tenable to assume that all positive emotions have identical response profiles or effects upon motivation and cognition.”
- Mogilner et al. (2012) found that positioning products as exciting or calm — more discrete types of ‘happiness’ — made a difference in how consumers chose a brand of water. Marketing “happiness water” as exciting produced more choices among younger people with a future focus. However, marketing “happiness water” as calm produced more choices among older people with a present focus.
- Zeelenberg and Pieters (2004) found that for a ‘dissatisfying’ service (the higher-order common feeling), discrete regret led to more inertia and less complaining about the service, while discrete disappointment led to more complaining and less inertia about the service.
All of this leads to the importance of discrete secondary feelings in influencing consumer behavior. Using Mogilner et al. (2012) as an example, marketing water as “happiness water” would be less effective among certain consumer targets (and more than one, it seems) than being more discrete and marketing it as “calming” or “exciting” water.
Once the importance of discreteness is accepted, understanding which discrete secondary feelings influence product purchase becomes critical. Research techniques that can effectively reveal such discrete secondary feelings are needed. Some may turn to psychophysiological techniques such as brain scanning (e.g., EEG or fMRI), biometrics (e.g., skin conductance or heart rate), or facial coding (e.g., EMG or computer automated). However, at best, these techniques can reveal basic primary emotions, and more reliably valence and/or arousal at the dimensional level. They would be hard put to identify, for example, regret vs. disappointment, as in Zeelenberg and Pieter’s study.
Two techniques more suited for assessing discrete secondary feelings are text analytics and implicit association measurement.
- Text analytics use proprietary natural language algorithms to convert textual data (e.g., from social media posts or discussions, open-ended survey responses, personal interview transcripts, or customer service call transcripts) into discrete emotional categories. Many text analytics companies and solutions exist, each having their own approaches.
- Implicit association measurement comes from well-validated social/cognitive psychology approaches, many of which use “priming.” Generally speaking, priming works by quickly showing respondents a “stimulus of interest” (SOI; e.g., a brand name, a package design, a print ad, a slogan, etc.), but having implicit feelings about the SOI manifest indirectly. Many indirect measurement tasks exist, including measuring how quickly a person classifies an emotional word as positive or negative, or seeing whether or not abstract images (imagine Rorshach ink blots) convey targeted emotions. In these priming tasks, indirect responses are influenced by the implicit feelings respondents have toward the SOI in ways that are quantitatively measurable. Furthermore, if outcome measures related to the SOIs are included in the studies (e.g., purchase, purchase interest, preference, etc.), the most impactful implicit discrete feelings can be identified through regression-based statistical techniques.
Emotive Analytics conducts implicit association studies using its IE Pro Technology. IE Pro has been adapted from the Affect Misattribution Procedure, a well-credentialed implicit measurement technique developed by Dr. Keith Payne at the University of North Carolina, Chapel Hill (Payne et al., 2005). More specifically, Emotive Analytics offers IE Pro YOU®, its automated, online, DIY platform for conducting implicit (and explicit) association studies. Such studies are designed to help marketers identify discrete secondary feelings that are associated with their SOIs (i.e., brands, products, ads, package designs, slogans, etc.) — both implicitly and explicitly — and determine which are most impacting their desired business outcomes. (For more information about IE Pro YOU®, click here or on the logo below.)
As we opened, the interest and importance of assessing emotions associated with products and services, including those that are nonconscious, is upon us and well-accepted. In taking on this work, know that appropriately delving into “the fine points of feeling” (i.e., measuring discrete secondary feelings beyond the dimensions of emotion) can improve marketing strategies and executions by bringing them fine-tuned insights.
Mogilner, C., Aaker, J., and Kamvar, S.D. (2012). How Happiness Affects Choice. Journal of Consumer Research, Vol. 39, 429-443.
Payne, B.K., Cheng, C.M., Govorun, O., and Stewart, B.D. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89(3), 277-293.
Russell, J.A. (2009). Emotion, core affect, and psychological construction. Cognition and Emotion, 23 (7), 1259-1283.
Shiota, M. N., Campos, B., Oveis, C., Hertenstein, M., Simon-Thomas, E., & Keltner, D. (in press). Beyond happiness: Toward a science of discrete positive emotions. Manuscript accepted for publication in American Psychologist.
Zeelenberg, M. & Pieters, R. (2004). Beyond valence in customer dissatisfaction: A review and new findings on behavioral responses to regret and disappointment in failed services. Journal of Business Research, 57, 445-455.