One of the misconceptions about modeling that many students learn in their disciplinary education in the sciences (biology, chemistry, physics, engineering, economics, etc.) is that for every interesting phenomenon within their field there is a single best model, or best type of model, that is suitable for representing it.
Students are taught how to use particular classes of models to represent particular types of systems and solve particular classes of problems. This leads to a conception of models as primarily tools for representing nature, and an assumption that certain types of models are only used by researchers within certain fields or disciplines (“I’m a quantum physicist. Why would I use game theory models? Those are for economists.”).
The same bias is found in marketing research. The conventional wisdom is that one uses multiple regression analysis or structural equation modeling for loyalty studies, conjoint analysis for pricing studies, and Max-Diff for assessing a lengthy list of attribute’s relative importance. (“I’m a marketing researcher. Why would I use ethnographic research? That method is for anthropologists.”)
But as argued in our previous article, models can also be used as tools for critically and creatively thinking about the world. Scientists who work at the leading edge of disciplinary research are familiar with this more fluid and creative attitude toward models. This is because it is the primary mode through which original, creative work in a field is done.
Multi-model thinking
When one learns to think of models as tools for deepening our understanding of nature, it is more natural to think in terms of applying multiple models to make sense of complex phenomena. This has been called “multi-model thinking”, or “many-model thinking”.[1]
Multi-model thinking produces insight by exploiting a diverse ensemble of logical and conceptual frames. Different models highlight different perspectives on a phenomenon, focusing on different sets of forces or processes that provide insights that overlap and interweave. By engaging multiple models as conceptual frames for critical inquiry, we develop a more nuanced understanding of the phenomena we’re investigating.
The necessity of multi-model thinking in marketing
Multi-model thinking is particularly important for understanding complex social phenomena involving human actors. Human beings are diverse, socially influenced, error-prone, purposive, capable of learning, and capable of independent agency. Physical objects, like carbon atoms and billiard balls, have none of these features.
Any social system involving human actors is an extraordinarily complex system. Given the natural complexity of social phenomena, we should expect that the forces that influence market behavior will not be adequately captured by any single model, or any single class of models. We should expect that multiple models, and multiple types of models, will be needed to develop a nuanced understanding of market behaviors, and to develop effective strategies for influencing those behaviors.
Marketing is a multidisciplinary field
Marketing isn’t an isolated discipline. One can learn much about human behavior in markets from fields such as anthropology, neuroscience, and behavioral economics. Below we give three examples.
Anthropology and ethnography: Ethnography is a branch of anthropology that involves a systematic approach to studying individual cultures. The most common ethnographic approach is participant observation within the context of field research. The ethnographer becomes immersed in the subject culture as an active participant and records extensive field notes, from which insights about attitudes and practices within the culture can be drawn.
In one ethnographic study of cell phone buyers, consumers were observed shopping for cell phones in various retail outlets and interviewedpost-purchase. One outcome from this research was to reassess the relative influence that sales clerks had on brand selection compared to other factors such as the phone’s cost, perceived durability or ease of use.
Later quantitative research corroborated the finding of this ethnographic study that sales clerks play a more influential role in brand choice than had been earlier thought.
Neuroscience: Neuroscience is the scientific study of the nervous system, and is relevant to many areas of pure and applied science. One application of neuroscience in marketing involves testing consumer awareness of packaging and advertising. A brand may possess all the attributes consumers value in a product category. However,if consumers don’t notice the messaging on a package or advertisement, marketing efforts can suffer.
NeuroVision™ is an applied consumer research company that uses a modeling tool to examine how the nervous system processes visual stimuli in the form of an advertisement.[2] In Figure 2 we see that desirable product attributes expressed in the text—“finely engineered” and “exhilaration”—are not relatively salient in this Infinity advertisement.
heatmap color coding from LEAST salient regions (blue), to yellow, to orange and then red for MOST salient. The higher the saliency the more likely these items are going to be automatically noticed. Parameters such as contrast, brightness, density, angles, movement, colour composition and more are determinants of this process. The Heatmap reflects areas processed by the autonomic nervous system during the first micro seconds of exposure, and cannot be consciously affected by the consumer.
Figure 2. NeuroVision™ uses eye-tracking technology and machine learning to track consumer attention and assess the salience of various features in advertisements and brand packaging.
Behavioral economics (BE):. Behavioral economics blends insights of psychology and economics, and supplies a framework to understand when and how people are prone to make errors in judgment and decision-making. Lessons from behavioral economics can be used to create environments that nudge people toward wiser decisions and healthier lives.
Consider the phenomenon of “loss aversion”[3], one of hundreds of documented cognitive biases. The negative utility associated with losing something is greater than the positive utility associated with gaining something. People prefer to avoid losing $20 than to gain $20.
One marketing application of loss aversion is in how one should frame the risk/reward of a marketing offer. Compare:
- “Stop reducing your vehicle engine’s life by switching from a petroleum-based oil change to our Acme synthetic oil.”
- “Increase your engine’s life by switching from a petroleum-based oil change to our Acme synthetic oil.”
Consumers are more likely to act on (1) than (2)—more likely to take steps to avoid a perceived loss than take steps to acquire a perceived gain, even when the outcomes are equivalent.
Marketing researchers should adopt a multi-model approach to their field. Historically, the field of consumer marketing has restricted its focus to studies of behavior related to product consumption and brand choice. To obtain a more comprehensive view of factors motivating brand choice, researchers need to look to the contributions other fields are making to marketing such as anthropology, neuroscience, and behavioral economics.
[1] “Why “Many-Model Thinkers” Make Better Decisions.” 19 Nov. 2018, https://hbr.org/2018/11/why-many-model-thinkers-make-better-decisions.
[2] “NeuroVision.” https://neurovision.io/.
[3] “Loss aversion – Biases & Heuristics | The Decision Lab.” https://thedecisionlab.com/biases/loss-aversion/. Accessed 12 Apr. 2021.
