Editor’s note: Greg Gwiasda is vice president at market research firm Ipsos’ Behavioral Science Center, Cincinnati. Jeff Galak is associate professor, Tepper School of Business, Carnegie Mellon University.
Market researchers and brands want to better understand the digital consumer experience. We are faced with a slew of new questions: How to optimize digital communications? How do we personalize our messages? How do we win omnichannel? We have generated a proliferation of new tools to answer these questions. Machine learning, biometrics, artificial intelligence, text analytics and passive measurement are now essential options for learning and testing. While we strive to identify and understand what is next, we often end up creating solutions that solve for what people are doing today. Or worse, what they were doing yesterday. How many brands are focused exclusively on their e-commerce strategy in 2019 when they want to focus on a larger omnichannel approach? And do we really know what we should be doing in 2021?
The above is not an indictment. In fact, the work that our industry has done is remarkable and should be lauded. The amount and pace of change has been truly revolutionary. Moreover, this revolution provides an opportunity to leverage behavioral science to accelerate the value of this shift. Behavioral science provides a framework to contextualize the in-depth understanding we have of our consumers. By mapping learnings onto a larger theory of human understanding, our research and strategies not only answer how consumers behave today but also provide a structure to be applied to future business challenges.
We can see how this works by applying two behavioral science frameworks to a hypothetical purchase journey for sustainable laundry detergent. For simplicity, we will focus on three key touchpoints:
- awareness;
- consideration; and
- purchase decision.
Let’s start by considering the traditional purchase in a brick-and-mortar setting and then see how things change when bought online and how behavioral science lets us quickly revise in-market strategies. Our examples are intentionally simple – real shopping is much more complex. Nonetheless, these examples demonstrate that our ability to pivot improves when we ground our insights in a framework.
Thinking fast and thinking slow
Perhaps the best-known behavioral science theory is that our brain is guided by two operating systems, as outlined in his Daniel Kahneman’s seminal book, Thinking, Fast and Slow. Fast thinking is effortless, automatic and completely outside our active control, while slow thinking is comparative, deliberative and typically under our control. We are always using fast thinking, whereas slow thinking kicks in, when available, to either validate or override our intuitive decisions.
It is important to know which system is driving a decision, as each system is persuaded differently.
- Fast thinking needs information to be easy, intuitive and focused on the immediate reward.
- Slow thinking needs information that is compelling, wins the debate and is focused on long-term goals.
Behavioral science predicts whether our decisions are more likely to be determined by fast or slow thinking as a function of our ability (e.g., knowledge, time, cognitive resources) and motivation (e.g., desire, relevance) to think in that situation. Simply stated, when people have both the ability and motivation for a choice, decisions are based on deliberative processing. If not, decisions fall back to quick thinking.
From this vantage point, we can determine a consumer’s likely mind-set at each touchpoint and tailor information accordingly. Let’s consider how Mary, a traditional brick-and-mortar shopper, ultimately decides to buy a sustainable laundry detergent. First, Mary needs to become aware of the product. While she has the ability to seek out information on detergents, her motivation to do so is low. She becomes aware of products as information comes to her. As a result, brands need to use fast processing messaging signals to drive awareness. Once aware, Mary is more deliberate (i.e., motivated) when deciding whether to consider a specific brand. She wants to know that it meets her needs and is now receptive to more compelling and deliberative messaging. Ultimately, she decides whether to purchase the sustainable detergent during the weekly shopping trip she takes with her 5-year-old son to the grocery store. During this trip, she makes dozens of decisions – while keeping track of her son – and does not have the time (i.e., ability) or motivation to carefully consider each one. Thus, to be selected at this moment, the sustainable detergent needs to provide intuitive information and cues to nudge Mary’s fast decision.
But what happens if Mary opts to purchase her detergent online? To do so, she must go online and proactively search for the product; she is clearly motivated to purchase. Additionally, while searching for the product, the site displays several other detergent options in addition to the sustainable detergent, making it easier for her to compare across brands. When purchasing online, the shopping environment, coupled with the consumer’s mind-set, encourages Mary to use slow and deliberate decision-making. Thus, whereas the brand wants to facilitate the auto-pilot behavior when Mary is shopping in the store, it now needs to “win the debate” online to be purchased.
Construal-level theory
Construal-level theory is one of the most researched academic behavioral science frameworks and is a powerful tool for understanding consumer behavior. The theory, according to psychologists Yaacov Trope and Nira Liberman, asserts that the perceived “psychological distance” between an object and an individual influences whether a person's thoughts regarding that object are abstract or concrete. Psychological distance is different from actual physical distance and encompasses things like time (e.g., now vs. future), our senses (e.g., seeing vs. feeling) and social closeness (e.g., myself vs. others). When something seems far away (i.e., high construal), we are more likely to think about it abstractly, focus on our aspirations and think about why we want it. Conversely, when something is close (i.e., low construal), we think about its tangibility, our immediate needs and how we might achieve them. High construal language should be used when we want to create the desire, while low construal is effective for activating the desire.
We can also use construal-level theory to map Mary’s retailer shopper journey. Brand awareness and consideration are typically created outside the store – that is, when Mary perceives product usage to be distant. At this moment, the brand is focused more on creating the desire and should leverage high-level construal messaging to tell May why the product is beneficial to her. For instance, a sustainable laundry detergent might focus on the long-term and more abstract environmental benefits it provides. Once in the store, Mary is now likely to think about using the product and how it meets her needs. In-store product messaging should therefore demonstrate how it works (i.e., low-level construal).
Let’s now consider what impact moving the shopper journey online has on Mary. She searches for the product online and is exposed to several other options as well – tempting her to expand her consideration set. One consequence of online shopping is that Mary’s consideration and purchase decisions are more likely to occur nearly simultaneously, changing the type of information that will influence her behavior. During the traditional journey it makes sense to use abstract language to create desire because product consideration occurs when Mary is less likely to be thinking about how to use the product. In contrast, the proximity of consideration to purchase online suggests Mary is thinking about how to use the product at both moments. To connect with Mary, online messaging and imagery should not be aspirational, but should cue her to think about how using the product will improve her life. Brands need to leverage more concrete communication to entice her when shopping online.
Using behavioral science to build a dynamic strategy
The above examples demonstrate a few simple ways that the move from a traditional to online shopping journey can alter both a consumer’s mind-set and, in turn, the types of information needed to nudge them toward purchase. Of course, disruption is never as simple as these examples. We now live in an omnichannel shopping world where people interact with brands in stores as well as online – sometimes simultaneously.
Moreover, we have not accounted for the myriad ways that people are now informed – such as advertising, social media, reviews, social influencers, packaging and other in-store materials. But this is precisely the point; as the journey gets more complex, we can more easily make sense of our data when we are grounded in a behavioral science understanding of our consumers. For instance, in their book The Smarter Screen, Shlomo Benartzi and Jonah Lehrer demonstrate that online shopping differs depending on the device used – we are generally more likely to use fast thinking on a smartphone than on a laptop. Big data tools can help us identify the moments when small or large screens are being used and behavioral science can help us optimize those touchpoints.
As another example, a recent Journal of Consumer Research study showed that people have a lower preference for variety in the morning, as they are likely not yet cognitively awake and thus should be more receptive to intuitive over complex messaging. We can again use modern targeting tools to identify when variety shopping is low and behavioral science to create appropriate messaging. While our world continues to get more complex, our ability to understand and respond only improves when we understand why and how people are behaving at each moment.
A larger framework
It is not surprising that brands and market researchers are seeking tools to understand the new ways that consumers are engaging with brands. They should. However, we should also take this time to situate and contextualize our consumer insights in a larger framework of human understanding. While creating a framework may take a bit more time up front (though not as much as you may think – there is a vast amount of behavioral science knowledge out there waiting to be leveraged), it will pay huge dividends in the not-too-distant future. The pace of change is accelerating in our world. A strong behavioral science framework is a powerful tool that enables us to remain nimble and agile within it.