What is Behavioral Science & Why it Matters for Experiment Design
People often use the term behavioral science loosely- and incorrectly. When one doesn’t truly understand what behavioral science is, you’re unable to pinpoint a causal relationship between what your company does and how your users respond. At Next Step, a behavioral design agency I founded in 2004, we bring the theories of behavioral science out
People often use the term behavioral science loosely- and incorrectly. When one doesn’t truly understand what behavioral science is, you’re unable to pinpoint a causal relationship between what your company does and how your users respond.
At Next Step, a behavioral design agency I founded in 2004, we bring the theories of behavioral science out of academia and into the business world. We couple our teams of scientists and strategists with teams of designers, copywriters and developers to leverage appropriate behavioral science interventions for immediate business application.
As behavioral science has tremendous applications for Experiment Design, it’s vital to understand it to make best use of the field’s potential. Let’s start out by clarifying what behavioral science is and what it is not.
Behavioral science is also known as behavioral economics. It is a field that studies how humans really make decisions and encapsulates multiple domains of study; including cognitive-neuroscience, psychology, and economics.
In particular, behavioral science studies the way that the environment (both physical and digital), emotions and social factors influence our decisions- everything from browsing and purchasing to habit formation. We know from research that it’s very common for people to take mental shortcuts when it comes to making decisions. These “rules of thumb” show up in the most common behavioral science interventions. Read more [link to Optimizely glossary definition of behavioral science]
A Famous Example of Behavioral Science Changing Lives
By now, you may have possibly heard about the power of defaults.
Defaults are everywhere. Your smartphone screen layout, cafeteria layouts seating layouts, webpages, your TV remembering what channel you last watched and automatically returning to it when you turn it on, whether or not you want it to. You see defaults in play when subscriptions are automatically renewed unless you tell them to stop- doing nothing means your service continues.
Defaults work because people like to conserve mental energy and tend to shy away from making difficult decisions, preferring to stay in autopilot mode, or as Nobel Prize winning behavioral economists call it: System 1 thinking. (For more on this, see Daniel Kahneman’s groundbreaking book: “Thinking, Fast and Slow.”)
Today it feels intuitive to use defaults to make it easier for people to do things like automatically renew their subscription or receive newsletters. But the use of defaults came into the spotlight back in 2003 when behavioral scientists discovered that the incorrect use of defaults on public policy forms was actually limiting how many people signed up for organ donation.
Until a famous experiment identified this systemic failure, neighboring countries in Europe fell into two distinct camps when it came to organ donation: either over 85% of people signed up or under 20%. For example, despite having similar cultures and values, only 4% signed up in Denmark, versus Sweden’s 85.9%.
In the Netherlands, millions were spent on education and marketing campaigns encouraging organ donation, but the discrepancies remained until a no-action default toward organ donation was implemented. Now it was opting out that required extra cognitive and emotional work. Controlled experiments showed that swapping the defaults increased the rate of organ donation by 56.5%. If you are interested you can read the full study in Science.
With that as background, here are a few things to think about when it comes to Behavioral Science.
- Behavioral Science is NOT: Behavioral Data.
Behavioral data is simply tracking your users throughout their user journey. From page views and email sign-ups to requesting a demo, behavioral data measures how your customers engage with your business.
Behavioral data is essential: you wouldn’t be able to understand your business without it. In the above example about organ donation, scientists relied on behavioral data to see whether or not people signed up. But on its own, behavioral data leaves you guessing at the story behind the data.
Using behavioral data alone does not give you insight about why people do what they do. In the default study mentioned above, the cultural, political and marketing experts saw the behavioral data and tried to construct a story about why it was happening. But because none of them got the story right, none of them were able to achieve different results.
Behavioral science, on the other hand, starts with the story. You create the hypothesis, run the experiment and measure the results. You start with a theory, control for other variables, and measure a difference in behavior (i.e. your behavioral data). If your hypothesis is confirmed, then you’re that much closer to understanding the specific mechanisms that drive human behavior.
- Behavioral Science is NOT: Behavioral Analytics.
Behavioral analytics looks at a user’s past behavior to make future sales recommendations. You see this whenever you shop on Amazon and see a cluster of other products you may like, or when Netflix recommends films based on your past viewing habits.
Netflix doesn’t need to know why you like “Breaking Bad” in order to recommend that you check out, “House of Cards”. But if you’re interested in understanding and influencing human decision-making, behavioral analytics on its own won’t provide the insights you need.
Why behavioral science is critical for experiment design.
If you’re not using the sound methodologies inherent to behavioral science, you’ll either simply be tracking data or confusing correlation for causation, a common and costly mistake.
Causation: X causes Y. One event directly causes another event to occur:
Correlation: X and Y happen to occur at the same time.
Just because two variables are correlated, does not mean that one caused the other to happen.
Simply picking your favorite explanation of a long list of possibilities is not scientifically sound, though it does make for catchy clickbait.
This is the first in a series of blogs we are working on with Optimizely. Our next blog will be,“Are you making an impact? Three ways to determine whether the environment you’ve created is actually impacting decision-making.”
If you are curious about learning more about Behavioral Science and NextStep you can see more here.