I often joke that if a contest for the most mysterious job title in my company existed, I would be the clear frontrunner. It seems as if every time I’m introduced to someone, I’m asked the same question: “So, exactly what do you do?”
This week marked my two year anniversary of working as a behavioral scientist in a product-driven tech company, and I still don’t have an exact answer to that question. But, having worked my way through the company, I’m now in a comfortable position to think about the question more clearly at least.
My biggest realization about this role is a double-edged sword: Behavioral science actually overlaps with many roles in product-led tech companies. That’s good because that means behavioral science is becoming increasingly relevant to everyone, irrespective of what role one is in. That’s also bad because the onus of proving the importance of a stand-alone behavioral science role is entirely on us.
Based on my experience, I’ve put together some thoughts on where product organization and behavioral science overlap, and what differentiation a behavioral scientist can bring to an organization as a whole.
Is behavioral science the same as user research?
In a generic sense, yes. User research is, rather obviously, about understanding users. Behavioral scientists do the same; yet, the nuance is in the process.
User research teams conduct qualitative research through in-depth interviews, discussions, and usability tests. They also conduct quantitative research through surveys. This research is then condensed into a form that product and design teams can consume and use for the product development.
Behavioral science research is more focused on uncovering the reasons behind why people do what they do. While interviews and surveys are useful techniques, it is more important for behavioral scientists to understand the context under which decisions are being made. Doing so helps them use that context to map and diagnose the behavior of the user. In other words, putting into words what the user cannot say. This comes not just from interviews, but from experiments and observations and conducting literature reviews of existing theories.
Can user research benefit from behavioral science?
Absolutely. If you are a user researcher and you have a behavioral scientist in your organization, integrate them into your research to help you uncover biases and blindspots that are not necessarily visible in plain sight.
Is behavioral science the same as design?
As someone who loves all things design, I wish that this answer is a yes. A designer in a product organization focuses on putting the product into a visual form, taking into account the users’ and the business’ needs. This is a highly technical process that involves immense stakeholder management and a deep understanding of how various systems interact in the backend, with the goal of producing a front-end that is simple enough for the user.
Behavioral design is a subset of design that is concerned with using design elements to affect behavior change.1 Borrowing heavily from behavioral science, this stream helps designers design for the real user — the one who is busy, inattentive, biased and not perfect, as opposed to the perfect user (who may or may not exist).
Can design benefit from behavioral science?
Absolutely. Behavioral science helps designers understand the actual user, rather than the theoretical user. If you’re a designer and you want to involve a behavioral scientist, reach out to them in the initial phases of design. They can help you understand the ‘why’ behind what people really do. It is immensely beneficial to get a behavioral scientist to audit your design from a behavioral perspective so you can uncover fail points early on in the process.
Is behavioral science the same as data science?
No. Data science is a field that analyses structured and unstructured data using machine learning and algorithms to extract useful insights, as well as predictive models.2 Data Scientists typically use mathematics, statistics, analysis, and machine learning to investigate existing patterns in data. The models that they create feed directly into products, making them smarter. For instance, if you have used Spotify and love the recommendations the app gives you, that’s the output of a data science model at work, which takes into account your music preferences and a bunch of other variables to predict what songs you might like.