Steve Shu | Perspectives

In my free time I have been developing a course, tentatively called Applied Behavioral Science in the Digital Age to be taught to business school students at either the undergraduate or graduate level. In the course, students will study how the pervasive reach of digital technology into our lives affects our heuristics, biases and other behavioral patterns. In addition to learning about behavioral science theories in the digital age, students will then learn how to apply those key theoretical concepts through discussing actual, corporate case studies and participating in hands-on exercises related to nudging and experimental design. The class will discuss key elements to starting and implementing behavioral science initiatives within a company. The course will be especially geared toward those interested in professional careers within consulting, product development, marketing, services, and technology app (e.g., FinTech) settings.

As related to that course, I have started to develop a short book that will cover specimens and cases based on the real world, such as sample websites, app designs, email campaigns, and customer journeys with ideas about how to evaluate such designs though the lens of behavioral science. If you have interesting examples and specimens for me to consider including (can be disguised or made anonymous as needed), please feel free to correspond with me at sds77@cornell.edu. If the specimen is from your company and you are interested, I can potentially perform a behavioral audit on the materials provided.

This post is based on a question that I answered previously on Quora.

Although it’s not exclusively from the realm of behavioral economics, the notion of A/B testing is something that I often try to work with companies to include. On the one hand this includes the capabilities of companies to integrate specific aspects of their product management, software development, UX, data science, and marketing processes. But it also means developing a research mindset that comes from the experimental side of behavioral economics. For example, if one really wants to nail down which aspects of a UX or customer experience affect behavior and outcomes, the gold standard is using randomized assignment, A/B testing, and discipline that between testing conditions only one item is changed. In setting up the A and B test conditions for a behavioral insights based UX isolation test, one can add, subtract, or substitute a single element between two test conditions. If you change more than one element, then your findings will be confounded between the multiple elements changed, and you won’t be able to tell what change worked or didn’t. UX teams should become used to working in worlds that include testing harnesses like Visual Website Optimizer, Optimizely, and the like.

For a little more on A/B testing, see this WSJ article by one of my colleagues. It describes a simple, but extremely powerful A/B test we worked on with a FinTech company’s UX. It’s Time to A/B Test Your Financial Life

If you are interested in other aspects related to the digital UX world and behavioral economics, you might also want to check out a book that was written by two of my colleagues: The Smarter Screen: Surprising Ways to Influence and Improve Online Behavior.

This post is based on a previous question posed to me on Quora.

The role of a Chief Behavioral Officer (CBOs) varies, but a common theme I’ve seen is that they analyze, plan, innovate, and implement aspects of the business using insights and methods from the behavioral sciences (e.g., behavioral economics, psychology). Some of the companies with CBOs do mostly marketing communications or thought leadership (e.g., research) while others may get involved with bringing insights and designs to product development (e.g., applied research). Some CBOs may directly manage people, such as a team of PhDs, analysts, etc. as well as partnerships (e.g., with academic researchers). The approach of CBOs may also vary in terms of the science. For example, some may leverage pre-existing research. Others may work with big data (e.g., proprietary) and correlational or instrumental variable type analysis. Yet others may take an experimental approach (e.g., A/B testing) and work with product and service teams to directly measure how designs affect behavior and outcomes.

A key aspect of determining the activities of the CBO really come down to setting goals for the larger organization, assessing gaps and resources, and developing a tactical plan to meet the goals over time. As an example, for the past few CBOs I have helped, we often worked to develop 30–60–90 day plans to initially get the organization rolling with longer-term planning and thinking happening in parallel.

The example below is a working draft of material related to the Behavioral Science Casebook project and is intended to be used for educational purposes and helping both students and working professionals to think about digital designs and customer experiences using behavioral science concepts. High-level behavioral audit comments and potential approaches to consider are provided in some cases (for instructor purposes).

Things to Think About

• Goals
  • What is the apparent priority in terms of product versus service sales?
  • To what extent does this website target more knowledgeable versus less knowledgeable users?
• Choice Architecture and Design
  • What are some key elements of the design?
  • How do the design elements behaviorally address user attention?
  • How do the design elements support user decision-making?
  • How do the design elements inhibit user decision-making?

Behavioral Audit of Website Screen 1 (Instructor Notes)

• Goals
  • Products appear to be the priority, although some space is given to services.
  • For example, products appear as the first item in the menu bar (primacy effect).
  • The site appears to target somewhat more knowledgeable users as there is a fairly long list of choices to navigate without assisted help.
• Behavioral Architecture and Design
  • The design includes a long menu list (possibly triggering choice overload).
  • Photo thumbnails garner user attention although some photos not intuitive (e.g., not aligned with System 1 fast thinking, such as “Offers” and “Professional Installation”).
  • More than half of the menu items have a label of “New” which grabs attention, but to what extent is it clear what the label means?

Behavioral Audit of Website Screen 2 (Instructor Notes)

• Goals
  • Best sellers are given priority, but it is unclear what product priorities are given within the category.
• Behavioral Architecture and Design
  • “Best Seller” and “Save $X” graphic elements highlight attention points.
  • Unclear how the blue circle “Sale” icon differs from the “Save $X” label.
  • List prices are provided as reference points with sales prices also listed.
  • Orange “Buy” buttons make it clear where actions can be taken to buy, although it is not clear whether users have enough information at this point and are ready to buy.

Potential Behavioral Approaches to Consider (Instructor Notes)

• Reduce choice overload by creating a better way to navigate based on their behavioral need (e.g., users who know what they are looking for versus those that need help or want ideas).
• Reduce choice overload by changing the presentation method (e.g., fewer items per page or per line).
• Revalidate goals and measurement outcomes
• Create research and testing strategy, e.g.,
  • control versus challenger case of simplified design (A/B test)
  • research to assess how well website works for different users