Category: Behavioral Science

While I will address return on investment (ROI) considerations in a future post, probably one of the first rules of thumb I have is to use some degree of portfolio thinking and management processes for implementing behavioral science initiatives.

Research indicates that seemingly promising behavioral interventions sometimes do not work, and it can be hard to predict what will actually work (Milkman et al., 2021). Additionally, some studies indicate that there can be strong wisdom-of-crowds versus individual forecasting performance effects, such as average forecasts outperforming 96% of individual forecasts (DellaVigna and Pope, 2018).

It can be difficult to predict which projects will be successful. There are risk-return tradeoffs and innovating in this space requires process and discipline (which could also result in undesirable outcomes, such as if managers are overly loss averse and fail to innovate).

So based on these premises, some strategies for companies looking to implement behavioral science include:

1. Look for quick wins opportunistically (e.g., when getting started), while also being realistic.
2. Recognize that it can be hard to predict results ex-ante.
3. Develop behavioral intervention generation, vetting, and project portfolio management processes. (This last point is very loaded. I will likely have future posts on different dimensions of just this point. However, one example of portfolio management can be picking several lower-risk projects along with a higher-risk project that could pay off to a much greater extent, e.g., financial or social good).

In summary, to effectively implement behavioral science initiatives, it is important for companies to recognize the unpredictable nature of these interventions and adopt a portfolio approach that includes a process for generating, vetting, and managing projects in order to maximize return on investment and/or other outcome goals of the organization.

References:

• Milkman, Katherine L., Dena Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Pepi Pandiloski, Yeji Park et al. “Megastudies improve the impact of applied behavioural science.” Nature 600, no. 7889 (2021): 478-483.
• DellaVigna, Stefano, and Devin Pope. “Predicting experimental results: who knows what?.” Journal of Political Economy 126, no. 6 (2018): 2410-2456.

In 2023, I’m going to experiment with series of short posts and mixed media that will try to appeal to those that are thinking of starting behavioral economics initiatives or trying to take things to the next level. I have a unique background to bring this type of content as I have worked in the commercial world for more than 30 years while also having an academic research and teaching background in behavioral economics. I will try to leverage an intersection between what I implement as a behavioral economics advisor in the commercial world and what I teach experientially in the classroom.

Current LinkedIn connections and prior students may also feel free to join an Applied Behavioral Economics slack channel. We can use to this as a way to share ideas, post questions, or potentially arrange informal meetups. Please send me an email at steve@steveshuconsulting.com or if your organization is on an approved domain (e.g., cornell.edu) you should be able to join using the following link: https://join.slack.com/t/appliedbehavi-qow2205/shared_invite/zt-1makq3ojb-GYmDS_eGI50uG_YoLdh10w (link valid for 30 days).

In March 2023, I will be guest speaking to DBA students at Hong Kong University of Science and Technology about implementing behavioral finance initiatives in corporations, an area that I have worked in for more than a third of my three-decade-some working career. Much thanks to Prof. Anirban Mukhopadhyay for inviting me. While the details are still to be worked, I plan to cover a number of case studies relative to retirement, FinTech, wealth management, and investment management firms. In addition to covering some perspectives on implementing behavioral insights teams, I also plan to provide some behind-the-scenes perspectives on applied research studies, such as the one Hal Hershfield, Shlomo Benartzi, and I worked on with Acorns (linked here at Marketing Science).

While organizations and companies have come a long way in terms of implementing behavioral economics, we are still in a relatively early phase with some organizations falling behind or missing out. 2023 will be a big year of change for me. Will it be for you? Please feel free to reach out to explore possibilities. Perhaps we can help one another in either academic (e.g., I have two new behavioral courses under development) or corporate settings.

In one of my prior Applied Behavioral Economics lectures, I mentioned the notion of not only looking at individual studies but also finding meta studies (essentially studies of studies) to help inform behavioral perspectives. This article covers a meta study of behavioral architecture interventions: https://www.pnas.org/content/119/1/e2107346118 

In this article, there were two observations that really stuck out to me:

1) Considering a range of domains (health, food, environment, finance, prosocial) where behavioral architecture is applied, there is the highest effect on food choices and lowest effects in the financial domain; effects are potentially moderated by domain because of lower behavioral costs and lower perceived consequences in the former versus higher behavioral costs and higher perceived consequences in the latter. 

2) Decision structure changes (choice architecture) outperforms decision information (information architecture) and decision assistance approaches, potentially because choice architecture approaches require less demand on cognitive information processing, and there is low susceptibility to individual differences and goals. (But remember that we will start to address personalization and individual differences in upcoming classes).

If you are interested in learning more about meta studies and how to do them, I highly recommend the book: Borenstein, Michael, et al. Introduction to meta-analysis. https://www.amazon.com/gp/product/0470057246/ref=dbs_a_def_rwt_bibl_vppi_i2 

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.