Teams in Behavioral Science
In 2019, Behavioral Science & Policy Association (BSPA), Action Design Network (ADN), Steve Wendel, and Ingrid Melvaer Paulin began a Behavioral Science Team Directory to track the development of the field. When companies registered, they were asked to fill out a large survey describing their activities, pursuits, goals, and challenges. Last year, Steve released a new version of his (highly recommended) book Designing for Behavioral Change and with it came the first release of a report from the survey responses. We recommend that you review this report for answers to questions such as where firms are located, how old is the average team, and other introductory questions*. As BSPA and ADN are large contributors to the Applied Behavioral Science Association, the data from these surveys were then shared with Connor Joyce who authored the following overview and the subsequent articles.
Deep Dive Articles
The Current State of Behavioral Science Teams
Size – 63% are in groups sized 1-5 people, 20% are in groups 6-10, the rest are in larger groups.
Location – Responding groups are in 50 countries with the majority being in US, UK, Australia, and India.
Behavioral Science Mission – 59% of responding groups report applying behavioral science as their main mission.
Successes & Struggles
Overall, applying behavioral science works most of the time with an average success rate across all responses at 57%. Consulting companies report having the highest success rate followed by for-profit companies. On the other side, non-profits report the lowest success rate. When looking at change types (further definition here), Judicial and Legal change teams report the highest level of success followed by Health, HR, and Increasing Usage. Education and Finance report the lowest levels of success. Taking a distinct perspective, the success rate was compared to if the change recipient knew about the intervention. Surprisingly, there was a significant difference (+10%) in success rate for those where the end-user knew about the change compared to situation where they did not.
Diving into the struggles of these groups, the most common struggle across all groups was implementing an intervention with 42% of groups reporting this challenge. On the other hand, the lowest challenges were seen with gathering preliminary research and the creation of ideas. The former makes can be explained by the ease of searching for research brought by search engines while the latter is not as easily explained but a positive, nonetheless. For more information on struggles and ideas to overcome them check out the deep dive article here (Coming Soon).
Unsurprisingly, the most important goal of the Behavioral Science teams is the direct changing of behavior for their stakeholders. This is quickly followed by the sharing of insights with their stakeholders. Policy making is the least important in the aggregate. The development of product and marketing are split with almost equal parts suggesting that it is especially important or not at all important. When diving deeper into change types of an explanation begins to immerge, which can be further explored by clicking here.
Techniques for applying behavioral science were bucketed in 7 categories: Social, Friction, Incentives, Attention, Choice Set, Habits, and Organizational Architecture (more info on categories). The most used technique is the application of social norms while the least is the creation of habits. As further habit formation research (such as that coming out of the Behavioral Change for Good group) continues to grow this seems like an ideal opportunity to expand. For a deeper dive into the techniques used, check out this deep dive article.
Behavioral Science Teams taken together use the application of known behavioral science topics as the most common method for creating change. This suggests that the primary research those academic institutions create is helping drive the application by Behavioral Teams. The biggest area of growth is the use of Behavioral Data to create statistical and machine learning models. This space has seen some growth in the past few years with companies such as Humu and Fabulous App. When exploring the difference in methods based on change pursuit interesting differences arise, as seen in this deep dive report.
Used to determine the success of the methods above, different measuring techniques are used. A majority of groups report using Pre-Post and A/B tests. The former is the standard of academic teaching and thus likely what many practitioners learned in their formal education. The latter has grown in importance as technology has enabled the rapid redesign of interfaces and experiences. Less than a quarter of the groups use Machine Learning techniques, suggesting that the rapidly growing field has yet to have a significant impact on the work of Behavioral Science teams.
*The sample size of the work done for this report was larger than that used in Steve Wendel’s report, thus you may notice differences in specific numbers.
This work was completed by ABSA Co-Founder and Exec Committee Member Connor Joyce. If you have any questions, comments, or suggestions please click here to message him.