Data-Driven Behavior Change and Personalization - DRT S2E10
46. 2:24 Get to know Amy
3:46 Where did Amy start
10:23 Digital barriers and personalization
15:23 Summary of issues with personalization (prior to data driven approaches)
17:10 Is behavior change hard?
19:29 How do you go about changing behavior when there are barriers and motivation is not that strong
24:09 What types of intervention are possible to increase the likelihood of behavior change?
27:02 We need to do work on behavioral science but also on AI; which one comes first and how do they merge?
30:14 Recap about transfer learning; How do you deal with organizational silos when several technical teams are involved?
35:46 What is the right team composition for a product like this?
38:06 How can behavior scientists and data scientists find a common language in collaboration?
41:15 How do you ensure the behavior changes you introduce are ethical?
44:04 Amir's long rant about privacy tech and such
46:54 Verdicts and takeaways
Key Takeaways:
1. The typical approach used to be long surveys on people's motivations and tasks to build heuristic algorithms.
2. Constraints at the time included digital literacy, health literacy, scalability, ability to capture context, and ability to capture drift in behavior.
3. Personalization technology in the past couldn't accommodate for people changing over time.
4. Behavior change is hard and can depend on factors such as one's strengths, preferences, and context, level of interest in the behavior change.
5. Behavior change is harder for some people due to personal barriers and lack of resources.
6. Some people may struggle with behavior change, even if they understand the science behind it.
7. Health behavior changes, such as weight management and weight loss, can be difficult for some people due to personal experiences, such as a history of using food as a form of love.
8. A very detailed study of the issues by behavior scientists is usually the best starting point, but then there should be very close collaboration between them and AI devs
9. The collaboration would require establishing a common language and operating principles and lots of iterative experiments
10. There are many types of nudges possible, but the best approach would focus on just in time intervention focused on short term gains vs long term objectives