top of page

Micro Supports

Small doses of flourishing to support well-being in everyday life. Text-based micro interventions delivered on the basis of ML-detected states of ill-being.
Timeframe

2023-2024

Role

As a Program Manager and design partner, I worked with our multidisciplinary team to build a library of micro supports from conception to launch. I conducted research, designed content, prototyped, user interviews, analyzed feedback, iterated, and worked closely with the CTO, senior scientist, dev team, and designers.
As a subject matter expert, I brought two decades of meditation practice, content production, and psychological research experience with additional responsibilities of project management, writing copy, and product design.

Micro Support Story Cover.png
Results
  • Delivered a library of over 200 user-vetted micro supports tagged with a proprietary taxonomy framework for optimizing context, mechanism, and delivery. 

  • ​Conducted rigorous, multi-round UXR testing to gain proof of concept and then vet mico supports, leading to a 7% increase in approval rating scores.   

  • Pivoted to email implementation due to tech-debt blockers, resulting in a 108% increase open rates compared to our standard benchmarks.

Goal

Design short, personalized well-being interventions to support habit formation and behavior change.

The Challenge
Responding to our users’ needs for “bite-sized” practices that delivered real-time support, we began researching what type of interventions would work best for whom and under what conditions. In addition to conducting market research, we ideated device-specific options, developed user personas, and built a taxonomy framework of contexts and needs.
User Testing
After determining a text-based approach that would best meet user needs and business goals, I prototyped and tested a set of micro supports in collaboration with our Senior Scientist through a four-phased approach, including quantitative and qualitative surveys, user interviews, and a diary study.
Validation and Learning
Our analysis revealed key insights about users’ expectations, interests, and the effectiveness of micro supports both in terms of content and delivery. Incorporating these findings into a secondary recontact survey yielded a 7% increase in approval ratings.
Implementation 
Utilizing the vetted content, we deployed the micro supports via personalized web-push and the HMRP in response to states of ill-being detected by machine learning as apart of a multi-arm reseasrch study. To capitalize on the new content, we also released a “Weekly Wisdom” campaign to nurture existing users. While the research study is still underway, the email campaign showed a 108% increase in open rates compared to our standard benchmarks.
bottom of page