MyDataHelps allows you to leverage randomization to create dynamic participant experiences for your research study.
In practice, this feature can be used to facilitate:
- core components of the participant experience (e.g., defining participant cohorts)
- micro experiments (e.g., determining receptivity to sms vs push notifications)
- everything in between (e.g., survey delivery time randomization, etc.)
When thinking about randomization, keep in mind the following factors:
- When should the randomization occur? How often?
- What are the different randomization groups?
- What is the weighting for each of these groups?
Randomization relies on two platform components: schedules and custom fields. Schedules dictate when the randomization will run and what the possible randomization groups are. Custom fields store the information for which randomization group a participant has been assigned to. Custom fields can then be used across the platform to dictate the effects of this randomization.
Implementing Randomization in your Project
See the general steps below on how to incorporate randomization into your project:
- Create a custom field to store a participant’s randomization assignment
- This must be a “text” type custom field.
- Create a schedule to run the randomization
- Define the randomization groups in the “Update Custom Fields” schedule action
- Select Set Value To “Random Value From List”
- Enter the list of randomization groups into the scheduler
- Utilize the custom field throughout the rest of the platform
- This custom field can be used across the platform to curate unique participant experiences for each randomization group (e.g., deliver specific surveys or notifications to each randomization group, build dedicated segments)
Advanced Tip: Creating Non-Uniform Randomization Distribution
When aiming for a non-uniform randomization distribution (i.e., 25% Group A, and 75% Group B), you may choose to append an increment to the randomization groups (e.g., -1, -2, -3).
You can then use the segment feature and the “contains” filter to collect the incremented randomization groups into one cohort (e.g., Cohort B contains Cohort B-1, B-2, and B-3).
If your project requires more advanced randomization, please contact us for more details.