Identifying population segments for effective intervention design and targeting using unsupervised machine learning:...

Area of Science:

• Behavioral Science
• Public Health
• Data Science

Background:

• One-size-fits-all behavior change interventions are ineffective.
• Psycho-behavioral segmentation can tailor interventions but is rarely used at scale.
• Lack of guidance hinders program designers and data scientists in implementing segmentation.

Purpose of the Study:

• To provide an end-to-end guide for implementing psycho-behavioral segmentation.
• To illustrate critical choices and steps in segmentation design and analysis.
• To demonstrate the application of segmentation in global development, using a health intervention case study.

Main Methods:

• Conceptualization and selection of segmentation dimensions.
• Design of qualitative and quantitative primary research.
• Algorithm selection, analysis, and subjective evaluation of outputs.
• Prioritization of segments and matching interventions via appropriate channels.

Main Results:

• A comprehensive framework for psycho-behavioral segmentation is presented.
• The process involves planning, research design, analysis, and implementation.
• A case study on voluntary medical male circumcision demonstrates the method's utility.

Conclusions:

• Psycho-behavioral segmentation enables targeted interventions by understanding behavioral heterogeneity.
• The provided framework can guide scalable implementation in global development.
• The principles are applicable across various contexts where behavior change is crucial.