Granular Behavioral Microsegments of Your Ideal Patient Population

Traditional market segmentation modeling starts with a relatively small number of demographic and lifestyle characteristics. Each consumer is then slotted into the best fitting descriptor. The result is an artificially simplistic view of healthcare consumer groups that creates potential mismatches by forcing a “best fit”.  Swoop is changing this by aligning precise patient definitions with social determinants of health.

Psychometric Factors

Determine how healthcare consumer populations cluster by demographics, attitudes, values and beliefs.

Media Consumption Patterns
Discover healthcare consumer preferences for Programmatic Advertising, Social Media Marketing, Digital Marketing, Addressable TV, Linear TV and On Demand Audio message deployment.
Consumer Experience
Learn the uptake levels and behavioral effects of differentiated consumer healthcare messaging.
Decision Making Approaches
Uncover consumer approaches, critical factors and dependencies for healthcare decision making.
Message Receptiveness

Understand how brand promises, messaging and "beyond the pill" factors are perceived by healthcare consumers.

Activation Channels

Determine the blend of cross-channel activation channels including Programmatic Advertising, Social Media Marketing, Website Personalization, Search Engine Optimization, Addressable TV, Linear TV and On Demand Audio.

Advanced Topic Modeling Discovers Cohorts of Patients Relevant to Your Brand

We utilize ML and AI in conjunction with a real world data universe of 65 billion anonymous consumer transactions and over 300 million unique de-identified patients.

Natural Cluster Modeling


Considers all relevant demographic and lifestyle attribution rather than a few pre-determined characteristics.

Dimensional Spatiality


Measures the distance from any point in a multi-dimensional space where population clusters form around an attribute.

Commonality Extraction


By not using pre-determined categories, we uncover natural population clusters and then extract commonalities.