propella.ai uses Roy Morgan's Helix Persona Data data product as the basis for consumer profiling.
Helix Community distribution for workers in an office building in the Melbourne CBD
Helix Persona (Top 10) distribution for workers
The Single Source data that sits behind each Persona contains approximately 1,700 data points that provide the detailed demographic and psychographic attributes to support consumer profiling. We apply the Helix Persona distribution for the subject population to the Single Source data to generate estimates for the psychographics (example below) that we run across multiple categories (Food & Beverage, Health & Wellness, Personal Services, etc.).
The following table illustrates a sample of dining preference attributes for a cohort of workers from an office building.
- The Building Pop % describes the estimated percentage of the worker population identified for that particular item/attribute (e.g. estimated that 77% of the building workers went to a cafe for coffee or tea in the last 3 months).
- The Index provides a comparison to the reference population. The reference population is usually the general worker population for the city within which the analysis is being run. A value of 100 means the % Pop is the same as reference population for that Item. A value of 120 implies that the building worker cohort are 20% more likely on average to be associated with/belong to the Item (e.g. 20% more likely to go to a cafe for coffee or tea). A value of 85 implies 15% less likely.
- Any item attributes that indicate a high Population % (e.g. greater than 25%) AND a high Index (e.g. greater than 120) are considered significant psychographic attributes for the worker cohort.
We generate these psychographic attributes summaries across a number of key areas that are relevant for property professionals, including:
- Food & Beverage - Dining preferences, food types like eating, food segments, food cuisine preferences, major products purchases, quick service restaurants preferences, and more
- Health & Wellness - Activities and products, regular sports and activities, health attitudes
- Services purchased regularly
- Recreational activities
- Value segments - a segmentation model developed by Roy Morgan to explore mindsets, values and motivations
- Technology segments
- Media consumption habits
- and other areas