Factori | US People Data for AI Enrichment Pipelines

US People Data is gathered and aggregated via surveys, digital services, and public data sources. Proprietary profiling algorithms ensure only fresh and reliable data points are ingested, making it suitable for ML pipelines, feature engineering, and identity resolution workflows.

The dataset includes multiple attribute domains to address gaps in customer data, build holistic audience segments, and power AI-driven client experiences.

1. Geography — City, State, ZIP, County, CBSA, Census Tract

2. Demographics — Gender, Age Group, Marital Status, Language

3. Financial — Income Range, Credit Rating Range, Credit Type, Net Worth Range

4. Persona — Consumer Type, Communication Preferences, Family Type

5. Interests — Content, Brands, Shopping, Hobbies, Lifestyle

6. Household — Number of Children, Number of Adults, IP Address

7. Behaviours — Brand Affinity, App Usage, Web Browsing

8. Firmographics — Industry, Company, Occupation, Revenue

9. Retail Purchase — Store, Category, Brand, SKU, Quantity, Price

10. Auto — Car Make, Model, Type, Year

11. Housing — Home Type, Home Value, Renter/Owner, Year Built

anonymous_id
id_type
gender
age
carrier
make
model
os
os_version
home_country
home_geohash
work_geohash
device_price
device_age
affluence
brands_visited
place_categories
geo_behaviour
interests
travelled_countries

360-Degree Customer View — Aggregate internal and external data for complete customer profiles usable in supervised learning and segmentation models.

Feature Engineering — Multiple attribute domains map directly to ML feature vectors across financial, behavioral, demographic, and device signals.

Fraud Detection — Use multiple digital identities across web and mobile to verify real users and detect anomalies.

Audience Modeling — Build holistic audience segments using online-to-offline consumer profiles for targeted ML-driven campaigns.

Recommendation Systems — Leverage brand affinity, interests, and retail purchase signals for content and product recommendation pipelines.

Refresh cadence: Daily, weekly, or monthly Data is collected dynamically; each export reflects the most current available data

Talk to an expert: https://www.factori.ai/talk-to-expert/?utm_source=direct&utm_medium=referral&utm_campaign=opendatabay

Read the documentation: https://docs.factori.ai/docs/consumer-data-1?utm_source=direct&utm_medium=referral&utm_campaign=opendatabay