US Persona Structure

Complete field reference for US personas. Each dimension includes a description and all possible values.

Interests are not exhaustively documented here due to the large taxonomy size (300+ values).

Name

Basic identifying information.

`first_name`

Given name, ethnicity-aware and age-correlated.

`last_name`

Family name, ethnicity-aware.

Identity

Core demographic and identity characteristics.

`age`

Integer age (18+). Derived from birth_year.

`birth_year`

Year of birth (e.g., 1995).

`birthday`

Full date of birth in ISO format (e.g., 1995-03-15).

`gender`

Gender identity.

Value	Description
`male`	Male
`female`	Female
`non_binary`	Non-binary
`transgender_male`	Transgender male
`transgender_female`	Transgender female
`genderfluid`	Genderfluid
`agender`	Agender

`ethnicity`

US ethnicity categories aligned with Census Bureau classifications.

Value	Description
`white`	European ancestry
`hispanic_latino`	Hispanic or Latino origin
`black_american`	African American (descendants of slavery)
`black_caribbean`	Caribbean immigrant background
`black_african`	African immigrant background
`east_asian`	Chinese, Korean, Japanese ancestry
`southeast_asian`	Filipino, Vietnamese, Thai, Indonesian ancestry
`south_asian`	Indian, Pakistani, Bangladeshi ancestry
`native_american`	Indigenous peoples, Alaska Native
`middle_eastern_north_african`	Arab, Persian, Turkish ancestry
`pacific_islander`	Hawaiian, Samoan ancestry
`multiracial`	Two or more races

`sexual_orientation`

Sexual orientation.

Value
`heterosexual`
`gay`
`lesbian`
`bisexual`
`pansexual`
`asexual`
`demisexual`
`queer`
`questioning`

`religion`

Religious affiliation.

Value	Description
`christian_protestant`	Protestant denominations
`christian_catholic`	Roman Catholic
`christian_other`	Other Christian denominations
`jewish`	Jewish
`muslim`	Muslim
`other_religious`	Other religious traditions
`non_religious`	Atheist or agnostic
`spiritual_not_religious`	Spiritual but not religious

`religion_salience`

How important religion is in daily life.

Value	Description
`not_salient`	Religion plays no role
`low_salience`	Minor role in decisions
`moderate_salience`	Moderate influence
`high_salience`	Central to identity and decisions

`political_orientation`

Political ideology.

Value	Description
`progressive_left`	Progressive/far left
`liberal`	Liberal/center-left
`moderate`	Moderate/centrist
`conservative`	Conservative/center-right
`populist_right`	Populist/far right
`libertarian`	Libertarian
`other_or_mixed`	Other or mixed ideology

`vote_tendency`

Likely voting behavior in general elections. Not a literal voting history.

Value	Description
`democratic`	Tends to vote Democratic
`republican`	Tends to vote Republican
`third_party`	Tends to vote third party
`non_voter`	Does not typically vote

Origin

Geographic and cultural background.

`country_of_birth`

Country of birth, reflecting top immigrant populations to the US.

Value
`united_states`
`mexico`
`india`
`china`
`philippines`
`el_salvador`
`cuba`
`vietnam`
`dominican_republic`
`guatemala`
`korea`
`colombia`
`honduras`
`canada`
`jamaica`
`haiti`
`venezuela`
`brazil`
`ecuador`
`germany`
`peru`
`nigeria`
`ukraine`
`russia`
`iran`
`pakistan`
`poland`
`japan`
`united_kingdom`
`bangladesh`
`italy`
`ethiopia`
`egypt`
`iraq`
`ghana`
`other_country`

`immigration_status`

Immigration generation status.

Value	Description
`us_born_third_generation_plus`	US-born, grandparents also US-born
`us_born_second_generation`	US-born to immigrant parents
`first_generation_naturalized`	Foreign-born, naturalized citizen
`first_generation_permanent_resident`	Foreign-born, green card holder
`first_generation_recent_immigrant`	Foreign-born, recent arrival
`refugee_asylum_status`	Refugee or asylum status
`mixed_status_family`	Mixed immigration status family

`region`

US state of residence.

All 50 states plus puerto_rico (e.g., california, texas, new_york).

`local_area`

County of residence. Population-weighted selection from 3,144 US counties (e.g., los_angeles_county, cook_county).

`geographic_context`

Type of geographic setting.

Value	Description
`major_metropolitan`	Large metro areas (1M+ population)
`metropolitan_urban`	Mid-size cities and urban areas
`suburban`	Suburban areas
`small_city`	Cities under 50K people
`small_town`	Towns under 10K people
`rural`	Rural areas, farms

`languages`

Languages spoken (array). English is always included for US personas.

Value
`english`
`spanish`
`chinese`
`tagalog`
`vietnamese`
`arabic`
`french`
`korean`
`portuguese`
`hindi`
`haitian_creole`
`russian`
`german`
`telugu`
`italian`
`persian`
`urdu`
`polish`
`bengali`
`gujarati`
`japanese`
`punjabi`
`other_language`

`english_proficiency`

English language proficiency.

Value	Description
`native_or_primary_english`	Native speaker or primary language
`very_well`	Very high proficiency
`well`	Good proficiency
`limited`	Limited proficiency

Background

Socioeconomic and educational background.

`parental_education`

Highest education level achieved by parents.

Value
`less_than_high_school`
`high_school_graduate`
`some_college_no_degree`
`associate_degree`
`bachelor_degree`
`master_degree`
`professional_degree`
`doctoral_degree`

`childhood_socioeconomic_status`

Socioeconomic status during childhood.

Value	Description
`poverty`	Below poverty line
`working_class`	Blue-collar, hourly wages
`lower_middle`	Lower middle class
`middle_class`	Middle class
`upper_middle`	Upper middle class
`wealthy`	Wealthy/affluent

`education`

Personal educational attainment.

Value
`less_than_high_school`
`high_school_graduate`
`some_college_no_degree`
`associate_degree`
`bachelor_degree`
`master_degree`
`professional_degree`
`doctoral_degree`

Work

Employment, occupation, and income.

`occupation_domain`

Broad occupational category aligned with BLS Major Occupational Groups.

Value
`technology_computing`
`engineering_technical`
`healthcare_medical`
`science_research`
`education_teaching`
`business_finance`
`management_leadership`
`legal_professional`
`public_administration_civil_service`
`public_safety_security`
`arts_creative_media`
`sales_marketing`
`service_hospitality`
`personal_care_services`
`skilled_trades_construction`
`manufacturing_production`
`transportation_logistics`
`agriculture_natural_resources`
`administrative_clerical`
`social_community_services`
`religious_clergy`
`military_veteran`
`homemaker_caregiver`
`none`

`occupation_title`

Specific job title derived from the occupation domain (e.g., “Software Engineer”, “Registered Nurse”).

`employment_status`

Current employment situation.

Value	Description
`employed_full_time`	Full-time employee
`employed_part_time`	Part-time employee
`self_employed`	Self-employed/business owner
`unemployed_looking`	Unemployed, seeking work
`unemployed_not_looking`	Unemployed, not seeking
`retired`	Retired
`student`	Full-time student
`homemaker`	Homemaker
`unable_to_work`	Unable to work (disability)
`military_active`	Active military service

`employment_flexibility`

Work arrangement flexibility.

Value	Description
`traditional_fixed`	Fixed hours, on-site
`flexible_hybrid`	Hybrid/flexible schedule
`fully_remote`	Fully remote work
`shift_work`	Shift-based schedule
`gig_freelance`	Gig/freelance work
`not_applicable`	Not employed

`income_band`

Annual household income bracket (USD).

Value
`under_25k_usd`
`25k_to_50k_usd`
`50k_to_75k_usd`
`75k_to_100k_usd`
`100k_to_150k_usd`
`150k_to_200k_usd`
`over_200k_usd`

`income_source`

Primary source of income.

Value	Description
`employment_wages`	Wages from employment
`self_employment`	Self-employment income
`social_security`	Social Security benefits
`retirement_pension`	Retirement/pension income
`investment_passive`	Investment/passive income
`unemployment_benefit`	Unemployment benefits
`household_support`	Support from family
`benefits`	Government benefits
`student_aid`	Student financial aid

`military_background`

Boolean indicating military service history.

Household

Family structure, housing, and living situation.

`relationship_status`

Current relationship status.

Value
`single_never_married`
`married`
`divorced`
`widowed`
`separated`
`registered_partnership`
`cohabiting_partner`

`number_of_children`

Number of children (integer, 0+).

`household_composition`

Household structure.

Value
`single_no_children`
`single_with_children`
`couple_no_children`
`couple_with_children`
`other_household`

`life_stage_segment`

Current life stage.

Value	Description
`emerging_adult`	18-24, transitioning to independence
`early_career`	25-34, establishing career
`family_building`	Starting/raising family
`established_family`	Established family with older children
`midlife`	Midlife transition
`retirement`	Retirement years

`household_living_state`

Living arrangement.

Value
`living_with_family`
`shared_household`
`living_alone`
`living_with_partner`
`single_parent_household`
`family_household`
`supported_or_temporary`
`other_household_state`

`dependency_state`

Financial dependency level.

Value	Description
`dependent`	Financially dependent on others
`partially_independent`	Partially self-supporting
`independent`	Fully financially independent

`household_support_model`

How household costs are managed.

Value
`fully_independent`
`family_supported`
`shared_household_costs`
`partner_supported_or_shared_costs`
`retirement_income_supported`
`state_or_supported_housing_buffer`
`mixed_support_model`

`housing_tenure`

Home ownership/rental status.

Value	Description
`own_outright`	Owns home outright
`own_with_mortgage`	Owns with mortgage
`rent_market_rate`	Rents at market rate
`rent_subsidized`	Subsidized rental
`occupancy_no_rent`	Lives rent-free
`transitional_housing`	Transitional/temporary

`homeownership_pathway`

Path to current housing situation.

Value
`dependent_family_household`
`independent_market_renter`
`subsidized_or_supported_housing_pathway`
`shared_household_renter`
`partnered_household_renter`
`first_time_buyer_mortgage`
`family_household_mortgage`
`family_supported_owner`
`long_term_owner_outright`
`owner_pathway_unclear`

`housing_type`

Type of dwelling.

Value
`single_family_house`
`apartment`
`condominium`
`townhouse`
`mobile_home`
`group_quarters`
`temporary_housing`

`debt_band`

Total debt level (USD).

Value
`no_debt`
`under_5k_usd`
`5k_to_25k_usd`
`25k_to_50k_usd`
`50k_to_100k_usd`
`100k_to_200k_usd`
`over_200k_usd`

`debt_profile`

Primary type of debt burden.

Value	Description
`low_or_no_debt`	Minimal debt
`mortgage_led_debt`	Primarily mortgage debt
`student_loan_led_debt`	Primarily student loans
`consumer_credit_led_debt`	Primarily credit card/consumer debt
`mixed_debt_burden`	Mixed debt types

Behavior

Lifestyle, interests, and daily behaviors.

`interests`

Array of interests and hobbies (300+ possible values). Not exhaustively documented due to size.

`has_pets`

Boolean indicating pet ownership.

`physical_activity_level`

Physical activity level.

Value	Description
`sedentary`	Little to no exercise
`lightly_active_below_guidelines`	Some activity, below guidelines
`meets_activity_guidelines`	Meets recommended guidelines
`exceeds_activity_guidelines`	Exceeds recommended guidelines
`training_level_active`	Athlete/training level

`primary_transport_mode`

Primary mode of transportation.

Value
`drive_alone`
`carpool`
`public_transit`
`walking`
`bicycle`
`taxi_rideshare`
`motorcycle`
`work_from_home`
`other_method`

`technology_confidence`

Comfort level with technology.

Value	Description
`tech_avoidant`	Avoids technology
`basic_functional_user`	Basic functionality only
`comfortable_everyday_user`	Comfortable with everyday tech
`confident_advanced_user`	Confident with advanced features
`expert_power_user`	Expert/power user

`digital_engagement_level`

Digital engagement style.

Value
`tech_early_adopter`
`active_multi_platform`
`smartphone_dependent`
`selective_user`
`basic_communication`
`traditional_media`
`privacy_focused`
`digitally_reluctant`

`social_media_usage`

Social media usage patterns.

Value
`daily_multiple_platforms`
`active_few_platforms`
`casual_consumer`
`passive_lurker`
`professional_only`
`sporadic_user`
`former_user`
`never_adopted`

Psychology

Cognitive style and personality traits.

`literacy_level`

Prose literacy level.

Value	Description
`below_basic`	Below basic literacy
`basic`	Basic literacy
`intermediate`	Intermediate literacy
`proficient`	Proficient literacy

`numeracy_level`

Numerical literacy level.

Value	Description
`basic`	Basic numeracy
`functional`	Functional numeracy
`intermediate`	Intermediate numeracy
`advanced`	Advanced numeracy

`problem_solving_style`

Approach to problem solving.

Value	Description
`structured_analytical`	Systematic, step-by-step
`practical_heuristic`	Practical rules of thumb
`creative_exploratory`	Creative, experimental
`mixed`	Mixed approach

`cognitive_capability_band`

General cognitive capability.

Value
`foundational`
`applied`
`advanced`

Big Five Personality Traits

Each trait uses a five-point scale:

big_five_openness very_low_openness, low_openness, average_openness, high_openness, very_high_openness

big_five_conscientiousness very_low_conscientiousness, low_conscientiousness, average_conscientiousness, high_conscientiousness, very_high_conscientiousness

big_five_extraversion very_low_extraversion, low_extraversion, average_extraversion, high_extraversion, very_high_extraversion

big_five_agreeableness very_low_agreeableness, low_agreeableness, average_agreeableness, high_agreeableness, very_high_agreeableness

big_five_neuroticism very_low_neuroticism, low_neuroticism, average_neuroticism, high_neuroticism, very_high_neuroticism

Decisioning

Communication preferences and decision-making style.

`communication_preference`

Preferred communication style.

Value	Description
`concise_direct`	Brief, to-the-point
`warm_relational`	Warm, relationship-focused
`evidence_data_led`	Data and evidence driven
`visual_story_led`	Visual/narrative driven
`community_peer_led`	Community/peer influenced

`message_processing_style`

How information is processed.

Value	Description
`heuristic_fast`	Quick, intuitive decisions
`balanced_mixed`	Balanced approach
`analytical_deliberate`	Careful, analytical

`purchase_risk_tolerance`

Risk tolerance in purchasing decisions.

Value
`risk_averse`
`risk_balanced`
`risk_tolerant`

`persuasion_susceptibility`

Susceptibility to persuasion techniques.

Value
`low_susceptibility`
`moderate_susceptibility`
`high_susceptibility`

`political_engagement_level`

Level of political engagement.

Value
`high_engagement`
`medium_engagement`
`low_engagement`

Health

Health conditions and dietary restrictions.

`dietary_restrictions`

Array of dietary restrictions (can be empty).

Value
`vegetarian`
`vegan`
`gluten_free`
`dairy_free`
`halal`
`kosher`
`keto`
`paleo`
`mediterranean`
`food_allergies`

`health_conditions`

Array of health conditions (can be empty).

Value
`diabetes`
`hypertension`
`heart_disease`
`respiratory_condition`
`chronic_pain`
`mental_health_condition`
`autoimmune_condition`
`active_cancer`
`cancer_survivor`
`obesity`

Appearance

Physical attributes and clothing sizes.

`skin_tone`

Skin tone on an 8-point scale.

Value
`very_fair`
`fair_light`
`light_medium`
`medium`
`medium_tan`
`medium_deep`
`deep`
`very_deep`

`body_build`

Body build/type.

Value
`very_lean`
`lean`
`average`
`muscular_athletic`
`broad_stocky`
`overweight`
`obese`

`nose_shape`

Nose shape.

Value
`straight`
`upturned_button`
`aquiline`
`broad`
`narrow_refined`

`face_shape`

Face shape.

Value
`oval`
`round`
`square`
`oblong`
`heart`
`diamond`
`pear`
`long`

`hair`

Hair attributes (nested object).

hair.color

Value
`black`
`dark_brown`
`medium_brown`
`light_brown`
`dirty_blonde`
`blonde`
`platinum_blonde`
`strawberry_blonde`
`red`
`auburn`
`copper`
`silver`
`gray`
`white`
`dyed_unnatural`

hair.texture (Andre Walker hair typing system)

Value
`type_1_straight`
`type_2a_wavy_loose`
`type_2b_wavy_defined`
`type_2c_wavy_strong`
`type_3a_curly_loose`
`type_3b_curly_tight`
`type_3c_curly_coarse`
`type_4_coily`

`eyes`

Eye attributes (nested object).

eyes.color

Value
`dark_brown`
`medium_brown`
`light_brown`
`amber`
`hazel_brown`
`hazel_green`
`green`
`blue_green`
`light_blue`
`medium_blue`
`dark_blue`
`gray_blue`
`gray`

eyes.shape

Value
`almond`
`round`
`hooded`
`monolid`
`upturned`
`downturned`

`height`

Height measurements (nested object).

Field	Type	Description
`cm`	integer	Height in centimeters
`feet_and_inches`	string	Formatted height (e.g., “5’10"")

`weight`

Weight measurements (nested object).

Field	Type	Description
`kg`	integer	Weight in kilograms
`lbs`	integer	Weight in pounds
`bmi`	number	Body Mass Index

`clothing_sizes`

Clothing size estimates (nested object).

Field	Type	Values
`shirt_size`	string	`XS`, `S`, `M`, `L`, `XL`, `XXL`, `XXXL`
`waist_size`	string	`28`, `30`, `32`, `34`, `36`, `38`, `40`, `42`, `44`
`dress_size`	string	`0`, `2`, `4`, `6`, `8`, `10`, `12`, `14`, `16`, `18`, `20`, `22`, `24`
`shoe_size_local`	string	US shoe sizes (e.g., “10”, “10.5”)

`appearance_summary`

Natural language description of the persona’s physical appearance. String field.