Total Consumer Insights

Spreadsheet Version
# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
1
PID
32
VARCHAR
I
N/A
Tokenized ID
✓
✓
Up to 32-Character Alphanumeric Persistent ID
Persistent ID assigned to individuals identified in the Infutor Graph
2
HHID
96
VARCHAR
HH
N/A
Tokenized ID
✓
✓
Up to 96-Character Alphanumeric Household ID
A household ID is an indicator that groups individuals living in the same household
3
FNAME
50
VARCHAR
I
A
PII
✓
✓
First Name
First Name of the Individual
4
MNAME
50
VARCHAR
I
A
PII
✓
✓
Middle Name
Middle name or initial of the individual if available
5
LNAME
50
VARCHAR
I
A
PII
✓
✓
Surname
Last name of the individual
6
SUFFIX
10
VARCHAR
I
A
PII
✓
✓
II, III, IV, IX, JR, SR, V, VI, VII, VIII
The generational suffix of the individual if available
7
GENDER
1
VARCHAR
I
I
Core Demographics
✓
✓
"Gender Code: M – Male, F - Female"
The gender of an individual is typically gathered from self-reported or public information sources, or can be assigned based on the individuals name and/or title. In cases where the individual's first name and title are unknown/ambiguous and no additional sources reflect gender, the gender will be coded as NULL
8
AGE
2
INTEGER
I
I
Core Demographics
✓
✓
Estimated Age: 18-99
Adult Estimated Age is calculated from date of birth data. Age data is applied at the individual level and is compiled from a variety of sources that may include public data, buying activities, and self-reported information. This is a calculation of age based on the individual's year of birth. The calculation is current year minus the YOB.
9
DOB
8
INTEGER
I
A
Core Demographics
✓
✓
Date of Birth: 6-digit DOB (YYYYMM) or 4-digit DOB (YYYY)
The known month and Year of birth of the individual and date where availabe; in some cases where the birth month is unknown, this field value may contain only the 4 digit birth year. The Adult Date of Birth select will identify members of the household that meet the given birth date criteria. Dates must be entered for the birth month and year. Age data is applied at the individual level and is compiled from a variety of sources that may include public data, buying activities, and self-reported information. Exact age data is considered sensitive in nature and may require contractual restrictions.
10
ADDRID
32
VARCHAR
HH
A
Tokenized ID
✓
✓
Up to 32-character Alphanumeric Address ID
Persistent ID assigned to addresses identified in the Infutor Graph
11
ADDRESS
64
VARCHAR
HH
A
PII
✓
✓
Full Address
All address components, including House Number, Directional, Street Name, etc.
12
HOUSE
10
VARCHAR
HH
A
PII
✓
✓
House Number: Can contain numbers, letters and characters (- and /)
Physical Street Number
13
PREDIR
2
VARCHAR
HH
A
PII
✓
✓
Street PreDirectional: E, N, NE, NW, S, SE, SW, W
An address element that indicates geographic location such as N, S, E, W, NE, NW, SE, and SW that is placed to the left of (before) the street name such as E HOOVER ST.
14
STREET
28
VARCHAR
HH
A
PII
✓
✓
Street Name, PO Box Name, RR # Box Name, or HC # Box Name
Information found in the primary name field of the USPS ZIP+4 file that identifies the street and forms the principal component of the delivery address line. A street name can include qualifiers such as directionals (e.g., North, SE) before or after the street name and suffixes (e.g., ST, AVE). Street names can be words or numbers
15
STRTYPE
4
VARCHAR
HH
A
PII
✓
✓
Physical Street Suffix: ST, AVE, PL, BLVD, PKWY, etc
An address component that qualifies the street name by type of street such as AVE (avenue), DR (drive), or RD (road). Sometimes a street may have a double suffix such as AVENUE DR.
16
POSTDIR
2
VARCHAR
HH
A
PII
✓
✓
Street Post Direction: E,N,NE,NW,S,SE,SW,W
An address element that indicates geographic location such as N, S, E, W, NE, NW, SE, and SW that is placed to the right of (after) the street name or street name suffix such as BAY DR W.
17
APTTYPE
4
VARCHAR
HH
A
PII
✓
✓
Unit Designator: APT, STE, UNIT, etc.
An address element in the delivery address line that indicates an apartment, office, suite, or some other division (e.g., 102 MAIN ST STE 202)
18
APTNBR
8
VARCHAR
HH
A
PII
✓
✓
Unit Number: Can contain numbers, letters and characters (- and /)
An address element in the delivery address line that indicates number of an apartment, office, suite, or some other division (e.g., 102 MAIN ST STE 202)
19
CITY
28
VARCHAR
HH
A
Geographical
✓
✓
City Name
As listed in USPS Publication 26, Directory of Post Offices. Post Office names in excess of 28 positions have been abbreviated by USPS.
20
STATE
2
VARCHAR
HH
A
Geographical
✓
✓
State Abbreviation: AL, FL, IL, NY, etc.
Two-position alpha FIPS State code
21
ZIP
5
VARCHAR
HH
A
Geographical
✓
✓
Zip Code: Five digit numbers only, e.g. 60614
Five-position numeric as assigned in USPS publication 65, National Zip Code Directory
22
Z4
4
VARCHAR
HH
A
PII
✓
✓
Zip+4 Code: Four digit numbers only, e.g. 5392
Four-position numeric as assigned in USPS Publication 65, National Zip Code Directory
23
DPC
3
VARCHAR
HH
A
PII
✓
✓
Delivery Point Code with Check Digit
Delivery Point Code with Check Digit
24
Z4TYPE
1
VARCHAR
Z4
A
Geographical
✓
✓
Zip+4 Type: F - Firm or company address G - General delivery address H - High-rise or business complex P - PO Box address R - Rural route address S - Street or residential address
USPS Zip+4 Record Type
25
CRTE
4
STRING
HH
A
PII
✓
✓
Carrier Route Code
Carrier Route Code
26
DPV
1
VARCHAR
HH
A
Geographical
✓
✓
Delivery Point Validation Code: Y - Address DPV confirmed for both primary and (if present) secondary numbers D - Address DPV confirmed for the primary number only, and secondary number information was missing S - Address DPV confirmed for the primary number only, and secondary number information was present but unconfirmed N - Both Primary and (if present) Secondary number information failed to DPV Confirm
Delivery Point Validation
27
VACANT
1
VARCHAR
HH
A
Geographical
✓
✓
Vacant: Y - Physical Address Identified by USPS as vacant N - Someone living at that address
Designation by USPS of a vacant property
28
INTERNAL
255
TBD
N/A
N/A
TBD
✓
✓
Future Use
29
INTERNAL2
255
TBD
N/A
N/A
TBD
✓
✓
Future Use
30
INTERNAL3
255
TBD
N/A
N/A
TBD
✓
✓
Future Use

Geographic Delineations

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level Individual, Household, or Zip4
Assignment Modeled, Actual, or Inferred
Category Field Category
Description Description
31
MSA
4
VARCHAR
N/A
A
Geographical
✓
✓
See tab - Geographical Codes - Metropolitan Statistical Area (MSA)
A metropolitan statistical area (MSA), formerly known as a standard metropolitan statistical area (SMSA), is the formal definition of a region that consists of a city and surrounding communities that are linked by social and economic factors, as established by the U.S. Office of Management and Budget (OMB).
32
CBSA
5
VARCHAR
N/A
A
Geographical
✓
✓
See tab - Geographical Codes - Core Based Statistical Areas (CBSA)
New metropolitan and micropolitan statistical area definitions were announced by OMB on June 6, 2003, based on application of the 2000 standards with Census 2000 data. Metropolitan and Micropolitan Statistical Areas are collectively referred to as Core-Based Statistical Areas. Metropolitan statistical areas have at least one urbanized area of 50,000 or more population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties. Micropolitan statistical areas are a new set of statistical areas that have at least one urban cluster of at least 10,000 but less than 50,000 population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties.
33
STATECD
2
VARCHAR
N/A
A
Geographical
✓
✓
Numeric State Code
Federal Information Processing System (FIPS) Codes for States and Counties FIPS codes are numbers which uniquely identify geographic areas. This field contains the two-digit state code, e.g. Code 01 corresponds to ALABAMA.
34
CNTYCD
3
VARCHAR
N/A
A
Geographical
✓
✓
County Code
Federal Information Processing System (FIPS) Codes for States and Counties FIPS codes are numbers which uniquely identify geographic areas. This field contains the three-digit county code, e.g. Code 003 corresponds to Baldwin County.
35
CENSUSTRACT
6
VARCHAR
N/A
A
Geographical
✓
✓
Census Tract
A census tract, census area, census district or meshblock is a geographic region defined for the purpose of taking a census
36
CENSUSBLCK
4
VARCHAR
N/A
A
Geographical
✓
✓
Census Block
A census block is the smallest geographic unit used by the United States Census Bureau for tabulation of 100-percent data.
37
CNTYSIZECD
1
VARCHAR
N/A
A
Geographical
✓
✓
County Size Code: A - Any county located in the 25 largest U.S. cities or their consolidated statistical urban areas B - Any county not designated as an A County that has population over 150,000 or is part of a consolidated statistical area with population over 150,000 C - Any county or consolidated statistical area not designated as an A or B County that has populationover 40,000 D - Any county statistical area not designated as an A, B, or C County
A, B, C, or D counties are based on the population totals of U.S. counties and also their proximity to a metro area or anchor city. A counties are the largest U.S. counties by population, and D counties are the smallest. Counties are classified on the basis of data from the latest census, which takes place every 10 years.

Telephone Number Data

The Telephone Number select indicates if up to three telephone numbers are available for a given record. All telephone numbers are processed for DNC compliance with flags available, but telemarketers are encouraged to perform additional processing prior to a telemarketing campaign. A SAN number is required to output DNC flagged telephone numbers on an order.
# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
41
PHONE
10
INTEGER
HH, I
A
PII
✓
✓
Phone
Public directory assistance and household based telephone number 1
42
DNC
1
VARCHAR
HH, I
A
PII
✓
✓
Y - Do Not Call Flag
Phone Included in the federal level do not call list: Y or null
43
PHONE2
10
INTEGER
HH, I
A
PII
✓
✓
Phone 2
Public directory assistance and household based telephone number 2
44
DNC2
1
VARCHAR
HH, I
A
PII
✓
✓
Y - Do Not Call Flag 2
Phone Included in the federal level do not call list: Y or null
45
PHONE3
10
INTEGER
HH, I
A
PII
✓
✓
Phone 3
Public directory assistance and household based telephone number 3
46
DNC3
1
VARCHAR
HH, I
A
PII
✓
✓
Y - Do Not Call Flag 3
Phone Included in the federal level do not call list: Y or null

Household Demographic Data

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
48
HOMEOWNERCD
1
VARCHAR
HH
A, M
Core Demographics
✓
✓
Homeowner Codes: H - Homeowner is a deterministic value of known homeowners as recorded by property deed records and recorded home sales R - Renter is a deterministic value of known households living in properties that are not currently occupied by the homeowner. Data is sourced from property deed records and recorded home sales data. O - Probable Homeowner is a probabilistic value identifying medium-level confidence of a homeowner status. Data is based on a combination of survey data signals indicating a likely homeowner status. P - Probable Renter is a probabilistic value identifying medium-level confidence of a renter status. Data is based on a combination of survey data signals indicating a likely renter status. U - Unknown are records with no data signals to indicate a homeowner or renter status and are assigned as likely to be low-confidence renters. Previous versions recorded assigned these records as NULL.
Homeowner and renter status identifies if the occupant of the residence is the homeowner or a renter. Data comes from multiple sources including property deed records from County Assessors or County Recorders, new home sales transaction data, and self-reported survey data.
49
DWELLTYPE
1
VARCHAR
HH
A
Geographical
✓
✓
Dwelling Type: S - Single M - Multi
Estimated income level is a model and takes into consideration several known demographic attributes, self-reported information and Census demographics. Data is assigned at the household level.
50
EHI *DO NOT USE*
1
VARCHAR
HH
M
Core Demographics
✓
✓
*DO NOT USE* New Estimated Household Income field available as of June 20, 2023 Release - See Field #343 EHI_V2
*DO NOT USE* This field was formally retired and all values were nulled with the February 20, 2024 release. Please use EHI_V2 (Field #343).
51
MARRIEDCD
1
VARCHAR
HH
A, I, M
Core Demographics
✓
✓
Marital Status Code: M - Married S - Single
The Marital Status select indicates if member of the household is likely to be married or single. Data is applied at the individual level when available.
59
WEALTHSCR *DO NOT USE*
1
VARCHAR
HH
M
Core Demographics
✓
✓
*DO NOT USE* This field was formally retired and all values were nulled with the October 25, 2024 release. Please use WEALTHSCR_V2 (Field #345).
*DO NOT USE* This field was formally retired and all values were nulled with the October 25, 2024 release. Please use WEALTHSCR_V2 (Field #345).
64
ETHNICITYCD *DO NOT USE*
1
VARCHAR
I
A, I
Core Demographics
✓
✓
*DO NOT USE* Due to multi-state Sensitive Personal Information requirements individual-level Ethnicity data is no longer available as of May 25, 2024 release. Geo-demographic ethnicity/race variables (Fields #319-326) are still available.
*DO NOT USE* This field was formally retired and all values were nulled with the May 25, 2024 release. Geo-demographic ethnicity/race variables available (Fields #319-326).
65
RELIGIONCD *DO NOT USE*
1
VARCHAR
I
A, I
Supplemental
✓
*DO NOT USE* Due to multi-state Sensitive Personal Information requirements individual-level Religion data is no longer available as of May 25, 2024 release.
*DO NOT USE* This field was formally retired and all values were nulled with the May 25, 2024 release.
66
LANGUAGECD
2
VARCHAR
I
A, I
Core Demographics
✓
✓
Demo Codes - Language
Language of an individual as provided by self-reported participants and overlays from participating source data
67
CHILD
1
VARCHAR
HH
A, I, M
Core Demographics
✓
✓
Y - Presence of Children
The presence of children, children's age ranges and number of children selections reflects the probable presence of children under age 18 in a given household. Information is sourced from children's age and gender data which is gathered from a variety of sources that includes public records where they may be available, survey data as well as modeled Census based information.
68
CHILDAGECD_6
1
VARCHAR
HH
A, I, M
Core Demographics
✓
✓
Presence of Children under 6
The presence of children, children's age ranges and number of children selections reflects the probable presence of children under age 18 in a given household. Information is sourced from children's age and gender data which is gathered from a variety of sources that includes public records where they may be available, survey data as well as modeled Census based information.
69
CHILDAGECD_6_10
1
VARCHAR
HH
A, I, M
Core Demographics
✓
✓
Presence of Children Aged 6 - 10
The presence of children, children's age ranges and number of children selections reflects the probable presence of children under age 18 in a given household. Information is sourced from children's age and gender data which is gathered from a variety of sources that includes public records where they may be available, survey data as well as modeled Census based information.
70
CHILDAGECD_11_15
1
VARCHAR
HH
A, I, M
Core Demographics
✓
✓
Presence of Children Aged 11 - 15
The presence of children, children's age ranges and number of children selections reflects the probable presence of children under age 18 in a given household. Information is sourced from children's age and gender data which is gathered from a variety of sources that includes public records where they may be available, survey data as well as modeled Census based information.
71
CHILDAGECD_16_17
1
VARCHAR
HH
A, I, M
Core Demographics
✓
✓
Presence of Children Aged 16 - 17
The presence of children, children's age ranges and number of children selections reflects the probable presence of children under age 18 in a given household. Information is sourced from children's age and gender data which is gathered from a variety of sources that includes public records where they may be available, survey data as well as modeled Census based information.
72
CHILDNBRCD
1
VARCHAR
HH
A, I, M
Core Demographics
✓
✓
Number of Children Code: A - No Children B - Less Than 3 C - 3 - 5 D - 6+
The presence of children, children's age ranges and number of children selections reflects the probable presence of children under age 18 in a given household. Information is sourced from children's age and gender data which is gathered from a variety of sources that includes public records where they may be available, survey data as well as modeled Census based information.

Consumer Plus Premium Package

Geographic Delineations

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level Individual, Household, or Zip4
Assignment Modeled, Actual, or Inferred
Category Field Category
Description Description
38
LATITUDE
11
FLOAT
N/A
A
Geocodes
✓
Up to 6 decimal precision for non-PII version and 6 decimal precision for PII version
The geometrical location north or south of the equator assigned to the address. A value in decimal degrees to an accuracy of 4 decimal places is accurate to 11.1 meters, a value of 6 decimal places is accurate 0.111 meters.
39
LONGITUDE
11
DOUBLE
N/A
A
Geocodes
✓
Up to 6 decimal precision for non-PII version and 6 decimal precision for PII version
The geometrical location east or west of a north-south line, called the prime meridian, assigned to the address. A value in decimal degrees to an accuracy of 4 decimal places is accurate to 11.1 meters, a value of 6 decimal places is accurate 0.111 meters.
40
GEOLEVEL
2
VARCHAR
N/A
A
Geocodes
✓
GeoLevel Precision Assignment
The level of precision at which the Latitude and Longitude are assigned.

Household Demographic Data

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
47
LOR
2
INTEGER
HH, I
A,I
Supplemental
✓
Length of Residence: 00-15 Based on length of residence of the household at the same address Individual household LOR values range from 0, indicating less than 1 year, to 15, indicating 15 or more years."
Length of residence indicates the probable number of years an individual/household has been located at current address. Data comes from multiple sources including real estate data, public records and self-reported sources.
52
SGLPARENT
1
VARCHAR
HH
M
Supplemental
✓
Modeled select based on presence of children and married selection: Y - Single Parent
Potentially single parent determined by model of known demographic information. The Single Parent Household select is used to identify households with a potentially single parent present. Data is applied at the individual level and is sourced from self-reported survey data.
53
HHNBRSR
1
VARCHAR
HH
A, I
Supplemental
✓
Y - Individuals in household 65 years and older
Total number of individuals in a given household whose known birth year indicates an age of 65 or greater
54
HHNBR
1
INTEGER
HH
I
Supplemental
✓
Number of adult individuals in household
Total number of adult individuals in a given household
55
SPANISHSPCD
1
VARCHAR
I
A, M
Supplemental
✓
Y - Speaks Spanish at Home
Indicates an individual who has reported speaking Spanish in their home. Data is applied at the individual level and is sourced from self-reported survey and modeled data
56
SOHOCD
1
VARCHAR
HH
A
Supplemental
✓
Y - SOHO (Small Office Home Office) Indicator
Indicates an individual owns a small business or has a home office at this address
57
VETERANCD
1
VARCHAR
HH
A
Supplemental
✓
Y - Veteran in Household
Compilation of signals and self reported data indicating an individual within the household has served or is serving in the armed forces
58
CREDITCARD
1
VARCHAR
HH
A
Supplemental
✓
Known use of Credit Card for purchases: Y - Credit Card
Indicator that an individual has used a credit card for purchases of goods and services
60
CHARITYDNR
1
VARCHAR
I
A
Supplemental
✓
Y - Donor
Known donor to charitable causes received from self-reported and other transactional sources
61
MRKTHOMEVAL
1
VARCHAR
HH
A
Supplemental
✓
Estimated Home Market Value (Assessor-based Model): A - $1,000 - $24,999 B - $25,000 - $49,999 C - $50,000 - $74,999 D - $75,000 - $99,999 E - $100,000 - $124,999 F - $125,000 - $149,999 G - $150,000 - $174,999 H - $175,000 - $199,999 I - $200,000 - $224,999 J - $225,000 - $249,999 K - $250,000 - $274,999 L - $275,000 - $299,999 M - $300,000 - $349,999 N - $350,000 - $399,999 O - $400,000 - $449,999 P - $450,000 - $499,999 Q - $500,000 - $749,999 R - $750,000 - $999,999 S - $1,000,000+
Estimated market value of home as indicated by County Tax Assesor and deed records. The Home Market Value indicates the relative Home Market Value as compared to all of the homes within the same county. This data is applied at the address level.
62
EDUCATIONCD
1
VARCHAR
I
M
Supplemental
✓
Education Attainment Code: A - Completed High School B - Completed College C - Completed Graduate School D - Attended Vocational/Technical E - Some High School F - Some College
Education is defined as the level of education completed by the consumer. This is a probabilistic attribute based on self-reported information, surveys, and census sources.
63
OCCUPATIONCD *DO NOT USE*
1
VARCHAR
I
A
Supplemental
✓
*DO NOT USE* New Occupation field available as of June 20, 2023 Release - See Field #344 OCCUPATIONCD_V2
*DO NOT USE* This field was formally retired and all values were nulled with the February 20, 2024 release. Please use OCCUPATIONCD_V2 (Field #344).
65
RELIGIONCD *DO NOT USE*
1
VARCHAR
I
A, I
Supplemental
✓
*DO NOT USE* Due to multi-state Sensitive Personal Information requirements individual-level Religion data is no longer available as of May 25, 2024 release.
*DO NOT USE* This field was formally retired and all values were nulled with the May 25, 2024 release.

Real Estate Data based upon county assessor data

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
73
YRBLD
4
INTEGER
HH
A
Supplemental
✓
The year home was built according to real estate data - YYYY
Year dwelling was built based on real estate transaction and deed records from County Assessor or County Recorder offices
74
MOBHOMECD
1
VARCHAR
HH
A
Supplemental
✓
Y - Mobile Home
Residence is a mobile home as indicated by real estate transaction and deed records from County Assessor or County Recorder offices
75
POOL
1
VARCHAR
HH
A
Supplemental
✓
Y - Pool Owner
Residence has a pool as indicated by real estate transaction and deed records from County Assessor or County Recorder offices
76
FIREPLCD
1
VARCHAR
HH
A
Supplemental
✓
Y - Home with Fireplace
Residence has a fireplace as indicated by real estate transaction and deed records from County Assessor or County Recorder offices

MarketShare Demographics

Marketshare product has been continuously built since 1993. Data is compiled at the individual level with aggregated data elements from multiple sources which have provided detailed transactional data. The sources include catalogs, magazines, continuity clubs and online products and services. Each affinity provides the number of different companies from which the person made a purchase of that type of product – values for these range from 0 to 99. See tab Marketshare for more information.
# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
77
MS_ACCESSORY
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Accessory
Includes shoes, purses, belts, etc.
78
MS_APPAREL
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Apparel
Includes the purchase of clothing. MS_ACCESSORY, MS_KIDAPP, MS_MENAPP, and MS_WOMAPP are included in the overall MS_APPAREL variable.
79
MS_AUDIO
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Audio
Includes purchases related to audio equipment.
80
MS_AUTO
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Automotive
Includes purchases related to Cars/Trucks
81
MS_AVIATION
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Aviation
Includes purchases of aviation related items.
82
MS_BARGAINS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Bargain Seekers
Includes purchases of low-cost or on-sale products from catalogs, retail and websites
83
MS_BIBLE
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Bible
Includes purchases of religious items
84
MS_BOATSAIL
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Boating/Sailing
Includes boating, sailing, canoeing, kayaking, water skiing, rafting, etc
85
MS_BOOKS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Books
Number of distinct book company participants.
86
MS_BUSINESS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Business
Includes business news, and/or products usually associated with running a business or a career or operating an office
87
MS_CAMP
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Camping
Includes camping and hiking
88
MS_CATALOG
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Catalog
Number of different catalogs ordered from.
89
MS_COLLECTIBLES
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Collectibles
Includes dolls, coins, stamps, stuffed animals, plates, memorabilia, etc.
90
MS_COMPUTERS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Computers
Includes Computers, software and computer accessories
91
MS_COOKING
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Cooking
Includes recipes, cooking utensils and food products
92
MS_BEAUTY
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Cosmetics/Beauty Products
Includes purchases of Beauty & Cosmetic items
93
MS_CRAFTS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Crafts
Includes crochet, sewing, knitting, needlepoint, quilting, jewelry, ceramics, painting, etc.
94
MS_CULTUREARTS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Culture Arts
Includes art, antiques, opera, museums, the theatre, etc.
95
MS_CURREVENT
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Current Events
Includes periodicals, news magazines, websites & blogs
96
MS_DIY
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Do It Yourself
Includes home improvements and construction, wood working, auto repair, etc.
97
MS_ELECTRONICS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Electronics
Includes home and auto stereos, video equipment, etc. Does not include computers.
98
MS_EQUESTRIAN
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Equestrian
Includes purchases related to horses/horse riding
99
MS_FAMILY
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Family
Includes products designed for families with children.
100
MS_FICTION
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Fiction
Include books & magazines of fiction
101
MS_FISHING
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Fishing
Include fishing paraphenalia as well as books & publications related to fishings
102
MS_FITNESS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Fitness
Includes purchases of fitness equipment, clothing, accessories as well as subscribers to publications and books.
103
MS_FOOD
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Food And Drink
Includes purchases of any food item
104
MS_FUNDRAISING
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Fundraising
Number of different charitable organizations to which a donation was made.
105
MS_GAMES
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Games
Includes computer games, traditional board games, puzzles, etc.
106
MS_GARDEN
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Gardening
Includes purchases of gardening products, plants, seeds, books etc. Also includes subscribers to gardening publications
107
MS_MERCHANDISE
2
INTEGER
I
A
Behavioral & LIfestyle
✓
General Merchandise
Includes catalog items not classified under any other affinity.
108
MS_GIFTGIVR
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Gift Giver
Number of orders that were given as gifts.
109
MS_GIFTEE
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Giftee
Number of orders that were received as gifts.
110
MS_GIFTS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Gifts
Includes products usually given as gifts.
111
MS_GOURMET
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Gourmet
Includes upscale products, and fine foods, wines, and other expensive items.
112
MS_HEALTH
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Health
MS_FITNESS is included in MS_HEALTH. Also includes health related items which are not fitness
113
MS_HISTORY
2
INTEGER
I
A
Behavioral & LIfestyle
✓
History
Includes purchases of books/publications related to history
114
MS_HOLIDAY
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Holiday
Includes items related to the holidays, usually Christmas.
115
MS_HOMEDECR
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Home Decorating/Furnishings
Includes furniture as well as other home décor items such as rugs, vases, pictures etc.
116
MS_HOMELIV
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Home Living
MS_CAT_HOMELIV, MS_PUB_HOMELIV, MS_HOMEDECR, MS_PUB_HOMEDECR, MS_CAT_HOMEDECR, MS_CAT_GARDEN, MS_COOKING, MS_PUB_COOKING, MS_GARDEN, MS_PUB_GARDEN, and MS_HOUSEWARES
117
MS_HOUSEWARES
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Housewares
Includes housewares excluding large appliance purchases
118
MS_HUMOR
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Humor/Comics
Includes comic books and strips, cartoons, comedies, etc.
119
MS_HUNTING
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Hunting
Includes purchases of all types of hunting related items (including rifles, bows etc) as well as subscribers to hunting publications
120
MS_INSPIRATION
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Inspiration
Includes non-religious products, such as new age products, astrology, etc.
121
MS_KIDAPP
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Kids Apparel
Includes all types of children's apparel
122
MS_MAGS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Magazines
Number of distinct participating magazine titles.
123
MS_MENAPP
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Men's Apparel
Includes all types of men's apparel
124
MS_MOTORCYCLES
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Motorcycles
Includes all types of motorcycles/motorcycle related products and subscriptions to motorcycle publications
125
MS_MUSIC
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Music
Includes music, music playing equipment, and musical instruments.
126
MS_MONEYMAKING
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Op Seekers
Includes moneymaking opportunities, usually of the get-rich-quick nature.
127
MS_OUTDOORS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Outdoors
MS_BOATSAIL, MS_CAMP, MS_FISHING, and MS_HUNTING are included in MS_OUTDOORS.
128
MS_PFIN
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Personal Finance
Includes all types of personal finance (including magazines)
129
MS_PETS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Pets
Includes purchase of all types of pet products including cats and dogs
130
MS_PHOTOPROC
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Photo Processing
Includes companies that offer film processed via the mail or the Internet.
131
MS_PHOTO
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Photography
Includes products/magazines related to photography
132
MS_PUBLISH
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Publish
Number of distinct publishing participants, counting individual magazines and book companies
133
MS_PUB_COOKING
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Publish Cooking
Includes all types of cooking publications & books
134
MS_PUB_FAMILY
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Publish Family
Includes all types of family/children publications & books
135
MS_PUB_GARDEN
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Publish Gardening
Includes all types of gardening publications & books
136
MS_PUB_GIFTGIVR
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Publish Gift Giver
Indicates number of magazine subscriptions given as a gift
137
MS_PUB_GIFTEE
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Publish Giftee
Indicates number of magazine subscriptions or book orders received as a Gift.
138
MS_PUB_HOMEDECR
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Publish Home Decorating/Furnishings
Includes all types of publications & books related to home décor and furninishings
139
MS_PUB_HOMELIV
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Publish Home Living
Includes all types of publications & books related to home living
140
MS_PUB_OUTDOORS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Publish Outdoors
Includes all types of publications & books related to outdoors (includes hunting, fishing, camping, hiking, some watersports)
141
MS_SCIENCE
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Science
Includes products, magazines & books related to science
142
MS_SPORTS
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Sports
Includes products, magazines & books related to sports
143
MS_TRAVEL
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Travel
Includes products, magazines & books related traveling & vacations
144
MS_TVMOVIES
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Tv/Movies/Video
Includes purchases of videos, subscribers to streaming services as well as magazine subscribers
145
MS_WILDLIFE
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Wildlife/Environment
Includes purchasers of products related to Wildlife and the environment as well as donors & magazine subscribers
146
MS_WOMAN
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Woman
Includes generic women’s publications.
147
MS_WOMAPP
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Women's Apparel
Includes all types of women's apparel
148
MS_WOMFASH
2
INTEGER
I
A
Behavioral & LIfestyle
✓
Women's Fashion
Includes publications related to women’s fashion.

Consumer Passion Index (CPI)

CPI Indices are a measure of the strength of one's interest in a given CPI affinity. The indices range from 9 to 0, with 9 representing someone with the strongest interest in that affinity, and 1 represents someone with the weakest interest in that affinity. 0 indicates that there is no none interest in that affinity. All passions are based on a combination of purchases from direct marketing and retail channels, and/or from interests as determined by surveys or websites. The indices are derived using our propriety methodology. To start, each contributing source file is given a value based on its ability to identify the strength of its affinities. For example, survey files receive the lowest values, and magazines/catalogs receive the highest values – actually purchasing a product in a given affinity shows a greater degree of interest than just checking off a survey box. In addition, for survey files, double-counting is limited: If one checks off one box for fly fishing and another for saltwater fishing, that person only gets credit for fishing once. On the other hand, if one subscribes to a fly fishing and a saltwater magazine, that person gets credited twice for fishing. See Tab CPI for more information.
# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
149
CPI_HISTORY_AMERICAN_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi American History Index: 9-0
Interested specifically in American History, patriotic themes, and/or veteran’s issues. Included in CPI_HISTORY_INDEX.
150
CPI_APPAREL_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Apparel Index: 9-0
Interested in or purchasers of Apparel or fashion. Includes the CPI_APPAREL_ACCESSORIES, CPI_APPAREL_KIDS, CPI_APPAREL_MEN, CPI_APPAREL_MENFASH, CPI_APPAREL_WOMEN, CPI_APPAREL_WOMFASH indices below.
151
CPI_APPAREL_ACCESSORY_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Apparel/Accessories Index: 9-0
Interested in or purchaseres of shoes, purses, belts, etc. Included in CPI_APPAREL_INDEX.
152
CPI_APPAREL_KIDS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Apparel/Children's Apparel Index: 9-0
Interested in or purchasers of children’s apparel via direct marketing channels. Included in CPI_APPAREL_INDEX.
153
CPI_APPAREL_MEN_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Apparel/Men's Apparel Index: 9-0
Interested in or purchasers of men’s apparel . Included in CPI_APPAREL_INDEX.
154
CPI_APPAREL_MENFASH_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Apparel/Mens Fashion Index: 9-0
Interested in or purchasers of men’s fashion. Included in CPI_APPAREL_INDEX.
155
CPI_APPAREL_WOMEN_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Apparel/Women's Apparel Index: 9-0
Interested in or purchasers of women’s apparel via direct marketing channels. Included in CPI_APPAREL_INDEX.
156
CPI_APPAREL_WOMFASH_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Apparel/Womens Fashion Index: 9-0
Interested in or purchasers of women’s fashion. Included in CPI_APPAREL_INDEX.
157
CPI_INSURANCE_AUTO_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Auto Insurance: 9-0
Interested in purchasing Automobile Insurance
158
CPI_AUTO_TRUCKS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Auto/Trucks Index: 9-0
Interested specifically in trucks (excluding SUVs). Included in CPI_AUTO_INDEX.
159
CPI_AUTO_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Automotive Index: 9-0
Interested in anything automobile-related, such as auto parts and tools, restoration, performance, general and make/model specific enthusiast products, etc. Includes CPI_AUTO_RACING and CPI_AUTO_TRUCKS indices.
160
CPI_AVIATION_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Aviation Index: 9-0
Interested in airplanes and other things that really fly and can transport things and people. Does not include toy planes.
161
CPI_BARGAINS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Bargain Seekers Index: 9-0
Interested in saving money, including coupons, low-cost stores and discount clubs, money-saving sales, rewards programs, auction websites, free stuff, etc.
162
CPI_BEAUTY_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Beauty Index: 9-0
Interested in beauty-related products, including cosmetics, treatments, etc.
163
CPI_BIBLE_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Bible/Devotional Index: 9-0
Interested in religion, church-going, bible-oriented products, and the spiritual.
164
CPI_PUBLISH_BOOKS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Book Buyer Index: 9-0
Known to have purchased a book via direct marketing channels. Included in CPI_PUBLISH_INDEX. Includes CPI_NONFICTION, CPI_FICTION, and CPI_SCIFI passions.
165
CPI_BUSINESS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Business Index: 9-0
Interested in business news, career, and/or products usually associated with running a business. Includes CPI__BUSINESS_HOMEOFFICE index.
166
CPI_BUSINESS_HOMEOFFICE_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Business/Home Office Index: 9-0
Indication that the person has a Home Office. Included in CPI_BUSINESS_INDEX.
167
CPI_CATALOG_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Catalog Index: 9-0
Has purchased from a catalog.
168
CPI_DONOR_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Charitable Donor Index: 9-0
Has a known history of donating to charitable causes.
169
CPI_FAMILY_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Children/Family Index: 9-0
Those known to live in a family (i.e., adults and children) household, based on the presence of children and/or the purchase of children’s related products. Includes both CPI_FAMILY_TEEN_INDEX and CPI_FAMILY_YOUNG_INDEX.
170
CPI_FAMILY_TEEN_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Children/Family Teen Index: 9-0
Those known to live in a family (i.e., adults and children) household with at least one teenager, based on based the presence of children and/or the purchase of teen-related products. Included in CPI_FAMILY_INDEX.
171
CPI_FAMILY_YOUNG_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Children/Family Young Index: 9-0
Those known to live in a family (i.e., adults and children) household with at least one child under , based on the presence of children and/or the purchase of younger children’s related products. Included in CPI_FAMILY_INDEX.
172
CPI_COLLECTIBLES_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Collectibles Index: 9-0
Interested in collectibles, including dolls, coins, stamps, stuffed animals, plates, memorabilia, etc.
173
CPI_COLLEGE_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi College Index: 9-0
Interested in colleges and universities as an alumni.
174
CPI_COMPUTERS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Computers Index: 9-0
Interested in computers and products used with computers.
175
CPI_CONTINUITY_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Continuity Buyer Index: 9-0
Purchased a product offered via continuity.
176
CPI_COOKING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Cooking Index: 9-0
Interested in cooking, baking, and all things culinary, including recipes and cooking equipment. Included in CPI_HOMELIV_INDEX.
177
CPI_CRAFTS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Crafts Index: 9-0
Covers all crafts, including crochet, sewing, knitting, needlepoint, quilting, jewelry, ceramics, painting, etc. Includes the CPI_CRAFTS_CROCHET, CPI_CRAFTS_KNIT, CPI_CRAFTS_NEEDLEPOINT, CPI_CRAFTS_QUILT, CPI_CRAFTS_SEW passions listed below.
178
CPI_CRAFTS_CROCHET_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Crafts/Crochet Index: 9-0
Interested in crocheting. Included in CPI_CRAFTS Index
179
CPI_CRAFTS_KNIT_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Crafts/Knitting Index: 9-0
Interested in knititing. Included in CPI_CRAFTS Index
180
CPI_CRAFTS_NEEDLEPOINT_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Crafts/Needlepoint Index: 9-0
Interested in needlepoint. Included in CPI_CRAFTS Index
181
CPI_CRAFTS_QUILT_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Crafts/Quilting Index: 9-0
Interested in quilting. Included in CPI_CRAFTS Index
182
CPI_CRAFTS_SEW_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Crafts/Sewing Index: 9-0
Interested in the sewing craft. Included in CPI_CRAFTS_INDEX.
183
CPI_CC_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Credit Card User Index: 9-0
Has used a credit card to make a purchase
184
CPI_CREDIT_REPAIR_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Credit Repair: 9-0
Interested in repairing their credit
185
CPI_CREDIT_REPORT_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Credit Report: 9-0
Have purchased or obtained a copy of their credit report
186
CPI_CULTUREARTS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Culture/Arts Index: 9-0
Interested in culture and the arts, including art, antiques, opera, museums, the theatre, etc.
187
CPI_CURREVENT_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Current Events Index: 9-0
Interested in the news and politics.
188
CPI_DIY_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Do It Yourself Index: 9-0
Interested in Do It Yourself projects, including home improvements and construction, wood working, auto repair, etc.
189
CPI_EDUCATION_SEEKERS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Education Seekers: 9-0
Interested in furthering their education including online schools, brick and mortar schools and trade schools
190
CPI_ELECTRONICS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Electronics Index: 9-0
Interested in electronics, such as home and auto stereos, video equipment, etc. Does not include computers.
191
CPI_FICTION_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Fiction Index: 9-0
Interested in fiction books. Included in CPI_PUBLISH_INDEX and CPI_PUBLISH_BOOKS_INDEX.
192
CPI_GAMBLING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Gambling Index: 9-0
Interested in games of chance, including casino gambling and lotteries.
193
CPI_GAMES_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Games Index: 9-0
Interested in a variety of games, including computer games, traditional board games, puzzles, etc.
194
CPI_GARDENING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Gardening/Flowers Index: 9-0
Interested in gardening and plants, both indoors and outdoors, decorative and edible. Included in CPI_HOMELIV_INDEX.
195
CPI_GIFTGIVR_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Gift Giver Index: 9-0
Has been known to purchase gifts for others via a direct marketing channel.
196
CPI_GOURMET_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Gourmet Foods/Cooking Index: 9-0
Interested in gourmet and upscale products, such as fine foods, wines, and other expensive items.
197
CPI_HEALTH_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Health Index: 9-0
Interested in one’s health and/or self-improvement. Includes HEALTH_DIET and HEALTH_FITNESS indices below.
198
CPI_INSURANCE_HEALTH_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Health Insurance: 9-0
Interested in purchasing Health Insurance
199
CPI_HEALTH_DIET_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Health/Diet Index: 9-0
Interested in weight control or losing weight. Included in CPI_HEALTH_INDEX.
200
CPI_HEALTH_FITNESS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Health/Fitness & Exercise Index: 9-0
Interested in physical fitness and exercise, including running, biking, walking, weight lifting, aerobics, etc. Included in CPI_HEALTH_INDEX.
201
CPI_HIGHTECH_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi High Tech Enthusiast Index: 9-0
Interested in new and/or relatively rare high technology electronic products, such as HDTV’s, digital video cameras, digital video recorders, satellite radio, pagers, home fax machines, plasma TVs, etc. This is a moving target as products become more accepted and as new products are invented.
202
CPI_HISPANIC_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Hispanic Index: 9-0
Race indicated as Hispanic, speaks Spanish, and/or interested in Hispanic-oriented products.
203
CPI_HISTORY_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi History Index: 9-0
Interested in History. Includes CPI_HISTORY_AMERICAN index.
204
CPI_HOBBIES_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Hobbies Index: 9-0
Interested in unspecified hobbies or in various hobbies not included in other passions.
205
CPI_HOMEDECR_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Home Decorating/Furnishings Index: 9-0
Interested in Home Decorating, such as furnishings, wall and window treatments, layouts, etc. Included in CPI_HOMELIV_INDEX.
206
CPI_HOMELIV_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Home Living Index: 9-0
Interested in things related to the home. These include the CPI_COOKING, CPI_GARDENING, and CPI_HOMEDECR passions listed below, but also include house wares, linens, and the like.
207
CPI_EQUESTRIAN_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Horses Index: 9-0
Interested in horses and riding.
208
CPI_INSPIRATION_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Inspirational Index: 9-0
Interested in non-religious inspiration, including new age products, astrology, etc.
209
CPI_INSURANCE_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Insurance: 9-0
Interested in purchasing insurance (includes Life, Health & Auto)
210
CPI_INTERNET_ACCESS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Internet Access Index: 9-0
Known only to have internet access; does not necessarily include an internet purchase. Included in CPI_INTERNET_INDEX.
211
CPI_INTERNET_BUY_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Internet Buyer Index: 9-0
Known to have purchased a product via the internet. Included in CPI_INTERNET_INDEX.
212
CPI_INTERNET_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Internet Index: 9-0
Known to be on the internet. Includes the INTERNET_ACCESS and INTERNET_BUY passions.
213
CPI_JOB_SEEKERS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Job Seekers: 9-0
Users of online job searching sites
214
CPI_PUBLISH_MAGS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Magazine Subscriber Index: 9-0
Known to have purchased a magazine via direct marketing channels. Included in CPI_PUBLISH_INDEX.
215
CPI_PUBLISH_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Magazine/Book Buyer Index: 9-0
Known to have purchased a book or magazine via direct marketing channels. Includes CPI_PUBLISH_MAGS and CPI_PUBLISH_BOOKS passions.
216
CPI_MOBILE_APPS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Mobile Apps/Ringtones: 9-0
Have purchased a ring tone or app for a mobile device
217
CPI_MOTORCYCLES_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Motorcycles Index: 9-0
Interested in motorcycles and ATVs.
218
CPI_MUSIC_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Music Index: 9-0
Interested in music and/or music playing equipment.
219
CPI_NONFICTION_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Nonfiction Index: 9-0
Interested in non-fiction books. Included in CPI_PUBLISH_INDEX and CPI_PUBLISH_BOOKS_INDEX.
220
CPI_MONEYMAKING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Op Seekers Index: 9-0
Interested in moneymaking opportunities, usually of the get-rich- quick nature.
221
CPI_OUTDOORS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Outdoor Sports Index: 9-0
Interested in outdoor, sportsman-type activities. Includes the CPI_OUTDOORS_BOATSAIL, CPI_OUTDOORS_CAMP, CPI_OUTDOORS_FISHING, CPI_OUTDOORS_HUNTING, and CPI_OUTDOORS_HUNTFISH passions listed below.
222
CPI_OUTDOORS_BOATSAIL_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Outdoors/Boating & Sailing Index: 9-0
Interested in boating, sailing, canoeing, kayaking, water skiing, rafting, etc. Included in CPI_OUTDOORS_INDEX.
223
CPI_OUTDOORS_CAMP_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Outdoors/Camping & Hiking Index: 9-0
Interested in camping, hiking, climbing, etc. Included in CPI_OUTDOORS_INDEX.
224
CPI_OUTDOORS_FISHING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Outdoors/Fishing Index: 9-0
Interested specifically in fishing, saltwater and/or freshwater. Included in CPI_OUTDOORS_INDEX.
225
CPI_OUTDOORS_HUNTING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Outdoors/Hunting Index: 9-0
Interested specifically in hunting or guns. Included in CPI_OUTDOORS_INDEX.
226
CPI_OUTDOORS_HUNTFISH_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Outdoors/Hunting Or Fishing Index: 9-0
Interested in hunting and/or fishing. Source data covers both (general sportsman activities), not specific to one or the other. Included in CPI_OUTDOORS_INDEX.
227
CPI_PFIN_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Personal Finance Index: 9-0
Interested in personal finance, investments, retirement planning, etc.
228
CPI_EGO_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Personalized Index: 9-0
Interested in personalized products, such as monograms.
229
CPI_PETS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Pets Index: 9-0
Interested in or an owner of pets, mostly dogs and cat. Includes the CPI_PETS_CATS and CPI_PETS_DOGS passions below.
230
CPI_PETS_CATS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Pets/Cats Index: 9-0
Interested in or an owner of cats. Included in CPI_PETS_INDEX.
231
CPI_PETS_DOGS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Pets/Dogs Index: 9-0
Interested in or an owner of dogs. Included in CPI_PETS_INDEX.
232
CPI_PHOTOPROC_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Photo Processing Index: 9-0
Known to have had film processed via the mail or the internet.
233
CPI_PHOTOG_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Photography Index: 9-0
Interested in photography.
234
CPI_CONSERVATIVE_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Politically Conservative Index: 9-0
Expressed a preference for a conservative political point of view or party.
235
CPI_LIBERAL_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Politically Liberal Index: 9-0
Expressed a preference for a liberal political point of view or party.
236
CPI_SOCIAL_NETWORKING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Social Networking: 9-0
Users of online social networking services such as facebook, twitter etc.
237
CPI_SPORTS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports Index: 9-0
Interested in sports, mostly spectator and/or paraphernalia purchaser, but includes participation. Includes the CPI_SPORTS_BASEBALL, CPI_SPORTS_BASKETBALL, CPI_SPORTS_FOOTBALL, CPI_SPORTS_GOLF, CPI_SPORTS_HOCKEY, CPI_SPORTS_SKIING, CPI_SPORTS_SOCCER and CPI_SPORTS_TENNIS passions below.
238
CPI_SPORTS_BASEBALL_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Baseball Index: 9-0
Interested in the specific sport of Baseball as a spectator, paraphernalia purchaser and/or participant.
239
CPI_SPORTS_BASKETBALL_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Basketball Index: 9-0
Interested in Basketball
240
CPI_SPORTS_BIKING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Biking Index: 9-0
Interested in Biking
241
CPI_SPORTS_FOOTBALL_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Football Index: 9-0
Interested in Football
242
CPI_SPORTS_GOLF_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Golf Index: 9-0
Interested in Golf
243
CPI_SPORTS_HOCKEY_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Hockey Index: 9-0
Interested in Hockey
244
CPI_SPORTS_RUNNING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Running Index: 9-0
Interested in Running
245
CPI_SPORTS_SKI_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Ski Index: 9-0
Interested in Skiing
246
CPI_SPORTS_SOCCER_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Soccer Index: 9-0
Interested in Soccer
247
CPI_SPORTS_SWIMMING_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Swimming Index: 9-0
Interested in Swimming
248
CPI_SPORTS_TENNIS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sports/Tennis Index: 9-0
Interested in Tennis
249
CPI_SWEEPS_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Sweepstakes Index: 9-0
Interested in offers utilizing sweepstakes.
250
CPI_TRAVEL_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Travel Index: 9-0
Interested in traveling. Includes the CPI_TRAVEL_CRUISE, CPI_TRAVEL_RV and CPI_TRAVEL_US passions.
251
CPI_TRAVEL_CRUISE_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Travel/Cruises Index: 9-0
Interested in boat cruises. Included in CPI_TRAVEL_INDEX.
252
CPI_TRAVEL_RV_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Travel/Recr Vehicles Index: 9-0
Interested in recreational vehicles or known to own one. Included in CPI_TRAVEL_INDEX.
253
CPI_TRAVEL_US_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Travel/Us Index: 9-0
Interested in travel within the US. Included in CPI_TRAVEL_INDEX.
254
CPI_TVMOVIES_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Tv/Movies/Video Index: 9-0
Interested in television, movies, videos, DVDs, etc.
255
CPI_WILDLIFE_INDEX
1
INTEGER
I
M
Behavioral & LIfestyle
✓
Cpi Wildlife/Environment Index: 9-0
Interested in the environment and/or wildlife.

Connex Segmentation Clusters

Groups of consumers who exhibit similar demographic, lifestyle and media consumption characteristics, empowering marketers to understand the unique attributes that comprise their most profitable consumer segments. Armed with this rich data, data scientists can drive analytics and modeling to power their brand’s unique marketing initiatives.
# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
256
HHCLSTRDCD
3
VARCHAR
HH
M
Connex
✓
Connex Household Clusters
257
NEIGHBORHOOD_CLSTRDCD
3
VARCHAR
Z4
M
Connex
✓
Connex Neighborhood Clusters
258
FMCLSTRDCD
3
VARCHAR
Z4
M
Connex
✓
Connex Family Clusters
259
MESSAGING_CLSTRDCD
3
VARCHAR
HH
M
Connex
✓
Connex Messaging Clusters
260
DIGITALCLSTRDCD
3
VARCHAR
HH
M
Connex
✓
Connex Digital Clusters
261
GENERATION_CLSTRDCD
3
VARCHAR
HH
M
Connex
✓
Connex Generation Clusters
262
GENERATION_GRPCD
3
VARCHAR
HH
M
Connex
✓
Connex Generation Groups
263
LIFESTG_CLSTRD
3
VARCHAR
HH
M
Connex
✓
Connex Life Stage Clusters
264
LIFESTG_GRPCD
3
VARCHAR
HH
M
Connex
✓
Connex Life Stage Groups

Connex Targets

Connex targets indicate consumer propensities at the household level based on their unique preferences. Infutor's robust prospecting database includes more than 5,400 Connex Targets across 86 categories. Contact Infutor's sales team for more information about our connex database.
# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
265
CT_MEDIA_HEAVYUSAGE_MAGAZINE
1
VARCHAR
HH
M
Connex
✓
Y - Frequent reader of magazines
266
CT_MEDIA_HEAVYUSAGE_NEWSPAPER
1
VARCHAR
HH
M
Connex
✓
Y - Frequent reader of newspapers
267
CT_MEDIA_HEAVYUSAGE_RADIO
1
VARCHAR
HH
M
Connex
✓
Y - Frequent listener to radio
268
CT_MEDIA_HEAVYUSAGE_PTRADIO
1
VARCHAR
HH
M
Connex
✓
Y - Frequent listener to radio primetime
269
CT_MEDIA_HEAVYUSAGE_TV
1
VARCHAR
HH
M
Connex
✓
Y - Frequent watcher of television
270
CT_MEDIA_HEAVYUSAGE_INTERNET
1
VARCHAR
HH
M
Connex
✓
Y - Frequent user of internet
271
CT_MEDIA_HEAVYUSAGE_ODRMDA
1
VARCHAR
HH
M
Connex
✓
Y - Frequent reader of outdoor media
272
CT_SOCIALUSAGE30_FB
1
VARCHAR
HH
M
Connex
✓
Y - Social media, photo or video-sharing services visited or used in the last 30 days: Facebook
273
CT_SOCIALUSAGE30_INSTA
1
VARCHAR
HH
M
Connex
✓
Y - Social media, photo or video-sharing services visited or used in the last 30 days: Instagram
274
CT_SOCIALUSAGE30_LNKIN
1
VARCHAR
HH
M
Connex
✓
Y - Social media, photo or video-sharing services visited or used in the last 30 days: LinkedIn
275
CT_SOCIALUSAGE30_PINT
1
VARCHAR
HH
M
Connex
✓
Y - Social media, photo or video-sharing services visited or used in the last 30 days: Pinterest
276
CT_SOCIALUSAGE30_TWITTER
1
VARCHAR
HH
M
Connex
✓
Y - Social media, photo or video-sharing services visited or used in the last 30 days: Twitter
277
CT_SOCIALUSAGE30_YOUTUBE
1
VARCHAR
HH
M
Connex
✓
Y - Social media, photo or video-sharing services visited or used in the last 30 days: YouTube
278
CT_STRMSUB_PRIME
1
VARCHAR
HH
M
Connex
✓
Y - Subscribes to Prime Video for streaming video services
279
CT_STRMSUB_HULU
1
VARCHAR
HH
M
Connex
✓
Y - Subscribes to Hulu for streaming video services
280
CT_STRMSUB_NETFLIX
1
VARCHAR
HH
M
Connex
✓
Y - Subscribes to Netflix for streaming video services
281
CT_SMRTPHN_TYPEOWNS_ANDROID
1
VARCHAR
HH
M
Connex
✓
Y - Owns an Android smartphone (any brand)
282
CT_SMRTPHN_TYPEOWNS_IPHONE
1
VARCHAR
HH
M
Connex
✓
Y - Owns an Apple iPhone smartphone
283
CT_HOMEIMPROVE12_ANY
1
VARCHAR
HH
M
Connex
✓
Y - Made improvements to home
284
CT_HOMEREMODEL12_ANY
1
VARCHAR
HH
M
Connex
✓
Y - Home Remodeling - Summary: Any: In last 12 months
285
CT_POLITICAL_PARTYAFF_DEMOCRAT
1
VARCHAR
HH
M
Connex
✓
Y - Democratic political affiliation
286
CT_POLITICAL_PARTYAFF_GOP
1
VARCHAR
HH
M
Connex
✓
Y - Republican political affiliation
287
CT_POLITICAL_PARTYAFF_IND
1
VARCHAR
HH
M
Connex
✓
Y - Independent/no party affiliation
288
CT_POLITICAL_OUTLK_VCONSERV
1
VARCHAR
HH
M
Connex
✓
Y - Political outlook is very conservative
289
CT_POLITICAL_OUTLK_SWCONSERV
1
VARCHAR
HH
M
Connex
✓
Y - Political outlook is somewhat conservative
290
CT_POLITICAL_OUTLK_MID
1
VARCHAR
HH
M
Connex
✓
Y - Political outlook is middle of the road
291
CT_POLITICAL_OUTLK_SWLIBERAL
1
VARCHAR
HH
M
Connex
✓
Y - Political outlook is somewhat liberal
292
CT_POLITICAL_OUTLK_VLIBERAL
1
VARCHAR
HH
M
Connex
✓
Y - Political outlook is very liberal
293
CT_ONLINESHOPSEG_OFFLINE
1
VARCHAR
HH
M
Connex
✓
Y - Online Shoppers Segments: Offline-Only Shoppers
294
CT_ONLINESHOPSEG_TRAD
1
VARCHAR
HH
M
Connex
✓
Y - Online Shoppers Segments: Traditional Consumers
295
CT_ONLINESHOPSEG_STRAITFWD
1
VARCHAR
HH
M
Connex
✓
Y - Online Shoppers Segments: Straightforward Shoppers
296
CT_ONLINESHOPSEG_DEALSEEK
1
VARCHAR
HH
M
Connex
✓
Y - Online Shoppers Segments: Passionate Deal Seekers
297
CT_ONLINESHOPSEG_QUALSEEK
1
VARCHAR
HH
M
Connex
✓
Y - Online Shoppers Segments: Active Quality Seekers
298
CT_TECHTUDESEG_PHOBES
1
VARCHAR
HH
M
Connex
✓
Y - Technology Attitude Segments: Techno-Phobes
299
CT_TECHTUDESEG_LAGGRDS
1
VARCHAR
HH
M
Connex
✓
Y - Technology Attitude Segments: Techno-Laggards
300
CT_TECHTUDESEG_XPLOIT
1
VARCHAR
HH
M
Connex
✓
Y - Technology Attitude Segments: Tech-Sploiters
301
CT_TECHTUDESEG_GAMER
1
VARCHAR
HH
M
Connex
✓
Y - Technology Attitude Segments: Techno-Gamers
302
CT_TECHTUDESEG_THUSIAST
1
VARCHAR
HH
M
Connex
✓
Y - Technology Attitude Segments: Tech-Thusiasts
303
CT_TECHTUDESEG_XPLOR
1
VARCHAR
HH
M
Connex
✓
Y - Technology Attitude Segments: Tech-Splorers
304
CT_DNR_CONTRIB_PBS
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to Public Broadcasting Service (PBS)
305
CT_DNR_CONTRIB_NPR
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to National Public Radio (NPR)
306
CT_DNR_CONTRIB_RELIGIOUS
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to religious organization(s)
307
CT_DNR_CONTRIB_ARTS
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to arts/cultural organization(s)
308
CT_DNR_CONTRIB_EDU
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to educational organization(s)
309
CT_DNR_CONTRIB_ENVIRO
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to environmental organization(s)
310
CT_DNR_CONTRIB_HEALTH
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to health organization(s)
311
CT_DNR_CONTRIB_POL
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to political organization(s)
312
CT_DNR_CONTRIB_SOCSERV
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to social service organization(s)
313
CT_DNR_CONTRIB_NONREL
1
VARCHAR
HH
M
Connex
✓
Y - Contributed to other non-religious organization(s)
314
CT_DNR_CONTRIBAMT_HIGH
1
VARCHAR
HH
M
Connex
✓
Y - Contributed $500 or more to organization(s)
315
CT_VOLUNTEER_CHTYORG
1
VARCHAR
HH
M
Connex
✓
Y - Volunteered for a charitable organization

Geographic Demographics

Census-based; includes interpolated Census data
# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category Field Category
Description Description
316
CENSPCT_WATER
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Water: 0-100
Percentage of Water in a given zip code
317
CENS_POP_DENSITY
6
INTEGER
Z4
A
Supplemental
✓
Census: Pop Density
Population Density per square mile in a given zip code
318
CENS_HU_DENSITY
6
INTEGER
Z4
A
Supplemental
✓
Census: Hu Density
Housing Units Density per square mile in a given zip code
319
CENSPCT_POP_WHITE
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop White: 0-100
Percentage of White population in a given zip code
320
CENSPCT_POP_BLACK
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop Black: 0-100
Percentage of Black population in a given zip code
321
CENSPCT_POP_AMERIND
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop Amer Indian: 0-100
Percentage of Amer Indian population in a given zip code
322
CENSPCT_POP_ASIAN
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop Asian: 0-100
Percentage of Asian population in a given zip code
323
CENSPCT_POP_PACISL
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop Pac Islander: 0-100
Percentage of Pac Islander population in a given zip code
324
CENSPCT_POP_OTHRACE
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop Othrace: 0-100
Percentage of Other Races population in a given zip code
325
CENSPCT_POP_MULTIRACE
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop Multirace: 0-100
Percentage of Multi Race population in a given zip code
326
CENSPCT_POP_HISPANIC
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop Hispanic: 0-100
Percentage of Hispanic population in a given zip code
327
CENSPCT_POP_AGELT18
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop Age 0-17: 0-100
Percentage Population Under Age 18 in a given zip code
328
CENSPCT_POP_MALES
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Pop Males: 0-100
Percentage Males in a given zip code
329
CENSPCT_ADULT_AGE1824
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Adult Age 18-24: 0-100
Percentage Adult Age 18-24 in a given zip code
330
CENSPCT_ADULT_AGE2534
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Adult Age 25-34: 0-100
Percentage Adult Age 25-34 in a given zip code
331
CENSPCT_ADULT_AGE3544
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Adult Age 35-44: 0-100
Percentage Adult Age 35-44 in a given zip code
332
CENSPCT_ADULT_AGE4554
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Adult Age 45-54: 0-100
Percentage Adult Age 45-54 in a given zip code
333
CENSPCT_ADULT_AGE5564
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Adult Age 55-64: 0-100
Percentage Adult Age 55-64 in a given zip code
334
CENSPCT_ADULT_AGEGE65
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Adult Age 65+: 0-100
Percentage Adult Age 65+ in a given zip code
335
CENS_POP_MEDAGE
3
INTEGER
Z4
A
Supplemental
✓
Census: Pop Median Age: 0-100
Population Median Age in a given zip code
336
CENS_HH_AVGSIZE
10
DOUBLE
Z4
A
Supplemental
✓
Census: Hh Avg Size
Average Household Size in a given zip code (not rounded to the nearest whole)
337
CENSPCT_HH_FAMILY
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Hh Family: 0-100
Percentage Hh Family in a given zip code
338
CENSPCT_HH_FAMILY_HUSBWIFE
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Hh Family Husb & Wife: 0-100
Percentage Hh Family Husband & Wife in a given zip code
339
CENSPCT_HU_OCCUPIED
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Hu Occupied: 0-100
Percentage Housing unit Occupied in a given zip code
340
CENSPCT_HU_OWNED
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Hu Owned: 0-100
Percentage Housing unit Owned in a given zip code
341
CENSPCT_HU_RENTED
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Hu Rented: 0-100
Percentage Housing unit Rented in a given zip code
342
CENSPCT_HU_VACANTSEASONAL
3
INTEGER
Z4
A
Supplemental
✓
Census: Pct Hu Vacant/Seasonal: 0-100
Percentage Housing unt Vacant/Seasonal in a given zip code

Additional Fields

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Category
Description Description
343
EHI_V2
1
VARCHAR
HH
M
Core Demographics
✓
Estimated Household Income:
A - Less than $20,000
B - $20,000-$29,999
C - $30,000-$39,999
D - $40,000-$49,999
E - $50,000-$59,999
F - $60,000-$74,999
G - $75,000-$99,999
H - $100,000-$124,999
I - $125,000-$149,999
J - $150,000-$199,999
K - $200,000-$249,999
L - $250,000-$499,999
M - $500,000+
Estimated household income level is a model that takes into consideration 80+ model predicators across a diverse set of info including demographics, transactional & behavioral data, property ownership and mortgages, vehicle ownership and geo-demographics. The data originates from a range of deterministic and probalistic data sources such as county assessor records, self-reported information (e.g. surveys), Census demographics, and many more. EHI_V2 field, released in June 2023, replacing EHI - Field #50.
344
OCCUPATIONCD_V2
1
VARCHAR
I
A, I
Supplemental
✓
Demo Codes - Occupation
Derived from self-reported data and/or state license bureau data indicating an individual's occupation. OCCUPATIONCD_V2 field, released in June 2023, replacing OCCUPATIONCD - Field #63.
345
WEALTHSCR_V2
1
VARCHAR
HH
M
Core Demographics
✓
Wealth Score:
A - $0 OR LESS
B - $1 - $24,999
C - $25,000 - $49,999
D - $50,000 - $74,999
E - $75,000 - $99,999
F - $100,000 - $149,999
G - $150,000 - $249,999
H - $250,000 - $374,999
I - $375,000 - $499,999
J - $500,000 - $749,999
K - $750,000 - $999,999
L - $1,000,000+
The net worth selection is a model which predicts household net worth. It takes into consideration 60+ model predictors across a diverse set of info including demographics, transactional & behavioral data, property ownership and mortgages, vehicle ownership and geo-demographics. The data originates from a range of deterministic and probalistic data sources such as county assessor records, self-reported information (e.g. surveys), Census demographics, and many more. WEALTHSCR_V2 field, released in March 2024, replacing WEALTHSCR - Field #59.
346
AGE_RANGE_ESTIMATED
1
VARCHAR
I
I
Core Demographics
✓
Estimated Age Range
A - Estimated Age 18-24
B - Estimated Age 25-34
C - Estimated Age 35-44
D - Estimated Age 45-54
E - Estimated Age 55-64
F - Estimated Age 65-74
G - Estimated Age 75+
Adult Estimated Age Range is calculated from date of birth data. Age data is applied at the individual level and is compiled from a variety of sources that may include public data, buying activities, and self-reported information. This is a calculation of age based on the individual's year of birth. The calculation is current year minus the YOB, which is then used to assign the record to an appropriate age range.
347
AGE_RANGE_INFERRED
1
VARCHAR
Z4
I
Core Demographics
✓
Inferred Age Range.
A - Inferred Age 18-24
B - Inferred Age 25-34
C - Inferred Age 35-44
D - Inferred Age 45-54
E - Inferred Age 55-64
F - Inferred Age 65-74
G - Inferred Age 75+
Adult Inferred Age Range uses known age of adults within the same ZIP+4 to assign an age range. The originating known age data is compiled from a variety of sources that may include public data, buying activities data, and self-reported information. A value is only assigned for this field if a record for the AGE_RANGE_ESTIMATED field is not available.
348
AGE_RANGE_COMBINED
1
VARCHAR
I,Z4
I
Core Demographics
✓
Combined (Estimated + Inferred) Age Range
A - Combined Age 18-24
B - Combined Age 25-34
C - Combined Age 35-44
D - Combined Age 45-54
E - Combined Age 55-64
F - Combined Age 65-74
G - Combined Age 75+
Adult Combined Age Range assigns an age range value to all records, using the applicable value from either AGE_RANGE_ESTIMATED and AGE_RANGE_INFERRED fields. Use this field if you are looking for 100% age range coverage within the TCI universe.

Auto - Premium Package

TCI Linkage Information

# Field Number
Field Name Field Name
Length Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Description Description
1
PID
96
VARCHAR
Persistent ID assigned to individuals identified in the Infutor Graph
I
✓
✓
N/A

Auto Attributes

# Field Number
Field Name Field Name
Length Max Field Length
Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Description Description
2
VIN1
17
VARCHAR
Vehicle Identification Number (VIN) (Vehicle 1)
HH
✓
✓
A
3
MAKE1
30
VARCHAR
Vehicle Make (Vehicle 1)
HH
✓
✓
A
4
MODEL1
30
VARCHAR
Vehicle Model (Vehicle 1)
HH
✓
✓
A
5
YEAR1
30
VARCHAR
Vehicle Year - YYYY
HH
✓
✓
A
6
VEH_CLASS1
15
VARCHAR
Vehicle Class Description: CROSSOVER FULL SIZE CAR FULL SIZE SUV FULL SIZE TRUCK FULL SIZE VAN MID SIZE CAR MID SIZE TRUCK MID SIZE SUV MINIVAN SMALL CAR SMALL SUV SMALL TRUCK"
HH
✓
✓
A
7
FUELTYPECD1
30
VARCHAR
Vehicle Fuel Code: B - Biodieselcolumn D - Diesel F - Flex-Fuel G - Gasoline H - Fuel Cell I - Plug-In Electric L - Electric/Gas N - CNG (Compressed Natural Gas) P - Propane Y - Hybrid
HH
✓
✓
A
8
MFGCD1
1
VARCHAR
Auto Manufacturer Code: A - Antique C - Chrysler Products F- Ford Products G - GM Products H - High end Imports L - Low End Imports O - Other
HH
✓
✓
A
9
STYLECD1
10
VARCHAR
Vehicle Style Code: CONV CPE2DR CUV HATCHBACK LUXURY PICKUP SEDAN SPORT UTIL VAN WAGON
HH
✓
✓
A
10
MILEAGECD1
1
VARCHAR
Mileage Code: A-Z in increments of 10,000 0-10000-A and 260,000+-Z
HH
✓
✓
A
11
INFERREDDATE1
1
VARCHAR
Inferred Purchase Date Code: A - 0-6 months B - 7-12 months C - 13-18 months D - 19-24 months E - 25-36 months F - 37-48 months G - 49+ months
HH
✓
✓
A
12
VIN2
17
VARCHAR
Vehicle Identification Number (VIN) (Vehicle 2)
HH
✓
✓
A
13
MAKE2
30
VARCHAR
Vehicle Make (Vehicle 2)
HH
✓
✓
A
14
MODEL2
30
VARCHAR
Vehicle Model (Vehicle 2)
HH
✓
✓
A
15
YEAR2
4
VARCHAR
Vehicle Year - YYYY
HH
✓
✓
A
16
VEH_CLASS2
15
VARCHAR
Vehicle Class Description: CROSSOVER FULL SIZE CAR FULL SIZE SUV FULL SIZE TRUCK FULL SIZE VAN MID SIZE CAR MID SIZE TRUCK MID SIZE SUV MINIVAN SMALL CAR SMALL SUV SMALL TRUCK
HH
✓
✓
A
17
FUELTYPECD2
1
VARCHAR
Vehicle Fuel Code: B - Biodieselcolumn D - Diesel F - Flex-Fuel G - Gasoline H - Fuel Cell I - Plug-In Electric L - Electric/Gas N - CNG (Compressed Natural Gas) P - Propane Y - Hybrid
HH
✓
✓
A
18
MFGCD2
1
VARCHAR
Auto Manufacturer Code: A - Antique C - Chrysler Products F- Ford Products G - GM Products H - High end Imports L - Low End Imports O - Other
HH
✓
✓
A
19
STYLECD2
10
VARCHAR
Vehicle Style Code: CONV CPE2DR CUV HATCHBACK LUXURY PICKUP SEDAN SPORT UTIL VAN WAGON
HH
✓
✓
A
20
MILEAGECD2
1
VARCHAR
Mileage Code: A-Z in increments of 10,000 0-10000-A and 260,000+-Z
HH
✓
✓
A
21
INFERREDDATE2
1
VARCHAR
Inferred Purchase Date Code: A - 0-6 months B - 7-12 months C - 13-18 months D - 19-24 months E - 25-36 months F - 37-48 months G - 49+ months
HH
✓
✓
A
22
VIN3
17
VARCHAR
Vehicle Identification Number (VIN) (Vehicle 3)
HH
✓
✓
A
23
MAKE3
30
VARCHAR
Vehicle Make (Vehicle 3)
HH
✓
✓
A
24
MODEL3
30
VARCHAR
Vehicle Model (Vehicle 3)
HH
✓
✓
A
25
YEAR3
4
VARCHAR
Vehicle Year - YYYY
HH
✓
✓
A
26
VEH_CLASS3
15
VARCHAR
Vehicle Class Description: CROSSOVER FULL SIZE CAR FULL SIZE SUV FULL SIZE TRUCK FULL SIZE VAN MID SIZE CAR MID SIZE TRUCK MID SIZE SUV MINIVAN SMALL CAR SMALL SUV SMALL TRUCK
HH
✓
✓
A
27
FUELTYPECD3
1
VARCHAR
Vehicle Fuel Code: B - Biodieselcolumn D - Diesel F - Flex-Fuel G - Gasoline H - Fuel Cell I - Plug-In Electric L - Electric/Gas N - CNG (Compressed Natural Gas) P - Propane Y - Hybrid
HH
✓
✓
A
28
MFGCD3
1
VARCHAR
Auto Manufacturer Code: A - Antique C - Chrysler Products F- Ford Products G - GM Products H - High end Imports L - Low End Imports O - Other
HH
✓
✓
A
29
STYLECD3
10
VARCHAR
Vehicle Style Code: CONV CPE2DR CUV HATCHBACK LUXURY PICKUP SEDAN SPORT UTIL VAN WAGON
HH
✓
✓
A
30
MILEAGECD3
10
VARCHAR
Mileage Code: A-Z in increments of 10,000 0-10000-A and 260,000+-Z
HH
✓
✓
A
31
INFERREDDATE3
10
VARCHAR
Inferred Purchase Date Code: A - 0-6 months B - 7-12 months C - 13-18 months D - 19-24 months E - 25-36 months F - 37-48 months G - 49+ months
HH
✓
✓
A
32
VIN4
17
VARCHAR
Vehicle Identification Number (VIN) (Vehicle 4)
HH
✓
✓
A
33
MAKE4
30
VARCHAR
Vehicle Make (Vehicle 4)
HH
✓
✓
A
34
MODEL4
30
VARCHAR
Vehicle Model (Vehicle 4)
HH
✓
✓
A
35
YEAR4
4
VARCHAR
Vehicle Year - YYYY
HH
✓
✓
A
36
VEH_CLASS4
15
VARCHAR
Vehicle Class Description: CROSSOVER FULL SIZE CAR FULL SIZE SUV FULL SIZE TRUCK FULL SIZE VAN MID SIZE CAR MID SIZE TRUCK MID SIZE SUV MINIVAN SMALL CAR SMALL SUV SMALL TRUCK
HH
✓
✓
A
37
FUELTYPECD4
15
VARCHAR
Vehicle Fuel Code: B - Biodieselcolumn D - Diesel F - Flex-Fuel G - Gasoline H - Fuel Cell I - Plug-In Electric L - Electric/Gas N - CNG (Compressed Natural Gas) P - Propane Y - Hybrid
HH
✓
✓
A
38
MFGCD4
15
VARCHAR
Auto Manufacturer Code: A - Antique C - Chrysler Products F- Ford Products G - GM Products H - High end Imports L - Low End Imports O - Other
HH
✓
✓
A
39
STYLECD4
10
VARCHAR
Vehicle Style Code: CONV CPE2DR CUV HATCHBACK LUXURY PICKUP SEDAN SPORT UTIL VAN WAGON
HH
✓
✓
A
40
MILEAGECD4
1
VARCHAR
Mileage Code: A-Z in increments of 10,000 0-10000-A and 260,000+-Z
HH
✓
✓
A
41
INFERREDDATE4
1
VARCHAR
Inferred Purchase Date Code: A - 0-6 months B - 7-12 months C - 13-18 months D - 19-24 months E - 25-36 months F - 37-48 months G - 49+ months
HH
✓
✓
A
42
INTERNAL1
255
VARCHAR
Future Use
N/A
✓
✓
N/A
43
INTERNAL2
255
VARCHAR
Future Use
N/A
✓
✓
N/A
44
INTERNAL3
255
VARCHAR
Future Use
N/A
✓
✓
N/A
45
INTERNAL4
255
VARCHAR
Future Use
N/A
✓
✓
N/A

Auto Intelligence Models (In-Market and Affinities)

# Field Number
Field Name Field Name
Length Max Field Length
Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Description Description
46
IN_MARKET_NEW_SEGMENTS
1
INTEGER
Predicts the likelihood that a consumer is likely to purchase a new vehicle, segment-based. 1 = Very Low 2 = Low 3 = Medium 4 = High 5 = Very High (5x)These segments are derived from the IN_MARKET_NEW_SCORE values, where 1= scores ranged 80-100 2= scores ranged 61-80 3= scores ranged 41-60 4= scores ranged 21-40 5= scores ranged 1-20
HH
✓
✓
M
47
IN_MARKET_NEW_SCORE
3
INTEGER
Score that predicts the likelihood that a consumer is likely to purchase a new vehicle (for machine learning algorithms), Numeric values 1-100. 1 = highest propensity, 100 = lowest propensity.
HH
✓
✓
M
48
IN_MARKET_USED_SEGMENTS
1
INTEGER
Predicts the likelihood that a consumer is likely to purchase a used vehicle, segment-based. 1 = Very Low 2 = Low 3 = Medium 4 = High 5 = Very High (3x)These segments are derived from the IN_MARKET_USED_SCORE values, where 1= scores ranged 80-100 2= scores ranged 61-80 3= scores ranged 41-60 4= scores ranged 21-40 5= scores ranged 1-20
HH
✓
✓
M
49
IN_MARKET_USED_SCORE
3
INTEGER
Score that predicts the likelihood that a consumer is likely to purchase a used vehicle. Numeric values 1-100 (for machine learning algorithms). 1 = highest propensity, 100 = lowest propensity.
HH
✓
✓
M
50
CHEVROLET_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Chevrolet brand and a consumer´s dedication to purchase Chevrolet vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
51
DODGE_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Dodge brand and a consumer´s dedication to purchase Dodge vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
52
FORD_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Ford brand and a consumer´s dedication to purchase Ford vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
53
GMC_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the GMC brand and a consumer´s dedication to purchase GMC vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
54
HONDA_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Honda brand and a consumer´s dedication to purchase Honda vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
55
HYUNDAI_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Hyundai brand and a consumer´s dedication to purchase Hyundai vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
56
TOYOTA_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Toyota brand and a consumer´s dedication to purchase Toyota vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
57
NISSAN_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Nissan brand and a consumer´s dedication to purchase Nissan vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
58
JEEP_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Jeep brand and a consumer´s dedication to purchase Jeep vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
59
CHRYSLER_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Chrysler brand and a consumer´s dedication to purchase Chrysler vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
60
LEXUS_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Lexus brand and a consumer´s dedication to purchase Lexus vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
61
CADILLAC_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Cadillac brand and a consumer´s dedication to purchase Cadillac vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
62
BMW_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the BMW brand and a consumer´s dedication to purchase BMW vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
63
MERCEDESBENZ_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Mercedes-Benz brand and a consumer´s dedication to purchase Mercedes-Benz vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
64
VOLVO_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Volvo brand and a consumer´s dedication to purchase Volvo vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
65
INFINITI_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Infinity brand and a consumer´s dedication to purchase Infinity vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
66
AUDI_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Audi brand and a consumer´s dedication to purchase Audi vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
67
LINCOLN_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Lincoln brand and a consumer´s dedication to purchase Lincoln vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
68
LANDROVER_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Land Rover brand and a consumer´s dedication to purchase Land Rover vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
69
TRUCK_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Truck auto style and a consumer´s dedication to purchase Trucks. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
70
SUV_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the SUV auto style and a consumer´s dedication to purchase SUV vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
71
COUPE_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the 2 door Coupe auto style and a consumer´s dedication to purchase Coupe vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
72
SEDAN_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Sedan auto style and a consumer´s dedication to purchase Sedan vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
73
VAN_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Van auto style and a consumer´s dedication to purchase Vans. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
74
HYBRID_AFFINITY
1
INTEGER
Score that predicts the affinity or loyalty towards the Hybrid auto style and a consumer´s dedication to purchase Hybrid vehicles. Numeric values 1-5. 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity
HH
✓
✓
M
75
INTERNAL5
255
VARCHAR
Future Use
N/A
✓
✓
N/A
76
INTERNAL6
255
VARCHAR
Future Use
N/A
✓
✓
N/A
77
INTERNAL7
255
VARCHAR
Future Use
N/A
✓
✓
N/A
78
INTERNAL8
255
VARCHAR
Future Use
N/A
✓
✓
N/A
79
INTERNAL9
255
VARCHAR
Future Use
N/A
✓
✓
N/A
80
INTERNAL10
255
VARCHAR
Future Use
N/A
✓
✓
N/A
81
INTERNAL11
255
VARCHAR
Future Use
N/A
✓
✓
N/A
82
INTERNAL12
255
VARCHAR
Future Use
N/A
✓
✓
N/A
83
INTERNAL13
255
VARCHAR
Future Use
N/A
✓
✓
N/A
84
INTERNAL14
255
VARCHAR
Future Use
N/A
✓
✓
N/A
85
INTERNAL15
255
VARCHAR
Future Use
N/A
✓
✓
N/A

Property - Premium Package

TCI Linkage Information

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
1
ADDRID
50
VARCHAR
Persistent ID assigned to addresses identified in the Infutor Graph

General Property Information

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
2
PROP_OWNEROCC
1
VARCHAR
Y - Property is Owner Occupied
3
PROP_IND
3
VARCHAR
A general code used to easily recognize specific property types (e.g, Residential, Condominium, Commercial). Please see "PROP_IND" table for code descriptions and/or click on the field name to view individual codes descriptions
4
PROP_MOBHOME
1
VARCHAR
Y - Mobile Home present on the Parcel
5
PROP_STYLE
3
VARCHAR
Type of building style (e.g., Colonial, Cape Code, Bungalow). Please see "PROP_STYLE" table for code descriptions and/or click on the field name to view individual codes descriptions
6
PROP_STORIESCD
3
VARCHAR
Type / number of stories (e.g., Split Foyer, Tri Level, 2 Story). Please see "PROP_STORIESCD" table for code descriptions and/or click on the field name to view individual codes descriptions
7
PROP_QLTY
3
VARCHAR
"Type of construction quality of building: QAV - Average QBA - Below Average QVV - Above Average QPO - Poor QEX - Excellent QLU - Luxury 999 - Bypass QGO - Good QLO - Low QFA - Fair QEC - Economical"
8
PROP_COND
3
VARCHAR
This represents the physical condition of the building (e.g., Good, Fair, Under Construction). Please see "PROP_COND" table for code descriptions and/or click on the field name to view individual codes descriptions
9
PROP_CNSTRTYPE
3
VARCHAR
The primary method of construction (e.g., Steel / Glass, Concrete Block, Log). Please see "PROP_CNSTRTYPE" table for code descriptions and/or click on the field name to view individual codes descriptions
10
PROP_EXTNW
3
VARCHAR
The type and/or finish of the exterior walls (e.g., Vinyl Siding, Brick Veneer, Frame / Stone). Please see "PROP_EXTNW" table for code descriptions and/or click on the field name to view individual codes descriptions
11
PROP_VALCALC
11
VARCHAR
The total (e.g., Land + Improvement) Value closest to current market value used for assessment by county or local taxing authorities
12
PROP_VAL_CALCIND
1
VARCHAR
"Type of values used to seed the total value calculated field: A - Assessed M - Market P - Appraised T - Transitional"
13
PROP_IMP_VALCALC
11
VARCHAR
The "IMPROVEMENT" Value closest to current market value used for assessment by county or local taxing authorities
14
PROP_IMP_VALCALC_IND
1
VARCHAR
"The ""IMPROVEMENT"" Value closest to current market value used for assessment by county or local taxing authorities: A - Assessed M - Market P - Appraised T - Transitional"
15
PROP_ASSED_VAL
11
VARCHAR
The Total Assessed Value of the Parcel's Land & Improvement values as provided by the county or local taxing/assessment authority
16
PROP_ASSED_IMPVAL
1
VARCHAR
The Assessed Improvement Values as provided by the county or local taxing/assessment authority
17
PROP_MRKTVAL
11
VARCHAR
Total Market Value of the Parcel's Land & Improvement values as provided by the county or local taxing/assessment authority
18
PROP_MRKT_IMPVAL
11
VARCHAR
Market Improvement Values as provided by the county or local taxing/assessment authority
19
PROP_APPRAISED_VAL
11
VARCHAR
The Total Appraised Value of the Parcel's Land & Improvement values as provided by the county or local taxing/assessment authority
20
PROP_APPRAISED_IMPVAL
11
VARCHAR
The Appraised Improvement Values as provided by the county or local taxing/assessment authority

Property Attributes

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
21
PROP_YRBLD
4
VARCHAR
The Appraised Improvement Values as provided by the county or local taxing/assessment authority
22
PROP_EFFYRBLD
4
VARCHAR
The Appraised Improvement Values as provided by the county or local taxing/assessment authority. This is the first year the building was assessed with its current components (e.g., YYYY, a building is originally constructed in 1960 and a bedroom and bath was added to the building in 1974. The Year Built would be 1960 and the Effective Year Built would be 1974)
23
PROP_VIEW
3
VARCHAR
View from building (e.g., Gulf, Mountains, Pool). Please see "PROP_VIEW " table for code descriptions and/or click on the field name to view individual codes descriptions
24
PROP_LCTN_INFL
3
VARCHAR
Positive or negative aspects associated with the location of the parcel (e.g., waterfront, flood plane, airport). Please see "PROP_LCTN_INFL" table for code descriptions and/or click on the field name to view individual codes descriptions.
25
PROP_ACRES
13
VARCHAR
Total land mass in Acres. (4 decimal points)Example: 13000 = 1.3 Acres
26
PROP_LANDSQFT
9
VARCHAR
Total land mass is Square Feet
27
PROP_UNVBLDSQFT
9
VARCHAR
The Building Square Footage that can most accurately be used for assessments or comparables (e.g., Living, Adjusted, Gross)
28
PROP_BLDSQFTIND
1
VARCHAR
"The codes appearing in this field indicate the source used to populate the UNIVERSAL BUILDING SQUARE FEET field: R - GROUND FLOOR LEVEL H - HEATED AREA M - MAIN OR BASE AREA B - BUILDING A - ADJUSTED L - LIVING G - GROSS"
29
PPROP_BLDSQFT
9
VARCHAR
The size of the building in Square Feet. This field is most commonly populated as a cumulative total when a county does not differentiate between Living and Non-living areas
30
PROP_LIVINGSQFT
7
VARCHAR
Area of a building that is used for general living. This is typically the area of a building that is heated or air conditioned and does not include Garage, Porch or Basement square footage
31
PROP_GROSSSQFT
7
VARCHAR
Square footage for the entire building. Typically this represents all square feet under the roof
32
PROP_ADJGROSSSQFT
7
VARCHAR
Square footage used by the county or local taxing / assessment authority to determine Improvement Value. This figure is typically 100% of the living area, plus lower percentage of non-living area
33
PROP_RMS
5
VARCHAR
Total number of rooms contained in the primary building
34
PROP_BEDRMS
5
VARCHAR
Total number of bedrooms contained in the primary building
35
PROP_BATHSCALC
5
VARCHAR
Total number of Bathrooms in whole numbers (e.g., a home containing 2 1/2 baths would have the number 3 stored in this field as, three actual rooms have been designated for this purpose). (e.g., 200, 300, 400)
36
PROP_BATHS
5
VARCHAR
Total number of Bathrooms as provided by our data sources (e.g., 4.00, 2.50, 1.75)
37
PROP_FULLBATHS
5
VARCHAR
Total number of Full Baths (typically comprised of a sink, toilet, and bathtub / shower stall). A home containing 2 1/2 baths would have the number 2 stored in this field
38
PROP_AC
3
VARCHAR
The type of air conditioning method used to cool the building (e.g., Central, Wall Unit, Evaporative). Please see "PROP_AC" table for code descriptions and/or click on the field name to view individual codes descriptions
39
PROP_FRPL
1
VARCHAR
Y - Fireplace is located within the building
40
PROP_FND
3
VARCHAR
The type of foundation (e.g., Continuous Footing, Pier, Mud Sill). Please see "PROP_FND" table for code descriptions and/or click on the field name to view individual codes descriptions
41
PROP_FLR
3
VARCHAR
The type of floor construction (e.g., Concrete, Plywood). Please see "PROP_FLR" table for code descriptions and/or click on the field name to view individual codes descriptions
42
PROP_GAR
3
VARCHAR
Type of garage or carport present (e.g., Attached Finished, Enclosed Carport, Basement Garage). Please see "PROP_GAR" table for code descriptions and/or click on the field name to view individual codes descriptions
43
PROP_HEAT
3
VARCHAR
Type or method of heating (e.g., Hot Water, Heat Pump, Baseboard, Radiant). Please see "PROP_HEAT" table for code descriptions and/or click on the field name to view individual codes descriptions
44
PROP_POOL
1
VARCHAR
Y - Pool Present in Parcel
45
PROP_ROOFCOVER
3
VARCHAR
Type of roof covering (e.g., Clay Tile, Aluminum, Shake). Please see "PROP_ROOFCOVER" table for code descriptions and/or click on the field name to view individual codes descriptions
46
PROP_ROOFTYPE
3
VARCHAR
Type of roof shape (e.g., Gambrel, Gable, Flat, Mansard). Please see "PROP_ROOFTYPE" table for code descriptions and/or click on the field name to view individual codes descriptions
47
PROP_ENERGY
3
VARCHAR
Type of electricity or energy use within the building (e.g., Average Wiring, Underground Wired, Private Source). Please see "PROP_ENERGY" table for code descriptions and/or click on the field name to view individual codes descriptions
48
PROP_FUEL
3
VARCHAR
Type of fuel used for heating of water and building (e.g., Solar, Gas, Oil). Please see "PROP_FUEL" table for code descriptions and/or click on the field name to view individual codes descriptions
49
PROP_SEWER
3
VARCHAR
"Type Of Sewer System On The Parcel: 0 - None 999 - Bypass SPU - Public SCE - Cesspool SSE - Septic SPR - Private STR - Storm SCO - Commercial"
50
PROP_WATER
3
VARCHAR
"Type Of Water Service On The Parcel: 0 - None 999 - Bypass WPU- Public WSC - Spring/Creek WWE - Well WPR - Private WCO - Commercial WPW - Public Well WCI - Cistern"

Assessor/Deed Information

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
51
PROP_HOMESTEAD
1
VARCHAR
Y - Owner qualified for a Homeowner/Homestead exemption
52
PROP_XMTVET
10
VARCHAR
"If State has such an exemption and it has been granted then a flag: Combat Vet Veteran"
53
PROP_XMT_DISABLED
10
VARCHAR
"If State has such an exemption and it has been granted then a flag: Disabled Blind"
54
PROP_TAXAMT
11
VARCHAR
The Total Tax amount provided by the county or local taxing/assessment authority
55
PROP_SALESDEEDCD
1
VARCHAR
"The type of deed used to record the sales transaction: U - Foreclouse Q - Quit Claim X - Multi CNTY/ST or Open-End-MTG T - Deed of Trust G - Deed D - Release of Deed of Trust/MTG F - Final Judgement J - Mechanic Liens L - Lis Pendens N - Notice of Default R - Release/Recision S - Loan Assignment"
56
PROP_SALEDATE
8
VARCHAR
Date the sales transaction was legally completed -YYYYMMDD
57
PROP_SALEAMT
11
VARCHAR
Price of the sale as depicted on the recorded sales transaction
58
PROP_SALECD
1
VARCHAR
"Financial Consideration Code: V - Verified R - Lease P - Sale Price (Partial) C - Confirmed N - Stamps on Back/Non-Disclosed F - Sale Price (Full) E - Estimated L - Commited"
59
PROP_SALESTRANSCD
2
VARCHAR
"This identifies situations associated with the sale: 1 - Resale 2 - Refinance 3 - Subdivision/new construction 4 - Timeshare 6 - Construction Loan 7 - Seller Carryback 9 - Nominal D - Release of Deed S - Assignment of Deed of Trust"

Mortgage Information

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
60
PROP_OWNERCD
3
VARCHAR
The type of ownership in terms of: owners in common; joint. Please see "PROP_OWNERCD" table for code descriptions and/or click on the field name to view individual codes descriptions
61
PROP_LOANTOVAL
3
VARCHAR
If provided on the note and recorded, loan to value ratio (e.g., 47,118,61,185)
62
PROP_MTGAMT
11
VARCHAR
Amount of Loan
63
PROP_MTGDATE
8
VARCHAR
Date Mortgage was initiated - YYYYMMDD
64
PROP_MTGLOANCD
5
VARCHAR
"Associated with the Mortgage type: WRP - Wrap-Around Mortgage VA - Veterans Affairs FHA - Federal Housing Administration CNV - Conventional SBA - Small Business Administration PP - Private Party Lender CDA - Community Development Authority CNS - Construction LH - Lease Hold Mortgage PMM - Purchase Money Mortgage"
65
PROP_MTGTERM
5
VARCHAR
The length of time per the mortgage (e.g., 15 years, 30 years, etc)
66
PROP_MTGDUEDATE
8
VARCHAR
Date Mortgage becomes due - YYYYMMDD
67
PROP_LENDERNAME
60
VARCHAR
Name of Lender
68
PROP_MTGREFICD
1
VARCHAR
"Was it a refinance of existing/prior mortgage: Y - Yes T - Trust"
69
PROP_RMSEQUITYCD
1
VARCHAR
Y - If equity in property was acknowledged
70
PROP_MTGINTRATE
6
VARCHAR
"Rate Associated with Mortgage (4 decimal) Example: 47300 - 4.73%"
71
PROP_MTGINTRATETYPE
3
VARCHAR
"Mortgage Rate Type: FIX - Fixed ADJ - Adjustable VAR - Variable BAL - Balloon"
72
PROP_MTGAMT2
11
VARCHAR
Amount of 2nd mortgage
73
PROP_MTGDATE2
8
VARCHAR
Date 2nd Mortgage was initiated - YYYYMMDD
74
PROP_MTGLOANCD2
5
VARCHAR
"Associated with the 2nd Mortgage type: WRP - Wrap-Around Mortgage VA - Veterans Affairs FHA - Federal Housing Administration CNV - Conventional SBA - Small Business Administration PP - Private Party Lender CDA - Community Development Authority CNS - Construction LH - Lease Hold Mortgage PMM - Purchase Money Mortgage"
75
PROP_MTGDEEDCD2
6
VARCHAR
2nd Deed used for recording. Please see "PROP_MTGDEEDCD" table for code descriptions and/or click on the field name to view individual codes descriptions
76
PROP_MTGTERM2
5
VARCHAR
The length of time per the 2nd mortgage (e.g., 15 years, 30 years, etc)
77
PROP_MTGDUEDATE2
8
VARCHAR
Date 2nd Mortgage becomes due - YYYYMMDD
78
PROP_MTGASSUMPTIONAMT2
9
VARCHAR
Amount of 2nd mortgage was assumed and rolled. (e.g., 65750, 130000, 162500, 50000)
79
PROP_LENDERNAME2
60
VARCHAR
Name of Lender 2
80
PROP_MTGREFICD2
1
VARCHAR
"Was it a refinance of existing/prior 2nd mortgage: Y - Yes T - Trust"
81
PROP_RMSEQUITYCD2
1
VARCHAR
Y - If 2nd equity in property was acknowledged
82
PROP_MTGINTRATE2
6
VARCHAR
"2nd Rate Associated with Mortgage (4 decimal) Example: 47300 - 4.73%"
83
PROP_MTGINTRATETYPE2
3
VARCHAR
"Mortgage Rate Type 2: FIX - Fixed ADJ - Adjustable VAR - Variable BAL - Balloon"
84
PROP_MTGAMT3
11
VARCHAR
Amount of 3rd mortgage
85
PROP_MTGDATE3
8
VARCHAR
Date 3rd Mortgage was initiated - YYYYMMDD
86
PROP_MTGLOANCD3
5
VARCHAR
"Associated with the 3rd Mortgage type: WRP - Wrap-Around Mortgage VA - Veterans Affairs FHA - Federal Housing Administration CNV - Conventional SBA - Small Business Administration PP - Private Party Lender CDA - Community Development Authority CNS - Construction LH - Lease Hold Mortgage PMM - Purchase Money Mortgage"
87
PROP_MTGDEEDCD3
6
VARCHAR
3rd Deed used for recording. Please see "PROP_MTGDEEDCD" table for code descriptions and/or click on the field name to view individual codes descriptions
88
PROP_MTGTERM3
5
VARCHAR
The length of time per the 3rd mortgage (e.g., 15 years, 30 years, etc)
89
PROP_MTGDUEDATE3
8
VARCHAR
Date 3rd Mortgage becomes due - YYYYMMDD
90
PROP_MTGASSUMPTIONAMT3
9
VARCHAR
Amount of 3rd mortgage was assumed and rolled. (e.g., 65750, 130000, 162500, 50000)
91
PROP_LENDERNAME3
60
VARCHAR
Name of Lender 3
92
PROP_MTGREFICD3
1
VARCHAR
"Was it a refinance of existing/prior 3rd mortgage: Y - Yes T - Trust"
93
PROP_RMSEQUITYCD3
1
VARCHAR
Y - If 3rd equity in property was acknowledged
94
PROP_MTGINTRATE3
6
VARCHAR
"3rd Rate Associated with Mortgage (4 decimal) Example: 47300 - 4.73%"
95
PROP_MTGINTRATETYPE3
3
VARCHAR
"Mortgage Rate Type 3: FIX - Fixed ADJ - Adjustable VAR - Variable BAL - Balloon"

Property Intelligence (AVM & Home Equity)

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
97
HEQUITY_EST
12
FLOAT
Estimate of Homeowner Equity in the Property in Dollars
98
HEQUITY_CONF
1
VARCHAR
"Home Equity Estimate Confidence Level (from 1 to 4) 1 - High confidence Home Equity prediction 2 - Medium confidence Home Equity predictions 3 - Medium-Low confidence Home Equity predictions 4 - Low confidence Home Equity predictions"
99
AVM_ESTIMATE
12
FLOAT
Estimate of Property Value in Dollars
100
AVM_ESTIMATE_ERROR
7
FLOAT
Estimate Error in Dollars (based on zip or national if zip not available)
101
AVM_ESTIMATE_PCT_ERROR
3
FLOAT
Estimate Error as a percentage of Estimate Value (000-999, based on zip or national if zip not available)
102
INTERNAL1
255
VARCHAR
Internal Use
103
INTERNAL2
255
VARCHAR
Internal Use
104
INTERNAL3
255
VARCHAR
Internal Use
105
INTERNAL4
255
VARCHAR
Internal Use
106
INTERNAL5
3
VARCHAR
Internal Use
107
INTERNAL6
255
VARCHAR
Internal Use
108
INTERNAL7
3
VARCHAR
Internal Use

Green - Premium Package

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Description Description
1
PID
32
VARCHAR
I
Up to 32-Character Alphanumeric Persistent ID. Use this field to link to TCI.
N/A
2
GV_SEGMENT
2
INTEGER
I
See tab - Green Vue Segments
M
3
GV_LUXURY_EURO_CENT
2
INTEGER
I
This value is a measure of the likelihood of a green consumer to purchase a European luxury car brand (i.e BMW, Mercedes-Benz, Audi, Volvo, Volkswagen) expressed in percentiles from 1-99. Values are represented in one percent increments allowing for granular analysis. The Green propensities utilize a variety of data sources including publicly available records (e.g. Census, County Assessor), opted-in transactional/ purchasing data (e.g. retail channels, direct marketing) and self-reported information (e.g. survey respondents, social media content). The resulting dataset includes a combination of deterministic, inferred, and modeled attributes.
"Luxury Vehicles European propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity) "
M
4
GV_LUXURY_EURO
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase an European luxury car brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Luxury Vehicles European propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
5
GV_LUXURY_ASIA_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase an Asian luxury car brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Luxury Vehicles Asian propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
6
GV_LUXURY_ASIA
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase an Asian luxury car brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Luxury Vehicles Asian propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
7
GV_LUXURY_DOMESTIC_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase a domestic luxury car brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Luxury Vehicles Domestic propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
8
GV_LUXURY_DOMESTIC
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase a domestic luxury car brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Luxury Vehicles Domestic propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
9
GV_EV_EURO_CROSS_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase an electric Crossover car from an European brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Electric Crossover Vehicles European propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
10
GV_EV_EURO_CROSS
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase an electric Crossover car from an European brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Electric Crossover Vehicles European propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
10
GV_EV_EURO_CROSS
2
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase an electric Crossover car from an European brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Electric Crossover Vehicles European propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
11
GV_EV_EURO_SED_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase an electric Sedan car from an European brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Electric Sedan Vehicles European propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
12
GV_EV_EURO_SED
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase an electric Sedan car from an European brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Electric Sedan Vehicles European propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
13
GV_EV_DOMESTIC_CROSS_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase an electric Crossover car from a domestic brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Electric Crossover Vehicles Domestic propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
14
GV_EV_DOMESTIC_CROSS
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase an electric Crossover car from a domestic brand expressed in a 1-5 score. It is a probalistic segment, modeled from known behaviors & consumer purchase activity from Market Vue.
"Electric Crossover Vehicles Domestic propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
15
GV_EV_DOMESTIC_PICKUP_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase an electric Pickup car from a domestic brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Electric Pickup Vehicles Domestic propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
16
GV_EV_DOMESTIC_PICKUP
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase an electric Pickup car from a domestic brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Electric Pickup Vehicles Domestic propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
17
GV_EV_DOMESTIC_SED_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase an electric Sedan car from a domestic brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Electric Sedan Vehicles Domestic propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
18
GV_EV_DOMESTIC_SED
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase an electric Sedan car from a domestic brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Electric Sedan Vehicles Domestic propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
19
GV_HYB_ASIA_SED_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase a hybrid Sedan car from an Asian brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Hybrid Sedan Vehicles Asian propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
20
GV_HYB_ASIA_SED
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase a hybrid Sedan car from an Asian brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Hybrid Sedan Vehicles Asian propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
21
GV_HYB_ASIA_CROSS_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase a hybrid Crossover car from an Asian brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Hybrid Crossover Vehicles Asian propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
22
GV_HYB_ASIA_CROSS
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase a hybrid Crossover car from an Asian brand expressed in a 1-5 score. It is a probalistic segment, modeled from known behaviors & consumer purchase activity from Market Vue.
"Hybrid Crossover Vehicles Asian propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
23
GV_HYB_ASIA_PICKUP_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase a hybrid Pickup car from an Asian brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Hybrid Pickup Vehicles Asian propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
24
GV_HYB_ASIA_PICKUP
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase a hybrid Pickup car from an Asian brand expressed in a 1-5 score. It is a probalistic segment, modeled from known behaviors & consumer purchase activity from Market Vue.
"Hybrid Pickup Vehicles Asian propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
25
GV_HYB_DOMESTIC_SED_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase a hybrid Sedan car from a domestic brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Hybrid Sedan Vehicles Domestic propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
26
GV_HYB_DOMESTIC_SED
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase a hybrid Sedan car from a domestic brand expressed in a 1-5 score. It is a probalistic segment, modeled from known behaviors & consumer purchase activity from Market Vue.
"Hybrid Sedan Vehicles Domestic propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
27
GV_HYB_DOMESTIC_CROSS_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase a hybrid Crossover car from a domestic brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Hybrid Crossover Vehicles Domestic propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
28
GV_HYB_DOMESTIC_CROSS
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase a hybrid Crossover car from a domestic brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Hybrid Crossover Vehicles Domestic propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
29
GV_HYB_DOMESTIC_PICKUP_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase a hybrid pickup car from a domestic brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Hybrid Pickup Vehicles Domestic propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
30
GV_HYB_DOMESTIC_PICKUP
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase a hybrid Pickup car from a domestic brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Hybrid Pickup Vehicles Domestic propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
31
GV_HYB_EURO_SED_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase a hybrid Sedan car from an European brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from known behaviors & consumer purchase activity from Market Vue.
Hybrid Sedan Vehicles European propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
32
GV_HYB_EURO_SED
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase a hybrid Sedan car from an European brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Hybrid Sedan Vehicles European propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M
33
GV_HYB_EURO_CROSS_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase a hybrid Crossover car from an European brand expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Hybrid Crossover Vehicles European propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
34
GV_HYB_EURO_CROSS
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to purchase a hybrid Crossover car from an European brand expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Hybrid Crossover Vehicles European propensity score: 1 = Very Low Affinity 2 = Low Affinity 3 = Medium Affinity 4 = High Affinity 5 = Very High Affinity"
M

Green Products & Services Models

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Description Description
35
GV_ENERGYEFF_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to have an energy efficient home as expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Energy Efficient Home propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
36
GV_ENERGYEFF
1
INTEGER
I
This selection is a measure of the likelihood of a consumer to have an energy efficient home as expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Energy Efficient Home propensity score: 1 = Highly Unlikely 2 = Very Unlikely 3 = Neutral 4 = Very likely 5 = Highly Likely"
M
37
GV_SOLAR_CENT
2
INTEGER
I
This value is a measure of the likelihood to have solar panels in the household or interest in solar energy expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Solar energy propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
38
GV_SOLAR
1
INTEGER
I
This selection is a measure of the likelihood to have solar panels in the household or interest in solar energy expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Solar energy propensity score: 1 = Highly Unlikely 2 = Very Unlikely 3 = Neutral 4 = Very likely 5 = Highly Likely"
M
39
GV_ORGANIC_CENT
2
INTEGER
I
This value is a measure of the likelihood of a consumer to purchase organic & natural foods expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Organic and natural foods propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
40
GV_ORGANIC
1
INTEGER
I
This selection is a measure of the likelihood pf a consumer to purchase organic & natural foods expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Organic and natural foods propensity score: 1 = Highly Unlikely 2 = Very Unlikely 3 = Neutral 4 = Very likely 5 = Highly Likely"
M
41
GV_NONTOXIC_CENT
2
INTEGER
I
This value is a measure of the likelihood for a consumer to purchase environmentaly safe cleaning and non toxic cleaners expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Non Toxic Cleaning Supplies propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
42
GV_NONTOXIC
1
INTEGER
I
This selection is a measure of the likelihood for a consumer to purchase environmentaly safe cleaning and non toxic cleaners expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Non Toxic Cleaning Supplies propensity score: 1 = Highly Unlikely 2 = Very Unlikely 3 = Neutral 4 = Very likely 5 = Highly Likely"
M
43
GV_APPLE_CENT
2
INTEGER
I
This value is a measure of the likelihood for a consumer to purchase Apple products expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Apple products propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
44
GV_APPLE
1
INTEGER
I
This selection is a measure of the likelihood for a consumer to purchase Apple products in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Apple products propensity score: 1 = Highly Unlikely 2 = Very Unlikely 3 = Neutral 4 = Very likely 5 = Highly Likely"
M
45
GV_COSMETIC_MED_CENT
2
INTEGER
I
This value is a measure of the likelihood for a consumer to engage in cosmetic procedure expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Apple products propensity score: 1 = Highly Unlikely 2 = Very Unlikely 3 = Neutral 4 = Very likely 5 = Highly Likely"
M
46
GV_COSMETIC_MED
1
INTEGER
I
This selection is a measure of the likelihood for a consumer to engage in cosmetic procedure expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Cosmetic procedures propensity score: 1 = Highly Unlikely 2 = Very Unlikely 3 = Neutral 4 = Very likely 5 = Highly Likely""High interest in discretionary procedures"
M
47
GV_CONCIERGE_MED_CENT
2
INTEGER
I
This value is a measure of the likelihood for a consumer to be interested in concierge & holistic medicine expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Concierge medicine propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
48
GV_CONCIERGE_MED
1
INTEGER
I
This selection is a measure of the likelihood for a consumer to be interested in concierge & holistic medicine expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Concierge medicine propensity score: 1 = Highly Unlikely 2 = Very Unlikely 3 = Neutral 4 = Very likely 5 = Highly Likely"
M
49
GV_DISC_MED_CENT
2
INTEGER
I
This value is a measure of the likelihood for a consumer to engage in discretionary procedures expressed in percentiles from 1-99. They are represented in one percent increments allowing for granular analysis. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
Discretionary medicine propensity values: 01 (Highly Unlikely/ Lowest Propensity)- 99 (Highly Likely/ Highest Propensity)
M
50
GV_DISC_MED
1
INTEGER
I
This selection is a measure of the likelihood for a consumer to engage in discretionary medical procedures expressed in a 1-5 score. It is a probalistic segment, modeled from a range of different data sets & attributes – e.g. demographics, financials, purchase activity, attitudinal surveys, property attributes, lifestyles, media and social media habits.
"Discretionary medicine propensity score: 1 = Highly Unlikely 2 = Very Unlikely 3 = Neutral 4 = Very likely 5 = Highly Likely"
M

INTERNAL

# Field Number
Field Name Field Name
Max
51
INTERNAL
255
✓
✓
52
INTERNAL
255
✓
✓
53
INTERNAL
255
✓
✓
54
INTERNAL
255
✓
✓
55
INTERNAL
255
✓
✓
56
INTERNAL
255
✓
✓
57
INTERNAL
255
✓
✓
58
INTERNAL
255
✓
✓
59
INTERNAL
255
✓
✓
60
INTERNAL
255
✓
✓
61
INTERNAL
255
✓
✓
62
INTERNAL
255
✓
✓
63
INTERNAL
255
✓
✓
64
INTERNAL
255
✓
✓
65
INTERNAL
255
✓
✓
66
INTERNAL
255
✓
✓
67
INTERNAL
255
✓
✓
68
INTERNAL
255
✓
✓
69
INTERNAL
255
✓
✓
70
INTERNAL
255
✓
✓
71
INTERNAL
255
✓
✓
72
INTERNAL
255
✓
✓
73
INTERNAL
255
✓
✓
74
INTERNAL
255
✓
✓
75
INTERNAL
255
✓
✓
76
INTERNAL
255
✓
✓
77
INTERNAL
255
✓
✓
78
INTERNAL
255
✓
✓
79
INTERNAL
255
✓
✓
80
INTERNAL
255
✓
✓

Geocredit - Premium Package

TCI Linkage Information

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Description Description
1
PID
32
VARCHAR
I
Up to 32-Character Alphanumeric Persistent ID. Use this field to link to TCI.
N/A

Geocredit Attributes

# Field Number
Field Name Field Name
LengthMax Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Description Description
2
VANTAGE_SCR
3
VARCHAR
Z4
Tri-bureau risk assessment model that predicts the likelihood of becoming a serious credit risk. The higher the score, the lower the risk.
M
3
VANTAGE_SCR_RNG
1
INTEGER
Z4
Credit Risk Score Ranges: A - 300 - 499 (Very Poor) B - 500 - 600 (Poor) C - 601 - 660 (Fair) D - 661 - 780 (Good) E - 781 - 850 (Excellent)
M
4
TOTALCR_PASTDUE_60D
3
INTEGER
Z4
Percentage of households with a credit relationship that has one more of accounts 60+ days past due. For example, the value represented for a Zip+4 = 40, this would be be interpreted as "For households in the Zip+4 area, 40% have one or more credit accounts that is 60 days or more past the due date". This measure is a key indicator for measuring financial stress.
M
5
TOTALCR_UTILIZATION
3
INTEGER
Z4
Percentage of households credit utilization, which represents the used credit balance relative to available credit limit. For example, if the value represented for a Zip+4 = 40, this would be interpreted as "For households in a Zip+4 area, 40% of available credit is being utilized". This measure is a key indicator for measuring capacity to pay.
M
6
ANYCR_INQUIRY
3
INTEGER
Z4
Percentage of households with any type of hard credit inquiry in the last 3 months. For example, if the value represented for a Zip+4 = 40, this would be interpreted as "For households in a Zip+4 area, 40% had a credit inquiry in the past 3 months". This measure is a key indicator to determine financial activity.
M
✓
7
TOTALCR_SEVDEROG
3
INTEGER
Z4
Percentage of households with a credit relationship that has one or more accounts in severe derogatory status. For example, if the value represented for a Zip+4 = 40, this would be interpreted as "For households in the Zip+4 area, 40% have one or more credit accounts in severe derogatory status". This measure is a key indicator to determine financial stress.
M
✓
8
TOTALCR_PASTDUE_3059D
3
INTEGER
Z4
Percentage of households with a credit relationship that has one more of accounts that are 30-59 days past due. For example, if the value represented for a Zip+4 = 40, this would be interpreted as "For households in the Zip+4 area, 40% have one or more credit accounts that is 30-59 days past the due date". This measure is a key indicator to determine financial stress.
M
✓
9
BANKCC_HAVEACCNT
3
INTEGER
Z4
Percentage of households with a bank credit card account. For example, if the value represented for a Zip+4 = 40, this would be interpreted as "For households in a Zip+4 area, 40% of households have a bank credit card account". This measure is a key indicator to determine capacity to pay. Bank credit cards are defined as unsecured or secured credit cards issued by a bank, national card company or credit union which includes revolving and open type accounts.
M
✓
10
BANKCC_UTILIZATION
3
INTEGER
Z4
Percentage of households bank credit card utilization, which represents the used bank credit card balance relative to the available bank card credit limit. For example, if the value represented for a Zip+4 = 40, this would be interpreted as "For households in a Zip+4 area, 40% of available bank card credit is being utilized". This measure is a key indicator to determine capacity to pay. Bank credit cards are defined as unsecured or secured credit cards issued by a bank, national card company or credit union which includes revolving and open type accounts.
M
✓
11
BANKCC_NEWAGE
3
INTEGER
Z4
Average minimum age of all bank card accounts on file (i.e. newest). This measure is a key indicator to determine capacity to pay. Bank credit cards are defined as unsecured or secured credit cards issued by a bank, national card company or credit union which includes revolving and open type accounts.
M
✓

Internal

# Field Number
Field Name Field Name
Max
12
Internal7
255
✓
✓
13
Internal8
255
✓
✓
14
Internal9
255
✓
✓
15
Internal10
255
✓
✓
16
Internal11
255
✓
✓
17
Internal12
255
✓
✓
18
Internal13
255
✓
✓
19
Internal14
255
✓
✓
20
Internal15
255
✓
✓
21
Internal16
255
✓
✓
22
Internal17
255
✓
✓

InMarket Scores - Premium Package

TCI Linkage Information

# Field Number
Field Name Field Name
Max Max Field Length
Format Field Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Description Description
1
PID
32
VARCHAR
I
Up to 32-Character Alphanumeric Persistent ID. Use this field to link to TCI.
N/A

InMarket Scores Attributes

# Field Number
Field Name Field Name
Length Max Field Length
Format
Level The level at which a given data element is matched. I: Individual HH: Household Z4: Zip4
Assignment M: Modeled A: Actual I: Inferred
Description Description
2
IMS_INSUR_HOME
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for home insurance. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Home Insurance Values Ranges: 1-8
1: Highest Likelihood to be In-market for Home Insurance
8: Lowest Likelihood to be In-market for Home Insurance
A,M
3
IMS_INSUR_AUTO
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for auto insurance. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Auto Insurance Values Ranges: 1-8
1: Highest Likelihood to be In-market for Auto Insurance
8: Lowest Likelihood to be In-market for Auto Insurance
A,M
4
IMS_INSUR_LIFE
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for life insurance. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Life Insurance Values Ranges: 1-8
1: Highest Likelihood to be In-market for Life Insurance
8: Lowest Likelihood to be In-market for Life Insurance
A,M
5
IMS_INSUR_GENERAL
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for insurance although the specific insurance category has not yet been identified. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket General Insurance Values Ranges: 1-8
1: Highest Likelihood to be In-market for Insurance (General)
8: Lowest Likelihood to be In-market for Insurance (General)
A,M
6
IMS_INSUR_HLTH_DENTAL
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for dental health insurance. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Dental Health Insurance Values Ranges: 1-8
1: Highest Likelihood to be In-market for Dental Health Insurance
8: Lowest Likelihood to be In-market for Dental Health Insurance
A,M
7
IMS_INSUR_HLTH_SHORTTERM
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for short-term health insurance. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Short-term Health Insurance Values Ranges: 1-8
1: Highest Likelihood to be In-market for Short-term Health Insurance
8: Lowest Likelihood to be In-market for Short-term Health Insurance
A,M
8
IMS_INSUR_HLTH_DISABILITY
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for disability health insurance. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Disability Health Insurance Values Ranges: 1-8
1: Highest Likelihood to be In-market for Disability Health Insurance
8: Lowest Likelihood to be In-market for Disability Health Insurance
A,M
9
IMS_INSUR_HLTH_MEDICARESUPP
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for Medicare Supplement health insurance. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Medicare Supplement Health Insurance Values Ranges: 1-8
1: Highest Likelihood to be In-market for Medicare Supplement Health Insurance
8: Lowest Likelihood to be In-market for Medicare Supplement Health Insurance
A,M
10
IMS_INSUR_HLTH_MEDICAREADV
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for Medicare Advantage health insurance. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Medicare Advantage Health Insurance Values Ranges: 1-8
1: Highest Likelihood to be In-market for Medicare Advantage Health Insurance
8: Lowest Likelihood to be In-market for Medicare Advantage Health Insurance
A,M
11
IMS_INSUR_HLTH_MEDICAREGENERAL
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for Medicare health insurance although the specific Medicare health insurance category has not yet been identified. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Medicare Health Insurance (General) Values Ranges: 1-8
1: Highest Likelihood to be In-market for Medicare Health Insurance (General)
8: Lowest Likelihood to be In-market for Medicare Health Insurance (General)
A,M
12
IMS_INSUR_HLTH_GENERAL
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for health insurance although the specific health insurance category has not yet been identified. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Health Insurance (General) Values Ranges: 1-8
1: Highest Likelihood to be In-market for Health Insurance (General)
8: Lowest Likelihood to be In-market for Health Insurance (General)
A,M
13
IMS_INSUR_HLTH_OVERALL
2
INTEGER
I
Verisk's InMarket Scores measures a consumer's likelihood to be in-market for insurance in one or more of the health insurance categories (dental, short-term, disability, Medicare and/or general interest). The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Health Insurance (Overall) Values Ranges: 1-8
1: Highest Likelihood to be In-market for Health Insurance (Overall)
8: Lowest Likelihood to be In-market for Health Insurance (Overall)
A,M
14
IMS_INSUR_OVERALL
2
INTEGER
I
Verisk's InMarket Scores measures a consumer's likelihood to be in-market for insurance in one or more of the insurance categories (life, auto, home and/or health). The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Insurance (Overall) Values Ranges: 1-8
1: Highest Likelihood to be In-market for Insurance (Overall)
8: Lowest Likelihood to be In-market for Insurance (Overall)
A,M
15
IMS_MTG_NEWHOME
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for a mortgage for a new home. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket New Home (Purchase) Mortgage Values Ranges: 1-8
1: Highest Likelihood to be In-market for New Home Mortgage
8: Lowest Likelihood to be In-market for New Home Mortgage
A,M
16
IMS_MTG_REFI
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market to refinance their mortgage. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Refinance Mortgage Ranges: 1-8
1: Highest Likelihood to be In-market for Refinance Mortgage
8: Lowest Likelihood to be In-market for Refinance Mortgage
A,M
17
IMS_MTG_HELOC
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for a home equity line of credit (HELOC). The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket HELOC Mortgage Values Ranges: 1-8
1: Highest Likelihood to be In-market for HELOC Mortgage
8: Lowest Likelihood to be In-market for HELOC Mortgage
A,M
18
IMS_MTG_REVERSE
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for a reverse mortgage. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Reverse Mortgage Values Ranges: 1-8
1: Highest Likelihood to be In-market for Reverse Mortgage
8: Lowest Likelihood to be In-market for Reverse Mortgage
A,M
19
IMS_MTG_GENERAL
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for a mortgage although the specific mortgage category has not yet been identified. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Mortgage (General) Values Ranges: 1-8
1: Highest Likelihood to be In-market for Mortgage (General)
8: Lowest Likelihood to be In-market for Mortgage (General)
A,M
20
IMS_MTG_OVERALL
2
INTEGER
I
Verisk's InMarket Scores measures a consumer's likelihood to be in-market for a mortgage in one or more of the mortgage categories (new home, refinance, HELOC, reverse and/or general interest). The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Mortgage (Overall) Values Ranges: 1-8
1: Highest Likelihood to be In-market for Mortgage (Overall)
8: Lowest Likelihood to be In-market for Mortgage (Overall)
A,M
21
IMS_EDUCATION
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for higher education. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Higher Education Values Ranges: 1-8
1: Highest Likelihood to be In-market for Higher Education
8: Lowest Likelihood to be In-market for Higher Education
A,M
22
IMS_JOBS
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for a job. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Jobs Values Ranges: 1-8
1: Highest Likelihood to be In-market for Jobs
8: Lowest Likelihood to be In-market for Jobs
A,M
23
IMS_HOMEBUYER
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market to buy a home. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Homebuyers Values Ranges: 1-8
1: Highest Likelihood to be In-market for Buying Home
8: Lowest Likelihood to be In-market for Buying Home
A,M
24
IMS_AUTOSALES
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for a new or used car. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Auto Sales Values Ranges: 1-8
1: Highest Likelihood to be In-market for Auto Sales
8: Lowest Likelihood to be In-market for Auto Sales
A,M
25
IMS_HOMESERVICES
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for home services (e.g. home improvement, home security system installation, solar panel installation). The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Home Services Values Ranges: 1-8
1: Highest Likelihood to be In-market for Home Services
8: Lowest Likelihood to be In-market for Home Services
A,M
26
IMS_FISERV_GENERAL
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be in-market for financial services. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Financial Services (General) Values Ranges: 1-8
1: Highest Likelihood to be In-market for Financial Services (General)
8: Lowest Likelihood to be In-market for Financial Services (General)
A,M
27
IMS_ONLINE_LEADGEN
2
INTEGER
I
Verisk's InMarket Scores measure a consumer's likelihood to be online, exhibiting in-market behaviors. The score assesses a combination of recency, frequency, and depth of shopping behavior, as well as hundreds of household and consumer level characteristics to determine an individual's propensitity to exhibit in-market behavior. Records scored with a "1" represent the highest confidence levels of in-market behavior. Records scored with an "8" have the lowest likelihood to exhibit in-market behavior, representing records with the lowest confidence levels or lacking any intelligence to indicate the record is in-market.
InMarket Online Lead Generation Values Ranges: 1-8
1: Highest Likelihood to be In-market for Online Lead Gen
8: Lowest Likelihood to be In-market for Online Lead Gen
A,M

Internal

# Field Number
Field Name Field Name
Max
28
Internal1
255
✓
✓
29
Internal2
255
✓
✓
30
Internal3
255
✓
✓
31
Internal4
255
✓
✓
32
Internal5
255
✓
✓
33
Internal6
255
✓
✓
34
Internal7
255
✓
✓
35
Internal8
255
✓
✓
36
Internal9
255
✓
✓
37
Internal10
255
✓
✓
38
Internal11
255
✓
✓
39
Internal12
255
✓
✓
40
Internal13
255
✓
✓
41
Internal14
255
✓
✓
42
Internal15
255
✓
✓
43
Internal16
255
✓
✓
44
Internal17
255
✓
✓
45
Internal18
255
✓
✓
46
Internal19
255
✓
✓
47
Internal20
255
✓
✓
48
Internal21
255
✓
✓
49
Internal22
255
✓
✓
50
Internal23
255
✓
✓
51
Internal24
255
✓
✓
52
Internal25
255
✓
✓

Phone - Premium Package

TCI Linkage Information

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
1
PID
32
VARCHAR
Up to 32-Character Alphanumeric Persistent ID. Use this field to link to TCI.

Phone Information

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
2
PHONE1
10
VARCHAR
Phone #1
3
PHONE1_TYPE
1
VARCHAR
"Probable Phone Type: L - Land Line V - VOIP W - Wireless"
6
PHONE1_CONFIDENCE_CODE
1
VARCHAR
"H = High Level of Confidence M = Medium Level of Confidence (includes household phones)"
7
INTERNAL1
255
VARCHAR
Internal Use
8
INTERNAL2
255
VARCHAR
Internal Use
9
INTERNAL3
255
VARCHAR
Internal Use
10
INTERNAL4
255
VARCHAR
Internal Use
11
INTERNAL5
255
VARCHAR
Internal Use
12
PHONE2
10
VARCHAR
Phone #2
13
PHONE2_TYPE
1
VARCHAR
"Probable Phone Type: L - Land Line V - VOIP W - Wireless"
16
PHONE2_CONFIDENCE_CODE
1
VARCHAR
"H = High Level of Confidence M = Medium Level of Confidence (includes household phones)"
17
INTERNAL6
255
VARCHAR
Internal Use
18
INTERNAL7
255
VARCHAR
Internal Use
19
INTERNAL8
255
VARCHAR
Internal Use
20
INTERNAL9
255
VARCHAR
Internal Use
21
INTERNAL10
255
VARCHAR
Internal Use
22
PHONE3
10
VARCHAR
Phone #3
23
PHONE3_TYPE
1
VARCHAR
"Probable Phone Type: L - Land Line V - VOIP W - Wireless"
26
PHONE3_CONFIDENCE_CODE
1
VARCHAR
"H = High Level of Confidence M = Medium Level of Confidence (includes household phones)"
27
INTERNAL11
255
VARCHAR
Internal Use
28
INTERNAL12
255
VARCHAR
Internal Use
29
INTERNAL13
255
VARCHAR
Internal Use
30
INTERNAL14
255
VARCHAR
Internal Use
31
INTERNAL15
255
VARCHAR
Internal Use
32
INTERNAL16
255
VARCHAR
Internal Use
33
INTERNAL17
255
VARCHAR
Internal Use
34
INTERNAL18
255
VARCHAR
Internal Use
35
INTERNAL19
255
VARCHAR
Internal Use
36
INTERNAL20
255
VARCHAR
Internal Use

Email - Premium Package

TCI Linkage Information

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
1
PID
32
VARCHAR
Up to 32-Character Alphanumeric Persistent ID. Use this field to link to TCI.

Email Information

# Field Number
Field Name Field Name
Length
Format
Values Description Values Description
2
EMAIL1
100
VARCHAR
Email address #1 (best email for a given individual)
3
EMAIL1_VALIDATION_CODE
1
VARCHAR
"Email validation codes: H - Clean Email V - Clean Email and recently verified as deliverable X - Do Not Email"
4
EMAIL1_CONFIDENCE_CODE
1
VARCHAR
"H = High Level of Confidence M = Medium Level of Confidence (includes household emails)"
5
INTERNAL1
255
VARCHAR
Internal Use
6
INTERNAL2
255
VARCHAR
Internal Use
7
INTERNAL3
255
VARCHAR
Internal Use
8
INTERNAL4
255
VARCHAR
Internal Use
9
INTERNAL5
255
VARCHAR
Internal Use
10
EMAIL2
100
VARCHAR
Email address #2
11
EMAIL2_VALIDATION_CODE
1
VARCHAR
"Email validation codes: H - Clean Email V - Clean Email and recently verified as deliverable X - Do Not Email"
12
EMAIL2_CONFIDENCE_CODE
1
VARCHAR
"H = High Level of Confidence M = Medium Level of Confidence (includes household emails)"
13
INTERNAL6
255
VARCHAR
Internal Use
14
INTERNAL7
255
VARCHAR
Internal Use
15
INTERNAL8
255
VARCHAR
Internal Use
16
INTERNAL9
255
VARCHAR
Internal Use
17
INTERNAL10
255
VARCHAR
Internal Use
18
EMAIL3
100
VARCHAR
Email address #3
19
EMAIL3_VALIDATION_CODE
1
VARCHAR
"Email validation codes: H - Clean Email V - Clean Email and recently verified as deliverable X - Do Not Email"
20
EMAIL3_CONFIDENCE_CODE
1
VARCHAR
"H = High Level of Confidence M = Medium Level of Confidence (includes household emails)"
21
INTERNAL11
255
VARCHAR
Internal Use
22
INTERNAL12
255
VARCHAR
Internal Use
23
INTERNAL13
255
VARCHAR
Internal Use
24
INTERNAL14
255
VARCHAR
Internal Use
25
INTERNAL15
255
VARCHAR
Internal Use
26
EMAIL4
100
VARCHAR
Email address #4
27
EMAIL4_VALIDATION_CODE
1
VARCHAR
"Email validation codes: H - Clean Email V - Clean Email and recently verified as deliverable X - Do Not Email"
28
EMAIL4_CONFIDENCE_CODE
1
VARCHAR
"H = High Level of Confidence M = Medium Level of Confidence (includes household matches)"
29
INTERNAL16
255
VARCHAR
Internal Use
30
INTERNAL17
255
VARCHAR
Internal Use
31
INTERNAL18
255
VARCHAR
Internal Use
32
INTERNAL19
255
VARCHAR
Internal Use
33
INTERNAL20
255
VARCHAR
Internal Use
34
EMAIL5
100
VARCHAR
Email address #5
35
EMAIL5_VALIDATION_CODE
1
VARCHAR
"Email validation codes: H - Clean Email V - Clean Email and recently verified as deliverable X - Do Not Email"
36
EMAIL5_CONFIDENCE_CODE
1
VARCHAR
"H = High Level of Confidence M = Medium Level of Confidence (includes household matches)"
37
INTERNAL21
255
VARCHAR
Internal Use
38
INTERNAL22
255
VARCHAR
Internal Use
39
INTERNAL23
255
VARCHAR
Internal Use
40
INTERNAL24
255
VARCHAR
Internal Use
41
INTERNAL25
255
VARCHAR
Internal Use
42
INTERNAL26
255
VARCHAR
Internal Use
43
INTERNAL27
255
VARCHAR
Internal Use
44
INTERNAL28
255
VARCHAR
Internal Use
45
INTERNAL29
255
VARCHAR
Internal Use
46
INTERNAL30
255
VARCHAR
Internal Use
47
INTERNAL31
255
VARCHAR
Internal Use
48
INTERNAL32
255
VARCHAR
Internal Use
49
INTERNAL33
255
VARCHAR
Internal Use
50
INTERNAL34
255
VARCHAR
Internal Use
51
INTERNAL35
255
VARCHAR
Internal Use
52
INTERNAL36
255
VARCHAR
Internal Use

The information in this document may not be altered, changed, or recreated without written consent of Infutor Data Solutions Inc. – Infutor Confidential and Proprietary