Third-Party Onboarding
Implementation guide for data onboarding
Experian data onboarding allows data providers to monetize their audience data by offering syndicated audience data on a public shelf for advertisers
You can leverage our programmatic and TV destination integrations through this service to deliver your segments.
This guide will help you understand the process and provide specific information to help you construct the files required to onboard your data.
Quickly jump to a section using these links:
Once the contract is signed, Experian will host a meeting to discuss your integration. Below are the topics we’ll discuss.
Below is a high-level flow of the steps to onboard and distribute your data. You can choose to upload your data using a file-based approach or Snowflake Share.
Using this guide, you will create the files required for onboarding (Metadata, Data, and Trigger).
Upload all files to the location you and your Project Manager agree on.
Email your Project Manager notifying them of the file upload (sample email below).
Experian will ingest the files and evaluate the data submitted to ensure it is formatted correctly, valid, and meets initial file ingest requirements.
Experian will resolve the consumer identifiers on your Data file to an Experian household or LUID (Living Unit ID).
Experian will update the Data file with the Experian LUID, removing your consumer identifiers.
Experian will process the files through our onboarding tools and distribute your segments to the specified end platform.
Using this guide, you will create the Snowflake data share tables (Taxonomy, Audiences, Trigger, and Tracking).
Upload data to the tables that will be shared with Experian.
Experian’s tracking table process will identify when new data has been published. Once updated, it will pull your data to create the onboarding files.
Experian will resolve the consumer identifiers within your audience table to an Experian household or LUID (Living Unit ID).
Experian will create the required onboarding files and evaluate the data to ensure it is valid and meets the initial file ingest requirements.
Experian will process the files through our onboarding tools and distribute your segments to the specified end platform.
We support the standard delivery locations below. Your Project Manager will work with you on any additional steps/approvals needed.
Amazon (AWS) S3 Data Provider Bucket (preferred)
Secure Transport (aka Data Provider SFTP, STS)
Snowflake data share is available in the US-West-2 region (US West – Oregon). If using Snowflake Share, skip to Section 4
Monthly data updates are expected. The monthly update allows you to update information within your metadata, if needed, and refresh the consumer identifiers in your audience data file. If you have no updates to your metadata or audience data, please contact your Project Manager so that we can ensure your segments remain active.
For Snowflake Share updates, you can update your data throughout the month; however, please only update the date in your tracker table once per month when you are ready for us to pull the data.
The table below summarizes the required data onboarding files with specific file requirements that follow in this section
| File | Purpose
|
Data Input
|
| Metadata | Includes the segment specific detail that will be displayed at the end-platform. | See this section. Segment code, description, rates, lder path |
Audience Data
|
Defines which consumer identifiers belong in each segment code. | See this section. Identifiers and all segment codes |
| Trigger | Classifies the data type and use. | See this section. Data type and use (3rd), data provider ID, number of data files |
The audience data, metadata, and trigger file are submitted together. The trigger should always be uploaded last.
The metadata file makes up your data taxonomy. It includes details that describe your segments and define how they will be displayed and priced at the end platform. The details describing your segments must be descriptive, well organized, and include terms that are searchable for optimal performance.
The file and field requirements are outlined below. Several file types are accepted. Please follow these guidelines closely to avoid file errors.
Segment Code (segmentCode)
Segment Description (segmentDescription)
Rates (cpm and percentOfMedia)
Folder Path (folderPath)
Please refer to the Column/Keywords in see this section for required fields and specific requirements for field length, type, and character restrictions.
Specific file naming is required for the system to process correctly. The first four name attributes of the Metadata, Data, and Trigger file must match.
Name format
AM_[clientfilename]_[version]_[YYYYMMDD]_Metadata
| Name Attribute | Definition |
| AM | Maintain as part of the file name |
| clientfilename | Client created, insert a name that makes sense for you. No spaces or special characters. Can use your brand name. |
| version | Number NNNN |
| ccyymmdd | Date, current date |
| Metadata | Maintain as part of file name |
Name example
AM_ABCClient_1_20241024_Metdata.psv, .csv, or .json
For visual purposes only. The below shows how the data is organized in Excel before converting to a delimited file (do not send data in Excel)
| segmentCode | segmentDescription | cpm | percentOfMedia | folderPath |
| DISCOUNT | Consumers that shop at discount sotres. | 0.75 | 10 | Shoppers | In-Store | Discount |
| LUXURY | Online shoppers that spend at luxury department stores. | 1.1 | 15 | Shoppers | Online | Luxury Department |
| HOME_IMPROVEMENT | DIY consumers that shop at home improvement stores. | 1.1 | 15 | Shoppers | Home Improvement |
| PET_SUPPLIES | Consumers that shop in-store & online, and have higher spend $ for their pets. | 0.75 | 10 | Shoppers | Pet Food & Supplies |
| 4SPORTS | Sports lovers and sports fanatics that spend at sports-related stores. | 1 | 12 | Shoppers | In-Store | Sports |
| After conversion, comma delimited (.csv) |
| segmentCode, segmentDescription, cpm, percentOfMedia, folderPath DISCOUNT, Consumers that shop at discount sotres., 0.75, 10, Shoppers | In-Store | Discount LUXURY, Online shoppers that spend at luxury department stores., 1.1, 15, Shoppers | Online | Luxury Department HOME_IMPROVEMENT, DIY consumers that shop at home improvement stores., 1.1, 15, Shoppers | Home Improvement PET_SUPPLIES, "Consumers that shop in-store & online, and have higher spend $ for their pets.", 0.75, 10, Shoppers | Pet Food & Supplies 4SPORTS, Sports lovers and sports fanatics that spend at sports-related stores., 1, 12, Shoppers | In-Store | Sports |
| After conversion, pipe delimited (.psv) |
| segmentCode | segmentDescription | cpm | percentOfMedia | folderPath DISCOUNT | Consumers that shop at discount sotres. | 0.75 | 10 | "Shoppers | In-Store | Discount" LUXURY | Online shoppers that spend at luxury department stores. | 1.1 | 15 | "Shoppers | Online | Luxury Department" HOME_IMPROVEMENT | DIY consumers that shop at home improvement stores. | 1.1 | 15 | "Shoppers | Home Improvement" PET_SUPPLIES | Consumers that shop in-store & online, and have higher spend $ for their pets. | 0.75 | 10 | "Shoppers | Pet Food & Supplies" 4SPORTS | Sports lovers and sports fanatics that spend at sports-related stores. | 1 | 12 | "Shoppers | In-Store | Sports" |
| After conversion, JSON |
| { "segmentCode": "DISCOUNT", "segmentDescription": "Consumers that shop at discount stores.", "cpm": 0.75, "percentOfMedia": 10 "folderPath": "Shoppers | In-Store | Discount" }, { "segmentCode": "LUXURY", "segmentDescription": "Online shoppers that spend at luxury department stores.", "cpm": 1.1, "percentOfMedia": 15 "folderPath": Shoppers | Online | Luxury Department" }, { "segmentCode": "HOME_IMPROVEMENT", "segmentDescription": "DIY consumers that shop at home improvement stores.", "cpm": 1.1, "percentOfMedia": 15 "folderPath": "Shoppers | Home Improvement" }, { "segmentCode": "PET_SUPPLIES", "segmentDescription": "Consumers that shop in-store & online, and have higher spend $ for their pets.", "cpm": 0.75, "percentOfMedia": 10 "folderPath": "Shoppers | Pet Food & Supplies" }, { "segmentCode": "4SPORTS", "segmentDescription": "Sports lovers and sports fanatics that spend at sports-related stores.", "cpm": 1, "percentOfMedia": 12 "folderPath": "Shoppers | In-Store | Sports" } ] |
The data file defines which consumers are in each of your segments. The file will include your consumer identifiers (offline, online, or the Experian ID) and unique segment codes. Each row will represent a consumer, and each column will represent a unique segment code. For consumers that belong to a segment, you’ll add a ‘1’ (one). Add ‘0’ (zero) for consumers not in a segment. Segment codes in the data file must match a segment code in the metadata file.
The file and field requirements are outlined below. Several file types are accepted. Please follow these guidelines closely to avoid file errors.
Experian supports receiving the following offline and digital identifiers.
Offline: Known Identifiers, including Personally Identifiable Information (PII)
Digital: Device Identifiers
Client ID. Please add a unique ID so we can easily exchange information if we find an error or formatting issue.
Segment Codes
Please refer to the Column/Keywords in this section and this section for required fields and specific requirements for field length, type, and character restrictions.
Identifier columns: Each column represents an identifier type.
The header includes Column/Keywords for each identifier in the file and a client ID. For the consumer identifier Column/Keywords, refer to this section.
Column/Keywords can be in any order.
The contents of each column must match the column keyword.
A file can only contain one Column/Keywords; it cannot contain two columns with the same column keyword.
Do not include Column/Keywords for identifiers not present in the file.
If a consumer does not have one of the identifiers for the column keyword, leave an empty string (,, or ||).
The Column/Keywords table outlines the field-specific requirements (e.g., character length, special character use, etc.). Data input that does not comply with the field requirements will result in an error.
Do not include a Byte Order Mark (BOM).
Segment code columns. The segment code columns will follow the identifier columns.
Each column represents a unique segment code that is in the metadata file.
There should be only one column for each unique segment code.
Each data file should include all segment codes within the header.
You will add a 1 (one) or 0 (zero) in each column that defines if that consumer belongs in a segment:
1 – in the segment
0 – not in the segment
Each segment code must have consumers assigned to it. We cannot process segment codes that have no consumers assigned to it.
Each row represents a consumer with offline or digital identifiers and a client ID.
Formatting offline data:
Only one address is allowed per row. If you have multiple addresses for the same person, each address must be in separate rows.
If you have multiple emails for a consumer, you can include them in the same row; however, each email must be in its own column. Up to four plain text emails and four hashed emails of each type can be included in one row. Each email must appear in a separate column and correspond to the column keyword.
If you are unsure if an email belongs with other consumer identifiers, place it in a separate row.
If there is an embedded delimiter in the field, the field must include double quotes.
123 Main St., Apt B within a comma delimited file should appear as: “123 Main St., Apt B”
If there are special characters in the field, it is recommended that you use double quotes.
Formatting digital data:
Plaintext and hashed digital IDs can appear in the same file.
If sending both plaintext and digital IDs, ensure that the identifier appears in the correct column based on the column keyword.
If you have multiple digital identifiers for a consumer, each row can contain one plaintext, one MD-5, one SHA-1, and one SHA-256 for MAID and IP.
If you have multiple digital HEMs for a consumer, each row can contain 1 MD-5, 1 SHA-1, and 1 SHA-256.
If you are unsure if a digital identifier belongs to the same consumer, place it in a separate row.
Hashing data: Please hash identifiers using lowercase and trim before hashing.
IPv6 (raw and hashed): if sending IPv6, please ensure that the IP address is normalized.
Steps for normalization:
Original ipv6 address:
2603:8001:00f0:0da0:7cd1:6410:25fa:7bf8
Normalized/Truncated ipv6 address: (The format we need for IPv6)
2603:8001:f0:da0::
The number of data files you will need to create can vary and is driven by the number of identifiers and segment codes.
2603:8001:f0:da0::
The number of data files you will need to create can vary and is driven by the number of identifiers and segment codes.
Specific file naming is required for the system to process correctly. The first four name attributes of the Metadata, Data, and Trigger file must match.
Name format
AM_[clientfilename]_[version]_[YYYYMMDD]_Data_[#]
| Name Attribute | Definition |
| AM | Maintain as part of the file name |
| clientfilename | Client created, insert a name that makes sense for you. No spaces or special characters. Can use your brand name. |
| version | Number NNNN |
| ccyymmdd | Date, current date |
| Data | Maintain as part of file name |
| # | File number. If submitting ten files, number 1-10 |
Name example
AM_ABCClient_1_20241024_Data_1.psv or .csv
Name example if the partner sends in 3 files in a comma delimited file type.
Examples show that the data provider is sending in two audience data files
Data file #1 – PII + emails
| FNAME | LNAME | ADDR1 | ADDR2 | CITY | STATE | ZIP | EMAIL_1 | EMAIL_2 | DISCOUNT | LUXURY | HOME_IMPROVEMENT | PET_SUPPLIES | 4SPORTS |
| Jonathan | Jones | No 58 | Cleveland | OH | 44335 | 1 | 1 | 0 | 1 | 0 | |||
| jjadams@anyemail.com | johnadams1500@gmail.com | 0 | 1 | 0 | 1 | 1 | |||||||
| Mary | Tigton | 15 Sunset Lane, Apt 55 | Miami | FL | 33101 | MSip2018@yahoo.com | 1 | 1 | 1 | 0 | 1 | ||
| Robert | McManson | 29581 | 1 | 0 | 1 | 1 | 1 | ||||||
| 8626 County Road | Bozeman | MT | 59716 | 0 | 0 | 1 | 1 | 1 | |||||
| Bridget | Wahls | 95741 | BridgWhal123@email.com | 0 | 1 | 0 | 1 | 1 | |||||
Data file #2 – IP
| ip_address | DISCOUNT | LUXURY | HOME_IMPROVEMENT | PET_SUPPLIES | 4SPORTS |
| 192.158.10.15 | 1 | 1 | 1 | 1 | 0 |
| 10.155.171.5 | 0 | 0 | 1 | 0 | 1 |
| 192.15.177.254 | 1 | 0 | 0 | 1 | 0 |
| 55.185.27.144 | 0 | 1 | 0 | 1 | 1 |
For visual purposes only. The below shows how the data is organized in Excel before converting to a delimited file (do not send data in Excel)
Data file #1 – PII based
| After conversion, comma delimited (.csv) |
| "fname,lname,addr1,addr2,city,state,zip,email_1,email_2,DISCOUNT,LUXURY,HOME_IMPROVEMENT,PET_SUPPLIES,4SPORTS Jonathan,Jones,1155 Syracuse Ave,No 58,Cleveland,OH,44335,,, 1,1,0,1,0 ,,,,,,,jjadams@anyemail.com,johnadams1500@gmail.com,0,1,0,1,1 Mary,Tigton,"15 Sunset Lane, Apt 55",,Miami,FL,33101,MSip2018@yahoo.com,,1,1,1,0,1 Robert,McManson,,,,,29581,,,1,0,1,1,1 ,,8626 County Road,,Bozeman,MT,59716,,,0,0,1,1,1 Bridget,Wahls,,,,,95741,BridgWhal123@email.com,,0,1,0,1,1" |
addr1 description included an embedded comma for Apt 55. Thus, the conversion to CSV saved this field in double quotes
Data file #2 – IP based
| After conversion, comma delimited (.csv) |
| ip_address,DISCOUNT,LUXURY,HOME_IMPROVEMENT,PET_SUPPLIES,4SPORTS 192.158.10.15,1,1,1,1,0 10.155.171.5,0,0,1,0,1 192.15.177.254,1,0,0,1,0 55.185.27.144,0,1,0,1,1 |
Data file #1 – PII based
| After conversion, pipe delimited (.psv) |
| fname|lname|addr1|addr2|city|state|zip|email_1|email_2|DISCOUNT|LUXURY|HOME_IMPROVEMENT|PET_SUPPLIES|4SPORTS Jonathan|Jones|1155 Syracuse Ave|No 58|Cleveland|OH|44335||| 1|1|0|1|0 |||||||jjadams@anyemail.com|johnadams1500@gmail.com|0|1|0|1|1 Mary|Tigton|15 Sunset Lane, Apt 55||Miami|FL|33101|MSip2018@yahoo.com||1|1|1|0|1 Robert|McManson|||||29581|||1|0|1|1|1 ||8626 County Road||Bozeman|MT|59716|||0|0|1|1|1 Bridget|Wahls|||||95741|BridgWhal123@email.com||0|1|0|1|1 |
Data file #2 – IP based
| After conversion, pipe delimited (.psv) |
| ip_address|DISCOUNT|LUXURY|HOME_IMPROVEMENT|PET_SUPPLIES|4SPORTS 192.158.10.15|1|1|1|1|0 10.155.171.5|0|0|1|0|1 192.15.177.254|1|0|0|1|0 55.185.27.144|0|1|0|1|1 |
The trigger file provides taxonomy-level detail so that we can process and distribute the data accordingly.
File Types: Pipe (|), Comma (,), or JSON
Data Provider ID (dataProviderId)
Provided by Experian.
Taxonomy Type (taxonomyType)
Syndicated for syndicated taxonomies. Syndicated taxonomies are updated monthly and sit on a public shelf.
Data Usage Type (dataUsageType) is ‘3rd’ for Third Party.
Segment Data Files (segmentDataFiles)
This is the total number of Data files accompanying your metadata and trigger file. Do not include your Trigger and Metadata file in this count.
Segment Metadata Exists (segmentMetadataExists) is Y, Yes, T, or True.
Please refer to the Metadata Column/Keywords in this section for required fields and specific requirements for field length, type, and character restrictions.
For delimited files:
Header = Use Column/Keywords (this section)
Row = Only one row is required, and it will include the data mentioned in the section above.
Column/Keywords can be in any order.
The contents of each column must match the column keyword.
Do not include spaces before/after the delimiter.
Do not include a Byte Order Mark (BOM).
Specific file naming is required for the system to process correctly. The first four name attributes of the Metadata, Data, and Trigger file must match.
Name format
AM_[clientfilename]_[version]_[YYYYMMDD]_Trigger
| Name Attribute | Definition |
| AM | Maintain as part of the file name |
| clientfilename | Client created, insert a name that makes sense for you. No spaces or special characters. Can use your brand name. |
| version | Number NNNN |
| ccyymmdd | Date, current date |
| Trigger | Maintain as part of file name |
Name example
AM_ABCClient_1_20241024_Trigger.psv, .csv, or JSON
This is for visual purposes only. The below shows how the data is organized in Excel before converting to a delimited file (do not send data in Excel).
| dataProviderId | segmentDataFiles | taxonomyType | dataUsageType | segmentMetadataExists |
| 77 | 2 | Syndicated | 3rd | Y |
| After conversion, comma delimited (.csv) |
| dataProviderId,segmentDataFiles,taxonomyType,dataUsageType,segmentMetadataExists 77,2,Syndicated,3rd,Y |
| After conversion, pipe delimited (.psv) |
| dataProviderId|segmentDataFiles|taxonomyType|dataUsageType|segmentMetadataExists 77|2|Syndicated|3rd|Y |
| After conversion, JSON |
| { "dataProviderId": "77", "segmentDataFiles": "2", "taxonomyType": "Syndicated", "dataUsageType": "3rd", "segmentMetadataExists": "Y", } |
We offer the option to share data via Snowflake share, which is hosted in the Snowflake US West 2 (US West Oregon) region.
The table below summarizes the required Snowflake tables with specific requirements that follow in this section.
| Table Name | Purpose | Data Input |
EXPERIAN_TAXONOMY_SHARED
|
Includes the segment specific detail that will be displayed at the end-platform. | Segment code, description, rates, folder path |
| EXPERIAN_AUDIENCES_SHARED | Defines which consumer identifiers belong in each segment code. | Identifiers and all segment codes |
| EXPERIAN_TRIGGER_SHARED | Classifies the data type and use. | Data type and use (3rd), data provider ID, number of data files |
| EXPERIAN_TRACKING_SHARED | Identifies the date(s) that the tables were updated. | Load date |
The taxonomy, audiences, and trigger data are all pulled from Snowflake simultaneously. We pull data based on the load date you add to the tracking table.
The taxonomy table includes metadata that describes your segments and defines how they will be displayed and priced at the end platform. For optimal performance, the details describing your segments must be descriptive, well-organized, and include searchable terms.
The field requirements are outlined below. Please follow these guidelines closely to avoid errors.
Segment Code (SEGMENTCODE)
Each segment must have a unique segment code. The system will error if it detects two segments sharing the same segment code.
Segment codes should be consistent. Once a segment code is assigned to an audience, do not change it. Maintain the same segment code for the assigned audience for all ongoing updates.
If you retire/remove a segment from your taxonomy, try not to reuse that code.
If a segment code appears in the taxonomy table, it must also appear in the audience table.
Segment Description (SEGMENTDESCRIPTION)
Describe your segment. Ensure that the description is appropriate and easy for the end-user to understand.
Rates (CPM and PERCENTOFMEDIA)
Include your base rates for CPM (cost per thousand in dollars) and Percent of Media. If you require different rates for different end platforms, these can be customized by platform before distribution. Work with your PM to accomplish this.
Folder Path (FOLDERPATH)
The folder path represents a hierarchy of how your data will be displayed within the end platform using parent/child categorization. This allows users to drill down to the segment name (last folder populated). Examples are below.
Demographics|Age Range|20-25
Auto and Trucks|Jeep|Grand Cherokee
Purchase Data|Sports|Equipment|Baseball
Each segment within your taxonomy must have a unique folder path. The system will error if the same folder path exists for multiple segments.
Do not include your brand name in the folder path. Your segments will appear under your brand name at the end platform; however, it is not part of the folder path.
Do not include spaces before/after the pipe separator.
It is recommended that you maintain a consistent folder path. This ensures that downstream users can save, reuse, and find information easily.
We recommend limiting your folders to no more than 7 to improve discoverability and viewability.
Please refer to the Column/Keywords in this section. for required fields and specific requirements for field length, type, and character restrictions.
Table structure
Header = Use Column/Keywords Name (See this section.)
Row = Each row represents an individual segment
Column/Keywords can be in any order.
The contents of each column must match the column keyword.
All segments should be listed in one taxonomy table.
EXPERIAN_TAXONOMY_SHARED
| SegmentCode | SEGMENTDESCRIPTION | CPM | PERCENTOFMEDIA | FOLDERPATH |
| DISCOUNT | Consumers that shop at discount stores | 0.75 | 10 | Shoppers| In-Store| Discount |
| LUXURY | Online shoppers that spend at luxury department stores. | 1.1 | 15 | Shoppers | Online | Luxury Department |
| HOME_IMPROVEMENT | DIY consumers that shop at home improvement stores. | 1.1 | 15 | Shoppers | Home Improvement |
| PET_SUPPLIES | Consumers that shop in-store & online, and have higher spend for their pets | 0.75 | 10 | Shoppers | Pet Food & Supplies |
| SPORTS | Sports lovers and sports fanatics that spend at sports-related stores | 1 | 12 | Shoppers | In-Store | Sports |
The audience table defines which consumers are in each of your segments. The table will include your consumer identifiers (offline, online, or the Experian ID) and unique segment codes. Each row will represent a consumer, and each column will represent a unique segment code. For consumers that belong to a segment, you’ll add a ‘1’ (one). Add ‘0’ (zero) for consumers not in a segment. Segment codes in the audience table must match a segment code in the taxonomy table.
The field requirements are outlined below. Please follow these guidelines closely to avoid file errors
Experian supports receiving the following offline and digital identifiers.
Offline: Known Identifiers, including Personally Identifiable Information (PII)
Name
Postal Address
Email: plaintext or hashed with MD5, SHA-1 or SHA-256
Phone: plaintext or hashed with MD5, SHA-1 or SHA-256
Digital: Device Identifiers
Mobile device ID (MAID): plaintext or hashed with MD5, SHA-1 or SHA-256
Internet Protocol address (IP): plaintext or hashed with MD5, SHA-1 or SHA-256
Email: hashed with MD5, SHA-1 or SHA-256
Segment Codes
Each unique segment code in your taxonomy table will have its own column in the audience table.
Please refer to the Metadata Column/Keywords in this section. and this section for required fields and specific requirements for field length, type, and character restrictions.
Identifier columns: Each column represents an identifier type.
The header includes Column/Keywords for each identifier in the file. For the consumer identifier Column/Keywords, refer to this section.
Column/Keywords can be in any order.
The contents of each column must match the column keyword.
The table can only contain one Column/Keywords; it cannot contain two columns with the same column keyword.
Do not include Column/Keywords for identifiers not present in the file.
If a consumer does not have one of the identifiers for the column keyword, leave the cell empty.
The Column/Keywords table outlines the field-specific requirements (e.g., character length, special character use, etc.). Data input that does not comply with the field requirements will result in an error.
Segment code columns. The segment code columns will follow the identifier columns.
Each column represents a unique segment code that is in the taxonomy table.
There should be only one column for each unique segment code.
You will add a 1 (one) or 0 (zero) in each column that defines if that consumer belongs in a segment:
1 – in the segment
0 – not in the segment
Each segment code must have consumers assigned to it. We cannot process segment codes that have no consumers assigned to it.
Each row represents a consumer with offline or digital identifiers and a client ID.
Formatting offline data:
Only one address is allowed per row. If you have multiple addresses for the same person, each address must be in a separate row.
If you have multiple emails for a consumer, you can include them in the same row; however, each email must be in its own column. Up to four plain text emails and four hashed emails of each type can be included in one row. Each email must appear in a separate column and correspond to the column keyword.
Formatting digital data:
If sending both plaintext and digital IDs, ensure that the identifier appears in the correct column based on the column keyword.
If you have multiple digital identifiers for a consumer, each row can contain one plaintext, one MD-5, one SHA-1, and one SHA-256 for MAID and IP.
If you have multiple digital HEMs for a consumer, each row can contain 1 MD-5, 1 SHA-1, and 1 SHA-256.
If you are unsure if a digital identifier belongs to the same consumer, place it in a separate row.
Hashing data: Please hash identifiers using lowercase and trim before hashing.
IPv6 (raw and hashed): if sending IPv6, please ensure that the IP address is normalized.
Steps for normalization:
Original ipv6 address:
2603:8001:00f0:0da0:7cd1:6410:25fa:7bf8
Normalized/Truncated ipv6 address: (The format we need for IPv6)
2603:8001:f0:da0::
EXPERIAN_AUDIENCES_SHARED
| FNAME | LNAME | ADDR1 | ADDR2 | CITY | STATE | ZIP | EMAIL_1 | EMAIL_2 | DISCOUNT | LUXURY | HOME_IMPROVEMENT | PET_SUPPLIES | 4SPORTS |
| Jonathan | Jones | No 58 | Cleveland | OH | 44335 | 1 | 1 | 0 | 1 | 0 | |||
| jjadams@anyemail.com | johnadams1500@gmail.com | 0 | 1 | 0 | 1 | 1 | |||||||
| Mary | Tigton | 15 Sunset Lane, Apt 55 | Miami | FL | 33101 | MSip2018@yahoo.com | 1 | 1 | 1 | 0 | 1 | ||
| Robert | McManson | 29581 | 1 | 0 | 1 | 1 | 1 | ||||||
| 8626 County Road | Bozeman | MT | 59716 | 0 | 0 | 1 | 1 | 1 | |||||
| Bridget | Wahls | 95741 | BridgWhal123@email.com | 0 | 1 | 0 | 1 | 1 |
The trigger table provides taxonomy-level detail so that we can process and distribute the data accordingly.
Data Provider ID (DATAPROVIDERID)
Provided by Experian.
Taxonomy Type (TAXONOMYTYPE)
Data Usage Type (DATAUSAGETYPE) is ‘3rd’ for Third Party.
Segment Data Files (SEGMENTDATAFILES)
While you aren’t submitting data files, our automation requires this field. Please add the number ‘1’.
Segment Metadata Exists (SEGMENTMETADATAEXISTS) is Y, Yes, T, or True.
Please refer to the Metadata Column/Keywords in this section for required fields and specific requirements for field length, type, and character restrictions.
Header = Use Column/Keywords (this section)
Row = Only one row is required, and it will include the data mentioned in the section above.
Column/Keywords can be in any order.
The contents of each column must match the column keyword.
EXPERIAN.TRIGGER.SHARED
| DATAUSAGETYPE | SEGMENTDATAFILES | TAXONOMYTYPE | SEGMENTMETADATAEXISTS | DATAPROVIDERID |
| 3rd | 1 | SYNDICATED | YES | 28 |
The tracking table maintains the date the metadata and/or audience data is ready for a refresh. This date should only be updated if your tables have been updated and you want Experian to process new onboarding files. A typical syndicated file refresh is every 30 days.
Load Date (LOAD_DATE)
The load date will reflect the date you are ready for Experian to pull updated data. Experian will also maintain a tracking table that maintains your load date for the last date we pulled data from your tables.
The Experian tracking table process runs every 8 hours.
If the load date in your tracking table is less than or equal to the load date in our tracking table, no action will be taken.
If the load date in your tracking table is greater than the load date in our tracking table, our process will execute to pull data from your tables.
Field type: TIMESTAMP_LTZ
Example: 2025-04-18T08:38:08.959-07.00
The date 2025-04-18T08:38:08.959-07:00 is in ISO 8601 format, which is a standardized way to represent date and time. Here's a breakdown of its components:
2025-04-18: The date in YYYY-MM-DD format (Year-Month-Day).
T: The separator between the date and time.
08:38:08.959: The time in HH:mm:ss.SSS format (Hours:Minutes:Seconds.Milliseconds).
-07:00: The time zone offset from UTC (UTC-7 hours).
EXPERIAN_TRACKING_SHARED
| LOAD_DATE |
| 2025-04-18T08:38:08.959-07.00 |
Metadata reviews are required for all segment data uploaded to Experian systems.
As with your initial metadata review by Experian compliance, new and updated segment data will require a review before uploading into Experian systems. We request that you submit updates to your Project Manager at least two weeks before uploading your monthly data. This will give your Project Manager time to review with Experian compliance and coordinate any reviews to downstream end platforms.
The metadata information should be formatted in Excel. Example below.
| segmentCode | folderPath | Description |
| GOLF_SPND | Purchase | Sports | Golfers | Consumers who are likely golfers based on consistent spend at golf courses. |
| FB_FANS | Interest | TV | Sports | Football Fans | Consumers who watch football related events |
| AGE20_25 | Demographics | Age Range 20-25 | Consumers age 20-25 |
If Experian or a downstream platform does not approve a segment, you will be notified of the segment information and why it cannot be onboarded.
If Experian disapproves, do not include the segment within the metadata or data file. If an end platform does not approve, you will need to work with your Project Manager to ensure your configuration does not include the segment for distribution to the end platform that did not approve.
Camel case naming convention is used for the Column/Keywords property names.
| Column/Keywords Name | Max Character Length | Description / Field Requirements | Input Filed Format |
| segmentCode | 100 | Required Unique code assigned to your segment.
All unique segment codes will be listed in the segmentCode column. Metadata associated with the segmentCode should align in the corresponding columns Segment codes within the Metadata must appear in Data file A-Z, 0-9 and underscore (_) accepted No spaces allowed While not case sensitive, we will convert to all CAPS. Thus, you may want to use upper case if including alpha characters. |
text |
| segmentDescription | 255 | Describes the audience. For syndicated segments that will sit on a public shelf, be descriptive. Allowed Characters, A-Z, 0-9, underscore (_), dash (-), single quote (‘), double quote (“), slash (/), period (.), US dollars ($), comma (,), Exclamation (!), pound (#), up carrot (^), Ampersand (&), asterisk (*), open and close parentheses ( ), colon (:), semi-colon (;), at (@), percent (%) No backslash (\), double backslash (\\), double slash (//) |
string |
| cpm | 4 | Cost per thousand in dollars. .50 is equal to $0.50 Required |
float |
| percentOfMedia | 4 | Percent of media cost. 1.0 = one percent. Required | float |
| folderPath | 100 characters per folder | Converts to a hierarchal tree structure that allows you organize and represent data in a format that is easy to understand, navigate and search. Do not include your brand name within the folder path. Your segments will appear under your brand name at the end platform but it should not be a part of the folder path. Pipe-separated parent/child hierarchy (e.g. “Demographics|Education|Bachelor Degree”. Last folder = Segment name Must be unique within a taxonomy – another segment cannot have the same folder path Maintain a consistent folder path. Ensures downstream users can reuse/find Recommend no more than 7 folders (discoverability and viewability) Allowed Characters, A-Z, 0-9, underscore (_), dash (-), single quote (‘), slash (/), period (.), US dollars ($), comma (,), Exclamation (!), pound (#), up carrot (^), Ampersand (&), asterisk (*), open and close parentheses ( ), colon (:), semi-colon (;), at (@), percent (%) No double quote (“), backslash (\), double backslash (\\), double slash (//) Spaces are allowed in between words |
string |
| Column/Keywords Name | Max Character Length | Description / Field Requirements | Input Filed Format |
| Identifiers |
|
Only include column/keyword header for identifiers within your file. If only submitting name and address, only include those columns. |
|
| Each unique segment code should be represented as their own column | 100 | Required Column header contains each unique segment code. Segment code must appear in Metadata Unique code – cannot duplicate A-Z, 0-9 and underscore (_) accepted No spaces allowed While not case sensitive, we will convert to all CAPS. Thus, you may want to use upper case if including alpha characters. |
text |
| Column/Keywords Name | Max Character Length | Description / Field Requirements | Input Filed Format |
| dataProviderId | 4 | Assigned by Experian. Please ask your Project Manager for your Provider ID. | integer |
| segmentDataFiles | 4 | Number of audience Data files accompanying the Metadata and Trigger files that need to be processed. The count is specific to the data files, do not include the metadata or trigger within the count. |
integer |
| taxonomyType | 100 | Syndicated case insensitive |
string |
| dataUsageType | 100 | 3rd case insensitive |
string |
| segmentMetadataExists | 10 | Y, Yes, T, True – use when uploading a Metadata file | string |
(only include the column/keywords for the identifiers you are submitting in the file)
| Column/Keywords Name * | Max Character Length | Description | Input Field Format |
| prefix | 20 | Prefix (Salutation: Mr, Mrs, Dr, etc.) | Text |
| fname | 20 | First Name | Text |
| mname | 20 | Middle Name | Text |
| lname | 20 | Last Name | Text |
| suffix | 20 | Suffix (Generation: Jr, Sr, II, III, etc) | Text |
| fullname | 50 | Whole Name | Text |
| gender | 10 | Male, Female | Text |
| addr1 | 30 | Address 1 | Text |
| addr2 | 30 | Address 2 | Text |
| addr3 | 30 | Address 3 | Text |
| city | 20 | City | Text |
| state | 10 | State | Text |
| zip | 10 | ZIP Code | Text |
| email_1 | 50 | ||
| md5email_1 | 32 | Text | |
| sha1email_1 | 40 | Text | |
| sha256email_1 | 64 | Text | |
| maid | 50 | Text | |
| md5_maid | 32 | Text | |
| sha1_maid | 40 | Text | |
| sha256_maid | 64 | Text | |
| ip_address | 50 | Text | |
| sha1_ip | 40 | Text | |
| sha256_ip | 64 | Text | |
| md5_ip | 32 | Text | |
| ip_start_date | 20 | YYYY-MM-DD | |
| ip_end_date | 20 | YYYY-MM-DD | |
| ip_state_code | 10 | Text | |
| email_2 | 50 | ||
| md5email_2 | 32 | Text | |
| sha1email_2 | 40 | Text | |
| sha256email_2 | 64 | Text | |
| email_3 | 50 | ||
| md5email_3 | 32 | Text | |
| sha1email_3 | 40 | Text | |
| sha256email_3 | 64 | Text | |
| email_4 | 50 | ||
| md5email_4 | 32 | Text | |
| sha1email_4 | 40 | Text | |
| sha256email_4 | 64 | Text | |
| clientId | 32 | Unique, can be sequential. Required for Sync file. Recommended for identifier-based data files to assist with error resolution | Text |
| luid | 10 | Living Unit ID – client specific Specific to audience data files where the partner already has identities resolved to a LUID. |
String |
token_(type) example: token_1, token_2 |
64 | Column header will define type of token, Each row represents a consumer | Text |
| npi | 10 | National Provider Identifier | Text |
May 1, 2025
Trigger File—Removed Taxonomy field. The Experian system will associate updated taxonomies with the active taxonomy or create a taxonomy for the data provider if no active taxonomy exists.
Metadata File – removed segmentName. Experian will automatically apply the ‘last folder populated’ as the segment name.
Consumer Identifiers – removed phone fields for input.
Snowflake Share – added new data sharing option.
Removed references to custom audiences and first-party data. Custom and first-party audience onboarding will have a different set of onboarding requirements.
May 22, 2025
Added National Provider Identifier (NPI) as a consumer identifier we can receive and resolve to in Section 7.