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Formatting File Data

Once you’ve determined what data you’ll be including, and which audience you’ll be uploading the data to (if necessary), review the information in the sections below (including the sections pertaining to your file type) to make sure that the data are formatted in a way that LiveRamp can accept.

Note

If you're formatting one of the file types listed below, keep the following in mind:

Make sure to review the following guidelines for the LiveRamp workflow that you're using (in addition to our data guidelines in "Types of Data That Can Be Included in Files" and "LiveRamp Data Restrictions"):

  • The Activation workflow, which involves creating fields and segments from your data in Connect so that you can distribute those fields and segments to your desired destination platforms for activation. See "Activation Workflow Overview" for more information.

  • The Measurement Enablement workflow, which involves replacing the input identifiers (such as PII, cookies, mobile device IDs, CIDs, etc.) in the files you upload with their associated RampIDs and returning the files to the location you specify. See "Measurement Enablement Workflow" for more information.

Once you’ve made sure that all of the data are formatted correctly, finalize the file for uploading.

Note

See "Uploading Data" for an overview of the file creation and formatting process.

File Formatting Guidelines Highlights (Activation Workflow)

File Formatting Guidelines Highlights (Measurement Enablement Workflow)

Formatting Guidelines for All Files

For both column-based files and key-value files, follow the guidelines listed below (in addition to the appropriate guidelines for formatting column-based files and for formatting key-value files).

Use Quotation Marks When the Data Contains a Delimiter

Data containing punctuation characters are at risk of delimiter collision, and thus data bleed when any delimiter (such as a comma, pipe, or semicolon) also appears as part of the data values. This can cause LiveRamp to interpret data in a particular row as belonging to the wrong field.

To avoid this, enclose each value in a column-based file, and each key and value in a key-value file, in quotation marks ("), following the guidelines listed below:

  • Equals signs (in key-value files) and delimiters (such as commas or pipes) should be outside the quotation marks.

  • Make sure that all quotation marks are closed so that there is an even number of quotation mark characters per data row.

  • If using quotation marks, the best practice (but not required) is to enclose all values with quotation marks rather than only those with delimiter collision potential.

    Note

    It is not necessary to enclose empty or null fields with quotation marks.

  • If a value contains a quotation mark character, that value should be enclosed in quotation marks and each quotation mark character in the value should be properly escaped by putting a quotation mark character right before it, as shown in the examples below:

    • LCD TV,50" becomes "LCD TV,50"""

    • "early-bird" special becomes """early-bird"" special"

    • 5'8" becomes "5'8"""

Put All Segment Data Related to a Given Identifier on a Single Row

For example, if you have three different segments, put them all on one row, either as three columns or three key-value pairs, rather than listing the same identifier on three rows with one segment per row. The latter approach will make file processing take significantly longer.

Caution

Multi-valued data are an exception. If you have a column where there are multiple values for the same header, each column value should be on a separate row, along with the identifier. For more information, see "Multi-Value Fields".

Do Not Use Placeholders for Empty Values

If a given field entry for a particular row of data has no value, leave it blank. Do not use a placeholder such as "NULL" or "N/A."

Format Multi-Value Fields Correctly

When sending in files with multi-value fields, make sure that those field values are formatted correctly by sending separate rows for each value a consumer has in a particular field.

Note

When sending files with multi-value fields to a particular audience, make sure to include that information when creating a support case before uploading the first file so those fields get ingested correctly.

For example, to make a consumer with a customer ID of "123" a member of the segments "Pets_Owned=Dog", "Pets_Owned=Cat", and "Pets_Owned=Hamster", format a column-based file like the example below:

Customer_ID

Pets_Owned

123

Dog

123

Cat

123

Hamster

For a key-value file, format the file like the example below:

123,Pets_Owned=Dog
123,Pets_Owned=Cat
123,Pets_Owned=Hamster

For more information, see "Multi-Value Fields".

Use UTF-8 Encoding

UTF-8 is in wide adoption and ensures maximum compatibility across different systems. ASCII encoding is accepted as it is included in the UTF-8 standard. When you're saving your file, make sure not to save it as something other than a UTF-8 encoded file type.

Formatting Dates

You can include segment data fields that contain calendar date values (such as date of most recent purchase) in the files you upload.

If you want to use a date field to create derived segments, use one of our preferred date formats: YYYYMM or YYYYMMDD. Do not include forward slashes (/) or hyphens (-).

This creates a numeric field that LiveRamp can map into segment ID codes that you can later modify into segment ranges using LiveRamp’s Segment Creator.

Note

Privacy issues: Keep in mind that LiveRamp will not distribute any data that could easily identify a group of fewer than 25 individuals (or fewer than two individuals for measurement files). Using the YYYYMM format can help avoid this situation.

Formatting Segment List Data

Not commonly used, these are single-field identifier files, typically device-based identifiers such as cookies tied to single PII identifier.

LiveRamp accepts non-rectangular segment list-based data. These are usually files with a single identifier field (typically a device-based identifier, such as a cookie ID, or PII-based data tied only to a single email address or phone number).

  • The identifier should be the first field of each row:

    <identifier1>,seg1,seg2

    <identifier2>,seg2

  • Separate the identifier and each segment with one of the allowed delimiters.

  • Segments should be unique per row. That is, do not include the same segment multiple times on a single row.

Allowed File Types

The file types listed below can be accepted, as long as they use one of the allowed file delimiters and meet all other file formatting requirements:

  • Comma-separated values files (.csv)

  • Tab-separated values files (.tsv)

  • Pipe (|)-separated values files (.psv)

  • Semicolon-separated values files (.scsv)

  • Text files (.txt)

When creating files for upload, remember that files must be provided in a flat, text-based format. As a general rule of thumb, if the file contents cannot be previewed within a command-line terminal or simple text editor (like WordPad or TextEdit), we cannot accept it.

Files can be column-based with delimiters or in key-value format (except for EU data files, which can only be sent as column-based files). Files can also be compressed, which will result in a different file extension.

Caution

  • No Excel or Word files: Non-text-based file types, such as Microsoft Excel (.xls or .xlsx) or Word (.doc or .docx) files, cannot be accepted.

  • For Mac Excel files: If exporting data from Microsoft Excel for Mac, choose the "Windows Comma Separated (.csv)" option (do not use the MS-DOS or Macintosh CSV versions, or any non-UTF-8 option). Make sure that extra columns haven't been added during the export process before uploading.

  • EU data cannot be sent in TXT files. See "Creating Data Files for EMEA Countries" for complete information on formatting EU data files.

Allowed File Delimiters

Use one of the delimiters listed below to separate values when creating a file for uploading:

  • Commas (comma-separated values files, or .csv files)

  • Tabs (tab-separated values files, or .tsv files)

  • Pipes (|) (pipe-separated values files, or .psv files)

  • Semicolons (semicolon-separated values files, or .scsv files)

Make sure to follow all other guidelines for allowed file types and for formatting file data.

Note

Enclose values in quotation marks when the data contains a delimiter: Data containing punctuation characters is at risk of delimiter collision , and thus data bleed, when any delimiter (such as a comma, pipe, or semicolon) also appears as part of the data values. This can cause LiveRamp to interpret data in a particular row as belonging to the wrong field. To prevent this, enclose values in quotation marks ("). See "Formatting Guidelines for All Files" for more information.

Avoiding File Ingestion Failure

There are many possible considerations to keep in mind when creating files for upload, but the cause of file ingestion failure or delay often falls into one of the categories listed below.

To be able to automatically ingest files that you upload for processing (which speeds up and streamlines the ingestion process for your files), we create an automation configuration for that particular audience. Often we will use a file from another of your audiences, or the first file you upload to your new audience, as a "seed" or "template" to create this configuration.

Once this configuration has been enabled, it's very important that subsequent files that are uploaded for that audience stay consistent with the "seed" or "template" configuration. If you upload a file that is not consistent (such as a file with different headers or a different audience key), the file will be paused in the ingestion process until the file is manually mapped to the audience.

For more information on the automation configuration, see "The Ingestion Automation Process for File Uploads".

Note

If you need to upload a file that does not match the initial automation configuration, create a support case so we can make sure the file is ingested correctly.

To avoid ingestion issues, be sure to keep the same file formatting as the original seed file, especially including the following:

Note

  • Files uploaded for a particular audience are put into a queue for that audience, so a delay in the processing of a file containing issues will delay all subsequent files that have been uploaded to that audience.

  • If you upload a file with new segment data fields that weren't in the original file, those fields should upload without an issue. However, you might want to monitor the file upload on the Files page to make sure there are no problems. If the processing pauses or fails, use the appropriate Troubleshooting File Upload Issue quick case to have the Support team investigate the issue.

For examples of properly formatted files (including downloadable files that can be used as templates), as well as files containing common errors, see "File Formatting Examples."

Header Issues

Header issues are the most common cause of file upload failure. The most common header issues are:

  • A header for an identifier column (including an identifier column that is being used as the audience key) that was in the original file uploaded for that audience is missing, or is named differently (including being capitalized differently).

    Caution

    • When including segment data columns that were in previously-uploaded files, make sure that the headers for those columns also stay consistent. If a header for a segment data column changes, a new field will be created instead of having the data from that column update the existing field.

    • If you are using the "full refresh" option to update the audience, keep in mind that any previously-uploaded segment data columns need to be included in the file if you want to keep that data in the associated fields. Otherwise, if a previously-uploaded segment data column is not included, the associated field will have all the members removed. See "Ways to Update an Existing Audience" for more information.

  • The file contains two or more headers that are exactly the same (sometimes extra headers and columns are added when saving from Excel).

Make sure that:

  • Every column-based file you upload has a header row.

  • Make sure that the headers are unique and that headers for identifier columns (including identifier columns that are being used as the audience key) and headers for any previously-included segment data columns match the headers in the original file uploaded for that audience.

See "Formatting Column-Based Files" for more information.

Audience Key Issues

Keep in mind that every file uploaded to a particular audience in an Activation workflow must use the same audience key as the original file uploaded for that audience so that we can effectively deduplicate and consolidate all rows within that audience.

The most common audience key issues are:

  • The audience key is different from the one used for the first file uploaded for that audience.

  • An identifier that was used originally as the audience key is not included in the file, such as “city” when the audience key is name and postal address.

  • The header for an audience key column is different from the header used in the original file.

  • The audience key has a low fill rate. For best results, and to avoid having ingestion processing pause or fail, make sure the audience key has a fill rate of as close to 100% as possible.

Identifier Issues

  • An identifier is formatted incorrectly, such as including both the first and last name in the same field instead of having separate “first name” and “last name” fields.

See "Formatting Identifiers" for more information.

Delimiter Issues

  • There is an extra delimiter (such as a comma) in a row, making it appear as though the row has more fields in it than it should. When this happens, our system gets confused while trying to parse your file. Watch out for this, especially when creating CSV files from Excel files.

  • The data contains the delimiter but is not enclosed in quotation marks. Data containing punctuation characters are at risk of delimiter collision and thus data bleed, where the specified delimiter (such as a comma) also appears as part of the data values. This can cause LiveRamp to interpret data in a particular row as belonging to the wrong field. See "Formatting Guidelines for All Files" for more information.

Raw Field Issues

A "raw field" is a field in a given file that has over 250 distinct values. Most files uploaded to LiveRamp should not contain raw fields unless you have previously coordinated this with your LiveRamp account manager.

The values in raw fields cannot be separated out and managed individually in Connect. Many platforms also cannot accept raw fields.

For more information, see "Raw Field".

New Identifier Field Issues

After the upload of the first file in an audience, subsequent files need to use the same file formatting. All originally-included fields should be included with the exact same headers. If you include an additional identifier field (such as a new email address column in a column-based file) without having LiveRamp first adjust the mapping to include that new field, the file will not process until that mapping has been adjusted.

To avoid delays or processing failures, if you need to add a new identifier field to a subsequent file, first create a support case so that we can adjust the mapping before you upload that file.

Multi-Value Field Issues

The default format for fields in a file for ingestion (columns in a column-based file) is for each field to be a "single-value" field. A single-value field is a field where each consumer record can only be a member of one segment in that field. For example, a file might have a column for the field "Favorite_Pet", where each consumer record can only have one value associated with them (such as "Favorite_Pet=Dog" or "Favorite_Pet=Cat", but not both).

But for some fields you might want to allow each consumer record to have multiple values for that field. For example, you might want to create a field in Connect called "Pets_Owned" where each consumer record might be a member of multiple segments, such as "Pets_Owned=Dog", "Pets_Owned=Cat", and "Pets_Owned=Hamster". This type of field is called a "multi-value field".

For consumers that have multiple values for a particular multi-value field, make sure to create separate rows for each value. For more information on formatting multi-value fields, see "Formatting Guidelines for All Files".

Before you upload the first file to a new audience where the file will contain one or more multi-value fields, use the Set Up Audience for First File Upload (Activation) quick case to create a support case to make sure the audience is configured correctly to process those multi-value fields.

Once the ingestion automation process has been set up for the initial file, make sure to create another support case if you change any of the single-value fields to multi-value fields so that the file ingests properly.

For more information on multi-value fields, see "Multi-Value Fields".

In this section:

A field name entry for a particular column of data in a column-based file.

Identifiers that have been derived from, and/or can be associated with, PII (and are therefore considered PII) and that have not been pseudonymized yet. In addition to PII-based identifiers (such as name and postal address or email address), known identifiers also include AbiliTec IDs, client customer IDs, and (in some cases) custom IDs and IP addresses. Sometimes referred to as "offline identifiers."

A packet of data sent by an Internet server to a browser, which is returned by the browser each time it subsequently accesses the same server and used to identify the user or track their access to the server.

Unique identifiers that are given to mobile devices. This category of IDs is sometimes referred to as Mobile User IDs (MUID) or Mobile Advertising IDs (MAID).

Identifiers that are assigned to users by a specific platform, such as Google or Facebook.

LiveRamp's universal, pseudonymous identifier that is tied to devices in the LiveRamp Identity Graph.

A category of identifiers where the identifiers consist of PII (personally identifiable information). PII identifiers are sometimes also referred to as "offline identifiers."

A set of segment data that holds a specific category of values, such as "Car_Owned."

A value or set of values that are distinct from all other values for a given field in a data file.

The audience precision level determines whether a record that is onboarded is matched to records in the LiveRamp Identity Graph on an individual level or a household level (for offline-based data) or on a device level or individual level (for online-based data).

A type of file where every row has the same number of fields and the field names are contained in a header row, as opposed to a key/value file.

A type of data file where each row contains key/value pairs in the form of "key=value" (for example, "gender=female").

A character (such as a comma or pipe) that acts as a separator between two values in a data file.

Within a column of segment data in a data file, the entry for a given record that indicates a trait or attribute value, such as "BMW" in a field named "Car_Owned." A group of users with a specific field/value pairing creates a segment.

In a key-value file, the category for a particular value (for example, a key of "gender"). Similar to a header in a column-based file.

A group of your records that are defined by a specific attribute (a field/value pairing, such as "Gender=female"). For example, a field for "Gender" might contain segments for "Gender=Female" and for "Gender=Male."

Data that are used to identify something, such as a person, mobile device, computer browser, or household.

multi-value fields

A segment built with the Segment Creator in Connect by applying rules to previously uploaded segments within a given audience.

A category of identifiers where the identifiers are based on digital devices (for example, cookie IDs or mobile device IDs).

A column-based file where every row has the exact same number of delimiters and columns of data.

Data you use to group your records based on certain attributes (such as “Gender=Female” or “Income=25,000-49,999”) so you can distribute those records to one of LiveRamp’s partners, typically for targeting, measurement, or personalization.

Data LiveRamp uses to match your records to other identifiers in our Identity Graph, such as name and postal address, email address, RampIDs, cookie IDs, or mobile device IDs.

In the context of LiveRamp Connect, a way to group your consumers within LiveRamp.

The process through which customer files are uploaded and properly formatted in LiveRamp's system.

A field in your files that acts as a unique ID for all of the records in a given LiveRamp audience.

The first row of a column-based file, where the entries of that row indicate the field name for each column.

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