About Data Tables

A customer 360 database is built using standard outputs of the Stitch process that provide a unified view of customer data, including customer profile and interaction records that are linked together by the Amperity ID, organized, merged, and ready for use in segmentation.

About the data model

The data model represents the “out-of-the-box” tables that are available to every tenant. (AmpIQ tables are available only to tenants that have added AmpIQ to Amperity and have completed the process of enabling predictive attributes.)

Important

This data model represents the starting point for all tenants. It is common for a tenant to have additional tables that support specific data requirements and workflows.

Data model diagram

The following diagram shows the data model for core tables in Amperity. Color coded sections identify which groups of tables are associated with customer profiles, interactions records, Stitch QA, and AmpIQ.

Note

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There are four groups of tables in this diagram:

Group name	Description
Customer records	A customer profile is a collection of attributes that are associated with a single unique individual in the customer 360 database. The total number of customer profiles is equal to the total number of rows in the Customer 360 data table. This total correlates strongly, but not exactly, to the total number of Amperity IDs assigned to unique individuals in the same data set. The Customer_360 table represents your primary set of customer profiles and is the most common starting point for building segments. Each customer profile is built using a combination of the Merged_Customers, Customer_Attributes, Unified_Customer, and Unified_Coalesced tables.
Interaction records	An interaction record is a row in a customer data table that contains information about customer behavior, such as purchases (items bought, items returned, costs of items, etc.) and preferences (brands, products, cart adds, etc.). Interaction records rely on a series of tables: Transaction_Attributes, Transaction_Attributes_Extended, Unified_Itemized_Transactions, Unified_Transactions, and Unified_Product_Catalog. Each Amperity ID in the Customer_360 table can be associated to many rows in the Unified_Transactions table, and then each Amperity ID in the Unified_Transactions can be associated to many rows in the Unified_Itemized_Transactions table. Each Amperity ID in the Customer 360 table is associated to one Amperity ID in the Transaction_Attributes table. Note The Transaction_Attributes table has an associated “extended” table that captures additional attributes when data sources provide to Amperity the data required to complete the calculations for the attributes. It exists as a separate table in the customer 360 database, but should be viewed as a subset of transactions attributes data.
Stitch results	Stitch QA is a process that monitors the quality of Stitch results. Stitch QA has two components: a database and a set of queries. The results of these queries are analyzed to help identify values that should be labeled or blocklisted and discover situations where the results of the Stitch process require tuning to match your tenant’s data set. Stitch QA activities rely on a series of tables: Unified_Coalesced, Unified_Scores, Detailed_Examples, Unified_Preprocessed_Raw, Unified_Changes_Clusters, and Unified_Changes_PKS. These tables are the basis for the Stitch QA process ; the use of any specific table will vary from tenant to tenant. Togehter they provide visibility into how Amperity grouped (or did not group) individual customer records to a single Amperity ID.
AmpIQ	AmpIQ enables customer-centric marketing campaigns. Use segment insights to build high-value segments. Use those segments to add audiences to campaigns. Build campaigns that send those audiences to any combination of downstream marketing workflows. AmpIQ tables are enabled for users of AmpIQ and are the results of the configuration and tuning of Amperity for predictive analytics. These tables rely on the Merged_Customers, Unified_Itemized_Transactions, and Unified_Transactions tables for predictions, but there is not a 1:1 or 1:many relationship between those three tables and AmpIQ tables. The Predicted_CLV_Attributes table contains one row per Amperity ID, whereas the Predicted_Affinity table contains many rows per Amperity ID. The Campaign_Recipients table contains a history of all campaigns that have been sent from Amperity. This table is updated on a recurring basis and may be used like any other table in your customer 360 database.

Data model indicators

This diagram uses the following indicators to highlight relationships between tables and to call out fields that are primary keys, establish links between tables, or associate this table back to a domain table in the Sources tab:

Name	Description
1:1, 1:many	Indicates that this table has a 1:1 or 1:many relationship with another table. For most tables, this relationship is based on the Amperity ID.
Many:1	Indicates that this table has a many:1 relationship with another table. For most tables, this relationship is based on the Amperity ID.
Primary key	Indicates this column is the primary key for this table.
Linking key	Indicates this column links customer records, such as those associated with a customer key or a foreign key that were defined as part of a feed or custom domain table from the Sources tab.
Data source	Indicates this column is associated to an original customer data source in the Sources tab, with the value of this field being the name of that data source.

All_Opted_In_Emails

The All_Opted_In_Emails table contains all opted-in email addresses (and associated Amperity IDs). This table is built from the Email_Opt_Status table, after which it can be extended to support brands, region, email program, and language preferences.

Warning

The All_Opted_In_Emails table is not unique by Amperity ID and should not be used within the Segment Editor.

The All_Opted_In_Emails table contains the same columns as the Email_Opt_Status table. Unlike the Email_Opt_Status table, the All_Opted_In_Emails table should be made available to the Segment Editor. This ensures that the email attribute in this table is available to campaigns. (Choose the email attribute from the All_Opted_In_Emails table from the Edit Attributes menu.)

Campaign_Recipients

The Campaign_Recipients table contains a list of Amperity IDs associated with any campaign (i.e., sub-audiences) that were sent from Amperity. Each campaign is associated with the segment used to generate the list of recipients, the control and recipient groups (including sub-audiences) used for the campaign, its launch date, and all destinations to which the campaign was sent.

The Campaign_Recipients table is generated by Amperity, and then made available as a standard database table. Use this table to access campaign attributes, perform historical campaign analysis, and to build segments and multi-stage campaigns that use the results of previous campaigns.

Important

Data in the Campaign_Recipients table is updated after the next database run. If your database is configured to run once per day then data in the Campaign_Recipients table will be available on a 1-day delayed rolling basis.

Note

See Campaign_Recipients for more information about how this table is built and maintained within Amperity.

The Campaign_Recipients table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
campaign_id	String	The unique ID for a campaign.
campaign_name	String	The name of the campaign.
campaign_type	String	A campaign is a message or offer that is sent to a specific group of customers or recipients. A campaign may be one of the following types: One-time Recurring A one-time campaign represents a specific campaign message that is configured to be sent only once. A recurring campaign is sent automatically based on an updated/refreshed audience that a customer wants to receive in their outbound destination or a state change, such as an accepted return, a change to a loyalty program, or an alert based on credit card status, with a predefined campaign message and cadence to a list of recipients.
database_id	String	The unique ID for the database.
database_name	String	The name of the database.
dataset_version	String	A unique ID for the dataset used with this set of campaign recipients.
delivery_datetime	Datetime	The date and time at which the associated campaign ID was delivered to the destination.
destination_id	String	The unique ID for a destination.
destination_name	String	The name of the destination to which the associated campaign ID was sent.
is_control	Boolean	A flag that indicates if the treatment_id represents a control group.
launch_datetime	Datetime	The date and time at which the associated campaign ID was sent from Amperity to its downstream workflow.
pk	String	The combination of data source and primary key allows Amperity to uniquely identify every row in every data table across the entirety of customer data input to Amperity.
segment_id	String	The unique ID for the segment that generated the list of recipients for the associated campaign ID.
segment_name	String	The name of the segment used with the associated campaign ID.
sub_audience_id	String	The unique ID for the sub-audience to which the associated campaign was sent.
sub_audience_name	String	The name of the sub-audience to which the associated campaign was sent.
target_id	String	The unique ID for the data template that was used to send campaign data to a downstream channel or workflow.
target_name	String	The name of the data template that was used to send campaign data to a downstream channel or workflow.
treatment_id	String	The ID for the recipient group to which the associated campaign was sent.
treatment_name	String	The name of the recipient group to which the associated campaign was sent. One or more recipient groups, along with a control group, are used to measure the quality of a campaign.
workflow_id	String	The unique ID for the workflow that managed the associated campaign.

Customer_360

The Customer_360 table is the unified view of the customer across all points of engagement, including attributes that cross systems. This table does not exist by default and must be created within the customer 360 database. Each row represent a complete record for a unique individual, including their Amperity ID, merged PII data, and summary attributes.

Note

See Customer360 for more information about how this table is built and maintained within the customer 360 database.

Note

The columns that appear in the Customer_360 table will vary, depending on the SQL statement used to add columns to the table. The set of columns must include the Amperity ID and should include columns that contain profile (PII) data, along with columns that contain summary attributes for interaction records.

The following table represents a Customer_360 table with profile data and a few summary attributes based on interaction records for transaction data:

Column Name	Data type	PII	Description
amperity_id	String		The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
given_name	String		The first name that is associated with an individual customer record. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
surname	String		The last name that is associated with an individual customer record. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
email	String		The email address that is associated with an individual customer record. A customer record may be associated with multiple email addresses. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
phone	String		The phone number that is associated with an individual customer record. A customer record may be associated with multiple phone numbers. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
address	String		The address that is associated with the location of an individual customer record. For example: 123 Main Street. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
city	String		The city that is associated with the location of an individual customer record. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
state	String		The state or province that is associated with the location of an individual customer record. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
postal	String		The zip code or postal code that is associated with the location of an individual customer record. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
birthdate	Date		The date of birth that is associated with an individual customer record. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
gender	String		The gender that is associated with an individual customer record. Also in: Merged_Customers, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw

Customer_Attributes

The Customer_Attributes table contains a series of flags that identify attributes about individuals, such as if that individual can be contacted, if a marketable email address, physical address, or phone number is available, if they are an employee, reseller, or if the individual represents a test account, along with identifying each individuals’s revenue relationship with a brand.

The Customer_Attributes table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
churn_trigger	String	The churn status for a customer. Possible values: Active Cooling down At risk Highly at risk Lost Blank Tip Use these values as part of your churn prevention campaigns.
churn_trigger_start_datetime	Datetime	The date and time at which the churn_trigger status begins.
contactable_address	Boolean	A flag that indicates if a customer can be contacted by a physical mailing address.
contactable_email	Boolean	A flag that indicates if a customer can be contacted by an email address.
contactable_global	Boolean	A flag that indicates if a customer can be contacted by phone number, email address, or physical mailing address.
contactable_paid_social	Boolean	A flag that indicates if a customer has personally identifiable information (PII) that could be used to contact them using paid social media channels.
contactable_phone	Boolean	A flag that indicates if a customer can be contacted by phone.
historical_purchaser_lifecycle_status	String	The status for a customer, based on their history and recency of interactions a brand. Possible values: “new”, “active”, “lapsed”, “dormant”, and “prospect”. Customer states are defined as “active”, “lapsed”, “dormant”, and “prospect”. Purchase behaviors are assigned across a 5 year window. A customer who has purchased within the previous 365 days (1 year) is assigned to “active” and within the previous 730 days (2 years) is assigned to “lapsed”. A customer who has not purchased within 2 years is assigned to “dormant”.
is_business	Boolean	A flag that indicates if a customer is a known or likely business.
is_employee	Boolean	A flag that indicates if a customer is or has been an employee of the brand at any time.
is_gift_giver	Boolean	A flag that indicates if a customer has purchased items as gifts.
is_no_pii_amperity_id	Boolean	A flag that indicates if the customer does not have personally identifiable information (PII) for name (given name, surname, full name), address (street address, city, state, postal code), email address, or phone number.
is_outlier	Boolean	A flag that indicates if the customer has abnormally high purchase behaviors in comparison to other purchasers.
is_primary_buyer_household	Boolean	A flag that indicates if a customer is the individual within a household who represents the highest lifetime revenue for that household. Note This attribute requires the Merged_Households table.
is_prospect	Boolean	A flag that indicates if a customer does not have a purchase history with a brand.
is_purchaser	Boolean	A flag that indicates if the customer has a purchase history with a brand.
is_reseller	Boolean	A flag that indicates if the customer is a known or likely reseller of a product. Tip A reseller should be defined as an early repeat purchaser with a high lifetime order frequency. Use the following fields from the Transaction_Attributes table to define a reseller: early_repeat_purchaser (set to true) lifetime_order_frequency (define an order frequency threshold, such as “greater than 50”)
is_test_account	Boolean	A flag that indicates if the customer is a known test account for a brand.

Detailed_Examples

The Detailed_Examples table contains detailed examples of Stitch results. Use these examples to help identify which features lead to scores with the biggest effect on overall Stitch results, including how they are associated with various combinations of fields that contain PII data.

Note

The Detailed_Examples table is a subset of the Unified_Scores table.

The Detailed_Examples table contains the following columns, as its starting point. This table is typically updated to add more pairs, enable use for blocklisted values, and to support additional tenant-specific use cases.

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
address_a address_b	String	The address that is associated with the location of an individual customer record. For example: 123 Main Street.
birthdate_a birthdate_b	String	The date of birth that is associated with an individual customer record.
case_count	String
city_a city_b	String	The city that is associated with the location of an individual customer record.
country_a country_b	String	The country that is associated with the location of an individual customer record.
datasource_a datasource_b	String	The name of the data source from which this customer record originated. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Coalesced table, which correlates to a single row in a domain table.
email_a email_b	String	The email address that is associated with an individual customer record. A customer record may be associated with multiple email addresses.
given_name_a given_name_b	String	The first name that is associated with an individual customer record.
match_category	String	A match category is a classifier that is applied by Amperity to an individual record-pair within a cluster of record-pairs. The match category is the result of this classification. Match Category Description Exact Amperity has the highest confidence that these records represent the same person because all profile data exactly matches. Excellent Amperity has near perfect confidence that these records belong to the same person, despite select types of profile data not matching. High Using deductive reasoning, Amperity has very high confidence that these records match, despite some profile data not matching. Moderate Amperity has moderate confidence that these records match, due to weak or fuzzy matches between highly unique customer attributes (email, phone, address). Weak Amperity lacks confidence, but if asked to guess, Amperity would assert these records do belong to the same individual, because they match on non-unique customer attributes (name, state, zip code). No conflict Amperity has high confidence that these records do NOT match, because core profile data is in conflict. Also in: Unified_Scores
phone_a phone_b	String	The phone number that is associated with an individual customer record. A customer record may be associated with multiple phone numbers.
pk_a pk_b	String	The combination of data source and primary key allows Amperity to uniquely identify every row in every data table across the entirety of customer data input to Amperity. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Coalesced table, which correlates to a single row in a domain table.
postal_a postal_b	String	The zip code or postal code that is associated with the location of an individual customer record.
score	Float	A score contains a value from 0.0 to 5.0 that represents the combined score assigned to the record pair by Stitch. There are two components of the score: the score itself, and then its strength. The record pair score correlates to the match category, which is a classifier that is applied by Amperity to individual record pairs. The record pair score corresponds to the classification: 5 for exact matches, 4 for excellent matches, 3 for high matches, 2 for moderate matches, 1 for weak matches, and 0 for no matches. The record pair strength represents the strength of the record pair score. It is a two digit number. For example: .31 is a lower strength and .93 is a very high strength. Note Scores are shown for records that end up in the same cluster, including any scores that are below threshold. Scores are not shown for records that do not end up in the same cluster. Also in: Unified_Scores
score_count	String
state_a state_b	String	The state or province that is associated with the location of an individual customer record.
surname_a surname_b	String	The last name that is associated with an individual customer record.

Email_Engagement_Attributes

The Email_Engagement_Attributes table contains all fields from the Email_Engagement_Summary table, unique by Amperity ID and by email address. This table may be extended for related email engagement and transaction attributes.

The Email_Engagement_Attributes table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
brand	String	The brand or company from which an email was sent.
email	String	The email address to which an email was sent.
event_datetime	Datetime	The date and time at which email event occurred.
event_type	String	The type of email event. Possible values: Open Click Sent Optin Bounce Converted Unsubscribe Important The values for email event types must spelled as expected to ensure the Email_Engagement_Summary can properly summarize email events. For example: “Optin” may not be “Opt-in”.
region	String	The region or location from which an email was sent. The region or location is typically associated to a single brand.
send_id	String	The unique identifier for the email that was sent to an email address at a specific date and time. If a data source does not provide a send ID a unique key is generated.

Email_Engagement_Summary

The Email_Engagement_Summary table contains a summary of email event statistics, such as counts for opens and clicks, the first open, and the most recent click, unique by email address.

The Email_Engagement_Summary table contains the following columns:

Column Name	Data type	Description
brand	String	The brand or company from which an email was sent.
email	String	The email address to which an email was sent.
email_clicks_last_x_day	Integer	The number of email clicks in the last 1, 3, 5, 7, or 14 days.
email_clicks_last_x_months	Integer	The number of email clicks in the last 3, 6, 9, or 12 months.
email_opens_last_x_day	Integer	The number of email opens in the last 1, 3, 5, 7, or 14 days.
email_opens_last_x_months	Integer	The number of email opens in the last 3, 6, 9, or 12 months.
engagement_frequency_last_15_months	Varchar	A classification that measures engagement frequency click rates for email addresses that have received a low volume of emails. Possible values: Non-recipient (0 received emails) New (fewer than 5 received emails) Active (currently engaging with received emails) Inactive (not currently engaging with received emails) Important Send rates must be available.
engagement_status_last_15_months	Varchar	A classification that measures click rates for email addresses that have received a low volume of emails. Possible values: Non-recipient (0 received emails) New (fewer than 5 received emails) Low (modeled open rate is in the lower third) Medium (modeled open rate is in the middle third) High (modeled open rate is in the upper third) Important Send rates must be available.
first_email_click_datetime	Datetime	The date and time at which an email was first clicked.
first_email_open_datetime	Datetime	The date and time at which an email was first opened.
first_email_send_datetime	Datetime	The date and time at which an email was sent.
most_recent_email_bounce_datetime	Datetime	The date and time for the most recent bounced email.
most_recent_email_click_datetime	Datetime	The date and time at which a customer most recently clicked a link or offer within an opened email.
most_recent_email_open_datetime	Datetime	The date and time at which a customer most recently opened an email.
most_recent_email_optin_datetime	Datetime	The date and time at which a customer most recently opted-in to receiving email.
most_recent_email_optout_datetime	Datetime	The date and time at which a customer most recently opted-out from receiving email.
most_recent_email_send_datetime	Datetime	The date and time at which an email was most recently sent.

Lifecycle_Events

The Lifecycle_Events table enables the use of event-driven customer behaviors within campaigns by providing a set of fields that combine predictions for customer behaviors with transaction details from the Unified_Transactions table.

The Lifecycle_Events table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
event_status	String	The status for a lifecycle event. Possible values: Active Cooling down At risk Highly at risk Lost
event_status_start_date	Datetime	The date on which the current lifecycle event started.
event_type	String	The type of lifecycle event. For example: “Predicted Lifecycle Status”.
is_current	Boolean	A flag that indicates if this is the most recent lifecycle event associated with the Amperity ID. Tip When the value of is_current is true only the most recent event_status is returned.
previous_event_status	String	The lifecycle event status for the previous lifecycle event associated with the Amperity ID.
prev_event_status_start_date	Datetime	The date on which the previous lifecycle event started. This value is NULL when there is no previous lifecycle event.

Merged_Customers

The Merged_Customers table is a data table that must be added to a customer 360 database. The purpose of the Merged_Customers table is to collect rows from the Unified_Coalesced table, and then collapse those into a single row per Amperity ID. Columns in the Merged_Customers table are made available to segmentation via the Customer_360 table.

Note

See Merged_Customers for more information about how this table is built and maintained within the customer 360 database.

The Merged_Customers table contains the following columns:

Column Name	Data type	Description
amperity_id	String	This column is input to AmpIQ predictive modeling. The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
address	String	The address that is associated with the location of an individual customer record. For example: 123 Main Street. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
address2	String	Additional address information, such as an apartment number or a post office box, that is associated with the location of an individual customer record. For example: Apt #9. Also in: Unified_Coalesced, Unified_Customer
birthdate	Date	This column is input to AmpIQ predictive modeling. The date of birth that is associated with an individual customer record. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
city	String	This column is input to AmpIQ predictive modeling. The city that is associated with the location of an individual customer record. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
company	String	The company, typically an employer or small business, that is associated with an individual customer record.
country	String	The country that is associated with the location of an individual customer record. Also in: Unified_Coalesced, Unified_Customer
create_dt	String	Apply the create-dt semantic tag to columns in customer records that identify when the data was created. The field to which this semantic is applied must be a datetime field type. Also in: Unified_Coalesced, Unified_Customer
email	String	This column is input to AmpIQ predictive modeling. The email address that is associated with an individual customer record. A customer record may be associated with multiple email addresses. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
full_name	String	A combination of given name (first name) and surname (last name) that is associated with an individual customer record and is stored as a combined value in a single field within customer data. A full name may include a middle name or initial. Also in: Unified_Coalesced, Unified_Customer
gender	String	This column is input to AmpIQ predictive modeling. The gender that is associated with an individual customer record. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
generational_suffix	String	The suffix that identifies to which family generation a customer record belongs. For example: Jr., Sr. II, and III. Also in: Unified_Coalesced, Unified_Customer
given_name	String	This column is input to AmpIQ predictive modeling. The first name that is associated with an individual customer record. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
loyalty_id	String	The identifier for a loyalty program that is associated with an individual customer record. Also in: Unified_Coalesced
phone	String	This column is input to AmpIQ predictive modeling. The phone number that is associated with an individual customer record. A customer record may be associated with multiple phone numbers. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
postal	String	This column is input to AmpIQ predictive modeling. The zip code or postal code that is associated with the location of an individual customer record. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
state	String	This column is input to AmpIQ predictive modeling. The state or province that is associated with the location of an individual customer record. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
surname	String	This column is input to AmpIQ predictive modeling. The last name that is associated with an individual customer record. Also in: Customer_360, Unified_Coalesced, Unified_Customer, Unified_Preprocessed_Raw
title	String	The title that precedes a full name that is associated with an individual customer record. For example: Mr., Mrs, and Dr. Also in: Unified_Coalesced, Unified_Customer
update_dt	String	Apply the update-dt semantic tag to columns in customer records that identify when the data was last updated. The field to which this semantic is applied must be a datetime field type. At least one customer record must have this semantic tag applied to ensure that the update_dt column is created in the Unified_Coalesced table and to ensure that the Merged_Customers table behaves correctly. Also in: Unified_Coalesced, Unified_Customer

Additional columns in the Merged_Customer table

The Merged_Customers table contains additional columns that help you understand how and why customer profile values are present in the Merged_Customers table.

These column names start with one of name, address, email, phone, birthday, or gender, and then are grouped as described in the following table. For example: name_completion, name_datasource, name_pk, name_priority, and name_update_dt.

Column Suffix	Description
_completion	The number of NOT NULL values that are present in a set of data, as defined in Merged_Customers. Combine the use of this column with _priority to understand why a record was selected. A name has three possible values (given_name, surname, and full_name). When all three values are NOT NULL, the value in the name_completion column will be 3. An address has four possible values (address, city, state, and postal). When all four values are NOT NULL, the value in the address_completion column will be 4. An email address has one possible value (email). When this value is NOT NULL, the value in the email_completion column will be 1. A phone number has one possible value (phone). When this value is NOT NULL, the value in the phone_completion column will be 1. A birthdate has one possible value (birthdate). When this value is NOT NULL, the value in the birthdate_completion column will be 1. Gender has one possible value (gender). When this value is NOT NULL, the value in the gender_completion column will be 1.
_datasource	The source data table from which the customer profile value originates. Combine the use of this column with _pk to find the record in the source domain table.
_pk	The primary key for the record in _datasource. Combine the use of this column with _datasource to find the record in the source domain table.
_priority	The priority that is assigned to the source domain table in Merged_Customers. Combine the use of this column with _completion to understand why a record was selected.
_update_dt

Opt-in status tables

Standard output tables are available that make available your customer’s preferences for email and phone communications from your brand. Use these preferences to determine when and how your brand can use email and SMS to communicate with your customers.

1. Use the Email_Opt_Status table when your brand comunnicates with your customers using their email address.

2. Use the SMS_Opt_Status table when your brand comunnicates with your customers using their phone number.

Email_Opt_Status

The Email_Opt_Status table contains a row for each unique combination of email address, brand, region, and email program. The email address is unique by Amperity ID.

This table is generated when email-opt/ semantic tags are applied to data sources that contain data that indicates when customers have given their consent to your brand for using their email address as part of your marketing campaign.

Note

The Email_Opt_Status table represents the current status for every email address for which you have customer consent. There should be only one consent status by combination of email address, brand, region, or email program. If you have multiple brands, regions or email programs, it’s possible for the same email address to have more than one customer consent status. If brand, region, or email program does not exist, there should be only one customer consent status for each unique email address.

The Email_Opt_Status table contains the following columns:

Semantic Name	Datatype	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles.
brand	String	The brand to which the opt-in status applies.
email	String	The email address that is associated with an individual customer record. A customer record may be associated with multiple email addresses.
email_frequency	String	The preferred frequency for email messages.
email_program	String	The email program to which the customer has opted-in.
is_email_opted_in	Boolean	Indicates whether a customer has given consent to being contacted by your brand using the customer’s email address.
language_preference	String	The customer’s preferred language for email messages.
region	String	The region to which the opt-in status applies.

SMS_Opt_Status

The SMS_Opt_Status table contains a row for each unique combination of phone number, brand, region, and SMS program. The phone number is unique by Amperity ID.

This table is generated when sms-opt/ semantic tags are applied to data sources that contain data that indicates when customers have given their consent to your brand for using their phone number as part of your marketing campaign.

Note

The SMS_Opt_Status table represents the current status for every phone number for which you have customer consent. There should be only one consent status by combination of phone number, brand, region, or SMS program. If you have multiple brands, regions or SMS programs, it’s possible for the same phone number to have more than one customer consent status. If brand, region, or SMS program does not exist, there should be only one customer consent status for each unique phone number.

The SMS_Opt_Status table contains the following columns:

Semantic Name	Datatype	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles.
brand	String	The brand to which the opt-in status applies.
is_sms_opted_in	Boolean	Indicates whether a customer has opted-in to being contacted by your brand using the customer’s phone number.
language_preference	String	The customer’s preferred language for SMS messages.
phone	String	The phone number that is associated with an individual customer record. A customer record may be associated with multiple phone numbers.
region	String	The region to which the opt-in status applies.
sms_frequency	String	The preferred frequency for SMS communications.
sms_program	String	The SMS program to which the customer has opted-in.

Predicted_Affinity

An Affinity table associates individual customers to the products they are most likely to purchase. Use an Affinity table to help deliver personalized experiences to your customers.

Note

See Predicted_Affinity for more information about how this table is built and maintained within the customer 360 database.

An Affinity table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
audience_size_large	Boolean	A flag that indicates the recommended audience size. When this value is True the recommended audience size is large. A large audience is predicted to include ~90% of future purchasers, while also including a high number of non-purchasers.
audience_size_medium	Boolean	A flag that indicates the recommended audience size. When this value is True the recommended audience size is medium. A medium audience is predicted to include ~70% of future purchasers, though it may also include a moderate number of non-purchasers.
audience_size_small	Boolean	A flag that indicates the recommended audience size. When this value is True the recommended audience size is small. A small audience is predicted to incude ~50% of future purchasers, while including the fewest non-purchasers. Use a small audience size to help prevent wasted spend and reduce opt-outs.
product_attribute	String	The field against which product affinity is measured. For example: a category, a class, or a brand.
ranking	Integer	A ranking of customers by their score for this product. A rank that is less than or equal to X will provide the top N customers with an affinity for this product.
score	Float	The strength of a customers’s affinity for this product, shown as an uncalibrated probability. Tip This score is used internally by AmpID and does not directly correlate to ranking and/or audience size. Sort results by ranking, and then compare to audience sizes. Higher rankings within smaller audience sizes correlate with stronger affinity.

Predicted_CLV_Attributes

The Predicted_CLV_Attributes table contains information, for each individual Amperity ID, about predicted future spend and the probability of churn.

Note

See Predicted_CLV_Attributes for more information about how this table is built and maintained within the customer 360 database and how it interacts with features within AmpIQ.

The Predicted_CLV_Attributes table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
predicted_average_order_revenue_next_365d	Decimal	The predicted average order revenue over the next 365 days.
predicted_clv_next_365d	Decimal	The total predicted customer spend over the next 365 days.
predicted_customer_lifecycle_status	String	A probabilistic grouping of a customer’s likelihood to purchase again. For repeat customers, groupings include the following tiers: Active (likelihood to purchase is greater than 60%) Cooling down (likelihood to purchase is between than 50% and 60%) At risk (likelihood to purchase is between than 35% and 50%) Highly at risk (likelihood to purchase is between than 20% and 35%) Lost (likelihood to purchase is less than 20%) For one-time buyers, groupings include the following tiers: Active (purchased within 60 days) Cooling down (purchased 60-120 days ago) At risk (purchased 120-180 days ago) Highly at risk (purchased 180-240 days ago) Lost (purchased 240+ days ago)
predicted_customer_lifetime_value_tier	String	A percentile grouping of customers by predicted CLV. Groupings include: Platinum: top 1% of customers Gold: top 1%-5% of customers Silver: top 5%-10% of customers Bronze: top 10%-25% of customers Medium: top 25%-50% of customers Low: bottom 50% of customers Note This attribute returns only the customers who belong to the selected value tier. For example, to return all of your top 10% customers, you must choose platinum, gold, and silver. Silver by itself will return 5% of your customers, specifically those are in your 5-10%.
predicted_order_frequency_next_365d	Decimal	The predicted number of orders over the next 365 days.
predicted_probability_of_transaction_next_365d	Decimal	The probability a customer will purchase again in the next 365 days.

Stitch_BadValues

The Stitch_BadValues table contains all of the values that were added to the bad-values blocklist.

The Stitch_BadValues table contains the following columns:

Column Name	Data type	Description
datasource	String	The name of the data source from which the value originated.
semantic	String	The semantic tag that is associated with the value that was added to the bad-values blocklist.
value	String	The value that was added to the bad-values blocklist.
num_values	String	The number of times the value appeared in the data.
num_proxy	String	The number of proxies to which the value was associated. When this number exceeds the threshold defined for in the blocklist a value is added to the bad-values blocklist.
domain_table	String	The domain table from which the value originated.
is_preprocessed	String	Indicates if the value was changed during pre-processing by Stitch. For example, phone numbers often have dashes within their values. Dashes are removed by Stitch during pre-processing. When this column is true, and a phone number value is “12065551212”, the original value for that phone number in the associated datasource may be “1-206-555-1212”.

Stitch_Blocking_Keys

The Stitch_Blocking_Keys table contains all of the blocking keys that are associated with the Stitch process.

The Stitch_Blocking_Keys table contains the following columns:

Column Name	Data type	Description
bk	String	A blocking key defines a specific combination of characters to be used as a blocking strategy. For example, the first three characters in given-name, the first character in surname, and birthdate represent a blocking key.
datasource	String	The name of the data source from which this customer record originated. Tip The combination of pk and datasource uniquely identifies a row in the Stitch_Blocking_Keys table, which correlates to a single row in a domain table.
pk	String	The combination of data source and primary key allows Amperity to uniquely identify every row in every data table across the entirety of customer data input to Amperity. Tip The combination of pk and datasource uniquely identifies a row in the Stitch_Blocking_Keys table, which correlates to a single row in a domain table.
strategy	String	A blocking strategy acts like a filter against a very large data set. Each blocking strategy applies its filter and all records that match are grouped together into a block. Each record that matches a blocking strategy is a blocking key.

Stitch_BlocklistValues

Note

The Stitch_BlocklistValues table may be added after the bad-values blocklist is configured for your tenant.

The Stitch_BlocklistValues table contains the following columns:

Column Name	Data type	Description
datasource	String	The name of the data source from which the blocklisted value originated. A value of * means a blocklisted value originated from more than one source.
domain_table	String	The name of the domain table from which the blocklisted value originated.
num_proxy	Integer	The distinct number of individuals to which num_value is associated.
num_values	Integer	The total number of values.
pk	String
semantic	String	The semantic type associated with the value that was blocklisted. For example: email.
value	String	The value that was blocklisted. For example: paul.jackson@amperity.com

Stitch_Scores

The Stitch_Scores table contains all scores generated by Stitch, including scores that are not associated with an Amperity ID. Use this table to help understand why records were not associated with an Amperity ID.

The Stitch_Scores table contains the following columns:

Column Name	Data type	Description
amperity_id1	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. The Amperity ID for the first of two compared records. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
amperity_id2	String	The Amperity ID for the second of two compared records.
match_category	String	A match category is a classifier that is applied by Amperity to an individual record-pair within a cluster of record-pairs. The match category is the result of this classification. Match Category Description Exact Amperity has the highest confidence that these records represent the same person because all profile data exactly matches. Excellent Amperity has near perfect confidence that these records belong to the same person, despite select types of profile data not matching. High Using deductive reasoning, Amperity has very high confidence that these records match, despite some profile data not matching. Moderate Amperity has moderate confidence that these records match, due to weak or fuzzy matches between highly unique customer attributes (email, phone, address). Weak Amperity lacks confidence, but if asked to guess, Amperity would assert these records do belong to the same individual, because they match on non-unique customer attributes (name, state, zip code). No conflict Amperity has high confidence that these records do NOT match, because core profile data is in conflict.
match_type	String	The type of score being applied. Possible values are as follows: “scored”, “scored_transitive”, and “trivial_duplicate”.
pk1	String
pk2	String
score	Float	A score contains a value from 0.0 to 5.0 that represents the combined score assigned to the record pair by Stitch. There are two components of the score: the score itself, and then its strength. The record pair score correlates to the match category, which is a classifier that is applied by Amperity to individual record pairs. The record pair score corresponds to the classification: 5 for exact matches, 4 for excellent matches, 3 for high matches, 2 for moderate matches, 1 for weak matches, and 0 for no matches. The record pair strength represents the strength of the record pair score. It is a two digit number. For example: .31 is a lower strength and .93 is a very high strength.
source1	String
source2	String

Transaction_Attributes

The Transaction_Attributes table describes the behavior and history of customer transactions, such as purchase history and frequency, brand preferences, order history, time between purchases, and so on. This table is added automatically when semantics related to transactions are applied to interaction records.

The Transaction_Attributes table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. This column is added when a transaction is associated with an Amperity ID from the Unified_Transactions table. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
early_repeat_purchaser	Boolean	Early repeat purchaser is a flag that indicates if a customer made a repeat purchase within the previous 90 days. Tip Use the early_repeat_purchaser attribute as a leading indicator for overall conversion rate of one-time buyers to repeat customers, even when it does not capture the total number of one-time buyers who have returned to purchase again. This column is calculated from the first_order_datetime and second_order_datetime columns in the Transaction_Attributes table. Also in: Transaction_Attributes_Extended
first_order_datetime	Datetime	First order date is the date on which the first order was placed by a customer. This column is calculated from the order_datetime column in the Unified_Transactions table, which is created when the order-datetime semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes_Extended
first_order_id	String	First order ID is the order ID for the first order that was made by a customer. This column is calculated from the order_id column in the Unified_Transactions and Unified_Itemized_Transactions tables, which is created when the order-id semantic tag is applied to interaction records that contain transactions and itemized transactions data. Also in: Transaction_Attributes_Extended
first_order_is_retained	Boolean	First order is retained is a flag that indicates if a customer has made a repeat purchase within 365 days of their first order.
first_order_revenue	Decimal	First order revenue is the total revenue that is associated with a customer’s first order, ignoring returned items and/or cancelled items. This column is calculated from the order_revenue column in the Unified_Transactions table, which is created when the order-revenue semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes_Extended
first_order_total_items	Integer	First order total items represents the number of items that were purchased as part of a customer’s first order, ignoring returned items and/or cancelled items. This column is calculated from the order_quantity column in the Unified_Transactions table, which is created when the order-quantity semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes_Extended
latest_order_datetime	Datetime	Latest order date is the date (and time) on which the most recent order was placed by a customer. This column is calculated from the order_datetime column in the Unified_Transactions table, which is created when the order-datetime semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes_Extended
latest_order_id	String	Latest order ID is the order ID for the most recent order that was made by a customer. This column is calculated from the order_id column in the Unified_Transactions and Unified_Itemized_Transactions tables, which is created when the order-id semantic tag is applied to interaction records that contain transactions and itemized transactions data. Also in: Transaction_Attributes_Extended
latest_order_revenue	Decimal	Latest order revenue is the total revenue that is associated with a customer’s most recent order, ignoring returned items and/or cancelled items. This column is calculated from the order_revenue column in the Unified_Transactions table, which is created when the order-revenue semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes_Extended
latest_order_total_items	Integer	Latest order total items is the number of items that were purchased as part of a customer’s most recent order, ignoring returned items and/or cancelled items. This column is calculated from the order_quantity column in the Unified_Transactions table, which is created when the order-quantity semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes_Extended
lifetime_average_item_price	Decimal	The average individual item price for all orders, ignoring returns and/or cancellations. This column is calculated from the order_quantity and order_revenue columns in the Unified_Transactions table, which are created when the order-quantity and order-revenue semantic tags are applied to interaction records that contain transactions data.
lifetime_average_num_items	Decimal	The average number of items purchased for all orders, ignoring returns and/or cancellations. This column is calculated from the order_id and order_quantity columns in the Unified_Transactions table, which are created when the order-id and order-quantity semantic tags are applied to interaction records that contain transactions data.
lifetime_average_order_value	Decimal	The average lifetime revenue for all orders, ignoring returns and/or cancellations. This column is calculated from the order_id and order_revenue columns in the Unified_Transactions table, which are created when the order-id and order-revenue semantic tags are applied to interaction records that contain transactions data.
lifetime_largest_order_value	Decimal	Lifetime largest order value identifies the largest order that is associated with a customer, ignoring returns and/or cancellations, for a customer’s entire purchase history. This column is calculated from the order_revenue column in the Unified_Transactions table, which is created when the order-revenue semantic tag is applied to interaction records that contain transactions data.
lifetime_order_frequency	Integer	A lifetime order frequency is the total number of orders that that a customer has made during their entire relationship with your brand. This column is calculated from the order_id column in the Unified_Transactions and Unified_Itemized_Transactions tables, which is created when the order-id semantic tag is applied to interaction records that contain transactions and itemized transactions data.
lifetime_order_revenue	Decimal	The lifetime revenue for all items, ignoring returns and/or cancellations. This column is calculated from the order_revenue column in the Unified_Transactions table, which is created when the order-revenue semantic tag is applied to interaction records that contain transactions data.
lifetime_preferred_purchase_brand	String	The most frequent brand for all orders.
lifetime_preferred_purchase_channel	String	The most frequent purchase-channel for all orders.
lifetime_total_items	Integer	The lifetime number of individual items associated with the transaction, ignoring returns and/or cancellations. This column is calculated from the order_quantity column in the Unified_Transactions table, which is created when the order-quantity semantic tag is applied to interaction records that contain transactions data.
multi_purchase_brand	Boolean	A flag that indicates if a customer has interacted with more than one brand. This column is calculated from the purchase_brand column in the Unified_Transactions table, which is created when the purchase-brand semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes_Extended
multi_purchase_channel	Boolean	Multi-purchase channel is a flag that indicates if a customer has purchased from more than one channel. This column is calculated from the purchase_channel column in the Unified_Transactions table, which is created when the purchase-channel semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes_Extended
one_and_done	Boolean	One and done is a flag that indicates if a customer has made only one purchase. Important Amperity uses the range of data that is provided to it to identify one-time buyers. For example, if Amperity is provided data for the years 2015-2022, purchases made in 2014 are not used to identify one-time buyers. This column is calculated from the lifetime_order_frequency column in the Transaction_Attributes table. Also in: Transaction_Attributes_Extended
second_order_datetime	Datetime	Second order date is the date on which the second order was placed by a customer. This column is calculated from the order_datetime column in the Unified_Transactions and Unified_Itemized_Transactions tables, which is created when the order-datetime semantic tag is applied to interaction records that contain transactions and itemized transactions data. Also in: Transaction_Attributes_Extended
second_order_id	String	Second order ID is the order ID for the second order that was made by a customer. This column is calculated from the order_id column in the Unified_Transactions and Unified_Itemized_Transactions tables, which are created when the order-id semantic tag is applied to interaction records that contain transactions and itemized transactions data. Also in: Transaction_Attributes_Extended
second_order_is_retained	Boolean	Second order is retained is a flag that indicates if a customer has made a repeat purchase within 365 days of their second order.
second_order_revenue	Decimal	Second order revenue is the total revenue that is associated with a customer’s second order, ignoring returned items and/or cancelled items. This column is calculated from the order_revenue column in the Unified_Transactions table, which is created when the order-revenue semantic tag is applied to interaction records that contain transactions data.
second_order_total_items	Integer	Second order total items is the number of items that were purchased as part of a customer’s second order, ignoring returned items and/or cancelled items. This column is calculated from the order_quantity column in the Unified_Transactions table, which is created when the order-quantity semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes_Extended

Transaction attributes, extended

The Transaction_Attributes_Extended table contains additional attributes for customer flags, customer orders, data differences, time period rollups, and RFM scores, many of which are represented by duration (days, months, years, lifetime), order position (first, second, last), frequency, and revenue.

Extended transaction attributes–customer flags, customer orders, date differences, time period rollups, and RFM–are also calculated by Amperity based on data sources that contain interaction records that were tagged with transactions and itemized transaction semantics.

Extended transaction attributes are added to the Transaction_Attributes_Extended table, which is an output of Stitch. Extended transaction attributes are fully available to the customer 360 database and to segments.

Extended transaction attributes are presented as a single table (with many columns), including an Amperity ID, and fit into the following categories:

• Customer flags

• Customer orders

• Date differences

• Time period rollups

• RFM

Customer flags

Each record has a set of flags that indicate if a customer has purchased, the number of brand interactions, the number of brand channels, and if that customer is an early repeat purchaser.

Column Name	Data type	PII	Description
amperity_id	String		An Amperity ID is a patented unique identifier that is assigned to clusters of customer records. A single Amperity ID represents a single individual. Unlike other systems, the Amperity ID is reassessed every day for the most comprehensive view of your customers.
early_repeat_purchaser	Boolean		Early repeat purchaser is a flag that indicates if a customer made a repeat purchase within the previous 90 days. Tip Use the early_repeat_purchaser attribute as a leading indicator for overall conversion rate of one-time buyers to repeat customers, even when it does not capture the total number of one-time buyers who have returned to purchase again. Also in: Transaction_Attributes
multi_purchase_brand	Boolean		A flag that indicates if a customer has interacted with more than one brand. This column is calculated from the purchase_brand column in the Unified_Transactions table, which is created when the purchase-brand semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes
multi_purchase_channel	Boolean		Multi-purchase channel is a flag that indicates if a customer has purchased from more than one channel. This column is calculated from the purchase_channel column in the Unified_Transactions table, which is created when the purchase-channel semantic tag is applied to interaction records that contain transactions data. Also in: Transaction_Attributes
one_and_done	Boolean		One and done is a flag that indicates if a customer has made only one purchase. Important Amperity uses the range of data that is provided to it to identify one-time buyers. For example, if Amperity is provided data for the years 2015-2022, purchases made in 2014 are not used to identify one-time buyers. Also in: Transaction_Attributes

Customer orders

Each record contains a subset of order data from a customers first, second, and latest order. Each set of attributes is prefixed by first, second, or latest, depending on the order data that is being summarized.

Column Name	Data type	PII	Description
<x>_order_datetime	Datetime		The datetime on which the order was made. Available columns: first_order_datetime second_order_datetime latest_order_datetime Also in: Transaction_Attributes
<x>_order_id	String		The ID of the order. Available columns: first_order_id second_order_id latest_order_id Also in: Transaction_Attributes
<x>_order_purchase_brand	String		The brand of the order made by the customer. Available columns: first_order_purchase_brand second_order_purchase_brand latest_order_purchase_brand Also in: Transaction_Attributes
<x>_order_purchase_channel	String		The channel in which the customer’s order was made. Available columns: first_order_purchase_channel second_order_purchase_channel latest_order_purchase_channel Also in: Transaction_Attributes
<x>_order_revenue	Decimal		The total revenue for orders at each interval. Available columns: first_order_revenue second_order_revenue latest_order_revenue Also in: Transaction_Attributes
<x>_store_id	String		The ID of the store where the customer made their order. This value may be NULL if the associated channel is not retail or some equivalent. Available columns: first_order_store_id second_order_store_id latest_order_store_id
<x>_total_items	Integer		The total number of items in the order. Available columns: first_order_total_items second_order_total_items latest_order_total_items Also in: Transaction_Attributes

Date differences

Each record contains three attributes that specify the number of days between certain events.

Column Name	Data type	PII	Description
days_since_latest_order	Integer		Days since latest order measures the number of days that have elapsed since a customer has placed an order. This column is calculated from the latest_order_datetime column in the Transaction_Attributes table.
first_to_latest_order_days	Integer		First-to-latest order days is the number of days that have elapsed between the date of the first order and the date of the latest order. This column is calculated from the first_order_datetime and latest_order_datetime columns in the Transaction_Attributes table.
first_to_second_order_days	Integer		First-to-second order days is the number of days that have elapsed between the date of the first order and the date of the second order. This column is calculated from the first_order_datetime and second_order_datetime columns in the Transaction_Attributes table.

Time period rollups

Each record contains time period rollups of the customer’s transaction history. The time periods used are lifetime, L12M (the 12 months of transaction history starting 12 months ago), LY12M (the 12 months of transaction history starting 24 months ago), and L30D (the last 30 days).

Column Name	Data type	PII	Description
<x>_average_item_price	Decimal		The average item price during the time period. Available columns: L30D_average_item_price L3M_average_item_price L6M_average_item_price L12M_average_item_price LY12M_average_item_price lifetime_average_item_price
<x>_average_num_items	Decimal		The average number of items during the time period. Available columns: L30D_average_num_items L3M_average_num_items L6M_average_num_items L12M_average_num_items LY12M_average_num_items lifetime_average_num_items
<x>_average_order_value	Decimal		The average order value during the time period. Available columns: L30D_average_order_value L3M_average_order_value L6M_average_order_value L12M_average_order_value LY12M_average_order_value lifetime_average_order_value
<x>_order_frequency	Integer		The count of distinct order IDs that are associated with the customer during the time period. Available columns: L30D_order_frequency L3M_order_frequency L6M_order_frequency L12M_order_frequency LY12M_order_frequency lifetime_order_frequency
<x>_order_revenue	Decimal		The total revenue for orders in the time period. Available columns: L30D_order_revenue L3M_order_revenue L6M_order_revenue L12M_order_revenue LY12M_order_revenue lifetime_order_revenue
<x>_preferred_purchase_brand	String		The brand with the greatest number of orders during the time period. Available columns: L30D_preferred_purchase_brand L3M_preferred_purchase_brand L6M_preferred_purchase_brand L12M_preferred_purchase_brand LY12M_preferred_purchase_brand lifetime_preferred_purchase_brand
<x>_preferred_purchase_channel	String		The channel with the greatest number of orders during the time period. Available columns: L30D_preferred_purchase_channel L3M_preferred_purchase_channel L6M_preferred_purchase_channel L12M_preferred_purchase_channel LY12M_preferred_purchase_channel lifetime_preferred_purchase_channel
<x>_preferred_store_id	String		The store ID with the greatest number of orders during the time period. Available columns: L30D_preferred_store_id L3M_preferred_store_id L6M_preferred_store_id L12M_preferred_store_id LY12M_preferred_store_id lifetime_preferred_store_id
<x>_purchase_brands	Integer		The count of the distinct brands a customer interacted with during the time period. Available columns: L30D_purchase_brands L3M_purchase_brands L6M_purchase_brands L12M_purchase_brands LY12M_purchase_brands lifetime_purchase_brands
<x>_purchase_channels	Integer		The count of the distinct channels (online, in store, etc.) that the customer interacted with during during the time period. Available columns: L30D_purchase_channels L3M_purchase_channels L6M_purchase_channels L12M_purchase_channels LY12M_purchase_channels lifetime_purchase_channels
<x>_stores	Integer		The count of the distinct stores that the customer interacted with during the time period. Available columns: L30D_stores L3M_stores L6M_stores L12M_stores LY12M_stores lifetime_stores
<x>_total_items	Integer		The total number of items purchased by the customer during the time period. Available columns: L30D_total_items L3M_total_items L6M_total_items L12M_total_items LY12M_total_items lifetime_total_items

RFM

Amperity calculates RFM scores against transactions that occurred within the last 12 months.

Each of the recency (R), frequency (F), and monetary (M) scores are represented by a number between 0 and 9. The final RFM score is a concatenation of the individual scores: recency first, then frequency, monetary last. The final RFM score is a number between 0 and 999.

Note

RFM uses approximate calculations to optimize the performance of the Transaction_Attributes table.

Column Name	Data type	PII	Description
L12M_rfm_score	Integer		The RFM score for the customer is based on transactions that occurred within the last 12 months. The RFM score is represented as an integer between “111” and “101010”. This is a concatenated score that uses each of the individual recency, frequency, and monetary scores. The order is recency, then frequency, and then monetary. For example, you can build an audience that contains your top 20% customers for recency, your top 30% customers for frequency, and your top 10% customers for monetary by setting the L12M RFM Score attribute to “9810” (or “9” for recency, “8” for frequency, and then “10” for monetary).
L12M_recency	Integer		L12M Recency is a score that sorts customers by how recently they purchased during the previous 12 months. Each RFM score is split into ten percentile groups. The lowest percentile is 1 and the highest percentile is 10. Each percentile represents 10% of the customers who belong to that segment. 10 represents the 90-100th percentile and the customers who have the highest recency, frequency, or monetary scores. 9 represents the 80-90th percentile 8 represents the 70-80th percentile 7 represents the 60-70th percentile 6 represents the 50-60th percentile 5 represents the 40-50th percentile 4 represents the 30-40th percentile 3 represents the 20-30th percentile 2 represents the 10-20th percentile 1 represents the 0-10th percentile and the customers who have the lowest recency, frequency, or monetary scores. Tip Combine percentiles to build larger groups of customers. For example 9 and 10 together represent the “top 20%” while 8, 9, and 10 represent the “top 30%”.
L12M_frequency	Integer		L12M Frequency is a score that sorts customers by purchase frequency during the previous 12 months. Each RFM score is split into ten percentile groups. The lowest percentile is 1 and the highest percentile is 10. Each percentile represents 10% of the customers who belong to that segment. 10 represents the 90-100th percentile and the customers who have the highest recency, frequency, or monetary scores. 9 represents the 80-90th percentile 8 represents the 70-80th percentile 7 represents the 60-70th percentile 6 represents the 50-60th percentile 5 represents the 40-50th percentile 4 represents the 30-40th percentile 3 represents the 20-30th percentile 2 represents the 10-20th percentile 1 represents the 0-10th percentile and the customers who have the lowest recency, frequency, or monetary scores. Tip Combine percentiles to build larger groups of customers. For example 9 and 10 together represent the “top 20%” while 8, 9, and 10 represent the “top 30%”.
L12M_monetary	Integer		L12M Monetary is a score that sorts customers by spend amount during the previous 12 months. Each RFM score is split into ten percentile groups. The lowest percentile is 1 and the highest percentile is 10. Each percentile represents 10% of the customers who belong to that segment. 10 represents the 90-100th percentile and the customers who have the highest recency, frequency, or monetary scores. 9 represents the 80-90th percentile 8 represents the 70-80th percentile 7 represents the 60-70th percentile 6 represents the 50-60th percentile 5 represents the 40-50th percentile 4 represents the 30-40th percentile 3 represents the 20-30th percentile 2 represents the 10-20th percentile 1 represents the 0-10th percentile and the customers who have the lowest recency, frequency, or monetary scores. Tip Combine percentiles to build larger groups of customers. For example 9 and 10 together represent the “top 20%” while 8, 9, and 10 represent the “top 30%”.

Unified_Changes

The Unified_Changes table contains a 30-day rolling history of every change that occurred during a Stitch run. This table is also useful for downstream processes associated with non-customer 360 databases, such as synchronizing Amperity output to external customer-owned systems and the Stitch QA process.

DEPRECATED

This table has been replaced by the Unified_Changes_Clusters and Unified_Changes_PKS tables. Please refer to those sections.

Unified_Changes_Clusters

The Unified_Changes_Clusters table contains a history of changes to cluster graphs, relative to the previous Stitch run.

The Unified_Changes_Clusters table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
change_type	String	The type of change that occurred within the Stitch job. Possible values include: added, deleted, and updated: The added change type indicates a new cluster was created during this run. The deleted change type indicates a cluster was deleted during this run. The updated change type indicates the cluster existed in the past run, and continues to exist in this run but the set of PKs that comprise it has changed. Also in: Unified_Changes_PKS
job_id	String	The job ID for database table generation. For example: db-gen-20210202-12345-AbCd67EFgH8. Also in: Unified_Changes_PKS
timestamp	Datetime	The timestamp that is associated with job_id. Each row that shares the same job ID will have the same value for timestamp. Also in: Unified_Changes_PKS

Unified_Changes_PKS

The Unified_Changes_PKS table contains a history of changes to primary keys, relative to the previous Stitch run.

The Unified_Changes_PKS table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
amperity_id_prev	String	The Amperity ID that was previously associated with the primary key (pk) as of the most recent Stitch run.
change_type	String	The type of change that occurred within the Stitch job. Possible values include: add, delete, and updated: The added change type indicates a primary key was added to the cluster. The deleted change type indicates a primary key was deleted from the cluster. The updated change type indicates the primary key existed in the past run, and continues to exist in this run but its cluster assignment has changed. Also in: Unified_Changes_Clusters
datasource	String	The name of the data source from which this customer record originated. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Coalesced table, which correlates to a single row in a domain table.
job_id	String	The job ID for database table generation. For example: db-gen-20210202-12345-AbCd67EFgH8. Also in: Unified_Changes_Clusters
pk	String	The combination of data source and primary key allows Amperity to uniquely identify every row in every data table across the entirety of customer data input to Amperity. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Coalesced table, which correlates to a single row in a domain table.
timestamp	Datetime	The timestamp that is associated with job_id. Each row that shares the same job ID will have the same value for timestamp. Also in: Unified_Changes_Clusters

Unified_Coalesced

The Unified_Coalesced table contains every row from every stitched table that is associated with an Amperity ID, with defined semantics coalesced into a single column. A unique Amperity ID may appear in more than one row. Columns are added to this table when semantic values match values in the bad-values blocklist.

The Unified_Coalesced table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
address	String	The address that is associated with the location of an individual customer record. For example: 123 Main Street. Values in this column depend on fields that are tagged with the address semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
address2	String	Additional address information, such as an apartment number or a post office box, that is associated with the location of an individual customer record. For example: Apt #9. Values in this column depend on fields that are tagged with the address2 semantic. Also in: Merged_Customers, Unified_Customer
birthdate	Date	The date of birth that is associated with an individual customer record. Values in this column depend on fields that are tagged with the birthdate semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
blv_address	Boolean	When true, the address on this customer record matches a blocklist value.
blv_email	Boolean	When true, the email on this customer record matches a blocklist value.
blv_given_name	Boolean	When true, the given-name on this customer record matches a blocklist value.
blv_phone	Boolean	When true, the phone on this customer record matches a blocklist value.
blv_surname	Boolean	When true, the surname on this customer record matches a blocklist value.
city	String	The city that is associated with the location of an individual customer record. Values in this column depend on fields that are tagged with the city semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
ck	String	The ck semantic tag may be applied to a column that contains pre-existing, tenant-specific customer IDs. When customer keys are applied, Amperity compares them to the Amperity ID as part of the deduplication process. Tip What happens to customer keys in the Unified_Coalesced table? Records may have NULL customer keys. There may be only one customer key per data source. There may be multiple customer keys per Amperity ID. This is because customer keys may also be tagged as foreign keys. Also in: Unified_Customer
component_id	Integer	An identifier that represents a set of records that are transitively connected with a score above threshold as an outcome of blocking and initial scoring. Records that share a component ID, but have different Amperity IDs, were split during hierarchical comparison. Tip Records with different component_id values may show as having been blocked together. This can occur when a connecting record pair was removed because it scored below the pairwise comparison threshold. Also in: Unified_Preprocessed_Raw
country	String	The country that is associated with the location of an individual customer record. Values in this column depend on fields that are tagged with the country semantic. Important The country field is added to the Unified_Coalesced table when fields are tagged with the country profile semantic. Also in: Merged_Customers, Unified_Customer
create_dt	String	Apply the create-dt semantic tag to columns in customer records that identify when the data was created. The field to which this semantic is applied must be a datetime field type. Also in: Merged_Customers, Unified_Customer
datasource	String	The name of the data source from which this customer record originated. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Coalesced table, which correlates to a single row in a domain table.
email	String	The email address that is associated with an individual customer record. A customer record may be associated with multiple email addresses. Values in this column depend on fields that are tagged with the email semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
fk_[name]	String	The fk-[namespace] semantic tag identifies a field as a foreign key. A foreign key semantic tag must be namespaced. For example: fk-customer, fk-interaction, fk-audience, or fk-brand. A column is added for each foreign key that is defined in the Sources tab. Tip What happens to foreign keys in the Unified_Coalesced table? Records may have NULL foreign keys. There may be multiple foreign keys in the data source, but there may not be duplicate foreign keys. There may be multiple foreign keys per Amperity ID. There should not be multiple Amperity IDs per foreign key. Also in: Unified_Customer, Unified_Preprocessed_Raw
full_name	String	A combination of given name (first name) and surname (last name) that is associated with an individual customer record and is stored as a combined value in a single field within customer data. A full name may include a middle name or initial. Values in this column depend on fields that are tagged with the full-name semantic. Also in: Merged_Customers, Unified_Customer
gender	String	The gender that is associated with an individual customer record. Values in this column depend on fields that are tagged with the gender semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
generational_suffix	String	The suffix that identifies to which family generation a customer record belongs. For example: Jr., Sr. II, and III. Also in: Merged_Customers, Unified_Customer
given_name	String	The first name that is associated with an individual customer record. Values in this column depend on fields that are tagged with the given-name semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
has_blv	Boolean	The has_blv column indicates if blocklist values for address, email, phone, given-name, or surname are present in customer records.
is_supersized	Boolean	Indicates when a rough heuristic is applied to the first grouping of records (rep_pk) to partition supersized records into smaller components. Supersized records occur when more than 500 groups are associated with the first grouping of records.
loyalty_id	String	The identifier for a loyalty program that is associated with an individual customer record. Also in: Merged_Customers
phone	String	The phone number that is associated with an individual customer record. A customer record may be associated with multiple phone numbers. Values in this column depend on fields that are tagged with the phone semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
pk	String	The combination of data source and primary key allows Amperity to uniquely identify every row in every data table across the entirety of customer data input to Amperity. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Coalesced table, which correlates to a single row in a domain table.
postal	String	The zip code or postal code that is associated with the location of an individual customer record. Values in this column depend on fields that are tagged with the postal semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
rep_ds	Integer	The rep_ds column shows the datasource that is associated with the rep_pk column.
rep_pk	Integer	The rep_pk column is an identifier that represents the first grouping of records done by Stitch. This grouping is based on identical semantic patterns. Tip The combination of rep_ds and rep_pk represent qualified trivial duplications, which are records with enough identical PII to score 3.0 (or greater) and were grouped together by Stitch early in the identity resolution process. All qualified trivial duplicates are treated as a single record by downstream Stitch processes. The rep_ds and rep_pk fields are included in the Unified_Coalesced table to help with situations where it’s necessary to understand why two records were not clustered together.
state	String	The state or province that is associated with the location of an individual customer record. Values in this column depend on fields that are tagged with the state semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
supersized_id	Integer	An identifier that represents supersized records that were partitioned into smaller components. Also in: Unified_Preprocessed_Raw
surname	String	The last name that is associated with an individual customer record. Values in this column depend on fields that are tagged with the surname semantic. Also in: Customer_360, Merged_Customers, Unified_Customer, Unified_Preprocessed_Raw
title	String	The title that precedes a full name that is associated with an individual customer record. For example: Mr., Mrs, and Dr. Also in: Merged_Customers, Unified_Customer
update_dt	String	Apply the update-dt semantic tag to columns in customer records that identify when the data was last updated. The field to which this semantic is applied must be a datetime field type. At least one customer record must have this semantic tag applied to ensure that the update_dt column is created in the Unified_Coalesced table and to ensure that the Merged_Customers table behaves correctly. Also in: Merged_Customers, Unified_Customer

Unified_Compliance

The Unified_Compliance table consists of all the tables tagged with the email, request-id, request-strategy, and request-type semantic tags, which are used to suppress data per customer deletion requests or a data subject access request (DSAR).

The Unified_Compliance table consolidates sources tagged with:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
ck	String	The ck semantic tag may be applied to a column that contains pre-existing, tenant-specific customer IDs. When customer keys are applied, Amperity compares them to the Amperity ID as part of the deduplication process. Also in: Unified_Custoers
Customer profile	String	This contains the columns for the following customer profile fields: address, birthdate, city, email, gender, given_name, loyalty_id, phone, postal, surname, and state
datasource	String	The name of the data source from which this customer record originated. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Coalesced table, which correlates to a single row in a domain table.
fk_[name]	String	The fk-[namespace] semantic tag identifies a field as a foreign key. A foreign key semantic tag must be namespaced. For example: fk-customer, fk-interaction, fk-audience, or fk-brand. A column is added for each foreign key that is defined in the Sources tab. Tip What happens to foreign keys in the Unified_Coalesced table? Records may have NULL foreign keys. There may be multiple foreign keys in the data source, but there may not be duplicate foreign keys. There may be multiple foreign keys per Amperity ID. There should not be multiple Amperity IDs per foreign key. Also in: Unified_Customer, Unified_Preprocessed_Raw
pk	String	The combination of data source and primary key allows Amperity to uniquely identify every row in every data table across the entirety of customer data input to Amperity.
request_datasource	String	An identifier that represents the first grouping of records done by Stitch. This grouping is based on identical semantic patterns.
request_email	String	The email address for the customer. This is used to find their records in Amperity.
request_id	Integer	The tracking identifier for the customer’s compliance workflow. This ID should be provided by the customer and must be unique.
request_strategy	String	The compliance request strategy used for matching exact email data, semantic tags, and Amperity IDs.
request_type	String	The type of compliance request. Possible values: delete or data subject access request (DSAR).

Unified_Customer

The Unified_Customer table contains every row of every stitched table with all semantics coalesced into a single column, with two exceptions that are removed from the table: values from a bad-values blocklist and values associated with supersized clusters. A unique Amperity ID may appear in more than one row.

Note

See Unified_Customers for more information about how this table is built and maintained within the customer 360 database.

Note

This table is similar to the Unified_Coalesced table, but will not contain columns related to blocklists or supersized clusters.

The Unified_Customer table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
address	String	The address that is associated with the location of an individual customer record. For example: 123 Main Street. Values in this column depend on fields that are tagged with the address semantic. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
address2	String	Additional address information, such as an apartment number or a post office box, that is associated with the location of an individual customer record. For example: Apt #9. Values in this column depend on fields that are tagged with the address2 semantic. Also in: Merged_Customers, Unified_Coalesced
birthdate	Date	The date of birth that is associated with an individual customer record. Values in this column depend on fields that are tagged with the birthdate semantic. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
city	String	The city that is associated with the location of an individual customer record. Values in this column depend on fields that are tagged with the city semantic. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
ck	String	The ck semantic tag may be applied to a column that contains pre-existing, tenant-specific customer IDs. When customer keys are applied, Amperity compares them to the Amperity ID as part of the deduplication process. Also in: Unified_Coalesced
country	String	The country that is associated with the location of an individual customer record. Values in this column depend on fields that are tagged with the country semantic. Also in: Merged_Customers, Unified_Coalesced
create_dt	String	Apply the create-dt semantic tag to columns in customer records that identify when the data was created. The field to which this semantic is applied must be a datetime field type. Also in: Merged_Customers, Unified_Coalesced
datasource	String	The name of the data source from which this customer record originated.
email	String	The email address that is associated with an individual customer record. A customer record may be associated with multiple email addresses. Values in this column depend on fields that are tagged with the email semantic. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
fk_[name]	String	The fk-[namespace] semantic tag identifies a field as a foreign key. A foreign key semantic tag must be namespaced. For example: fk-customer, fk-interaction, fk-audience, or fk-brand. A column is added for each foreign key that is defined in the Sources tab. Tip What happens to foreign keys in the Unified_Coalesced table? Records may have NULL foreign keys. There may be multiple foreign keys in the data source, but there may not be duplicate foreign keys. There may be multiple foreign keys per Amperity ID. There should not be multiple Amperity IDs per foreign key. Also in: Unified_Coalesced, Unified_Preprocessed_Raw
full_name	String	A combination of given name (first name) and surname (last name) that is associated with an individual customer record and is stored as a combined value in a single field within customer data. A full name may include a middle name or initial. Values in this column depend on fields that are tagged with the full-name semantic. Also in: Merged_Customers, Unified_Coalesced
gender	String	The gender that is associated with an individual customer record. Values in this column depend on fields that are tagged with the gender semantic. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
generational_suffix	String	The suffix that identifies to which family generation a customer record belongs. For example: Jr., Sr. II, and III. Also in: Merged_Customers, Unified_Coalesced
given_name	String	The first name that is associated with an individual customer record. Values in this column depend on fields that are tagged with the given-name semantic. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
loyalty_id	String	The identifier for a loyalty program that is associated with an individual customer record.
phone	String	The phone number that is associated with an individual customer record. A customer record may be associated with multiple phone numbers. Values in this column depend on fields that are tagged with the phone semantic. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
pk	String	The combination of data source and primary key allows Amperity to uniquely identify every row in every data table across the entirety of customer data input to Amperity.
postal	String	The zip code or postal code that is associated with the location of an individual customer record. Values in this column depend on fields that are tagged with the postal semantic. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
state	String	The state or province that is associated with the location of an individual customer record. Values in this column depend on fields that are tagged with the state semantic. Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
surname	String	The last name that is associated with an individual customer record. Values in this column depend on fields that are tagged with the surname semantic. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Preprocessed_Raw
title	String	The title that precedes a full name that is associated with an individual customer record. For example: Mr., Mrs, and Dr. Also in: Merged_Customers, Unified_Coalesced
update_dt	String	Apply the update-dt semantic tag to columns in customer records that identify when the data was last updated. The field to which this semantic is applied must be a datetime field type. At least one customer record must have this semantic tag applied to ensure that the update_dt column is created in the Unified_Coalesced table and to ensure that the Merged_Customers table behaves correctly. Also in: Merged_Customers, Unified_Coalesced

Unified_Email_Events

The Unified_Email_Events table contains individual email event information, such as sends, opens, clicks, opt-in and opt-out preferences, bounces, and conversions. This table is added as Stitch output when email event semantic tags are applied to data sources.

Important

This table is only generated when email-events semantic tags are applied to data sources that provide at least 15 months of data for raw email events.

The Unified_Email_Events table contains the following columns:

Column Name	Data type	Description
brand	String	The brand or company from which an email was sent. Values in this column depend on fields that are tagged with the email-event/brand semantic.
datasource	String
email	String	The email address to which an email was sent. Values in this column depend on fields that are tagged with the email-event/email semantic.
event_datetime	Datetime	The date and time at which email event occurred. Values in this column depend on fields that are tagged with the email-event/event-datetime semantic.
event_type	String	The type of email event. Possible values: Open Click Sent Optin Bounce Converted Unsubscribe Important The values for email event types must spelled as expected to ensure the Email_Engagement_Summary can properly summarize email events. For example: “Optin” may not be “Opt-in”. Values in this column depend on fields that are tagged with the email-event/event-type semantic.
pk	String
region	String	The region or location from which an email was sent. The region or location is typically associated to a single brand. Values in this column depend on fields that are tagged with the email-event/region semantic.
send_id	String	The unique identifier for the email that was sent to an email address at a specific date and time. If a data source does not provide a send ID a unique key is generated. Values in this column depend on fields that are tagged with the email-event/send-id semantic.

Unified_Itemized_Transactions

The Unified_Itemized_Transactions table contains every row of every stitched table with all transactional data summarized to the item level, and then coalesced into a single column for each unique combination of order ID and product ID. The order ID is associated with an Amperity ID.

Note

These columns are created when txn-item semantics are applied to interaction records that contain itemized transaction data.

The following diagram shows an example of the Unified_Itemized_Transactions table (click to view a larger diagram):

The Unified_Itemized_Transactions table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
customer_id	String	A custom semantic tag that is applied to interaction records to identify a field that is used in downstream processes to associate interaction records to the Amperity ID. A customer ID may appear once for each unique order ID in the itemized transactions table. Note A namespaced foreign key may be used instead of (or in addition to) a customer ID. Values in this column depend on fields that are tagged with the txn-item/customer-id semantic or a foreign key. Also in: Unified_Transactions
currency	String	Currency represents the type of currency that was used to pay for an item. For example: dollar. Note Currency must be consistent across all orders from the same data source. Values in this column depend on fields that are tagged with the txn-item/currency semantic.
digital_channel	String	The digital channel through which a transaction was made. For example: Facebook, Google Ads, email, etc. Note This column should only have values when purchase-channel specifies an online channel. Values in this column depend on fields that are tagged with the txn-item/digital-channel semantic.
is_cancellation	Boolean	A flag that indicates if the item was cancelled. Values in this column depend on fields that are tagged with the txn-item/is-cancellation semantic. Important This value should be TRUE when items are canceled and FALSE when items are purchases and NULL when the value is unknown.
is_return	Boolean	This column is input to AmpIQ predictive modeling. A flag that indicates if the item was returned. Values in this column depend on fields that are tagged with the txn-item/is-return semantic. Important This value should be TRUE when items are returns and FALSE when items are purchases and NULL when the value is unknown.
item_cost	Decimal	Item cost is the cost to produce all units of an item. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/item-cost semantic.
item_discount_amount	Decimal	Item discount amount is the discount amount that is applied to all units that are associated with a single item within a single transaction. This value should equal item quantity multiplied by unit discount amounts. This value is used by Amperity for discount sensitivity analysis. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/item-discount-amount semantic.
item_discount_percent	Decimal	Item discount percent is the percentage discount that is applied to all units that are associated with a single item within a single transaction. This value is used by Amperity for discount sensitivity analysis. Note This value must be between 0 and 1. Values in this column depend on fields that are tagged with the txn-item/item-discount-percent semantic.
item_list_price	Decimal	Item list price is the manufacturer’s suggested retail price (MSRP) for all units of this item. The manufacturer’s suggested retail price (MSRP) is the price before shipping costs, taxes, and/or discounts have been applied. MSRP is sometimes referred to as the base price. This value should equal item revenue plus item discount amount. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/item-list-price semantic.
item_profit	Decimal	Item profit represents the amount of profit that is earned when all units of an item are sold. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/item-profit semantic.
item_quantity	Integer	This column is input to AmpIQ predictive modeling. Item quantity is the total number of items in an order. When an item has been returned or an order has been cancelled, item quantity is the total number of items that were returned and/or cancelled. Note This value must be less than or equal to 0 when is-return or is-cancellation are true. Values in this column depend on fields that are tagged with the txn-item/item-quantity semantic.
item_revenue	Decimal	This column is input to AmpIQ predictive modeling. The total revenue for all units of an item, after discounts are applied. When an item has been returned or the order has been cancelled, the total revenue for all items that were returned and/or cancelled. This value should equal item quantity multiplied by unit revenue. Note This value must be less than or equal to 0 when is-return or is-cancellation are true. Values in this column depend on fields that are tagged with the txn-item/item-revenue semantic.
item_subtotal	Decimal	An item subtotal is the amount for an item, before discounts are applied. This value should equal unit list price times item quantity. This value is used by Amperity for discount sensitivity analysis. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/item-subtotal semantic.
item_tax_amount	Decimal	An item tax amount is the total amount of taxes that are associated with the purchase of an item. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/item-tax-amount semantic.
order_datetime	Datetime	This column is input to AmpIQ predictive modeling. Order datetime is the date (and time) on which an order was placed. Note Other dates associated with an order that are not specific to a transactions, such as dates associated with hotel stays and reservations, should be added to the Unified_Product_Catalog table. When is_return is TRUE, the date and time on which the order was returned. When is_cancellation is TRUE, the date and time on which the order was cancelled. Values in this column depend on fields that are tagged with the txn-item/order-datetime semantic. This column is used to calculate the following transaction attributes: first_order_datetime latest_order_datetime second_order_datetime Also in: Unified_Transactions
order_id	String	This column is input to AmpIQ predictive modeling. An order ID is the unique identifier for the order and links together all of the items that were part of the same transaction. When an item has been returned or when an order has been cancelled, the order ID is the unique identifier for the original order, including the returned or cancelled items. This field is often the primary key and associated with the pk semantic tag. Note For data that contains itemized transactions, where a single transaction includes more than one of the same item, the order ID will appear more than once. Note The order ID should never change, even when an item in the order is returned or cancelled. Caution If order IDs are recycled and/or are otherwise not guaranteed to be unique over time, the unique identifier for the order must be updated to be a combination of the order ID and the date on which the order occurred. This must be done using domain SQL similar to: CONCAT(order_id, order_date). Values in this column depend on fields that are tagged with the txn-item/order-id semantic. The combination of order_id and product_id must be unique for each row in this table. This column is used to calculate the following transaction attributes: first_order_id L30D_average_num_items L30D_average_order_value L30D_order_frequency latest_order_id lifetime_average_num_items lifetime_average_order_value lifetime_order_frequency second_order_id Also in: Unified_Transactions
payment_method	String	A payment method is how a customer chose to pay for the items they have purchased. For example: credit card, gift card, or cash. Values in this column depend on fields that are tagged with the txn-item/payment-method semantic.
Product catalogs	String	Optional Product catalog fields are added to the Unified_Itemized_Transactions table in two ways, depending on the approach your tenant used for defining your product catalog: When data sources use txn-item/ product catalog semantic tags. When product catalog fields are joined from the Unified_Product_Catalog table. Refer to the section in this topic about the columns that are available to product catalogs. Important The names of the columns that are available for product catalogs are identical. For example: “product_brand”, “product_category”, and “product_gender”. The difference is the outcome of the approach your tenant used to define your product catalog wihtin Amperity.
product_id	String	This column is input to AmpIQ predictive modeling. The unique identifier for a product. A stock keeping unit (SKU) is an identifier that captures all of the unique details of any individual product, including specific attributes that differentiate by color, size, material, and so on. For example, a shirt with the same color and material, but with three different sizes would be represented by three unique SKUs and would also be represented by three unique product IDs. Note For data that contains itemized transactions, where a single transaction includes more than one of the same product, the product ID will appear more than once. Caution Every customer has their own definition for SKUs and product IDs. Be sure to understand this defintition before applying semantic tags to fields with product IDs to ensure they accurately reflect the customer’s definition. Values in this column depend on fields that are tagged with the txn-item/product-id semantic. The combination of order_id and product_id must be unique for each row in this table; product quantity is counted using item_quantity. Important This column is recommended when using a product catalog. A product catalog is a requirement for certain predictive features of AmpIQ, such as for product affinity and audience sizes.
purchase_brand	String	The brand for which a transaction was made. Note This column should only have values when interaction records contain transactions data for more than one brand. Values in this column depend on fields that are tagged with the txn-item/purchase-brand semantic. This column is used to calculate the following transaction attributes: multi_purchase_brand.
purchase_channel	String	This column is input to AmpIQ predictive modeling. A purchase channel is the channel from which a transaction was made. For example: in-store or online. Values in this column depend on fields that are tagged with the txn-item/purchase-channel semantic.
store_id	String	A store ID is a unique identifier that is identified with the location of a store. Values in this column depend on fields that are tagged with the txn-item/store-id semantic.
unit_cost	Decimal	Unit cost is the cost to produce a single unit of one item. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/unit-cost semantic.
unit_discount_amount	Decimal	Unit discount amount is the discount amount that is applied to a single unit of one item. This discount is often applied to all units of the same item within a single transaction. This value is used by Amperity for discount sensitivity analysis. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/unit-discount-amount semantic.
unit_list_price	Decimal	Unit list price is the manufacturer’s suggested retail price (MSRP) for a single unit of an item. The manufacturer’s suggested retail price (MSRP) is the price before shipping costs, taxes, and/or discounts have been applied. MSRP is sometimes referred to as the base price. This value should equal the unit discount amount plus the unit subtotal. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/unit-list-price semantic.
unit_profit	Decimal	Unit profit represents the amount of profit that is earned when a single unit of an item is sold. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/unit-profit semantic.
unit_revenue	Decimal	The total revenue for a single unit of an item. When an item has been returned or the order has been cancelled, the total revenue for a single unit of an item that was returned and/or cancelled. Note This value must be less than or equal to 0 when is-return or is-cancellation are true. Values in this column depend on fields that are tagged with the txn-item/unit-revenue semantic.
unit_subtotal	Decimal	A unit subtotal is the amount for a single unit of one item, before discounts have been applied. This value is used by Amperity for discount sensitivity analysis. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/unit-subtotal semantic.
unit_tax_amount	Decimal	A unit tax amount is the total amount of taxes that are associated with a single unit. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn-item/unit-tax-amount semantic.
Custom attributes	Varies	You may extend the Unified_Itemized_Transactions table to define custom attributes for use with a product catalog. Important This column is recommended when using a product catalog. A product catalog is a requirement for certain predictive features of AmpIQ, such as for product affinity and audience sizes. Add custom attributes to the Unified_Itemized_Transactions table by extending it to add columns that support using a product catalog. Custom attributes should include the columns to which product catalog semantic tags were applied, but may include additional custom attributes that are unique to your tenant and your brands. Important The Unified_Product_Catalog table represents the taxonomy for your products and brands. Attributes are added to the Unified_Product_Catalog table when pc/ semantic tags are applied to your data sources. All pc/ semantic tags are optional. Use the ones that best define the shape of your product catalog and best describe the individual items within it. The product ID is used as an input to AmpIQ predictive modeling.

Unified_Preprocessed_Raw

The Unified_Preprocessed_Raw table is an output of Stitch. Use this table as part of Stitch QA to view data exactly how values were used by Stitch after preprocessing and blocklisting. This table contains one row for each trivial duplicate. Only the semantics used by Stitch are shown. Semantic values represent pre-processing values and/or bad values that were removed (and replaced by NULL).

Tip

Because there is only one row per trivial duplicate, join this table on the Unified_Coalesced table for ongoing usage. For example:

SELECT *
FROM unified_coalesced uc
JOIN unified_preprocessed_raw up
ON uc.rep_ds=up.datasource AND uc.rep_pk=up.pk

The Unified_Preprocessed_Raw table contains the following columns:

Column Name	Data type	Description
amperity_id	String	The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
address	String	The address that is associated with the location of an individual customer record. For example: 123 Main Street. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer
birthdate	Date	The date of birth that is associated with an individual customer record. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer
city	String	The city that is associated with the location of an individual customer record. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer
component_id	Integer	An identifier that represents a set of records that are transitively connected with a score above threshold as an outcome of blocking and initial scoring. Records that share a component ID, but have different Amperity IDs, were split during hierarchical comparison. Tip Use this field during the Stitch QA process to help identify why certain records were grouped (or not grouped) together. Also in: Unified_Preprocessed_Raw
datasource	String	The name of the data source from which this customer record originated. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Preprocessed_Raw table, which correlates to a single row in a domain table.
email	String	The email address that is associated with an individual customer record. A customer record may be associated with multiple email addresses. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer
fk_[name]	String	The fk-[namespace] semantic tag identifies a field as a foreign key. A foreign key semantic tag must be namespaced. For example: fk-customer, fk-interaction, fk-audience, or fk-brand. A column is added for each foreign key that is defined in the Sources tab. Note If foreign keys are linked together by a trivial duplicate they will appear in the Unified_Preprocessed_Raw table as a comma-separated list. Also in: Unified_Coalesced, Unified_Customer
gender	String	The gender that is associated with an individual customer record. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer
given_name	String	The first name that is associated with an individual customer record. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer
int_id	Integer	An identifier that represents a unique combination of datasource and pk (primary key) and their position in the Stitch record sort order after Stitch has identified (and removed) trivial duplicates. Important This identifier is based on Stitch record sort order and is determined each time Stitch runs. Records will not always have the same position in the record sort order; you should not expect this ID to be stable over time. Tip Use this field during the Stitch QA process to help identify why certain records were grouped (or not grouped) together.
login_trimmed	String
phone	String	The phone number that is associated with an individual customer record. A customer record may be associated with multiple phone numbers. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer
pk	String	The combination of data source and primary key allows Amperity to uniquely identify every row in every data table across the entirety of customer data input to Amperity. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Preprocessed_Raw table, which correlates to a single row in a domain table.
po_box	String	Additional address information, such as an apartment number or a post office box, that is associated with the location of an individual customer record. For example: Apt #9.
postal	String	The zip code or postal code that is associated with the location of an individual customer record. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer
sk_generational_suffix	String
sk_given_name	String
state	String	The state or province that is associated with the location of an individual customer record. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer
supersized_id	Integer	The identifier for a supersized record. Indicates when a rough heuristic is applied to the first grouping of records (rep_pk) to partition supersized records into smaller components. Supersized records occur when more than 500 groups are associated with the first grouping of records. Also in: Unified_Coalesced
surname	String	The last name that is associated with an individual customer record. Also in: Customer_360, Merged_Customers, Unified_Coalesced, Unified_Customer

Unified_Product_Catalog

The Unified_Product_Catalog table contains a row for every item in your product catalog, including descriptive attributes, such as name, brand, size, and color, grouping attributes, such as category, sub-category, class, and sub-class, and other attributes like MSRP, gender, and description.

Important

The Unified_Product_Catalog table represents the taxonomy for your products and brands. Attributes are added to the Unified_Product_Catalog table when pc/ semantic tags are applied to your data sources. All pc/ semantic tags are optional. Use the ones that best define the shape of your product catalog and best describe the individual items within it. The product ID is used as an input to AmpIQ predictive modeling.

The Unified_Product_Catalog table contains the following columns:

Column Name	Data type	Description
datasource	String	The name of the data source from which this customer record originated. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Coalesced table, which correlates to a single row in a domain table.
pk	String	The combination of data source and primary key allows Amperity to uniquely identify every row in every data table across the entirety of customer data input to Amperity. Tip The combination of pk and datasource uniquely identifies a row in the Unified_Coalesced table, which correlates to a single row in a domain table.
product_brand	String	The brand name of a product or item.
product_brand_id	String	The ID for the brand name of a product or item.
product_category	String	This column is input to AmpIQ predictive modeling when joined to Unified_Itemized_Transactions. A category to which the product belongs.
product_category_id	String	The ID for the category to which a product belongs.
product_class	String	The name of the class (or grouping) to which a product or item belongs.
product_class_id	String	The ID for the name of the class (or grouping) to which a product or item belongs.
product_collection	String	The name of the collection to which a product or item belongs.
product_collection_id	String	The ID for the name of the collection to which a product or item belongs.
product_color	String	The color of a product or item.
product_color_id	String	The ID for the color of a product or item.
product_department	String	The department to which a product or item belongs.
product_department_id	String	The ID for the department to which a product or item belongs.
product_description	String	This column is input to AmpIQ predictive modeling when joined to Unified_Itemized_Transactions. A description of the product.
product_division	String	The division to which a product or item belongs.
product_division_id	String	The ID for the division to which a product or item belongs.
product_fabric	String	The fabric used for a product or item.
product_fabric_id	String	The ID for the fabric used for a product or item.
product_gender	String	A list of gender options for products. For example: F, M, unisex, NULL (for unknown).
product_group	String	The group to which a product or item belongs.
product_id	String	The unique identifier for a product. Values in this column depend on fields that are tagged with the pc/product-id semantic.
product_material	String	The material used for a product or item.
product_material_id	String	The ID for the material used for a product or item.
product_msrp	String	The manufacturer’s suggested retail price (MSRP) for a product or item. The manufacturer’s suggested retail price (MSRP) is the price before shipping costs, taxes, and/or discounts have been applied. MSRP is sometimes referred to as the base price.
product_name	String	The name of the product or item.
product_season	String	The season to which a product or item is associated.
product_season_id	String	The ID for the season to which a product or item is associated.
product_silhouette	String
product_size	String	The size of a product or item.
product_size_id	String	The ID for the size of a product or item.
product_sku	String	The stock keeping unit, or SKU, for the product or item. A stock keeping unit (SKU) is an identifier that captures all of the unique details of any individual product, including specific attributes that differentiate by color, size, material, and so on.
product_style	String	The style of a product or item.
product_subcategory	String	This column is input to AmpIQ predictive modeling when joined to Unified_Itemized_Transactions. A subcategory or secondary variant to which a product belongs.
product_subcategory_id	String	The ID for the subcategory or secondary variant to which a product belongs.
product_subclass	String	The subclass to which a product or item is assigned.
product_subclass_id	String	The ID for the subclass to which a product or item is assigned.
product_subdepartment	String	The sub-department to which a product or item is assigned.
product_subdepartment_id	String	The ID for the sub-department to which a product or item is assigned.
product_type	String	The type assigned to a product or item.
product_upc	String	The UPC code for the product or item. A Universal Product Code (UPC or UPC code) is a barcode that is widely used to track items in stores.

Unified_Scores

The Unified_Scores table records all of the pairwise comparison scores and match categories for all groups of records, and then for each group of records all of the pairwise scores that are present between records within that group.

Note

See Unified_Scores for more information about how this table is built and maintained within the customer 360 database.

The Unified_Scores table contains the following columns:

Column Name	Data type	PII	Description
amperity_id	String		The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
source1	String
source2	String
pk1	String
pk2	String
score	Float		A score contains a value from 0.0 to 5.0 that represents the combined score assigned to the record pair by Stitch. There are two components of the score: the score itself, and then its strength. The record pair score correlates to the match category, which is a classifier that is applied by Amperity to individual record pairs. The record pair score corresponds to the classification: 5 for exact matches, 4 for excellent matches, 3 for high matches, 2 for moderate matches, 1 for weak matches, and 0 for no matches. The record pair strength represents the strength of the record pair score. It is a two digit number. For example: .31 is a lower strength and .93 is a very high strength. Note Scores are shown for records that end up in the same cluster, including any scores that are below threshold. Scores are not shown for records that do not end up in the same cluster. Also in: Detailed_Examples
match_category	String		A match category is a classifier that is applied by Amperity to an individual record-pair within a cluster of record-pairs. The match category is the result of this classification. Match Category Description Exact Amperity has the highest confidence that these records represent the same person because all profile data exactly matches. Excellent Amperity has near perfect confidence that these records belong to the same person, despite select types of profile data not matching. High Using deductive reasoning, Amperity has very high confidence that these records match, despite some profile data not matching. Moderate Amperity has moderate confidence that these records match, due to weak or fuzzy matches between highly unique customer attributes (email, phone, address). Weak Amperity lacks confidence, but if asked to guess, Amperity would assert these records do belong to the same individual, because they match on non-unique customer attributes (name, state, zip code). No conflict Amperity has high confidence that these records do NOT match, because core profile data is in conflict. Also in: Detailed_Examples
match_type	String		The type of score being applied. Possible values are as follows: “scored”, “scored_transitive”, and “trivial_duplicate”. A match is assigned “scored_transitive” when that match was not identified during blocking. For example: three records (A, B, and C). If records A and B and records B and C were identified as matching during blocking, all three records will end up in the same group of records for pairwise comparison. Records A and C have a transitive connection.

Unified_Transactions

The Unified_Transactions table contains one row for each unique transaction record, with each order ID associated to an Amperity ID.

Note

These columns are created when txn semantics are applied to interaction records that contain transactions data.

The following diagram shows an example of the Unified_Transactions table (click to view a larger diagram):

The Unified_Transactions table contains the following columns:

Column Name	Data type	Description
amperity_id	String	This column is input to AmpIQ predictive modeling. The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
currency	String	Currency represents the type of currency that was used to pay for an item. For example: dollar. Note Currency must be consistent across all orders from the same data source. Values in this column depend on fields that are tagged with the txn/currency semantic.
customer_id	String	A custom semantic tag that is applied to interaction records to identify a field that is used in downstream processes to associate interaction records to the Amperity ID. A customer ID may appear once for each order ID in the transactions table. When a foreign key is not present, the customer ID is used to join interaction records (transactions and itemized transactions) to tables that contain the Amperity ID. Note A namespaced foreign key should be used along with a customer ID. When a foreign key is added to transactions data it: Must match a foreign key in a table that is output by Stitch Must be well-distributed across the data source (a high percentage of values must not be 0) May contain a NULL value Must be unique for each order ID in the Unified_Transactions table. Values in this column depend on fields that are tagged with the txn/customer-id semantic or a foreign key. Also in: Unified_Itemized_Transactions
digital_channel	String	The digital channel through which a transaction was made. For example: Facebook, Google Ads, email, etc. Note This column should only have values when purchase-channel specifies an online channel. Values in this column depend on fields that are tagged with the txn/digital-channel semantic.
order_cancelled_quantity	Integer	This column is input to AmpIQ predictive modeling. The total number of items in the original transaction that were cancelled. This value should match the sum of all items in the itemized transactions that were cancelled for the same order ID. Important This value must be less than or equal to 0 when is_cancelled is TRUE. Values in this column depend on fields that are tagged with the txn/order-cancelled-quantity semantic.
order_cancelled_revenue	Decimal	This column is input to AmpIQ predictive modeling. The total amount of revenue for all cancelled items in the transaction. This value should match the sum of the revenue for all items in the itemized transactions that were cancelled. Important This value must be less than or equal to 0 when is_cancelled is TRUE. Values in this column depend on fields that are tagged with the txn/order-cancelled-revenue semantic.
order_cost	Decimal	Order cost represents the total cost of goods sold (COGS) for a single transaction. Cost of goods sold (COGS) are the direct costs of producing goods that are sold by a brand, including the costs of materials and labor to produce the item, but excluding indirect expenses like distribution or sales. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Warning Only one of order-profit and order-cost may be present for a transaction. Values in this column depend on fields that are tagged with the txn/order-cost semantic.
order_datetime	Datetime	This column is input to AmpIQ predictive modeling. Order datetime is the date (and time) on which an order was placed. The order date: Must have a consistent time zone across all dates in the transactions data. Should be a local time zone. Should be a timestamp, which is converted to datetime automatically when a date is present in the timestamp. Note Other dates associated with an order that are not specific to a transactions, such as dates associated with hotel stays and reservations, should be added to the Unified_Product_Catalog table. Values in this column depend on fields that are tagged with the txn/order-datetime semantic. This column is used to calculate the following transaction attributes: first_order_datetime latest_order_datetime second_order_datetime Also in: Unified_Itemized_Transactions
order_discount_amount	Decimal	This column is input to AmpIQ predictive modeling. Order discount amount is the total discount amount that is applied to the entire order. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. This value is used by Amperity for discount sensitivity analysis. Caution This value should match the customer’s definition of an order-level discount. For example, some customers associate this value to order value, whereas others associate this value to a subtotal. Use domain SQL to update this field for the desired calculation. Values in this column depend on fields that are tagged with the txn/order-discount-amount semantic.
order_discount_percent	Decimal	Order discount percent is the percentage discount that is applied to the order value for the entire transaction, in addition to any item or unit-specific discount percentages. This value may be used as an input to order discount amount. Note This value must be between 0 and 1. This value is used by Amperity for discount sensitivity analysis. Caution This value should match the customer’s definition of an order-level discount percentage. For example, some customers associate this value to order value, whereas others associate this value to a subtotal. Use domain SQL to update this field for the desired calculation. Values in this column depend on fields that are tagged with the txn/order-discount-percent semantic.
order_id	String	This column is input to AmpIQ predictive modeling. An order ID is the unique identifier for the order and links together all of the items that were part of the same transaction. When an item has been returned or when an order has been cancelled, the order ID is the unique identifier for the original order, including the returned or cancelled items. This column is the primary key and must be associated with the pk semantic tag. Each unique order ID must: Appear only once in the Unified_Transactions. Match an order ID in the Unified_Itemized_Transactions table. (This order ID might be associated to multiple items within a single transactions.) Note The order ID should never change, even when an item in the order is returned or cancelled. Caution If order IDs are recycled and/or are otherwise not guaranteed to be unique over time, the unique identifier for the order must be updated to be a combination of the order ID and the date on which the order occurred. This must be done using domain SQL similar to: CONCAT(order_id, order_date). Values in this column depend on fields that are tagged with the txn/order-id semantic. This column is used to calculate the following transaction attributes: first_order_id L30D_average_num_items L30D_average_order_value L30D_order_frequency latest_order_id lifetime_average_num_items lifetime_average_order_value lifetime_order_frequency second_order_id Also in: Unified_Itemized_Transactions
order_list_price	Decimal	Order list price is the total value for a transaction, as defined by the manufacturer’s suggested retail price (MSRP) for all units of this item. The manufacturer’s suggested retail price (MSRP) is the price before shipping costs, taxes, and/or discounts have been applied. MSRP is sometimes referred to as the base price. This value should match the sum of item list price amounts in the itemized transactions that are associated with the same order ID. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn/order-list-price semantic.
order_profit	Decimal	Order profit is the amount of profit that is earned from a single transaction. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Warning Only one of order-profit and order-cost may be present for a transaction. Values in this column depend on fields that are tagged with the txn/order-profit semantic.
order_quantity	Integer	This column is input to AmpIQ predictive modeling. Order quantity is the total number of individual items associated with the transaction. This value should match the sum of all items in the itemized transactions that have not been returned or cancelled for the same order ID. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn/order-quantity semantic. This column is used to calculate the following transaction attributes: first_order_total_items L30D_average_item_price L30D_average_num_items L30D_total_items latest_order_total_items lifetime_average_item_price lifetime_average_num_items lifetime_total_items second_order_total_items
order_returned_quantity	Integer	This column is input to AmpIQ predictive modeling. Order returned quantity is the total number of items in the original transaction that were returned. This value should match the sum of all items in the itemized transaction that were returned for the same order ID. Note This value must be less than or equal to 0. Important This value must be less than or equal to 0 when is_return is TRUE. Values in this column depend on fields that are tagged with the txn/order-returned-quantity semantic.
order_returned_revenue	Decimal	This column is input to AmpIQ predictive modeling. Order returned revenue total amount of revenue for all returned items in a transaction. This value should match the sum of the revenue for all items in the itemized transactions that were returned. Important This value must be less than or equal to 0 when is_return is TRUE. Values in this column depend on fields that are tagged with the txn/order-returned-revenue semantic.
order_revenue	Decimal	This column is input to AmpIQ predictive modeling. The total amount of revenue for all items in a transaction after discounts are applied, ignoring returns and/or cancellations. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn/order-revenue semantic. This column is used to calculate the following transaction attributes: first_order_revenue L30D_average_item_price L30D_average_order_value L30D_largest_order_value L30D_order_revenue latest_order_revenue lifetime_average_item_price lifetime_average_order_value lifetime_largest_order_value lifetime_order_revenue second_order_revenue
order_shipping_amount	Decimal	The order shipping amount is the total cost of shipping all items in the same transaction. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn/order-shipping-amount semantic.
order_subtotal	Decimal	An order subtotal is the amount for an order, before discounts are applied. This value should match the sum of item subtotal revenue in the itemized transactions that are associated with the same order ID. Values in this column depend on fields that are tagged with the txn/order-subtotal semantic.
order_tax_amount	Decimal	An order tax amount is the total amount of taxes that are associated with an entire order. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn/order-tax-amount semantic.
payment_method	String	A payment method is how a customer chose to pay for the items they have purchased. For example: credit card, gift card, or cash. Values in this column depend on fields that are tagged with the txn/payment-method semantic.
purchase_brand	String	This column is input to AmpIQ predictive modeling. The brand for which a transaction was made. Note This column should only have values when interaction records contain transactions data for more than one brand. Values in this column depend on fields that are tagged with the txn/purchase-brand semantic. This column is used to calculate the following transaction attributes: multi_purchase_brand.
purchase_channel	String	This column is input to AmpIQ predictive modeling. A purchase channel is the channel from which a transaction was made. For example: in-store or online. Values in this column depend on fields that are tagged with the txn/purchase-channel semantic.
store_id	String	This column is input to AmpIQ predictive modeling. A store ID is a unique identifier that is identified with the location of a store. Values in this column depend on fields that are tagged with the txn/store-id semantic.
sum_item_discount_amount	Decimal	The sum of discount amounts is the total of all discount amounts that were applied to each item within a transaction. This value should match the sum of item discount amounts in the itemized transactions that are associated with the same order ID. Values in this column depend on fields that are tagged with the txn/sum-item-discount-amount semantic.
sum_item_revenue	Decimal	The sum of itemized revenue for the original order, not including returns and/or cancellations. This value may be used as an input to order revenue. This value should match the sum of item revenue in the itemized transactions that are associated with the same order ID. Note This value must be greater than or equal to 0 for purchases, but less than or equal to 0 for returns or cancellations. Values in this column depend on fields that are tagged with the txn/sum-item-revenue semantic.