Shopify-based customer segment filters

Use this reference guide to understand the filter names, operators, and values that are used to build customer segments that are based on the default Shopify filters.

Note

When you use until to specify an end date for an event, the segment includes customers up until that set date.

For example, if you create a segment that's until 2024-01-31, then the segment displays event information for up until 2024-01-30 at 23:59.

If you want a segment to display event information that includes the date 2024-01-31, then ensure that you set the end date until 2024-02-01.

Abandoned checkout date

abandoned_checkout_date

Includes customers by the date that they last abandoned their cart.

Operators	Exactly on date: = Not on date: != On or before date: <= Before date: < On or after date: >= After date: > Between dates: BETWEEN <date1> AND <date2> Doesn't exist: IS NULL Exists: IS NOT NULL
Values
Format	Absolute date: YYYY-MM-DD Date offset examples: -4w, -10y Named date: Today: = today Yesterday: = yesterday In the last 7 days: >= 7_days_ago In the last 30 days: >= 30_days_ago In the last 90 days: >= 90_days_ago In the last 12 months: >= 12_months_ago The named dates are default values and can't be changed. For custom dates, use a date offset
Example	Include customers who last abandoned their cart within the last week: abandoned_checkout_date >= 7_days_ago Include customers who last abandoned their cart within the last eight months: abandoned_checkout_date > -8m
Notes	Date values are based on entire days and depend on which time zone your store is in.

Anniversary

anniversary()

Includes customers by the date of the event associated with the date parameter.

Function parameters	date (required): Use this parameter to specify which event you want to filter on.
Operators	Exactly on date: = Between dates: BETWEEN <date1> AND <date2>
Values
Format	Absolute date: YYYY-MM-DD Date offset examples: +4w, +3m Named date: Today: = today In the next 7 days: BETWEEN today AND +7d In the next 30 days: BETWEEN today AND +30d
Example	Include customers with a birthday in the next 30 days: anniversary(date: 'metafields.facts.birth_date') BETWEEN today AND +30d
Notes	The year isn't used to segment when using absolute dates. Date values are based on entire days and depend on which time zone your store is in. To filter on birth dates, you need to either enable the facts.birth_date standard metafield or create your own custom metafield. Learn more about adding standard metafields or creating custom metafield definitions.

Amount spent

amount_spent

Includes customers based on how much money they have spent in your store.

Operators	Is equal to: = Is not equal to: != Greater than: > Smaller than: < Smaller or equal to: <= Greater or equal to: >= Between: BETWEEN
Values
Format	Number range: # AND # Number: # Decimal number: The decimal point (.) is used as the decimal separator. Thousand separators, such as commas or spaces, are not accepted. Language-specific formatted numbers are not accepted.
Example	Include customers who have spent 1 to 999.99 in your store: amount_spent BETWEEN 1 AND 999.99
Notes	The currency that is used is based on the currency that is selected for your store. Don't specify which currency is used by entering a currency symbol. BETWEEN includes both the start and the end values. For example, amount_spent BETWEEN 1 AND 100 includes customers who have spent at least 1 and as much as 100.

Cities

customer_cities

Includes customers who have an address in the specified city. Customers who have multiple addresses might be included in more than one customer segment that uses this filter.

Operators	Contains this exact city: CONTAINS Doesn't contain this exact city: NOT CONTAINS Doesn't exist: IS NULL Exists: IS NOT NULL
Values
Format	countryCode-regionCode-cityCode
Example	Include customers who have an address in New York City: customer_cities CONTAINS 'US-NY-NewYorkCity'
Notes	To find a city, you can start typing the name of the city, and then select the appropriate value from the list that is displayed.

Companies

Plan requirement

The Companies filter is available only to stores on the Shopify Plus plan. If you previously added companies while on the Shopify Plus plan, then you can still create and use segments with this filter.

companies

Includes customers from companies that have been configured as B2B customers.

Operators	Contains this exact company ID: CONTAINS Doesn't contain this exact company ID: NOT CONTAINS Doesn't exist: IS NULL Exists: IS NOT NULL
Values	Company ID
Format
Example	Is a B2B customer: companies IS NOT NULL Is not a B2B customer: companies IS NULL Includes customers who are affiliated with a specific company: companies CONTAINS 3778915041302
Notes	When you create the customer segment, you can select the company by its name from the list that's displayed. Alternatively, you can start typing the title of the company, and then select it from the list. The company ID, not the company name, is entered into the code. When you hover your cursor over the company ID, the company name is displayed.

Countries or regions

customer_countries

Includes customers who have an address in the specified country or region. Customers who have multiple addresses might be included in more than one customer segment that uses this filter.

Operators	Contains this exact location: CONTAINS Doesn't contain this exact location: NOT CONTAINS Doesn't exist: IS NULL Exists: IS NOT NULL
Values	Use the ISO two-letter country code.
Format
Example	Include customers who have an address in the United States: customer_countries CONTAINS 'US'
Notes	To find a country, you can start typing the name of the country, and then select the appropriate value from the list that is displayed.

Created by app id

created_by_app_id

Includes customers who have been created by the specified app.

Operators	Is equal to: = Is not equal to: !=
Values	The ID of the app to segment on.
Format	App ID
Example	Include customers who have been created in the Shopify admin : created_by_app_id = 1830279
Notes	When you create the customer segment, you can select an app by its name from the list that's displayed. Alternatively, you can start typing the name of the app, and then select it from the list. The app ID, not the app name, is entered into the code. When you hover your cursor over the app ID, the app name is displayed.

Customer account status

customer_account_status

Includes the customers who have the specified customer account status.

Operators	Is equal to: = Is not equal to: !=
Values	Declined: 'DECLINED' The customer was invited to create an account, but declined. Disabled: 'DISABLED' The customer has not created an account. Enabled: 'ENABLED' The customer created an account. Invited: 'INVITED' The customer has been invited to create an account.
Format
Example	Include customers who have been invited to create an account, but declined: customer_account_status = 'DECLINED'
Notes

Customer added date

customer_added_date

Includes customers based on the date that they were added to your store.

Operators	Exactly on date: = Not on date: != On or before date: <= Before date: < On or after date: >= After date: > Between dates: BETWEEN <date1> AND <date2>
Values
Format	Absolute date: YYYY-MM-DD Date offset examples: -4w, -10y Named date: Today: = today Yesterday: = yesterday In the last 7 days: >= 7_days_ago In the last 30 days: >= 30_days_ago In the last 90 days: >= 90_days_ago In the last 12 months: >= 12_months_ago The named dates are default values and can't be changed. For custom dates, use a date offset.
Example	Include customers who were added within the last week: customer_added_date >= 7_days_ago Include customers who were added within the last eight months: customer_added_date > -8m Include customers who were added during a specific date range: customer_added_date BETWEEN 2022-12-01 AND 2022-12-31
Notes	Date values are based on entire days and depend on which time zone your store is in.

Customer email domain

customer_email_domain

Includes customers whose email address belongs to the specified domain. The domain name is the part of the email address after the @ symbol, for example, gmail.com.

Operators	Is equal to: = Is not equal to: != Doesn't exist: IS NULL Exists: IS NOT NULL
Values	The following domain names are offered as suggestions. You're not limited to these domain names. You can manually enter any other valid domain names. gmail.com: 'gmail.com' yahoo.com: 'yahoo.com' hotmail.com: 'hotmail.com' aol.com: 'aol.com' msn.com: 'msn.com' live.com: 'live.com' outlook.com: 'outlook.com' yahoo.ca: 'yahoo.ca'
Format
Example	Include customers whose email domain is shopify.com: customer_email_domain = 'shopify.com'
Notes

Customer language

customer_language

Includes customers based on the language that the customer uses to communicate with your store.

Operators	Is equal to: = Is not equal to: != Doesn't exist: IS NULL Exists: IS NOT NULL
Values	Use the ISO 639-1 two-letter language code.
Format	The following values are examples of some common ISO language codes. Your data isn't limited to these language codes. You can manually enter any other valid language codes, but the values that are offered to you as suggested values in the editor are the only ones that are available in your customer data. English: 'en' French: 'fr' Spanish: 'es' German: 'de' Italian: 'it' Japanese: 'ja' Russian: 'ru'
Example	Include customers who communicate with your store in English: customer_language = 'en' Exclude customers who communicate with your store in Canadian English: customer_language != 'en‑CA'
Notes	You can add the locale ISO code to the value to specify a dialect for that language. For example, you can use 'en‑US' for the United States and 'en‑GB' for the United Kingdom, or 'pt‑PT' for Portugal and 'pt‑BR' for Brazil. The filter value acts as a wildcard if only the language prefix is specified. For example, if the filter value is 'en', then your results include customers whose language is set to 'en' and customers whose language is set to 'en‑GB', 'en‑CA', and so on.

Customer tags

customer_tags

Includes customers based on their tags.

Operators	Contains this exact tag: CONTAINS Doesn't contain this exact tag: NOT CONTAINS Doesn't exist: IS NULL Exists: IS NOT NULL
Values	The name of a customer tag.
Format
Example	Include customers who have the GoldStatus tag: customer_tags CONTAINS 'GoldStatus'
Notes	Tags are not case sensitive. Learn more about tags and their considerations.

Customer within distance

Includes customers within a specified distance of a saved location.

Function parameters	You can use only one distance parameter for each filter. coordinates (required): Use this parameter to specify the pin location you want to use to create your segment. distance_km (required): Use this parameter to specify the distance radius you want to search within for customers. distance_mi (required): Use this parameter to specify the distance radius you want to search within for customers.
Operators	Is equal to: =
Value	true, false
Format	Supported format for coordinates: Number (latitude), Number (longitude) Supported format for coordinates (latitude, longitude): Number: # Supported format for distance_mi, distance_km: Number: #
Example	This filter requires coordinates and one distance parameter to be valid. Filter customers who have an address within 10 miles of coordinates (40.624940, -111.833060): customer_within_distance(coordinates: (40.624940, -111.833060), distance_mi: 10 ) = true If your store has saved locations, then Shopify Magic automatically translates the coordinate pair to your location name in the magic translation. Customers who have an address within 10 miles of the location 'Salt Lake City Store'. Filter can be used in conjunction with other filters to narrow your customer list down even further. Filter customers who have address within 20 kilometers of coordinates (43.634,-79.412) and have placed at least one order: customer_within_distance ( coordinates: (43.634,-79.412), distance_km: 20 ) = true AND number_of_orders > 0 Filter customers who do not have an address within 50 kilometers of coordinates (45.502,-73.563): customer_within_distance ( coordinates: (45.502,-73.563), distance_km: 50 ) = false
Notes	Shopify Segmentation automatically converts your saved locations to a coordinate pair and will appear as a selectable value when using this filter.

Email events

shopify_email.EVENT()

Includes customers based on selected email events. Supported events (EVENT) include the following:

• Bounced: bounced
• Clicked: clicked
• Delivered: delivered
• Marked as spam: marked_as_spam
• Opened: opened
• Unsubscribed: unsubscribed

Function parameters	activity_id (optional): Use this parameter to select the marketing activity ID that you want to filter. count_at_least (optional): Use this parameter to specify the minimum number of times an email event occurred. count_at_most (optional): Use this parameter to specify the maximum number of times an email event occurred. count (optional): Use this parameter to specify the exact number of times an email event occurred. since (optional): Use this parameter to specify a start date for the event. until (optional): Use this parameter to specify an end date for the event.
Operators	Is equal to: = Is not equal to: !=
Value	true, false
Format	Supported formats for activity_id: ID (single value) List <ID>: A set of values with implicit "OR". A List is a set of comma-separated values wrapped with parentheses. For example: (1, 2, 3). There is a limit of 500 activity IDs in a list. Supported Date formats for since and until: Absolute date: YYYY-MM-DD Date offset examples: 7 days ago: :-7d 4 weeks ago: :-4w 3 months ago: :-3m 1 year ago: :-1y Named date: :today, :yesterday The named dates are default values and can't be changed. For custom dates, use a date offset. Email events are available for the last 26 months, with data starting on March 2022. Supported formats for count_at_least, count_at_most, count: Number: #
Example	Specify whether an email event happened using a = or != operator: shopify_email.opened(activity_id: 135195754518) = false shopify_email.opened(activity_id: 135195754518) != true Use the parameter activity_id to specify the marketing activity ID that you want to filter on: shopify_email.delivered(activity_id: 135195754518) = true Use the parameter since to specify a start date for an email event: shopify_email.delivered(activity_id: 135195754518, since: 2022-01-01) = false Use the parameter until to specify an end date for an email event: shopify_email.delivered(activity_id: 135195754518, until: 2022-01-01) = true Use the parameters since and until to specify both a start and end date for an email event: shopify_email.bounced(activity_id: 135195754518, since: 12_months_ago, until: today) = false
Notes	When you create a customer segment using the activity_id parameter, you can select the marketing activity by its name from the list that's displayed. Due to data retention, the absence of both since and until parameters means that results will be filtered for the last 26 months, with no set start or end date. Absence of activity_id means that your filter includes all Shopify email activities.

Email subscription status

email_subscription_status

Includes customers based on whether they are subscribed to your marketing email.

Operators	Is equal to: = Is not equal to: != Doesn't exist: IS NULL Exists: IS NOT NULL
Values	Not subscribed: 'NOT_SUBSCRIBED' The customer hasn't subscribed to your marketing email. Subscribed: 'SUBSCRIBED' The customer is subscribed to your marketing email. Pending: 'PENDING' The customer is in the process of subscribing to your marketing email. Invalid: 'INVALID' The customer’s email address marketing state is invalid.
Format
Example	Include customers who have subscribed to your email marketing: email_subscription_status = 'SUBSCRIBED'
Notes

Last order date

last_order_date

Includes customers who placed their last order on the date specified.

Operators	Exactly on date: = Not on date: != On or before date: <= Before date: < On or after date: >= After date: > Between dates: BETWEEN <date1> AND <date2> Doesn't exist: IS NULL Exists: IS NOT NULL
Values
Format	Absolute date: YYYY-MM-DD Date offset examples: -4w, -10y Named date: Today: = today Yesterday: = yesterday In the last 7 days: >= 7_days_ago In the last 30 days: >= 30_days_ago In the last 90 days: >= 90_days_ago In the last 12 months: >= 12_months_ago The named dates are default values and can't be changed. For custom dates, use a date offset.
Example	Include customers whose last order was placed since last week: last_order_date >= 7_days_ago Include customers whose last order was placed since eight months ago: last_order_date > -8m
Notes	Date values are based on entire days and depend on which time zone your store is in.

Number of orders

number_of_orders

Includes customers based on the number of orders that they have placed in your store.

Operators	Is equal to: = Is not equal to: != Greater than: > Smaller than: < Smaller or equal to: <= Greater or equal to: >= Between: BETWEEN
Values	The value that you enter must be a whole number.
Format	Number range: # AND # Number: #
Example	Include customers who have placed more than 10 orders: number_of_orders > 10
Notes	BETWEEN includes both the start and the end values. For example, number_of_orders BETWEEN 1 AND 100 includes customers who have placed at least 1 order and as many as 100 orders.

Orders placed

orders_placed()

Includes customers who placed orders or spent a certain amount during a specified date range.

Function parameters	count_at_least (optional): Use this parameter to specify the minimum number of times an order was placed. count_at_most (optional): Use this parameter to specify the maximum number of times an order was placed. count (optional): Use this parameter to specify the exact number of times an order was placed. amount_at_least (optional): Use this parameter to specify the minimum amount spent on an order. amount_at_most (optional): Use this parameter to specify the maximum amount spent on an order. amount (optional): Use this parameter to specify the exact amount spent on an order. sum_amount_at_least (optional): Use this parameter to specify the minimum amount spent on all orders. sum_amount_at_most (optional): Use this parameter to specify the maximum amount spent on all orders. sum_amount (optional): Use this parameter to specify the amount spent on all orders. since (optional): Use this parameter to specify a start date for the event. until (optional): Use this parameter to specify an end date for the event.
Operators	Is equal to: = Is not equal to: !=
Values	true, false
Format	Supported formats for count_at_least, count_at_most, count: Number: # Supported formats for amount_at_least, amount_at_most, amount: Number: # Supported formats for sum_amount_at_least, sum_amount_at_most, sum_amount: Number: # Supported Date formats for since and until: Absolute date: YYY-MM-DD Date offset examples: 7 days ago: :-7d 4 weeks ago: :-4w 3 months ago: :-3m 1 year ago: :-1y Named date: Today: :today Yesterday: :yesterday The named dates are default values and can't be changed.
Example	Specify whether an order has been placed using a = or != operator: orders_placed() = true orders_placed() = false Filter customers that placed more than 3 orders (inclusive) in the last 6 months: orders_placed(count_at_least:3, since:-6m) = true Filter customers that spent more than $1000 (inclusive) in the last 3 months: orders_placed(sum_amount_at_least: 1000, since:-90d) = true Filter customers that spent less than $100 (inclusive) last week: orders_placed(sum_amount_at_most: 100, since:-7d) = true Filter customers that spent more than $1000 (inclusive) and placed more than 3 orders (inclusive) since January 1, 2023: orders_placed(sum_amount_at_least: 1000, count_at_least: 3, since: 2023-01-01) = true In date ranges, the order of the since and until parameters doesn't matter. You can express between January 1, 2023 and June 1, 2023 (inclusive) in either of the following ways: Between January 1, 2023 and June 1, 2023 (inclusive): orders_placed(count_at_least:3, since: 2023-01-01, until: 2023-06-01) = true Between January 1, 2023 and June 1, 2023 (inclusive): orders_placed(count_at_least:3, until: 2023-06-01, since: 2023-01-01) = true
Notes	When you hover your cursor over the amount, the currency used to filter your customers is displayed. When you hover your cursor over the following syntax: amount_at_least,amount_at_most amount,sum_amount_at_least,sum_amount_at_most sum_amount the description of the syntax is displayed. Absence of parameters means that your filter includes all orders placed for all time.

Predicted spend tier

predicted_spend_tier

Includes customers who are within the specified predicted spend tier.

This filter is only available if your store made more than 100 sales.

Learn more about predicted spend tier.

Operators	Is equal to: = Is not equal to: != Doesn't exist: IS NULL Exists: IS NOT NULL
Values	'HIGH' 'MEDIUM' 'LOW'
Format
Example	Include customers who are in the HIGH tier: predicted_spend_tier = 'HIGH'
Notes

Product subscription status

product_subscription_status

Includes customers who have the specified product subscription status.

This filter is only available if you use a subscription app.

Operators	Is equal to: = Is not equal to: != Doesn't exist: IS NULL Exists: IS NOT NULL
Values	Active: 'SUBSCRIBED' The customer has an active product subscription. Cancelled: 'CANCELLED' The customer has canceled their product subscription. Expired: 'EXPIRED' The customer's product subscription has expired. Failed: 'FAILED' The customer has a failed payment. Never subscribed: 'NEVER_SUBSCRIBED' The customer never subscribed. Paused: 'PAUSED' The customer has paused their product subscription.
Format
Example	Include customers who have an active product subscription: product_subscription_status = 'SUBSCRIBED'
Notes

Products purchased

products_purchased()

Includes customers who purchased the specified product. In addition, you can include customers who bought the product during a specified date range.

Function parameters	id (optional): Use this parameter to specify the product a customer has purchased that you want to filter. quantity_at_least (optional): Use this parameter to specify the minimum quantity of products purchased per order. quantity_at_most (optional): Use this parameter to specify the maximum quantity of products purchased per order. quantity (optional): Use this parameter to specify the quantity of products purchased per order. sum_quantity_at_least (optional): Use this parameter to specify the minimum quantity of products purchased across all orders. sum_quantity_at_most (optional): Use this parameter to specify the maximum quantity of products purchased across all orders. sum_quantity (optional): Use this parameter to specify the quantity of products purchased across all orders. since (optional): Use this parameter to specify a start date for the event. tag (optional): Use this parameter to specify a product tag for purchased products that you want to filter. until (optional): Use this parameter to specify an end date for the event.
Operators	Is equal to: = Is not equal to: !=
Value	true, false
Format	Supported formats for tag: String (single value) Supported formats for id: ID (single value) List <ID>: A set of values provided as a list. For example: (1012132033639, 2012162031638, 32421429314657). There is a limit of 500 product IDs in a list. Supported Date formats for since and until: Absolute date: YYYY-MM-DD Date offset examples: 7 days ago: :-7d 4 weeks ago: :-4w 3 months ago: :-3m 1 year ago: :-1y Named date: :today, :yesterday The named dates are default values and can't be changed. Supported formats for quantity_at_least, quantity_at_most, quantity: Number: # Supported formats for sum_quantity_at_least, sum_quantity_at_most, sum_quantity: Number: #
Example	Specify whether a product has been purchased using a = or != operator: products_purchased() != true products_purchased(id: 2012162031638) = true products_purchased(id: (2012162031638, 1012132033639)) = false products_purchased(tag: 'red') = true Filter customers that purchased a specific product since January 1, 2022 until today: products_purchased(id: 1012132033639, since: 2022-01-01, until: today) = true Filter customers that purchased a product with the tag 'red' since January 1, 2022 until today: products_purchased(tag: 'Red', since: 2022-01-01, until: today) = true Within the last 30 days: products_purchased(since: -30d) = true Until January 1, 2022: products_purchased(until: 2022-01-01) = true In date ranges, the order of the since and until parameters doesn't matter. You can express between January 1, 2022 and June 1, 2022 (inclusive) in either of the following ways: Between January 1, 2022 and June 1, 2022 (inclusive): products_purchased(id: 1012132033639, since: 2022-01-01, until: 2022-06-01) = true Between January 1, 2022 and June 1, 2022 (inclusive): products_purchased(id: 1012132033639, until: 2022-06-01, since: 2022-01-01) = true Filter customers that have recently purchased a lot of products with a specific tag: products_purchased(tag: 'product_tag', sum_quantity_at_least: 3, since: -90d) = true
Notes	When you create the customer segment using the id parameter, you can select the product by its name or image from the list that's displayed. Alternatively, you can start typing the title of the product, and then select it from the list. The product ID, not the product title, is entered into the code. When you hover your cursor over the product ID, the product title and image are displayed. Absence of both since and until parameters means that results will be filtered for all time, with no set start or end date. Absence of parameters means that your filter includes all products purchased for all time

RFM group

rfm_group

Includes customers based on which RFM group they're categorized into.

Learn more about RFM customer analysis.

List of possible RFM group operators and values, including examples.

Operators	Is equal to: = Is not equal to: != Doesn't exist: IS NULL Exists: IS NOT NULL
Values	Dormant: 'DORMANT' At risk: 'AT_RISK' Previously loyal: 'PREVIOUSLY_LOYAL' Needs attention: 'NEEDS_ATTENTION' Almost lost: 'ALMOST_LOST' Loyal: 'LOYAL' Promising: 'PROMISING' Active: 'ACTIVE' New: 'NEW' Champions: 'CHAMPIONS' Prospects: 'PROSPECTS'
Format
Example	Include customers in the RFM group Needs Attention: rfm_group = 'NEEDS_ATTENTION'
Notes

SMS subscription status

sms_subscription_status

Includes customers based on whether they are subscribed to your marketing SMS text messages.

Learn more about collecting customer contact information.

Operators	Is equal to: = Is not equal to: != Doesn't exist: IS NULL Exists: IS NOT NULL
Values	Subscribed: 'SUBSCRIBED' The customer is subscribed to your marketing SMS text messages. Pending: 'PENDING' The customer is in the process of subscribing to your marketing SMS text messages. Redacted: 'REDACTED' The customer has pending redaction because of a GDPR erasure request Unsubscribed: 'UNSUBSCRIBED' The customer has unsubscribed from your marketing SMS text messages. Not subscribed: 'NOT_SUBSCRIBED' The customer never subscribed to your marketing SMS text messages.
Format
Example	Include customers who have subscribed to your marketing SMS text messages: sms_subscription_status = 'SUBSCRIBED'
Notes

States or provinces

customer_regions

Includes customers who have an address in the specified region within a country. Customers who have multiple addresses might be included in more than one customer segment that uses this filter.

Operators	Contains this exact location: CONTAINS Doesn't contain this exact location: NOT CONTAINS Doesn't exist: IS NULL Exists: IS NOT NULL
Values	Use the ISO country code with the ISO 3166-2 subdivision code.
Format
Example	Include customers who have an address in New York State: customer_regions CONTAINS 'US-NY'
Notes	To find a region, you can start typing the name of the region, and then select the appropriate value from the list that is displayed.

Store credit accounts

store_credit_accounts

Includes customers who have a store credit balance on your store.

Function parameters	balance (optional): Use this parameter to specify the customer's current store credit account balance. balance_at_least (optional): Use this parameter to specify the customer's minimum store credit balance. balance_at_most (optional): Use this parameter to specify the customer's maximum store credit account balance. currency (optional): Use this parameter to specify the currency of the customer's store credit balance. next_expiry_date (optional): Use this parameter to specify the date of the soonest expiring unspent store credit. next_expiry_date_at_least (optional): Use this parameter to specify the earliest date of the soonest expiring unspent store credit. next_expiry_date_at_most (optional): Use this parameter to specify the latest date of the soonest expiring unspent store credit. last_credit_date (optional): Use this parameter to specify the date when the customer last received store credit. last_credit_date_at_least (optional): Use this parameter to specify the earliest date when the customer last received store credit. last_credit_date_at_most (optional): Use this parameter to specify the latest date when the customer last received store credit.
Operators	Is equal to: = Is not equal to: !=
Values	true, false
Format	Supported formats for currency: Currency code: For example USD Supported formats for balance: Number: # Supported formats for next_expiry_date and last_credit_date: Absolute date: YYY-MM-DD Date offset examples: 7 days ago: -7d 4 weeks ago: -4w 3 months ago: -3m 1 year ago: -1y Named date: Today: today Yesterday: yesterday The named dates are default values and can't be changed.
Example	Filter customers who have a store credit account balance greater than or equal to 1 in any currency: store_credit_accounts(balance_at_least: 1) = true Filter customers who have a store credit account balance greater than or equal to $1 USD: store_credit_accounts(balance_at_least: 1, currency: 'USD') = true Filter customers with store credit expiring in the next 7 days: store_credit_accounts(next_expiry_date_at_most: +7d) = true Filter customers who last received store credit more than 1 month ago but still have a balance available to spend: store_credit_accounts(last_credit_date_at_most: -1m, balance_at_least: 1) = true
Notes	Customers have a store credit account if you've ever issued store credit to them. A customer's store credit balance can be greater than or equal to 0. Store credit accounts are specific to a currency. If you don't include a currency parameter in your segment, then your filter returns all store credit accounts regardless of the currency type. A customer can have 0 to many store credit accounts, depending on how many currencies you support. For example, if you issue store credit to a customer in both CAD and USD, then that customer has 2 store credit accounts.

Storefront Events

storefront.EVENT()

Includes customers based on storefront events. Supported events (EVENT) include the following:

• Product viewed: product_viewed
• Collection viewed: collection_viewed

Function parameters	id (optional): Use this parameter to specify the products or collections that you want to filter on. since (optional): Use this parameter to specify a start date for the event. until (optional): Use this parameter to specify an end date for the event. count_at_least (optional): Use this parameter to specify the minimum number of times a product or collection was viewed. count_at_most (optional): Use this parameter to specify the maximum number of times a product or collection was viewed. count (optional): Use this parameter to specify the exact number of times a product or collection was viewed.
Event-specific parameters	tag (optional): Use this parameter to specify the product tag you want to filter on. This behaves the same as filtering for every product ID with that tag.
Operators	Is equal to: = Is not equal to: !=
Value	true, false
Format	Supported formats for id: ID (single value) List <ID>: A set of values provided as a list. For example: (1012132033639, 2012162031638, 32421429314657). There is a limit of 500 product IDs in a list. Supported formats for tag: String (single value) Supported Date formats for since and until: Absolute date: YYYY-MM-DD Date offset examples: 7 days ago: :-7d 4 weeks ago: :-4w 3 month ago: :-3m 1 year ago: :-1y In the last 90 days: >= 90_days_ago Named date: Today: :today Yesterday: :yesterday Storefront events are available for the last 26 months, with data starting on May 2023. The named dates are default values and can't be changed. For custom dates, use a date offset.
Example	Specify whether a storefront event happened using a = or != operator: storefront.product_viewed() = true storefront.collection_viewed() = false Use the parameter id to specify the products that you want to filter on: storefront.product_viewed(id: 2012162031638) = true storefront.collection_viewed(id: (2012162031638, 456, 789)) = true Use the parameter tag to the product tags you want to filter on: storefront.product_viewed(tag: 'jeans') = true Use the parameter since to specify a start date for a storefront event: storefront.product_viewed(id: 2012162031638, since: 2023-04-03) = true storefront.collection_viewed(id: 2012162031638, since:-30d) = true Use the parameter until to specify an end date for a storefront event: storefront.product_viewed(id: 2012162031638, until: 2023-04-30) = true storefront.collection_viewed(id: 2012162031638, until:-7d) = true Use the parameters since and until to specify both a start and end date for a storefront event: storefront.product_viewed(id: 2012162031638, since: 2023-04-03, until: 2023-04-30) = true storefront.collection_viewed(id: 2012162031638, since: -90d, until: -30d) = true Filter customers that viewed a specific product in the last 30 days: storefront.product_viewed(id: 2012162031638, since: -30d) = true Filter customers that viewed a specific collection since January 1, 2023 until today: storefront.collection_viewed(id: 2012162031638, since: 2023-01-01, until: today) = true In date ranges, the order of the since and until parameters doesn't matter. You can express between January 1, 2023 and June 1, 2023 (inclusive) in either of the following ways: Between January 1, 2023 and June 1, 2023 (inclusive): storefront.collection_viewed(id: 2012162031638, since: 2023-01-01, until: 2023-06-01) = true Between January 1, 2023 and today (inclusive): storefront.collection_viewed(id: 2012162031638, until: today, since: 2023-01-01,) = true
Notes	When you create the customer segment using the id parameter, you can select the product or collection by its name or image from the list that's displayed. Alternatively, you can start typing the title of the product or collection, and then select it from the list. The product or collection ID, not the title, is entered into the code. When you hover your cursor over the ID, the product or collection title and image are displayed. Collections use the image saved as the collection thumbnail, if applicable. If not, a generic image placeholder will be displayed. Due to data retention, the absence of both since and until parameters means that results will be filtered for the last 26 months, with no set start or end date. Absence of id means that your filter includes all products.