Track the way credit for conversions or actions is assigned to different touch-points in a customer's journey, such as first-touch, last-touch, or multi-touch attribution models.
Group customers or entities based on shared characteristics or behaviors within a specific timeframe. These dimensions are often used for Cohort Analysis.
Categorize or predict behaviors or outcomes based on historical data patterns. These dimensions are generated or influenced by machine learning algorithms.
Group transactional data such as transaction IDs, dates, amounts, or types of transactions.
Attribution
Sub-Group
Dimension name
Description
Pre-calculated field?
-
Campaign ID | Name
The campaign ID and name.
No
Campaign Level Names
Level 1 Name
The name of the level 1 campaign.
No
Campaign Level Names
Level 2 Name
The name of the level 2 campaign.
No
Campaign Level Names
Level 3 Name
The name of the level 3 campaign.
No
Campaign Level Names
Level 4 Name
The name of the level 4 campaign.
No
Campaign Level Names
Level 5 Name
The name of the level 5 campaign.
No
Customer
Sub-Group
Dimension name
Description
Pre-calculated field?
Address
Customer City
The city from the customer’s primary address.
No
Address
Customer Country
The country from the customer’s primary address.
No
Address
Customer RBDI
The Residential or Business Delivery Indicator: A flag to mark whether the customer’s primary address is a business or residential address. Parcel delivery to residential addresses is more expensive than to business addresses (United States only).
Attribution window is a period of time you set between which transactions by customers included in a cohort / campaign can be credited to that same cohort / campaign. The default attribution window is [0:14], representing a range of 0 to 14 days from the campaign execution date. This period determines when transactions are credited to the campaign or cohort. You can adjust the window using [X] format, where X and Y are the lower and upper bounds in days. For customization, contact your CVM.
Yes
-
Campaign | Segment Name
The campaign name with the segment name.
Yes
Campaign Execution Date
Date
The specific date when the campaign or segment was executed.
Yes
Campaign Execution Date
Day of Month
The day of the month when the campaign or segment was executed.
Machine Learning
Sub-Group
Dimension name
Description
Pre-calculated field?
Behavior Based Cluster
Behavior Based Cluster - 1 Month Ago
The cluster of customers personas based on their purchase behavior, preferences, and spending patterns a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Behavior Based Cluster
Behavior Based Cluster - 2 Months Ago
The cluster of customers personas based on their purchase behavior, preferences, and spending patterns two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Behavior Based Cluster
Behavior Based Cluster - This Month
The cluster of customers personas based on their purchase behavior, preferences, and spending patterns this month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Behavior Based Cluster
Behavior Based Cluster - Today
Market Basket Analysis
Sub-Group
Dimension name
Description
Pre-calculated field?
First Item Details
First Category Name Level 1
The category name of the level 1 first item.
No
First Item Details
First Category Name Level 2
The category name of the level 2 first item.
No
First Item Details
First Category Name Level 3
The category name of the level 3 first item.
No
First Item Details
First Category Name Level 4
The category name of the level 4 first item.
No
First Item Details
First Category Name Level 5
The category name of the level 5 first item.
No
Product
Dimension name
Description
Pre-calculated field?
Product Brand
The brand name for product
No
Product ID
A Unique identifier for product
No
Product Item Status
Availability status for product. ‘Active’ means the product is in stock, available or available for order / preorder. ‘Inactive’ generally means the product is out of stock or no longer available. It’s null for products that we do not have the Availability for.
No
Product Name
Name for product
No
Product Category
Dimension name
Description
Pre-calculated field?
Product Category - Level 1
Customers purchased from 1 distinct product category.
No
Product Category - Level 2
Customers purchased from 2 distinct product categories.
No
Product Category - Level 3
Customers purchased from 3-5 distinct product categories.
No
Product Category - Level 4
Customers purchased from 6-10 distinct product categories.
No
Product Category - Level 5
Customers purchased from 10+ distinct product categories.
No
Sales Organization
Sub-Group
Dimension name
Description
Pre-calculated field?
Geography
Sales Org City
The city for sales organization.
No
Geography
Sales Org Country
The country for sales organization.
No
Geography
Sales Org State
The state for sales organization.
No
Geography
Sales Org Zip Code
The Zip Code for sales organization.
No
-
Sales Channel
The channel for sales organization. The following are the values:
Digital: Online stores
Transaction
Sub-Group
Dimension name
Description
Pre-calculated field?
Customer Transaction Sequence
Customer Transaction Sequence
A number to indicate the sequence of transactions for each customer after sorting their transactions by [Transaction Date] in ascending order. ‘1’ denotes the first transaction for a given customer, ‘2’ denotes the second transaction for the same customer. These values are calculated at the transaction header-level. If a customer places more than one transaction on the same day, each of those transactions have a different sequence number depending on the timestamp of the transaction and the unique identifier of the transaction.
Yes
Customer Transaction Sequence
Customer Transaction Sequence - New vs Repeat
A flag to indicate if a customer is a new customer or a repeat customer when the transaction was made. Transactions with [Customer Transaction Sequence] = 1 are marked as ‘New’ whereas transactions with [Customer Transaction Sequence] > 1 are marked as ‘Repeat’. Note that this field is calculated at the transaction level, and it does not indicate buyer status as of today. Instead, it indicates buyer status as of when the transaction was made.
Yes
Customer Transaction Sequence
Customer Transaction Sequence Group
The Bucketed version of [Customer Transaction Sequence] for transaction
Yes
Days since Customer First Transaction
Dimensions tables
The following is the list of tables for standard dimensions:
Track the way credit for conversions or actions is assigned to different touch-points in a customer's journey, such as first-touch, last-touch, or multi-touch attribution models.
Group customers or entities based on shared characteristics or behaviors within a specific timeframe. These dimensions are often used for Cohort Analysis.
Categorize or predict behaviors or outcomes based on historical data patterns. These dimensions are generated or influenced by machine learning algorithms.
Group transactional data such as transaction IDs, dates, amounts, or types of transactions.
Attribution
Sub-Group
Dimension name
Description
Pre-calculated field?
-
Campaign ID | Name
The campaign ID and name.
No
Campaign Level Names
Level 1 Name
The name of the level 1 campaign.
No
Campaign Level Names
Level 2 Name
The name of the level 2 campaign.
No
Campaign Level Names
Level 3 Name
The name of the level 3 campaign.
No
Campaign Level Names
Level 4 Name
The name of the level 4 campaign.
No
Campaign Level Names
Level 5 Name
The name of the level 5 campaign.
No
Customer
Sub-Group
Dimension name
Description
Pre-calculated field?
Address
Customer City
The city from the customer’s primary address.
No
Address
Customer Country
The country from the customer’s primary address.
No
Address
Customer RBDI
The Residential or Business Delivery Indicator: A flag to mark whether the customer’s primary address is a business or residential address. Parcel delivery to residential addresses is more expensive than to business addresses (United States only).
Attribution window is a period of time you set between which transactions by customers included in a cohort / campaign can be credited to that same cohort / campaign. The default attribution window is [0:14], representing a range of 0 to 14 days from the campaign execution date. This period determines when transactions are credited to the campaign or cohort. You can adjust the window using [X] format, where X and Y are the lower and upper bounds in days. For customization, contact your CVM.
Yes
-
Campaign | Segment Name
The campaign name with the segment name.
Yes
Campaign Execution Date
Date
The specific date when the campaign or segment was executed.
Yes
Campaign Execution Date
Day of Month
The day of the month when the campaign or segment was executed.
Machine Learning
Sub-Group
Dimension name
Description
Pre-calculated field?
Behavior Based Cluster
Behavior Based Cluster - 1 Month Ago
The cluster of customers personas based on their purchase behavior, preferences, and spending patterns a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Behavior Based Cluster
Behavior Based Cluster - 2 Months Ago
The cluster of customers personas based on their purchase behavior, preferences, and spending patterns two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Behavior Based Cluster
Behavior Based Cluster - This Month
The cluster of customers personas based on their purchase behavior, preferences, and spending patterns this month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Behavior Based Cluster
Behavior Based Cluster - Today
Market Basket Analysis
Sub-Group
Dimension name
Description
Pre-calculated field?
First Item Details
First Category Name Level 1
The category name of the level 1 first item.
No
First Item Details
First Category Name Level 2
The category name of the level 2 first item.
No
First Item Details
First Category Name Level 3
The category name of the level 3 first item.
No
First Item Details
First Category Name Level 4
The category name of the level 4 first item.
No
First Item Details
First Category Name Level 5
The category name of the level 5 first item.
No
Product
Dimension name
Description
Pre-calculated field?
Product Brand
The brand name for product
No
Product ID
A Unique identifier for product
No
Product Item Status
Availability status for product. ‘Active’ means the product is in stock, available or available for order / preorder. ‘Inactive’ generally means the product is out of stock or no longer available. It’s null for products that we do not have the Availability for.
No
Product Name
Name for product
No
Product Category
Dimension name
Description
Pre-calculated field?
Product Category - Level 1
Customers purchased from 1 distinct product category.
No
Product Category - Level 2
Customers purchased from 2 distinct product categories.
No
Product Category - Level 3
Customers purchased from 3-5 distinct product categories.
No
Product Category - Level 4
Customers purchased from 6-10 distinct product categories.
No
Product Category - Level 5
Customers purchased from 10+ distinct product categories.
No
Sales Organization
Sub-Group
Dimension name
Description
Pre-calculated field?
Geography
Sales Org City
The city for sales organization.
No
Geography
Sales Org Country
The country for sales organization.
No
Geography
Sales Org State
The state for sales organization.
No
Geography
Sales Org Zip Code
The Zip Code for sales organization.
No
-
Sales Channel
The channel for sales organization. The following are the values:
Digital: Online stores
Transaction
Sub-Group
Dimension name
Description
Pre-calculated field?
Customer Transaction Sequence
Customer Transaction Sequence
A number to indicate the sequence of transactions for each customer after sorting their transactions by [Transaction Date] in ascending order. ‘1’ denotes the first transaction for a given customer, ‘2’ denotes the second transaction for the same customer. These values are calculated at the transaction header-level. If a customer places more than one transaction on the same day, each of those transactions have a different sequence number depending on the timestamp of the transaction and the unique identifier of the transaction.
Yes
Customer Transaction Sequence
Customer Transaction Sequence - New vs Repeat
A flag to indicate if a customer is a new customer or a repeat customer when the transaction was made. Transactions with [Customer Transaction Sequence] = 1 are marked as ‘New’ whereas transactions with [Customer Transaction Sequence] > 1 are marked as ‘Repeat’. Note that this field is calculated at the transaction level, and it does not indicate buyer status as of today. Instead, it indicates buyer status as of when the transaction was made.
Yes
Customer Transaction Sequence
Customer Transaction Sequence Group
The Bucketed version of [Customer Transaction Sequence] for transaction
Yes
Days since Customer First Transaction
-
Campaign Name
The name of the campaign.
No
Campaign Start Date
Date
The date when the campaign started.
No
Campaign Start Date
Day of Month
The day of the month when the campaign started.
No
Campaign Start Date
Day of Week
The day of the week when the campaign started.
No
Campaign Start Date
Hour of Day
The hour of the day when the campaign started.
No
Campaign Start Date
Month
The month when the campaign started.
No
Campaign Start Date
Month Name
The name of the month when the campaign started.
No
Campaign Start Date
Quarter
The quarter when the campaign started.
No
Campaign Start Date
Quarter of Year
The quarter of the year when the campaign started.
No
Campaign Start Date
Time of Day
The time of the day when the campaign started.
No
Campaign Start Date
Week of Year
The week of the year when the campaign started.
No
Campaign Start Date
Year
The year when the campaign started.
No
The age of the customer.
No
Age
Age Group
The age of the customer, grouped into buckets for easier use.
No
-
Buyer Identification
The dimension that indicates whether the customer is an identified or unidentified buyer. All transactions that have a blank or null Customer get attributed to a generic Unidentified customer. This is primarily used with Sales Organization dimensions to view a distribution of revenue attributed to unidentified individuals for POS sales.
Yes
Buying Stats
AOV
The average order value for customers calculated using [Product Revenue] / [Transaction Count].
Yes
Buying Stats
Average Annual Transactions
The annual transaction frequency for customer calculated using [Transaction Count] * 365 / [Days since First Transaction].
Yes
Buying Stats
Average Discount Rate
The average discount rate for customers to identify promotion driven customers.
Yes
Buying Stats
First Transaction Revenue
The revenue from the customer’s first transaction. Note that ranges are inclusive at the higher bound. For example, ‘0-25’ includes up to 25, ‘25-50’ starts at 25.01.
Yes
Buying Stats
Last Transaction Interval
The number of months between the last and the second to last transaction of the customer.
Yes
Buying Stats
Revenue - 13-24 Months
The total revenue for customer’s purchases in the previous 13-24 month duration. Note that ranges are inclusive at the higher bound. For example, ‘0-100’ includes up to 100, ‘100-200’ starts at 100.01.
Yes
Buying Stats
Revenue - Last 12 Months
The total revenue for customer’s purchases in the last 12 month duration. Note that ranges are inclusive at the higher bound. For example: ‘0-100’ includes up to 100, ‘100-200’ starts at 100.01.
Yes
Buying Stats
Revenue - Lifetime
The total revenue for all purchases in the customer’s lifetime. Note that ranges are inclusive at the higher bound. For example: ‘0-100’ includes up to 100, ‘100-200’ starts at 100.01.
Yes
Buying Stats
Revenue Decile - 13-24 Months
The revenue decile for customers based on total revenue for all purchases in the previous 13-24 month duration. The following are the values:
1: Top 10%
10: Bottom 10%
0: The customers that have not purchased in a given time period.
Yes
Buying Stats
Revenue Decile - Last 12 Months
The revenue decile for customers based on total revenue for all purchases in the last 12 month duration. The following are the values:
1: Top 10%
10: Bottom 10%
0: The customers that have not purchased in a given time period.
Yes
Buying Stats
Revenue Segment - 13-24 Months
The revenue segment for customers based on total revenue for all purchases in the previous 13-24 month duration. The following are the values:
1-HighValue: Top 10% by revenue
2-MediumValue: 20% to 80% by revenue
3-LowValue: Bottom 20%
NonBuyer: The customers that have not purchased in the given time
period.
Yes
Buying Stats
Revenue Segment - Last 12 Months
The revenue segment for customers based on total revenue for all purchases in the last 12 month duration. The following are the values:
1-HighValue: Top 10% by revenue
2-MediumValue: 20% to 80% by revenue
3-LowValue: Bottom 20%
NonBuyer: The customers that have not purchased in the given time
period.
Yes
Buying Stats
Revenue Trend Segment
The revenue trend segment for customers derived by comparing revenue decile in the previous 13-24 months as compared to the last 12 months. It characterizes whether they spent less (‘Downward’), the same (‘Stable’), or more (‘Upward’) in the previous 13-24 months period as compared to the last 12 months. The following are the values:
Non Buyer: The customers that have not purchased in the given time period.
Unidentified: Indicates invalid data.
2PeriodInactive: The customers are buyers who did not make a purchase in the last 24 months but made a purchase before 24 months ago.
Lapsed: The customers who made a purchase in the last 13-24 months but did not make a purchase in the last 12 months.
Yes
Buying Stats
Transaction Count - 13-24 Months
The total number of transactions made by customers in the previous 13-24 month duration.
Yes
Buying Stats
Transaction Count - Last 12 Months
The total number of transactions made by customers in the last 12 month duration.
Yes
Buying Stats
Transaction Count - Lifetime
The total number of transactions made in a customer’s lifetime.
Yes
-
Category Count
The number of distinct product categories that customers purchased from in their lifetime.
Yes
-
Customer Gender
The gender of the customer as determined by the genderization algorithm of Customer Data Platform (CDP). Values are Female, Male, Neutral and Unknown. Neutral refers to names that are either male or female. If the gender can not be determined, CDP displays Unknown.
No
-
Customer Status
The buying status of the customer. Possible values include Buyer, Non Buyer, Unidentified.
Yes
Days since First Transaction
Days since First Transaction
The number of days since the customer’s first transaction, that is, the tenure of the customer in days.
Yes
Days since First Transaction
Days since First Transaction Group
The bucketed version of [Days since First Transaction] represented in months.
Yes
Days since Last Transaction
Days since Last Transaction
The number of days since the customer’s last transaction, that is, the recency of the customer in days.
Yes
Days since Last Transaction
Days since Last Transaction Group
The bucketed version of [Days since Last Transaction] represented in months.
Yes
Email Last Click Date
Date
The date when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Day of Month
The day of the month when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Day of Week
The day of the week when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Month
The month when the customer last clicked one of the email campaigns
Yes
Email Last Click Date
Month Name
The name of the month when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Quarter
The quarter when the customer last clicked one of the campaigns.
Yes
Email Last Click Date
Quarter of Year
The quarter of the year when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Week of Year
The week of the year when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Year
The year when the customer last clicked one of the email campaigns.
Yes
Email Last Open Date
Date
The date when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Day of Month
The day of the month when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Day of Week
The day of the week when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Month
The month when the customer last opened one of the email campaigns
Yes
Email Last Open Date
Month Name
The name of the month when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Quarter
The quarter when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Quarter of Year
The quarter of the year when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Week of Year
The week of the year when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Year
The year when the customer last opened one of the email campaigns.
Yes
Email Last Send Date
Date
The date when the customer was last sent an email.
Yes
Email Last Send Date
Day of Month
The day of the month when the customer was last sent an email.
Yes
Email Last Send Date
Day of Week
The day of the week when the customer was last sent an email.
Yes
Email Last Send Date
Month
The month when the customer was last sent an email.
Yes
Email Last Send Date
Month Name
The name of the month when the customer was last sent an email.
Yes
Email Last Send Date
Quarter
The quarter when the customer was last sent an email.
Yes
Email Last Send Date
Quarter of Year
The quarter of the year when the customer was last sent an email.
Yes
Email Last Send Date
Week of Year
The week of the year when the customer was last sent an email.
Yes
Email Last Send Date
Year
The year when the customer was last sent an email.
Yes
First Transaction (Digital) Date
Date
The date of the first transaction made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Day of Month
The day of the month of the first transaction made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Day of Week
The day of the week of the first transaction made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Month
The month when the first transaction was made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Month Name
The name of the month when the first transaction was made by the customer in the digital format.
Yes
First Transaction (Digital) Date
Quarter
The quarter when the first transaction was made by the customer in the digital format.
Yes
First Transaction (Digital) Date
Quarter of Year
The quarter of the year when the first transaction was made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Week of Year
The week of the year when the first transaction was made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Year
The year when the first transaction was made by a customer in the digital format.
Yes
First Transaction (Physical) Date
Date
The date of the first transaction made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Day of Month
The day of the month of the first transaction made by a customer in the physical format.
Yes
First Transaction (Physical) Date
Day of Week
The day of the week of the first transaction made by a customer in the physical format.
Yes
First Transaction (Physical) Date
Month
The month when the first transaction was made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Month Name
The name of the month when the first transaction was made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Quarter
The quarter when the first transaction was made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Quarter of Year
The quarter of the year when the first transaction was made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Week of Year
The week of the year when the first transaction was made by a customer in the physical format.
Yes
First Transaction (Physical) Date
Year
The year when the first transaction was made by a customer in the physical format.
Yes
First Transaction Date
Date
The date of the first transaction made by a customer.
Yes
First Transaction Date
Day of Month
The day of the month of the first transaction made by a customer.
Yes
First Transaction Date
Day of Week
The day of the week of the first transaction made by a customer.
Yes
First Transaction Date
Month
The month when the first transaction was made by a customer.
Yes
First Transaction Date
Month Name
The name of the month when the first transaction was made by a customer.
Yes
First Transaction Date
Quarter
The quarter when the first transaction was made by a customer.
Yes
First Transaction Date
Quarter of Year
The quarter of the year when the first transaction was made by a customer.
Yes
First Transaction Date
Week of Year
The week of the year when the first transaction was made by a customer.
Yes
First Transaction Date
Year
The year when the first transaction was made by a customer.
Yes
-
First Transaction Date - Is Before YTD (Yes / No)
A flag to indicate whether the day of year from customer’s [First Transaction Date] is before today’s day of year. This flag is typically used for Year over Year (YoY) reporting. You can create a report with [First Transaction Date - Month Name] dimension, pivot on [First Transaction Date - Year] dimension, and add [First Transaction Date - Is Before YTD] as a filter with ‘Yes’ value. Such a report accurately compares measures sliced by [First Transaction Date] in a YoY fashion.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Month Num
The fiscal month number of the customer’s first transaction date.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Quarter of Year
The fiscal quarter of the year of the customer’s first transaction date.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Week of Year
The fiscal week of the year of the customer’s first transaction date.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Year
The fiscal year of the customer’s first transaction date.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 1
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics, or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 2
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 3
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 4
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 5
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Product Category
First Transaction Product Category - Level 1
The product categories for products bought in a customer’s first transaction.
Yes
First Transaction Product Category
First Transaction Product Category - Level 2
The product categories for products bought in a customer’s first transaction.
Yes
First Transaction Product Category
First Transaction Product Category - Level 3
The product categories for products bought in a customer’s first transaction.
Yes
First Transaction Product Category
First Transaction Product Category - Level 4
The product categories for products bought in a customer’s first transaction.
Yes
First Transaction Product Category
First Transaction Product Category - Level 5
The product categories for products bought in a customer’s first transaction.
Yes
Last Transaction (Digital) Date
Date
The date of the last transaction made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Day of Month
The day of the month of the last transaction made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Day of Week
The day of the week of the last transaction made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Month
The month when the last transaction was made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Month Name
The name of the month when the last transaction was made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Quarter
The quarter when the last transaction was made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Quarter of Year
The quarter of the year when the last transaction was made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Week of Year
The week of the year when the last transaction was made by a customer in the digital format.
Yes
Last Transaction (Digital) Date
Year
The year when the last transaction was made by a customer in the digital format.
Yes
Last Transaction (Physical) Date
Date
The date of the last transaction made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Day of Month
The day of the month of the last transaction made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Day of Week
The day of the week of the last transaction made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Month
The month when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Month Name
The name of the month when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Quarter
The quarter when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Quarter of Year
The quarter of the year when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Week of Year
The week of the year when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Year
The year when the last transaction was made by the customer in the physical format.
Yes
Last Transaction Date
Date
The date of the last transaction made by the customer.
Yes
Last Transaction Date
Day of Month
The day of the month of the last transaction made by the customer.
Yes
Last Transaction Date
Day of Week
The day of the week of the last transaction made by the customer.
Yes
Last Transaction Date
Month
The month when the last transaction was made by the customer.
Yes
Last Transaction Date
Month Name
The name of the month when the last transaction was made by the customer.
Yes
Last Transaction Date
Quarter
The quarter when the last transaction was made by the customer.
Yes
Last Transaction Date
Quarter of Year
The quarter of the year when the last transaction was made by the customer.
Yes
Last Transaction Date
Week of Year
The week of the year when the last transaction was made by the customer.
Yes
Last Transaction Date
Year
The year when the last transaction was made by a customer.
Yes
-
Last Transaction Date - Is Before YTD (Yes / No)
A flag to indicate whether the day of year from customer’s [Last Transaction Date] is before today’s day of year. CDP uses this flag for Year over Year (YoY) reporting. You can create a report with [Last Transaction Date - Month Name] dimension, pivot on [Last Transaction Date - Year] dimension, and add [Last Transaction Date - Is Before YTD] as a filter with ‘Yes’ value. Such a report accurately compares measures sliced by [Last Transaction Date] in a YoY fashion.
Yes
Last Transaction Fiscal Date
Last Transaction Fiscal Month Num
The fiscal month number of the customer’s last transaction date.
Yes
Last Transaction Fiscal Date
Last Transaction Fiscal Quarter of Year
The fiscal quarter of the year of the customer’s last transaction date.
Yes
Last Transaction Fiscal Date
Last Transaction Fiscal Week of Year
The fiscal week of the year of the customer’s last transaction date.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Year
The fiscal year of the customer’s last transaction date.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 1
The product categories for products bought in the customer’s last transaction.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 2
The product categories for products bought in the customer’s last transaction.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 3
The product categories for products bought in the customer’s last transaction.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 4
The product categories for products bought in the customer’s last transaction.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 5
The product categories for products bought in the customer’s last transaction.
Yes
Marketing Status & Preferences
Address Certified
A Flag to indicate whether the customer’s primary address has been certified by the CDP Data Quality Engine (using USPS CASS certification): ‘True’ means that the address is CASS certified, ‘False’ means the address is not CASS certified (and likely invalid), ‘Unknown’ means that there is either no address, or that the address is not in the US / Canada (those are the only countries we can certify right now).
No
Marketing Status & Preferences
Address DPV Confirmed
A flag to indicate whether the customer’s primary address has been Delivery Point Validation confirmed. DPV is the process of verifying that an address is actually deliverable, meaning that mail can be sent to that address. A single street address may have multiple delivery points, such as individual units in an apartment building. ‘Y’ means the address is DPV confirmed whereas ‘N’ means the address is not DPV confirmed. Typically, all DPV confirmed addresses are also Certified but all Certified addresses may not be DPV confirmed.
No
Marketing Status & Preferences
Do Not Call
A flag to indicate whether the customer has opted-in or opted-out of the Call campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in.
No
Marketing Status & Preferences
Do Not Email
A flag to indicate whether the customer has opted-in or opted-out of the Email campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in.
No
Marketing Status & Preferences
Do Not Mail
A flag to indicate whether the customer has opted-in or opted-out of the Direct Mail campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in.
No
Marketing Status & Preferences
Do Not Text
A flag to indicate whether the customer has opted-in or opted-out of the SMS or Text campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in.
No
Marketing Status & Preferences
Email Domain
The domain of the customer’s email address.
No
Marketing Status & Preferences
Email Status
A flag to indicate the validity of the customer’s email address. The following are the values:
‘V’: ‘Verified’, the email address syntax is valid, the domain is good or known and the email address is not a known spam trap.
‘U’: ‘Unverified’, the email address syntax is valid but the domain is
unknown or bad.
-‘X’: ‘Invalid’, the email address syntax is invalid.
No
-
Primary Brand
The product brands that customers made most transactions during their lifetime.
Yes
-
Product Count
Number of distinct products purchased by customers during their lifetime.
Yes
Sales Organizations
Closest Store
The closest store for customers.
Yes
Sales Organizations
Distance to Closest Store
The distance, in miles, between customer and the closest store. This dimension works only for US, Canada and UK addresses (for both stores and customers).
Yes
Sales Organizations
First Transaction Sales Channel
The sales channel of the customer’s first transaction. The following are the values:
Digital: Online stores
Physical:Offline stores
Warehouse: Warehousing locations, if any
Other: The channels that do not fall into earlier categories such as call centers.
Yes
Sales Organizations
Last Transaction Sales Channel
The sales channel of the customer’s last transaction. The following are the values:
Digital: Online stores
Physical:Offline stores
Warehouse: Warehousing locations, if any
Other: The channels that do not fall into earlier categories such as call centers.
Yes
Sales Organizations
Primary Physical Store
The physical Store that customer made most transactions in during their lifetime
Yes
Sales Organizations
Primary Sales Channel
The sales Channel that the customer made most transactions in during their lifetime. The following are the values:
Digital: Online stores
Physical:Offline stores
Warehouse: Warehousing locations, if any
Other: The channels that do not fall into earlier categories such as call centers.
Yes
Sales Organizations
Sales Channel Count
Number of sales channels customers have purchased from in their lifetime.
Yes
Second to Last Transaction Date
Date
The date of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Day of Month
The day of the month of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Day of Week
The day of week of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Month
The month of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Month Name
The name of the month of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Quarter
The quarter of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Quarter of Year
The quarter of the year of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Week of Year
The week of the year of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Year
The year of the second to last transaction made by the customer.
Yes
Web First Visit Date
Date
The date when a customer first visited the website.
Yes
Web First Visit Date
Day of Month
The day of the month when a customer first visited the website.
Yes
Web First Visit Date
Day of Week
The day of the week when a customer first visited the website.
Yes
Web First Visit Date
Month
The month when a customer first visited the website.
Yes
Web First Visit Date
Month Name
The name of the month when a customer first visited the website.
Yes
Web First Visit Date
Quarter
The quarter when a customer first visited the website.
Yes
Web First Visit Date
Quarter of Year
The quarter of the year when a customer first visited the website.
Yes
Web First Visit Date
Week of Year
The week of the year when a customer first visited the website.
Yes
Web First Visit Date
Year
The year when a customer first visited the website.
Yes
Web Last Visit Date
Date
The date when a customer last visited the website.
Yes
Web Last Visit Date
Day of Month
The day of the month when the customer last visited the website.
Yes
Web Last Visit Date
Day of Week
The day of the week when the customer last visited the website.
Yes
Web Last Visit Date
Month
The month when the customer last visited the website.
Yes
Web Last Visit Date
Month Name
The name of the month when the customer last visited the website.
Yes
Web Last Visit Date
Quarter
The quarter when the customer last visited the website.
Yes
Web Last Visit Date
Quarter of Year
The quarter of the year when the customer last visited the website.
Yes
Web Last Visit Date
Week of Year
The week of the year when the customer last visited the website.
Yes
Web Last Visit Date
Year
The year when the customer last visited the website.
Yes
Yes
Campaign Execution Date
Day of Week
The day of the week when the campaign or segment was executed. For example, Monday, Tuesday, and so on.
Yes
Campaign Execution Date
Month
The numeric representation of the month when the campaign or segment was executed. For example, 1 for January, 2 for February, and so on.
Yes
Campaign Execution Date
Month Name
The name of the month when the campaign or segment was executed. For example, January, February, and so on.
Yes
Campaign Execution Date
Quarter
The quarter of the year when the campaign or segment was executed. For example, Q1, Q2, Q3, and Q4.
Yes
Campaign Execution Date
Quarter of Year
The numeric representation of the quarter when the campaign or segment was executed. For example, 1, 2, 3, and 4.
Yes
Campaign Execution Date
Time
The timestamp indicating the time when the campaign or segment was executed.
Yes
Campaign Execution Date
Time of Day
The specific time of day when the campaign or segment was executed. For example, morning, afternoon, evening, and so on.
Yes
Campaign Execution Date
Week of Year
The week number within the year when the campaign or segment was executed.
Yes
Campaign Execution Date
Year
The year when the campaign or segment was executed.
Yes
-
Campaign ID
The unique identifier associated with the campaign.
Yes
-
Campaign Name
The name or title of the campaign.
Yes
-
Segment ID
The unique identifier associated with the segment.
Yes
-
Segment Name
The name or title of the segment.
Yes
-
Variant Name
The specific variant or version of a campaign or segment, if applicable.
Yes
The cluster of customers personas based on their purchase behavior, preferences, and spending patterns today. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
CC Studio
Custom Calculation Dimension
The custom calculation dimension created within CC Studio for specific analytic needs.
Yes
Fuzzy Clustering
Most Likely Cluster Probability
The probability of a customer belonging to a cluster with the best match. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Most Likely Cluster Value
The cluster where the customer belongs to with the highest probability. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Second Most Likely Cluster Probability
The probability of a customer belonging to a cluster with the second best match. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Second Most Likely Cluster Value
The cluster where the customer belongs to with the second highest probability. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Third Most Likely Cluster Probability
The probability of a customer belonging to a cluster with the third best match. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Third Most Likely Cluster Value
The cluster where the customer belongs to with the third highest probability. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Likelihood to Buy
Likelihood to Buy - 1 Month Ago
The likelihood or probability that an existing customer makes a purchase in the near future based on behaviors a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Buy
Likelihood to Buy - 2 Months Ago
The likelihood or probability that an existing customer makes a purchase in the near future based on behaviors two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Buy
Likelihood to Buy - This Month
The likelihood or probability that an existing customer makes a purchase in the near future based on behaviors this month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Buy
Likelihood to Buy - Today
The likelihood or probability that an existing customer makes a purchase in the near future based on today’s behaviors. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Convert
Likelihood to Convert - 1 Month Ago
The likelihood or probability that a non-buyer makes a purchase in the near future learning from the past one month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Convert
Likelihood to Convert - 2 Months Ago
The likelihood or probability that a non-buyer makes a purchase in the near future learning from the past two months. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Convert
Likelihood to Convert - This Month
The likelihood or probability that a non-buyer makes a purchase in the near future learning from the past month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Convert
Likelihood to Convert - Today
The likelihood or probability that a non-buyer makes a purchase in the near future learning from today’s behavior. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Engage on Email
Likelihood to Engage on Email - 1 Month Ago
The likelihood or probability that someone opens an email in the near future learning from email events from a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Engage on Email
Likelihood to Engage on Email- 2 Months Ago
The likelihood or probability that someone opens an email in the near future learning from email events from two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Engage on Email
Likelihood to Engage on Email - This Month
The likelihood or probability that someone opens an email in the near future learning from email events from this month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Engage on Email
Likelihood to Engage on Email - Today
The likelihood or probability that someone opens an email in the near future learning from today’s email events. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Pay Full Price
Likelihood to Pay Full Price - 1 Month Ago
The likelihood or probability that someone pays full price in the near future learning from a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Pay Full Price
Likelihood to Pay Full Price- 2 Months Ago
The likelihood or probability that someone pays full price in the near future learning from two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Pay Full Price
Likelihood to Pay Full Price - This Month
The likelihood or probability that someone pays full price in the near future learning from this month. Likelihood to Pay Full Price- 2 Months Ago To set up the model, contact your CVM.
Yes
Likelihood to Pay Full Price
Likelihood to Pay Full Price - Today
The likelihood or probability that someone pays full price in the near future learning from today. Likelihood to Pay Full Price- 2 Months Ago To set up the model, contact your CVM.
Yes
Next Best Action
Next Best Channel
The next best channel that the customer is most likely to engage with.
Yes
Next Best Action
Second Best Channel
The second next best channel that the customer is most likely to engage with.
Yes
Predictive Lifetime Value
Predictive Lifetime Value - Decile - Today
The buckets the predictive lifetime value from a customer into groups.
Yes
Predictive Lifetime Value
Predictive Lifetime Value - Revenue Group - Today
The buckets the predictive lifetime revenue from a customer into groups.
Yes
Predictive Sends
Optimal Email Send Time - Overall
The optimum time when emails can be sent to a customer.
Yes
Predictive Sends
Optimal Email Send Time - Weekday
The optimum time during weekdays when emails can be sent to a customer.
Yes
Predictive Sends
Optimal Email Send Time - Weekend
The optimum time during the weekends when emails can be sent to a customer.
Yes
Product based Cluster
Product based Cluster - 1 Month Ago
The customer personas based on the products or product categories they purchased from a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Product based Cluster
Product based Cluster - 2 Months Ago
The customer personas based on the products or product categories they purchased from two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Product based Cluster
Product based Cluster - This Month
The customer personas based on the products or product categories they purchased from this month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your .
Yes
Product based Cluster
Product based Cluster - Today
The customer personas based on the products or product categories they purchased from. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
First Item Details
First Item Brand
The brand of the first item.
No
First Item Details
First Item Color
The color of the first item.
No
First Item Details
First Item Size
The size of the first item.
No
First Item Details
First Product ID
The ID of the first product.
No
First Item Details
First Product Name
The name of the first product.
No
First Item Details
Selected Dimension First Item
The first item of the selected dimension. The value of this field is determined by the value set in the Analysis By Dimension filter.
No
Second Item Details
Second Category Name Level 1
The category name of the level 1 second item.
No
Second Item Details
Second Category Name Level 2
The category name of the level 2 second item.
No
Second Item Details
Second Category Name Level 3
The category name of the level 3 second item.
No
Second Item Details
Second Category Name Level 4
The category name of the level 4 second item.
No
Second Item Details
Second Category Name Level 5
The category name of the level 5 second item.
No
Second Item Details
Second Item Brand
The brand of the second item.
No
Second Item Details
Second Item Color
The color of the second item.
No
Second Item Details
Second Item Size
The size of the second item.
No
Second Item Details
Second Product ID
The ID of the second product.
No
Second Item Details
Second Product Name
The name of the second product.
No
Second Item Details
Selected Dimension Second Item
The second item of the selected dimension. The value of this field is determined by the value set in the Analysis By Dimension filter.
No
Physical:Offline stores
Warehouse: Warehousing locations, if any
Other: The channels that do not fall into earlier categories such as call centers.
No
-
Sales Org ID
A unique identifier for sales organization
No
-
Sales Org Name
The name for sales organization
No
-
Sales Org Type
The type of sales organization, an optional level of categorization after the sales channel. For example, Physical sales organizations may have types such as ‘Retail’ or ‘Outlet’ whereas Digital sales organizations may have a type such as ‘eCommerce’. The possible values for your environment depend on data provided during implementation.
No
Days since Customer First Transaction
The number of days between a given transaction and the customer’s first transaction. Note that this field is calculated at the transaction level, and it does not indicate customer tenure as of today. Instead, it indicates customer tenure as of when the transaction was made.
Yes
Days since Customer First Transaction
Days since Customer First Transaction Group
The bucketed version of [Days since Customer First Transaction] for transaction.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 1
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics, or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 2
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 3
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 4
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 5
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Transaction Date
Date
The date of the transaction made by the customer.
No
Transaction Date
Day of Month
The day of the month of the transaction made by the customer.
No
Transaction Date
Day of Week
The day of the week of the transaction made by the customer.
No
Transaction Date
Month
The month when the transaction was made by the customer.
No
Transaction Date
Month Name
The name of the month when the transaction was made by the customer.
No
Transaction Date
Quarter
The quarter when the transaction was made by the customer.
No
Transaction Date
Quarter of Year
The quarter of the year when the transaction was made by the customer.
No
Transaction Date
Week of Year
The week of the year when the transaction was made by the customer.
No
Transaction Date
Year
The year when the transaction was made by the customer.
No
-
Transaction Date - Is Before YTD (Yes / No)
A flag to indicate whether the day of year from [Transaction Date] is before today’s day of year. This flag is typically used for Year over Year (YoY) reporting. You can create a report with [Transaction Date - Month Name] dimension, pivot on [Transaction Date - Year] dimension, and add [Transaction Date - Is Before YTD] as a filter with ‘Yes’ value. Such a report accurately compares measures sliced by [Transaction Date] in a YoY fashion.
No
Transaction Fiscal Date
Transaction Fiscal Month Num
The fiscal month number of the customer’s transaction date.
No
Transaction Fiscal Date
Transaction Fiscal Quarter of Year
The fiscal quarter of the year of the customer’s transaction date.
No
Transaction Fiscal Date
Transaction Fiscal Week of Year
The fiscal week of the year of the customer’s transaction date.
No
Transaction Fiscal Date
Transaction Fiscal Year
The fiscal calendar year of the customer’s transaction date.
No
Transaction Line Date
Date
The date of the transaction made by the customer at the line level.
No
Transaction Line Date
Day of Month
The day of the month of the transaction made by the customer at the line level.
No
Transaction Line Date
Day of Week
The day of the week of the transaction made by the customer at the line level.
No
Transaction Line Date
Month
The month when the transaction was made by the customer at the line level.
No
Transaction Line Date
Month Name
The name of the month when the transaction was made by the customer at the line level.
No
Transaction Line Date
Quarter
The quarter when the transaction was made by the customer at the line level.
No
Transaction Line Date
Quarter of Year
The quarter of the year when the transaction was made by the customer at the line level.
No
Transaction Line Date
Week of Year
The week of the year when the transaction was made by the customer at the line level.
No
Transaction Line Date
Year
The year when the transaction was made by the customer at the line level.
No
-
Transaction Line Date - Is Before YTD (Yes / No)
A flag to indicate whether the day of year from [Transaction Line Date] is before today’s day of year. This flag is typically used for Year over Year (YoY) reporting. You create a report with [Transaction Line Date - Month Name] dimension, pivot on [Transaction Line Date - Year] dimension, and add [Transaction Line Date - Is Before YTD] as a filter with ‘Yes’ value. Such a report accurately compares measures sliced by [Transaction Line Date] in a YoY fashion.
No
Transaction Line Fiscal Date
Transaction Line Fiscal Month Num
The fiscal month number of the customer’s transaction line date.
No
Transaction Line Fiscal Date
Transaction Line Fiscal Quarter of Year
The fiscal quarter of the year of the customer’s transaction line date.
No
Transaction Line Fiscal Date
Transaction Line Fiscal Week of Year
The fiscal week of the year of the customer’s transaction line date.
No
Transaction Line Fiscal Date
Transaction Line Fiscal Year
The fiscal calendar year of the customer’s transaction line date.
No
-
Transaction Line SubType
The subtype of transaction line, possible values are ‘Demand’, ‘Canceled’, ‘Shipped’, and ‘Returned’.
No
-
Transaction Line Type
The type of transaction line, default value is ‘Purchase’. Contact your CDP CVM for descriptions if you have custom transaction linetypes.
No
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-
Campaign Name
The name of the campaign.
No
Campaign Start Date
Date
The date when the campaign started.
No
Campaign Start Date
Day of Month
The day of the month when the campaign started.
No
Campaign Start Date
Day of Week
The day of the week when the campaign started.
No
Campaign Start Date
Hour of Day
The hour of the day when the campaign started.
No
Campaign Start Date
Month
The month when the campaign started.
No
Campaign Start Date
Month Name
The name of the month when the campaign started.
No
Campaign Start Date
Quarter
The quarter when the campaign started.
No
Campaign Start Date
Quarter of Year
The quarter of the year when the campaign started.
No
Campaign Start Date
Time of Day
The time of the day when the campaign started.
No
Campaign Start Date
Week of Year
The week of the year when the campaign started.
No
Campaign Start Date
Year
The year when the campaign started.
No
The age of the customer.
No
Age
Age Group
The age of the customer, grouped into buckets for easier use.
No
-
Buyer Identification
The dimension that indicates whether the customer is an identified or unidentified buyer. All transactions that have a blank or null Customer get attributed to a generic Unidentified customer. This is primarily used with Sales Organization dimensions to view a distribution of revenue attributed to unidentified individuals for POS sales.
Yes
Buying Stats
AOV
The average order value for customers calculated using [Product Revenue] / [Transaction Count].
Yes
Buying Stats
Average Annual Transactions
The annual transaction frequency for customer calculated using [Transaction Count] * 365 / [Days since First Transaction].
Yes
Buying Stats
Average Discount Rate
The average discount rate for customers to identify promotion driven customers.
Yes
Buying Stats
First Transaction Revenue
The revenue from the customer’s first transaction. Note that ranges are inclusive at the higher bound. For example, ‘0-25’ includes up to 25, ‘25-50’ starts at 25.01.
Yes
Buying Stats
Last Transaction Interval
The number of months between the last and the second to last transaction of the customer.
Yes
Buying Stats
Revenue - 13-24 Months
The total revenue for customer’s purchases in the previous 13-24 month duration. Note that ranges are inclusive at the higher bound. For example, ‘0-100’ includes up to 100, ‘100-200’ starts at 100.01.
Yes
Buying Stats
Revenue - Last 12 Months
The total revenue for customer’s purchases in the last 12 month duration. Note that ranges are inclusive at the higher bound. For example: ‘0-100’ includes up to 100, ‘100-200’ starts at 100.01.
Yes
Buying Stats
Revenue - Lifetime
The total revenue for all purchases in the customer’s lifetime. Note that ranges are inclusive at the higher bound. For example: ‘0-100’ includes up to 100, ‘100-200’ starts at 100.01.
Yes
Buying Stats
Revenue Decile - 13-24 Months
The revenue decile for customers based on total revenue for all purchases in the previous 13-24 month duration. The following are the values:
1: Top 10%
10: Bottom 10%
0: The customers that have not purchased in a given time period.
Yes
Buying Stats
Revenue Decile - Last 12 Months
The revenue decile for customers based on total revenue for all purchases in the last 12 month duration. The following are the values:
1: Top 10%
10: Bottom 10%
0: The customers that have not purchased in a given time period.
Yes
Buying Stats
Revenue Segment - 13-24 Months
The revenue segment for customers based on total revenue for all purchases in the previous 13-24 month duration. The following are the values:
1-HighValue: Top 10% by revenue
2-MediumValue: 20% to 80% by revenue
3-LowValue: Bottom 20%
NonBuyer: The customers that have not purchased in the given time
period.
Yes
Buying Stats
Revenue Segment - Last 12 Months
The revenue segment for customers based on total revenue for all purchases in the last 12 month duration. The following are the values:
1-HighValue: Top 10% by revenue
2-MediumValue: 20% to 80% by revenue
3-LowValue: Bottom 20%
NonBuyer: The customers that have not purchased in the given time
period.
Yes
Buying Stats
Revenue Trend Segment
The revenue trend segment for customers derived by comparing revenue decile in the previous 13-24 months as compared to the last 12 months. It characterizes whether they spent less (‘Downward’), the same (‘Stable’), or more (‘Upward’) in the previous 13-24 months period as compared to the last 12 months. The following are the values:
Non Buyer: The customers that have not purchased in the given time period.
Unidentified: Indicates invalid data.
2PeriodInactive: The customers are buyers who did not make a purchase in the last 24 months but made a purchase before 24 months ago.
Lapsed: The customers who made a purchase in the last 13-24 months but did not make a purchase in the last 12 months.
Yes
Buying Stats
Transaction Count - 13-24 Months
The total number of transactions made by customers in the previous 13-24 month duration.
Yes
Buying Stats
Transaction Count - Last 12 Months
The total number of transactions made by customers in the last 12 month duration.
Yes
Buying Stats
Transaction Count - Lifetime
The total number of transactions made in a customer’s lifetime.
Yes
-
Category Count
The number of distinct product categories that customers purchased from in their lifetime.
Yes
-
Customer Gender
The gender of the customer as determined by the genderization algorithm of Customer Data Platform (CDP). Values are Female, Male, Neutral and Unknown. Neutral refers to names that are either male or female. If the gender can not be determined, CDP displays Unknown.
No
-
Customer Status
The buying status of the customer. Possible values include Buyer, Non Buyer, Unidentified.
Yes
Days since First Transaction
Days since First Transaction
The number of days since the customer’s first transaction, that is, the tenure of the customer in days.
Yes
Days since First Transaction
Days since First Transaction Group
The bucketed version of [Days since First Transaction] represented in months.
Yes
Days since Last Transaction
Days since Last Transaction
The number of days since the customer’s last transaction, that is, the recency of the customer in days.
Yes
Days since Last Transaction
Days since Last Transaction Group
The bucketed version of [Days since Last Transaction] represented in months.
Yes
Email Last Click Date
Date
The date when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Day of Month
The day of the month when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Day of Week
The day of the week when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Month
The month when the customer last clicked one of the email campaigns
Yes
Email Last Click Date
Month Name
The name of the month when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Quarter
The quarter when the customer last clicked one of the campaigns.
Yes
Email Last Click Date
Quarter of Year
The quarter of the year when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Week of Year
The week of the year when the customer last clicked one of the email campaigns.
Yes
Email Last Click Date
Year
The year when the customer last clicked one of the email campaigns.
Yes
Email Last Open Date
Date
The date when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Day of Month
The day of the month when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Day of Week
The day of the week when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Month
The month when the customer last opened one of the email campaigns
Yes
Email Last Open Date
Month Name
The name of the month when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Quarter
The quarter when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Quarter of Year
The quarter of the year when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Week of Year
The week of the year when the customer last opened one of the email campaigns.
Yes
Email Last Open Date
Year
The year when the customer last opened one of the email campaigns.
Yes
Email Last Send Date
Date
The date when the customer was last sent an email.
Yes
Email Last Send Date
Day of Month
The day of the month when the customer was last sent an email.
Yes
Email Last Send Date
Day of Week
The day of the week when the customer was last sent an email.
Yes
Email Last Send Date
Month
The month when the customer was last sent an email.
Yes
Email Last Send Date
Month Name
The name of the month when the customer was last sent an email.
Yes
Email Last Send Date
Quarter
The quarter when the customer was last sent an email.
Yes
Email Last Send Date
Quarter of Year
The quarter of the year when the customer was last sent an email.
Yes
Email Last Send Date
Week of Year
The week of the year when the customer was last sent an email.
Yes
Email Last Send Date
Year
The year when the customer was last sent an email.
Yes
First Transaction (Digital) Date
Date
The date of the first transaction made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Day of Month
The day of the month of the first transaction made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Day of Week
The day of the week of the first transaction made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Month
The month when the first transaction was made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Month Name
The name of the month when the first transaction was made by the customer in the digital format.
Yes
First Transaction (Digital) Date
Quarter
The quarter when the first transaction was made by the customer in the digital format.
Yes
First Transaction (Digital) Date
Quarter of Year
The quarter of the year when the first transaction was made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Week of Year
The week of the year when the first transaction was made by a customer in the digital format.
Yes
First Transaction (Digital) Date
Year
The year when the first transaction was made by a customer in the digital format.
Yes
First Transaction (Physical) Date
Date
The date of the first transaction made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Day of Month
The day of the month of the first transaction made by a customer in the physical format.
Yes
First Transaction (Physical) Date
Day of Week
The day of the week of the first transaction made by a customer in the physical format.
Yes
First Transaction (Physical) Date
Month
The month when the first transaction was made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Month Name
The name of the month when the first transaction was made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Quarter
The quarter when the first transaction was made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Quarter of Year
The quarter of the year when the first transaction was made by the customer in the physical format.
Yes
First Transaction (Physical) Date
Week of Year
The week of the year when the first transaction was made by a customer in the physical format.
Yes
First Transaction (Physical) Date
Year
The year when the first transaction was made by a customer in the physical format.
Yes
First Transaction Date
Date
The date of the first transaction made by a customer.
Yes
First Transaction Date
Day of Month
The day of the month of the first transaction made by a customer.
Yes
First Transaction Date
Day of Week
The day of the week of the first transaction made by a customer.
Yes
First Transaction Date
Month
The month when the first transaction was made by a customer.
Yes
First Transaction Date
Month Name
The name of the month when the first transaction was made by a customer.
Yes
First Transaction Date
Quarter
The quarter when the first transaction was made by a customer.
Yes
First Transaction Date
Quarter of Year
The quarter of the year when the first transaction was made by a customer.
Yes
First Transaction Date
Week of Year
The week of the year when the first transaction was made by a customer.
Yes
First Transaction Date
Year
The year when the first transaction was made by a customer.
Yes
-
First Transaction Date - Is Before YTD (Yes / No)
A flag to indicate whether the day of year from customer’s [First Transaction Date] is before today’s day of year. This flag is typically used for Year over Year (YoY) reporting. You can create a report with [First Transaction Date - Month Name] dimension, pivot on [First Transaction Date - Year] dimension, and add [First Transaction Date - Is Before YTD] as a filter with ‘Yes’ value. Such a report accurately compares measures sliced by [First Transaction Date] in a YoY fashion.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Month Num
The fiscal month number of the customer’s first transaction date.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Quarter of Year
The fiscal quarter of the year of the customer’s first transaction date.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Week of Year
The fiscal week of the year of the customer’s first transaction date.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Year
The fiscal year of the customer’s first transaction date.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 1
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics, or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 2
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 3
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 4
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Last Marketing Touch - Online
First Transaction Last Marketing Touch - Online 5
The last of all online marketing touches, which are responsible for the customer’s first transaction. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
First Transaction Product Category
First Transaction Product Category - Level 1
The product categories for products bought in a customer’s first transaction.
Yes
First Transaction Product Category
First Transaction Product Category - Level 2
The product categories for products bought in a customer’s first transaction.
Yes
First Transaction Product Category
First Transaction Product Category - Level 3
The product categories for products bought in a customer’s first transaction.
Yes
First Transaction Product Category
First Transaction Product Category - Level 4
The product categories for products bought in a customer’s first transaction.
Yes
First Transaction Product Category
First Transaction Product Category - Level 5
The product categories for products bought in a customer’s first transaction.
Yes
Last Transaction (Digital) Date
Date
The date of the last transaction made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Day of Month
The day of the month of the last transaction made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Day of Week
The day of the week of the last transaction made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Month
The month when the last transaction was made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Month Name
The name of the month when the last transaction was made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Quarter
The quarter when the last transaction was made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Quarter of Year
The quarter of the year when the last transaction was made by the customer in the digital format.
Yes
Last Transaction (Digital) Date
Week of Year
The week of the year when the last transaction was made by a customer in the digital format.
Yes
Last Transaction (Digital) Date
Year
The year when the last transaction was made by a customer in the digital format.
Yes
Last Transaction (Physical) Date
Date
The date of the last transaction made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Day of Month
The day of the month of the last transaction made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Day of Week
The day of the week of the last transaction made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Month
The month when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Month Name
The name of the month when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Quarter
The quarter when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Quarter of Year
The quarter of the year when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Week of Year
The week of the year when the last transaction was made by the customer in the physical format.
Yes
Last Transaction (Physical) Date
Year
The year when the last transaction was made by the customer in the physical format.
Yes
Last Transaction Date
Date
The date of the last transaction made by the customer.
Yes
Last Transaction Date
Day of Month
The day of the month of the last transaction made by the customer.
Yes
Last Transaction Date
Day of Week
The day of the week of the last transaction made by the customer.
Yes
Last Transaction Date
Month
The month when the last transaction was made by the customer.
Yes
Last Transaction Date
Month Name
The name of the month when the last transaction was made by the customer.
Yes
Last Transaction Date
Quarter
The quarter when the last transaction was made by the customer.
Yes
Last Transaction Date
Quarter of Year
The quarter of the year when the last transaction was made by the customer.
Yes
Last Transaction Date
Week of Year
The week of the year when the last transaction was made by the customer.
Yes
Last Transaction Date
Year
The year when the last transaction was made by a customer.
Yes
-
Last Transaction Date - Is Before YTD (Yes / No)
A flag to indicate whether the day of year from customer’s [Last Transaction Date] is before today’s day of year. CDP uses this flag for Year over Year (YoY) reporting. You can create a report with [Last Transaction Date - Month Name] dimension, pivot on [Last Transaction Date - Year] dimension, and add [Last Transaction Date - Is Before YTD] as a filter with ‘Yes’ value. Such a report accurately compares measures sliced by [Last Transaction Date] in a YoY fashion.
Yes
Last Transaction Fiscal Date
Last Transaction Fiscal Month Num
The fiscal month number of the customer’s last transaction date.
Yes
Last Transaction Fiscal Date
Last Transaction Fiscal Quarter of Year
The fiscal quarter of the year of the customer’s last transaction date.
Yes
Last Transaction Fiscal Date
Last Transaction Fiscal Week of Year
The fiscal week of the year of the customer’s last transaction date.
Yes
First Transaction Fiscal Date
First Transaction Fiscal Year
The fiscal year of the customer’s last transaction date.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 1
The product categories for products bought in the customer’s last transaction.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 2
The product categories for products bought in the customer’s last transaction.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 3
The product categories for products bought in the customer’s last transaction.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 4
The product categories for products bought in the customer’s last transaction.
Yes
Last Transaction Product Category
Last Transaction Product Category - Level 5
The product categories for products bought in the customer’s last transaction.
Yes
Marketing Status & Preferences
Address Certified
A Flag to indicate whether the customer’s primary address has been certified by the CDP Data Quality Engine (using USPS CASS certification): ‘True’ means that the address is CASS certified, ‘False’ means the address is not CASS certified (and likely invalid), ‘Unknown’ means that there is either no address, or that the address is not in the US / Canada (those are the only countries we can certify right now).
No
Marketing Status & Preferences
Address DPV Confirmed
A flag to indicate whether the customer’s primary address has been Delivery Point Validation confirmed. DPV is the process of verifying that an address is actually deliverable, meaning that mail can be sent to that address. A single street address may have multiple delivery points, such as individual units in an apartment building. ‘Y’ means the address is DPV confirmed whereas ‘N’ means the address is not DPV confirmed. Typically, all DPV confirmed addresses are also Certified but all Certified addresses may not be DPV confirmed.
No
Marketing Status & Preferences
Do Not Call
A flag to indicate whether the customer has opted-in or opted-out of the Call campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in.
No
Marketing Status & Preferences
Do Not Email
A flag to indicate whether the customer has opted-in or opted-out of the Email campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in.
No
Marketing Status & Preferences
Do Not Mail
A flag to indicate whether the customer has opted-in or opted-out of the Direct Mail campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in.
No
Marketing Status & Preferences
Do Not Text
A flag to indicate whether the customer has opted-in or opted-out of the SMS or Text campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in.
No
Marketing Status & Preferences
Email Domain
The domain of the customer’s email address.
No
Marketing Status & Preferences
Email Status
A flag to indicate the validity of the customer’s email address. The following are the values:
‘V’: ‘Verified’, the email address syntax is valid, the domain is good or known and the email address is not a known spam trap.
‘U’: ‘Unverified’, the email address syntax is valid but the domain is
unknown or bad.
-‘X’: ‘Invalid’, the email address syntax is invalid.
No
-
Primary Brand
The product brands that customers made most transactions during their lifetime.
Yes
-
Product Count
Number of distinct products purchased by customers during their lifetime.
Yes
Sales Organizations
Closest Store
The closest store for customers.
Yes
Sales Organizations
Distance to Closest Store
The distance, in miles, between customer and the closest store. This dimension works only for US, Canada and UK addresses (for both stores and customers).
Yes
Sales Organizations
First Transaction Sales Channel
The sales channel of the customer’s first transaction. The following are the values:
Digital: Online stores
Physical:Offline stores
Warehouse: Warehousing locations, if any
Other: The channels that do not fall into earlier categories such as call centers.
Yes
Sales Organizations
Last Transaction Sales Channel
The sales channel of the customer’s last transaction. The following are the values:
Digital: Online stores
Physical:Offline stores
Warehouse: Warehousing locations, if any
Other: The channels that do not fall into earlier categories such as call centers.
Yes
Sales Organizations
Primary Physical Store
The physical Store that customer made most transactions in during their lifetime
Yes
Sales Organizations
Primary Sales Channel
The sales Channel that the customer made most transactions in during their lifetime. The following are the values:
Digital: Online stores
Physical:Offline stores
Warehouse: Warehousing locations, if any
Other: The channels that do not fall into earlier categories such as call centers.
Yes
Sales Organizations
Sales Channel Count
Number of sales channels customers have purchased from in their lifetime.
Yes
Second to Last Transaction Date
Date
The date of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Day of Month
The day of the month of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Day of Week
The day of week of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Month
The month of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Month Name
The name of the month of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Quarter
The quarter of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Quarter of Year
The quarter of the year of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Week of Year
The week of the year of the second to last transaction made by the customer.
Yes
Second to Last Transaction Date
Year
The year of the second to last transaction made by the customer.
Yes
Web First Visit Date
Date
The date when a customer first visited the website.
Yes
Web First Visit Date
Day of Month
The day of the month when a customer first visited the website.
Yes
Web First Visit Date
Day of Week
The day of the week when a customer first visited the website.
Yes
Web First Visit Date
Month
The month when a customer first visited the website.
Yes
Web First Visit Date
Month Name
The name of the month when a customer first visited the website.
Yes
Web First Visit Date
Quarter
The quarter when a customer first visited the website.
Yes
Web First Visit Date
Quarter of Year
The quarter of the year when a customer first visited the website.
Yes
Web First Visit Date
Week of Year
The week of the year when a customer first visited the website.
Yes
Web First Visit Date
Year
The year when a customer first visited the website.
Yes
Web Last Visit Date
Date
The date when a customer last visited the website.
Yes
Web Last Visit Date
Day of Month
The day of the month when the customer last visited the website.
Yes
Web Last Visit Date
Day of Week
The day of the week when the customer last visited the website.
Yes
Web Last Visit Date
Month
The month when the customer last visited the website.
Yes
Web Last Visit Date
Month Name
The name of the month when the customer last visited the website.
Yes
Web Last Visit Date
Quarter
The quarter when the customer last visited the website.
Yes
Web Last Visit Date
Quarter of Year
The quarter of the year when the customer last visited the website.
Yes
Web Last Visit Date
Week of Year
The week of the year when the customer last visited the website.
Yes
Web Last Visit Date
Year
The year when the customer last visited the website.
Yes
Yes
Campaign Execution Date
Day of Week
The day of the week when the campaign or segment was executed. For example, Monday, Tuesday, and so on.
Yes
Campaign Execution Date
Month
The numeric representation of the month when the campaign or segment was executed. For example, 1 for January, 2 for February, and so on.
Yes
Campaign Execution Date
Month Name
The name of the month when the campaign or segment was executed. For example, January, February, and so on.
Yes
Campaign Execution Date
Quarter
The quarter of the year when the campaign or segment was executed. For example, Q1, Q2, Q3, and Q4.
Yes
Campaign Execution Date
Quarter of Year
The numeric representation of the quarter when the campaign or segment was executed. For example, 1, 2, 3, and 4.
Yes
Campaign Execution Date
Time
The timestamp indicating the time when the campaign or segment was executed.
Yes
Campaign Execution Date
Time of Day
The specific time of day when the campaign or segment was executed. For example, morning, afternoon, evening, and so on.
Yes
Campaign Execution Date
Week of Year
The week number within the year when the campaign or segment was executed.
Yes
Campaign Execution Date
Year
The year when the campaign or segment was executed.
Yes
-
Campaign ID
The unique identifier associated with the campaign.
Yes
-
Campaign Name
The name or title of the campaign.
Yes
-
Segment ID
The unique identifier associated with the segment.
Yes
-
Segment Name
The name or title of the segment.
Yes
-
Variant Name
The specific variant or version of a campaign or segment, if applicable.
Yes
The cluster of customers personas based on their purchase behavior, preferences, and spending patterns today. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
CC Studio
Custom Calculation Dimension
The custom calculation dimension created within CC Studio for specific analytic needs.
Yes
Fuzzy Clustering
Most Likely Cluster Probability
The probability of a customer belonging to a cluster with the best match. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Most Likely Cluster Value
The cluster where the customer belongs to with the highest probability. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Second Most Likely Cluster Probability
The probability of a customer belonging to a cluster with the second best match. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Second Most Likely Cluster Value
The cluster where the customer belongs to with the second highest probability. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Third Most Likely Cluster Probability
The probability of a customer belonging to a cluster with the third best match. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Fuzzy Clustering
Third Most Likely Cluster Value
The cluster where the customer belongs to with the third highest probability. Customers are matched to clusters based on purchase behavior, preferences, and spending patterns.
Yes
Likelihood to Buy
Likelihood to Buy - 1 Month Ago
The likelihood or probability that an existing customer makes a purchase in the near future based on behaviors a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Buy
Likelihood to Buy - 2 Months Ago
The likelihood or probability that an existing customer makes a purchase in the near future based on behaviors two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Buy
Likelihood to Buy - This Month
The likelihood or probability that an existing customer makes a purchase in the near future based on behaviors this month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Buy
Likelihood to Buy - Today
The likelihood or probability that an existing customer makes a purchase in the near future based on today’s behaviors. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Convert
Likelihood to Convert - 1 Month Ago
The likelihood or probability that a non-buyer makes a purchase in the near future learning from the past one month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Convert
Likelihood to Convert - 2 Months Ago
The likelihood or probability that a non-buyer makes a purchase in the near future learning from the past two months. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Convert
Likelihood to Convert - This Month
The likelihood or probability that a non-buyer makes a purchase in the near future learning from the past month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Convert
Likelihood to Convert - Today
The likelihood or probability that a non-buyer makes a purchase in the near future learning from today’s behavior. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Engage on Email
Likelihood to Engage on Email - 1 Month Ago
The likelihood or probability that someone opens an email in the near future learning from email events from a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Engage on Email
Likelihood to Engage on Email- 2 Months Ago
The likelihood or probability that someone opens an email in the near future learning from email events from two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Engage on Email
Likelihood to Engage on Email - This Month
The likelihood or probability that someone opens an email in the near future learning from email events from this month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Engage on Email
Likelihood to Engage on Email - Today
The likelihood or probability that someone opens an email in the near future learning from today’s email events. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Pay Full Price
Likelihood to Pay Full Price - 1 Month Ago
The likelihood or probability that someone pays full price in the near future learning from a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Pay Full Price
Likelihood to Pay Full Price- 2 Months Ago
The likelihood or probability that someone pays full price in the near future learning from two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Likelihood to Pay Full Price
Likelihood to Pay Full Price - This Month
The likelihood or probability that someone pays full price in the near future learning from this month. Likelihood to Pay Full Price- 2 Months Ago To set up the model, contact your CVM.
Yes
Likelihood to Pay Full Price
Likelihood to Pay Full Price - Today
The likelihood or probability that someone pays full price in the near future learning from today. Likelihood to Pay Full Price- 2 Months Ago To set up the model, contact your CVM.
Yes
Next Best Action
Next Best Channel
The next best channel that the customer is most likely to engage with.
Yes
Next Best Action
Second Best Channel
The second next best channel that the customer is most likely to engage with.
Yes
Predictive Lifetime Value
Predictive Lifetime Value - Decile - Today
The buckets the predictive lifetime value from a customer into groups.
Yes
Predictive Lifetime Value
Predictive Lifetime Value - Revenue Group - Today
The buckets the predictive lifetime revenue from a customer into groups.
Yes
Predictive Sends
Optimal Email Send Time - Overall
The optimum time when emails can be sent to a customer.
Yes
Predictive Sends
Optimal Email Send Time - Weekday
The optimum time during weekdays when emails can be sent to a customer.
Yes
Predictive Sends
Optimal Email Send Time - Weekend
The optimum time during the weekends when emails can be sent to a customer.
Yes
Product based Cluster
Product based Cluster - 1 Month Ago
The customer personas based on the products or product categories they purchased from a month ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Product based Cluster
Product based Cluster - 2 Months Ago
The customer personas based on the products or product categories they purchased from two months ago. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
Product based Cluster
Product based Cluster - This Month
The customer personas based on the products or product categories they purchased from this month. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your .
Yes
Product based Cluster
Product based Cluster - Today
The customer personas based on the products or product categories they purchased from. You can turn off the ML dimension or the ML dimension may not have data in your environment, if the ML models are not trained. To set up the model, contact your CVM.
Yes
First Item Details
First Item Brand
The brand of the first item.
No
First Item Details
First Item Color
The color of the first item.
No
First Item Details
First Item Size
The size of the first item.
No
First Item Details
First Product ID
The ID of the first product.
No
First Item Details
First Product Name
The name of the first product.
No
First Item Details
Selected Dimension First Item
The first item of the selected dimension. The value of this field is determined by the value set in the Analysis By Dimension filter.
No
Second Item Details
Second Category Name Level 1
The category name of the level 1 second item.
No
Second Item Details
Second Category Name Level 2
The category name of the level 2 second item.
No
Second Item Details
Second Category Name Level 3
The category name of the level 3 second item.
No
Second Item Details
Second Category Name Level 4
The category name of the level 4 second item.
No
Second Item Details
Second Category Name Level 5
The category name of the level 5 second item.
No
Second Item Details
Second Item Brand
The brand of the second item.
No
Second Item Details
Second Item Color
The color of the second item.
No
Second Item Details
Second Item Size
The size of the second item.
No
Second Item Details
Second Product ID
The ID of the second product.
No
Second Item Details
Second Product Name
The name of the second product.
No
Second Item Details
Selected Dimension Second Item
The second item of the selected dimension. The value of this field is determined by the value set in the Analysis By Dimension filter.
No
Physical:Offline stores
Warehouse: Warehousing locations, if any
Other: The channels that do not fall into earlier categories such as call centers.
No
-
Sales Org ID
A unique identifier for sales organization
No
-
Sales Org Name
The name for sales organization
No
-
Sales Org Type
The type of sales organization, an optional level of categorization after the sales channel. For example, Physical sales organizations may have types such as ‘Retail’ or ‘Outlet’ whereas Digital sales organizations may have a type such as ‘eCommerce’. The possible values for your environment depend on data provided during implementation.
No
Days since Customer First Transaction
The number of days between a given transaction and the customer’s first transaction. Note that this field is calculated at the transaction level, and it does not indicate customer tenure as of today. Instead, it indicates customer tenure as of when the transaction was made.
Yes
Days since Customer First Transaction
Days since Customer First Transaction Group
The bucketed version of [Days since Customer First Transaction] for transaction.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 1
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics, or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 2
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 3
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 4
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Last Marketing Touch - Online
Last Marketing Touch - Online 5
The last of all online marketing touches, which are responsible for transactions. This data typically comes from web analytics providers such as Google Analytics, Adobe Analytics or IBM Coremetrics. Represented with the following levels: Source, Medium, Campaign, Adgroup, Keyword.
Yes
Transaction Date
Date
The date of the transaction made by the customer.
No
Transaction Date
Day of Month
The day of the month of the transaction made by the customer.
No
Transaction Date
Day of Week
The day of the week of the transaction made by the customer.
No
Transaction Date
Month
The month when the transaction was made by the customer.
No
Transaction Date
Month Name
The name of the month when the transaction was made by the customer.
No
Transaction Date
Quarter
The quarter when the transaction was made by the customer.
No
Transaction Date
Quarter of Year
The quarter of the year when the transaction was made by the customer.
No
Transaction Date
Week of Year
The week of the year when the transaction was made by the customer.
No
Transaction Date
Year
The year when the transaction was made by the customer.
No
-
Transaction Date - Is Before YTD (Yes / No)
A flag to indicate whether the day of year from [Transaction Date] is before today’s day of year. This flag is typically used for Year over Year (YoY) reporting. You can create a report with [Transaction Date - Month Name] dimension, pivot on [Transaction Date - Year] dimension, and add [Transaction Date - Is Before YTD] as a filter with ‘Yes’ value. Such a report accurately compares measures sliced by [Transaction Date] in a YoY fashion.
No
Transaction Fiscal Date
Transaction Fiscal Month Num
The fiscal month number of the customer’s transaction date.
No
Transaction Fiscal Date
Transaction Fiscal Quarter of Year
The fiscal quarter of the year of the customer’s transaction date.
No
Transaction Fiscal Date
Transaction Fiscal Week of Year
The fiscal week of the year of the customer’s transaction date.
No
Transaction Fiscal Date
Transaction Fiscal Year
The fiscal calendar year of the customer’s transaction date.
No
Transaction Line Date
Date
The date of the transaction made by the customer at the line level.
No
Transaction Line Date
Day of Month
The day of the month of the transaction made by the customer at the line level.
No
Transaction Line Date
Day of Week
The day of the week of the transaction made by the customer at the line level.
No
Transaction Line Date
Month
The month when the transaction was made by the customer at the line level.
No
Transaction Line Date
Month Name
The name of the month when the transaction was made by the customer at the line level.
No
Transaction Line Date
Quarter
The quarter when the transaction was made by the customer at the line level.
No
Transaction Line Date
Quarter of Year
The quarter of the year when the transaction was made by the customer at the line level.
No
Transaction Line Date
Week of Year
The week of the year when the transaction was made by the customer at the line level.
No
Transaction Line Date
Year
The year when the transaction was made by the customer at the line level.
No
-
Transaction Line Date - Is Before YTD (Yes / No)
A flag to indicate whether the day of year from [Transaction Line Date] is before today’s day of year. This flag is typically used for Year over Year (YoY) reporting. You create a report with [Transaction Line Date - Month Name] dimension, pivot on [Transaction Line Date - Year] dimension, and add [Transaction Line Date - Is Before YTD] as a filter with ‘Yes’ value. Such a report accurately compares measures sliced by [Transaction Line Date] in a YoY fashion.
No
Transaction Line Fiscal Date
Transaction Line Fiscal Month Num
The fiscal month number of the customer’s transaction line date.
No
Transaction Line Fiscal Date
Transaction Line Fiscal Quarter of Year
The fiscal quarter of the year of the customer’s transaction line date.
No
Transaction Line Fiscal Date
Transaction Line Fiscal Week of Year
The fiscal week of the year of the customer’s transaction line date.
No
Transaction Line Fiscal Date
Transaction Line Fiscal Year
The fiscal calendar year of the customer’s transaction line date.
No
-
Transaction Line SubType
The subtype of transaction line, possible values are ‘Demand’, ‘Canceled’, ‘Shipped’, and ‘Returned’.
No
-
Transaction Line Type
The type of transaction line, default value is ‘Purchase’. Contact your CDP CVM for descriptions if you have custom transaction linetypes.
No
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