Information for: DEVELOPERS   PARTNERS   SUPPORT

List of Standard Dimensions

The primary groups are:

Attribution

Sub-Group Dimension Name Description Pre-calculated field?
- Campaign ID | Name Campaign ID and name No
Campaign Level Names Level 1 Name Name of the level 1 campaign. No
Campaign Level Names Level 2 Name Name of the level 2 campaign. No
Campaign Level Names Level 3 Name Name of the level 3 campaign. No
Campaign Level Names Level 4 Name Name of the level 4 campaign. No
Campaign Level Names Level 5 Name Name of the level 5 campaign. No
- Campaign Name Name of the campaign. No
Campaign Start Date Date Date when the campaign started. No
Campaign Start Date Day of Month Day of the month when the campaign started. No
Campaign Start Date Day of Week Day of the week when the campaign started. No
Campaign Start Date Hour of Day Hour of the day when the campaign started. No
Campaign Start Date Month Month when the campaign started. No
Campaign Start Date Month Name Name of the month when the campaign started. No
Campaign Start Date Quarter Quarter when the campaign started. No
Campaign Start Date Quarter of Year Quarter of the year when the campaign started. No
Campaign Start Date Time of Day Time of the day when the campaign started. No
Campaign Start Date Week of Year Week of the year when the campaign started. No
Campaign Start Date Year Year when the campaign started. No

Customer

Sub-Group Dimension Name Description Pre-calculated field?
Address Customer City City from customer’s primary address. No
Address Customer Country Country from customer’s primary address. No
Address Customer RBDI Residential / 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). No
Address Customer State State from customer’s primary address. No
Address Customer Zip Code Zip Code from customer’s primary address. No
Age Age Age of customer. No
Age Age Group Bucketed version of [Age] for customer. No
- Buyer Identification Indicates whether customer is an identified or unidentified buyer. All transactions that have a blank / 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 Average order value for customer calculated using [Product Revenue] / [Transaction Count]. Yes
Buying Stats Average Annual Transactions Annual transaction frequency for customer calculated using [Transaction Count] * 365 / [Days since First Transaction]. Yes
Buying Stats Average Discount Rate Average discount rate for customer to identify promotion driven customers. Yes
Buying Stats First Transaction Revenue Revenue from customer’s first transaction. Note that ranges are inclusive at the higher bound. Example: ‘0-25’ includes up to 25, ‘25-50’ would start at 25.01. Yes
Buying Stats Last Transaction Interval Number of months between the last and the second to last transaction of customer. Yes
Buying Stats Revenue - 13-24 Months Total revenue for customer’s purchases in the previous 13-24 month duration. Note that ranges are inclusive at the higher bound. Example: ‘0-100’ includes up to 100, ‘100-200’ would start at 100.01. Yes
Buying Stats Revenue - Last 12 Months Total revenue for customer’s purchases in the last 12 month duration. Note that ranges are inclusive at the higher bound. Example: ‘0-100’ includes up to 100, ‘100-200’ would start at 100.01. Yes
Buying Stats Revenue - Lifetime Total revenue for all purchases in customer’s lifetime. Note that ranges are inclusive at the higher bound. Example: ‘0-100’ includes up to 100, ‘100-200’ would start at 100.01. Yes
Buying Stats Revenue Decile - 13-24 Months Revenue decile for customer based on total revenue for all purchases in the previous 13-24 month duration. ‘1’ means top 10% based on revenue, ‘10’ means bottom 10%, ‘0’ contains all customers that have not purchased in the given time period. Yes
Buying Stats Revenue Decile - Last 12 Months Revenue decile for customer based on total revenue for all purchases in the last 12 month duration. ‘1’ means top 10% based on revenue, ‘10’ means bottom 10%, ‘0’ contains all customers that have not purchased in the given time period. Yes
Buying Stats Revenue Segment - 13-24 Months Revenue segment for customer based on total revenue for all purchases in the previous 13-24 month duration. ‘1 - High Value’ means top 10% by revenue, ‘2 - Medium Value’ means top 20 to 80%, ‘3 - Low Value’ means bottom 20%, ‘Non Buyer’ contains all customers that have not purchased in the given time period. Yes
Buying Stats Revenue Segment - Last 12 Months Revenue segment for customer based on total revenue for all purchases in the last 12 month duration. ‘1 - High Value’ means top 10% by revenue, ‘2 - Medium Value’ means top 20 to 80%, ‘3 - Low Value’ means bottom 20%, ‘Non Buyer’ contains all customers that have not purchased in the given time period. Yes
Buying Stats Revenue Trend Segment Revenue trend segment for customer derived by comparing revenue decile in the previous 13-24 months vs the last 12 months. It characterizes whether they spent less (‘Downward’), the same (‘Stable’), or more (‘Upward’) in the previous 13-24 months period vs the last 12 months. If a customer was acquired in the last 12 months, then they get marked as ‘New’ instead. ‘Reactivated’ customers are buyers who made a purchase in the last 12 months, did not make a purchase in the previous 13-24 months, but did make a purchase before 24 months ago. ‘2 Period Inactive’ customers are buyers who did not make a purchase in the last 24 months but made a purchase before 24 months ago. ‘Lapsed’ customers are buyers 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 Total number of transactions made by customer in the previous 13-24 month duration. Yes
Buying Stats Transaction Count - Last 12 Months Total number of transactions made by customer in the last 12 month duration. Yes
Buying Stats Transaction Count - Lifetime Total number of transactions made in customer’s lifetime. Yes
- Category Count Number of distinct product categories that customer purchased from in their lifetime. Yes
- Customer Gender Gender of customer as determined by CDP’s genderization algorithm. Values could be Female, Male, Neutral and Unknown. Neutral refers to names that could be either male or female. Unknown is displayed if the gender can’t be determined. No
- Customer Status Buying status of customer. Possible values include Buyer, Non Buyer, Unidentified. Yes
Days since First Transaction Days since First Transaction Number of days since the customer’s first transaction i.e. the tenure of customer in days. Yes
Days since First Transaction Days since First Transaction Group Bucketed version of [Days since First Transaction] represented in months. Yes
Days since Last Transaction Days since Last Transaction Number of days since the customer’s last transaction i.e. the recency of customer in days. Yes
Days since Last Transaction Days since Last Transaction Group Bucketed version of [Days since Last Transaction] represented in months. Yes
Email Last Click Date Date Date when customer last clicked one of your email campaigns. Yes
Email Last Click Date Day of Month Day of the month when customer last clicked one of your email campaigns. Yes
Email Last Click Date Day of Week Day of the week when customer last clicked one of your email campaigns. Yes
Email Last Click Date Month Month when customer last clicked one of your email campaigns Yes
Email Last Click Date Month Name Name of the month when customer last clicked one of your email campaigns. Yes
Email Last Click Date Quarter Quarter when customer last clicked one of your email campaigns. Yes
Email Last Click Date Quarter of Year Quarter of the year when customer last clicked one of your email campaigns. Yes
Email Last Click Date Week of Year Week of the year when customer last clicked one of your email campaigns. Yes
Email Last Click Date Year Year when customer last clicked one of your email campaigns. Yes
Email Last Open Date Date Date when customer last opened one of your email campaigns. Yes
Email Last Open Date Day of Month Day of the month when customer last opened one of your email campaigns. Yes
Email Last Open Date Day of Week Day of the week when customer last opened one of your email campaigns. Yes
Email Last Open Date Month Month when customer last opened one of your email campaigns Yes
Email Last Open Date Month Name Name of the month when customer last opened one of your email campaigns. Yes
Email Last Open Date Quarter Quarter when customer last opened one of your email campaigns. Yes
Email Last Open Date Quarter of Year Quarter of the year when customer last opened one of your email campaigns. Yes
Email Last Open Date Week of Year Week of the year when customer last opened one of your email campaigns. Yes
Email Last Open Date Year Year when customer last opened one of your email campaigns. Yes
Email Last Send Date Date Date when customer was last sent an email. Yes
Email Last Send Date Day of Month Day of the month when customer was last sent an email. Yes
Email Last Send Date Day of Week Day of the week when customer was last sent an email. Yes
Email Last Send Date Month Month when customer was last sent an email. Yes
Email Last Send Date Month Name Name of the month when customer was last sent an email. Yes
Email Last Send Date Quarter Quarter when customer was last sent an email. Yes
Email Last Send Date Quarter of Year Quarter of the year when customer was last sent an email. Yes
Email Last Send Date Week of Year Week of the year when customer was last sent an email. Yes
Email Last Send Date Year Year when customer was last sent an email. Yes
First Transaction (Digital) Date Date Date of the first transaction made by customer in the digital format. Yes
First Transaction (Digital) Date Day of Month Day of the month of the first transaction made by customer in the digital format. Yes
First Transaction (Digital) Date Day of Week Day of the week of the first transaction made by customer in the digital format. Yes
First Transaction (Digital) Date Month Month when the first transaction was made by customer in the digital format. Yes
First Transaction (Digital) Date Month Name Name of the month when the first transaction was made by customer in the digital format. Yes
First Transaction (Digital) Date Quarter Quarter when the first transaction was made by customer in the digital format. Yes
First Transaction (Digital) Date Quarter of Year Quarter of the year when the first transaction was made by customer in the digital format. Yes
First Transaction (Digital) Date Week of Year Week of the year when the first transaction was made by customer in the digital format. Yes
First Transaction (Digital) Date Year Year when the first transaction was made by customer in the digital format. Yes
First Transaction (Physical) Date Date Date of the first transaction made by customer in the physical format. Yes
First Transaction (Physical) Date Day of Month Day of the month of the first transaction made by customer in the physical format. Yes
First Transaction (Physical) Date Day of Week Day of the week of the first transaction made by customer in the physical format. Yes
First Transaction (Physical) Date Month Month when the first transaction was made by customer in the physical format. Yes
First Transaction (Physical) Date Month Name Name of the month when the first transaction was made by customer in the physical format. Yes
First Transaction (Physical) Date Quarter Quarter when the first transaction was made by customer in the physical format. Yes
First Transaction (Physical) Date Quarter of Year Quarter of the year when the first transaction was made by customer in the physical format. Yes
First Transaction (Physical) Date Week of Year Week of the year when the first transaction was made by customer in the physical format. Yes
First Transaction (Physical) Date Year Year when the first transaction was made by customer in the physical format. Yes
First Transaction Date Date Date of the first transaction made by customer. Yes
First Transaction Date Day of Month Day of the month of the first transaction made by customer. Yes
First Transaction Date Day of Week Day of the week of the first transaction made by customer. Yes
First Transaction Date Month Month when the first transaction was made by customer. Yes
First Transaction Date Month Name Name of the month when the first transaction was made by customer. Yes
First Transaction Date Quarter Quarter when the first transaction was made by customer. Yes
First Transaction Date Quarter of Year Quarter of the year when the first transaction was made by customer. Yes
First Transaction Date Week of Year Week of the year when the first transaction was made by customer. Yes
First Transaction Date Year Year when the first transaction was made by customer. Yes
- First Transaction Date - Is Before YTD (Yes / No) 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 could 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 would accurately compare measures sliced by [First Transaction Date] in a YoY fashion. Yes
First Transaction Fiscal Date First Transaction Fiscal Month Num Fiscal month number of customer’s first transaction date. Yes
First Transaction Fiscal Date First Transaction Fiscal Quarter of Year Fiscal quarter of the year of customer’s first transaction date. Yes
First Transaction Fiscal Date First Transaction Fiscal Week of Year Fiscal week of the year of customer’s first transaction date. Yes
First Transaction Fiscal Date First Transaction Fiscal Year Fiscal year of customer’s first transaction date. Yes
First Transaction Last Marketing Touch - Online First Transaction Last Marketing Touch - Online 1 Last of all online marketing touches responsible for 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 Last of all online marketing touches responsible for 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 Last of all online marketing touches responsible for 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 Last of all online marketing touches responsible for 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 Last of all online marketing touches responsible for 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 Product categories for products bought in customer’s first transaction. Yes
First Transaction Product Category First Transaction Product Category - Level 2 Product categories for products bought in customer’s first transaction. Yes
First Transaction Product Category First Transaction Product Category - Level 3 Product categories for products bought in customer’s first transaction. Yes
First Transaction Product Category First Transaction Product Category - Level 4 Product categories for products bought in customer’s first transaction. Yes
First Transaction Product Category First Transaction Product Category - Level 5 Product categories for products bought in customer’s first transaction. Yes
Last Transaction (Digital) Date Date Date of the last transaction made by customer in the digital format. Yes
Last Transaction (Digital) Date Day of Month Day of the month of the last transaction made by customer in the digital format. Yes
Last Transaction (Digital) Date Day of Week Day of the week of the last transaction made by customer in the digital format. Yes
Last Transaction (Digital) Date Month Month when the last transaction was made by customer in the digital format. Yes
Last Transaction (Digital) Date Month Name Name of the month when the last transaction was made by customer in the digital format. Yes
Last Transaction (Digital) Date Quarter Quarter when the last transaction was made by customer in the digital format. Yes
Last Transaction (Digital) Date Quarter of Year Quarter of the year when the last transaction was made by customer in the digital format. Yes
Last Transaction (Digital) Date Week of Year Week of the year when the last transaction was made by customer in the digital format. Yes
Last Transaction (Digital) Date Year Year when the last transaction was made by customer in the digital format. Yes
Last Transaction (Physical) Date Date Date of the last transaction made by customer in the physical format. Yes
Last Transaction (Physical) Date Day of Month Day of the month of the last transaction made by customer in the physical format. Yes
Last Transaction (Physical) Date Day of Week Day of the week of the last transaction made by customer in the physical format. Yes
Last Transaction (Physical) Date Month Month when the last transaction was made by customer in the physical format. Yes
Last Transaction (Physical) Date Month Name Name of the month when the last transaction was made by customer in the physical format. Yes
Last Transaction (Physical) Date Quarter Quarter when the last transaction was made by customer in the physical format. Yes
Last Transaction (Physical) Date Quarter of Year Quarter of the year when the last transaction was made by customer in the physical format. Yes
Last Transaction (Physical) Date Week of Year Week of the year when the last transaction was made by customer in the physical format. Yes
Last Transaction (Physical) Date Year Year when the last transaction was made by customer in the physical format. Yes
Last Transaction Date Date Date of the last transaction made by customer. Yes
Last Transaction Date Day of Month Day of the month of the last transaction made by customer. Yes
Last Transaction Date Day of Week Day of the week of the last transaction made by customer. Yes
Last Transaction Date Month Month when the last transaction was made by customer. Yes
Last Transaction Date Month Name Name of the month when the last transaction was made by customer. Yes
Last Transaction Date Quarter Quarter when the last transaction was made by customer. Yes
Last Transaction Date Quarter of Year Quarter of the year when the last transaction was made by customer. Yes
Last Transaction Date Week of Year Week of the year when the last transaction was made by customer. Yes
Last Transaction Date Year Year when the last transaction was made by customer. Yes
- Last Transaction Date - Is Before YTD (Yes / No) Flag to indicate whether the day of year from customer’s [Last Transaction Date] is before today’s day of year. This flag is typically used for Year over Year (YoY) reporting. You could 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 would accurately compare measures sliced by [Last Transaction Date] in a YoY fashion. Yes
Last Transaction Fiscal Date Last Transaction Fiscal Month Num Fiscal month number of customer’s last transaction date. Yes
Last Transaction Fiscal Date Last Transaction Fiscal Quarter of Year Fiscal quarter of the year of customer’s last transaction date. Yes
Last Transaction Fiscal Date Last Transaction Fiscal Week of Year Fiscal week of the year of customer’s last transaction date. Yes
First Transaction Fiscal Date First Transaction Fiscal Year Fiscal year of customer’s last transaction date. Yes
Last Transaction Product Category Last Transaction Product Category - Level 1 Product categories for products bought in customer’s last transaction. Yes
Last Transaction Product Category Last Transaction Product Category - Level 2 Product categories for products bought in customer’s last transaction. Yes
Last Transaction Product Category Last Transaction Product Category - Level 3 Product categories for products bought in customer’s last transaction. Yes
Last Transaction Product Category Last Transaction Product Category - Level 4 Product categories for products bought in customer’s last transaction. Yes
Last Transaction Product Category Last Transaction Product Category - Level 5 Product categories for products bought in customer’s last transaction. Yes
Marketing Status & Preferences Address Certified 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 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 Flag to indicate whether the customer has opted-in / opted-out of your Call campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in. No
Marketing Status & Preferences Do Not Email Flag to indicate whether the customer has opted-in / opted-out of your Email campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in. No
Marketing Status & Preferences Do Not Mail Flag to indicate whether the customer has opted-in / opted-out of your Direct Mail campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in. No
Marketing Status & Preferences Do Not Text Flag to indicate whether the customer has opted-in / opted-out of your SMS / Text campaigns. ‘Y’ means customer has opted-out whereas ‘N’ means customer has opted-in. No
Marketing Status & Preferences Email Domain Domain of customer’s email address. No
Marketing Status & Preferences Email Status Flag to indicate the validity of customer’s email address. ‘V’ means ‘Verified’ i.e. the email address syntax is valid, the domain is good / known and the email address is not a known spam trap; ‘U’ means ‘Unverified’ i.e. the email address syntax is valid but the domain is unknown / bad; ‘X’ means ‘Invalid’ i.e. the email address syntax is invalid. No
- Primary Brand Product brand that customer made most transactions in during their lifetime. Yes
- Product Count Number of distinct products purchased by customer during their lifetime. Yes
Sales Organizations Closest Store Closest store for customer. Yes
Sales Organizations Distance to Closest Store Distance, in miles, between customer and the store closest to where they live. This dimension works only for US, Canada and UK addresses (for both stores and customers). Yes
Sales Organizations First Transaction Sales Channel Sales channel of customer’s first transaction. ‘Digital’ includes any online stores, ‘Physical’ includes any offline stores, ‘Warehouse’ typically refers to warehousing locations, if any, and ‘Other’ refers to channels that don’t fall into earlier categories which could include call centers. Yes
Sales Organizations Last Transaction Sales Channel Sales channel of customer’s last transaction. ‘Digital’ includes any online stores, ‘Physical’ includes any offline stores, ‘Warehouse’ typically refers to warehousing locations, if any, and ‘Other’ refers to channels that don’t fall into earlier categories which could include call centers. Yes
Sales Organizations Primary Physical Store Physical Store that customer made most transactions in during their lifetime Yes
Sales Organizations Primary Sales Channel Sales Channel that customer made most transactions in during their lifetime. ‘Digital’ includes any online stores, ‘Physical’ includes any offline stores, ‘Warehouse’ typically refers to warehousing locations, if any, and ‘Other’ refers to channels that don’t fall into earlier categories which could include call centers. Yes
Sales Organizations Sales Channel Count Number of sales channels customer has purchased from in their lifetime. Yes
Second to Last Transaction Date Date Date of the second to last transaction made by customer. Yes
Second to Last Transaction Date Day of Month Day of month of the second to last transaction made by customer. Yes
Second to Last Transaction Date Day of Week Day of week of the second to last transaction made by customer. Yes
Second to Last Transaction Date Month Month of the second to last transaction made by customer. Yes
Second to Last Transaction Date Month Name Name of the month of the second to last transaction made by customer. Yes
Second to Last Transaction Date Quarter Quarter of the second to last transaction made by customer. Yes
Second to Last Transaction Date Quarter of Year Quarter of the year of the second to last transaction made by customer. Yes
Second to Last Transaction Date Week of Year Week of the year of the second to last transaction made by customer. Yes
Second to Last Transaction Date Year Year of the second to last transaction made by customer. Yes
Web First Visit Date Date Date when customer first visited your website. Yes
Web First Visit Date Day of Month Day of the month when customer first visited your website. Yes
Web First Visit Date Day of Week Day of the week when customer first visited your website. Yes
Web First Visit Date Month Month when customer first visited your website. Yes
Web First Visit Date Month Name Name of the month when customer first visited your website. Yes
Web First Visit Date Quarter Quarter when customer first visited your website. Yes
Web First Visit Date Quarter of Year Quarter of the year when customer first visited your website. Yes
Web First Visit Date Week of Year Week of the year when customer first visited your website. Yes
Web First Visit Date Year Year when customer first visited your website. Yes
Web Last Visit Date Date Date when customer last visited your website. Yes
Web Last Visit Date Day of Month Day of the month when customer last visited your website. Yes
Web Last Visit Date Day of Week Day of the week when customer last visited your website. Yes
Web Last Visit Date Month Month when customer last visited your website. Yes
Web Last Visit Date Month Name Name of the month when customer last visited your website. Yes
Web Last Visit Date Quarter Quarter when customer last visited your website. Yes
Web Last Visit Date Quarter of Year Quarter of the year when customer last visited your website. Yes
Web Last Visit Date Week of Year Week of the year when customer last visited your website. Yes
Web Last Visit Date Year Year when customer last visited your website. Yes

Machine Learning

Sub-Group Dimension Name Description Pre-calculated field?
Behavior Based Cluster Behavior Based Cluster - 1 Month Ago Cluster of customers personas based on their purchase behavior, preferences, and spending patterns a month ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Behavior Based Cluster Behavior Based Cluster - 2 Months Ago Cluster of customers personas based on their purchase behavior, preferences, and spending patterns two months ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Behavior Based Cluster Behavior Based Cluster - This Month Cluster of customers personas based on their purchase behavior, preferences, and spending patterns this month. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Behavior Based Cluster Behavior Based Cluster - Today Cluster of customers personas based on their purchase behavior, preferences, and spending patterns today. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Fuzzy Clustering Most Likely Cluster Probability 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 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 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 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 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 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 Likelihood or probability that an existing customer makes a purchase in the near future based on behaviors a month ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Buy Likelihood to Buy - 2 Months Ago Likelihood or probability that an existing customer makes a purchase in the near future based on behaviors two months ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Buy Likelihood to Buy - This Month Likelihood or probability that an existing customer makes a purchase in the near future based on behaviors this month. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Buy Likelihood to Buy - Today Likelihood or probability that an existing customer makes a purchase in the near future based on today’s behaviors. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Convert Likelihood to Convert - 1 Month Ago Likelihood or probability that a non-buyer makes a purchase in the near future learning from past one month. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Convert Likelihood to Convert - 2 Months Ago Likelihood or probability that a non-buyer makes a purchase in the near future learning from past two months. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Convert Likelihood to Convert - This Month Likelihood or probability that a non-buyer makes a purchase in the near future learning from past month. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Convert Likelihood to Convert - Today Likelihood or probability that a non-buyer makes a purchase in the near future learning from today’s behavior. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Engage on Email Likelihood to Engage on Email - 1 Month Ago Likelihood or probability that someone opens an email in the near future learning from email events from a month ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Engage on Email Likelihood to Engage on Email- 2 Months Ago Likelihood or probability that someone opens an email in the near future learning from email events from two months ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Engage on Email Likelihood to Engage on Email - This Month Likelihood or probability that someone opens an email in the near future learning from email events from this month. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Engage on Email Likelihood to Engage on Email - Today Likelihood or probability that someone opens an email in the near future learning from today’s email events. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Pay Full Price Likelihood to Pay Full Price - 1 Month Ago Likelihood or probability that someone pays full price in the near future learning from a month ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Pay Full Price Likelihood to Pay Full Price- 2 Months Ago Likelihood or probability that someone pays full price in the near future learning from two months ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Pay Full Price Likelihood to Pay Full Price - This Month Likelihood or probability that someone pays full price in the near future learning from this month. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Likelihood to Pay Full Price Likelihood to Pay Full Price - Today Likelihood or probability that someone pays full price in the near future learning from today. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Next Best Action Next Best Channel Next best channel that the customer is most likely to engage with. Yes
Next Best Action Second Best Channel Second next best channel that the customer is most likely to engage with. Yes
Predictive Lifetime Value Predictive Lifetime Value - Decile - Today Buckets the predictive lifetime value from a customer into groups. Yes
Predictive Lifetime Value Predictive Lifetime Value - Revenue Group - Today Buckets the predictive lifetime revenue from a customer into groups. Yes
Predictive Sends Optimal Email Send Time - Overall Optimum time when emails can be sent to a customer. Yes
Predictive Sends Optimal Email Send Time - Weekday Optimum time during weekdays when emails can be sent to a customer. Yes
Predictive Sends Optimal Email Send Time - Weekend Optimum time during the weekends when emails can be sent to a customer. Yes
Product based Cluster Product based Cluster - 1 Month Ago Customer personas based on the products or product categories they purchased from a month ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Product based Cluster Product based Cluster - 2 Months Ago Customer personas based on the products or product categories they purchased from two months ago. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Product based Cluster Product based Cluster - This Month Customer personas based on the products or product categories they purchased from this month. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes
Product based Cluster Product based Cluster - Today Customer personas based on the products or product categories they purchased from. Note that ML dimensions may be turned off or may not have data in your environment if the models have not been trained yet. To set up the model, contact your CSM. Yes

Market Basket Analysis

Sub-Group Dimension Name Description Pre-calculated field?
First Item Details First Category Name Level 1 Category name of the level 1 first item. No
First Item Details First Category Name Level 2 Category name of the level 2 first item. No
First Item Details First Category Name Level 3 Category name of the level 3 first item. No
First Item Details First Category Name Level 4 Category name of the level 4 first item. No
First Item Details First Category Name Level 5 Category name of the level 5 first item. No
First Item Details First Item Brand Brand of the first item. No
First Item Details First Item Color Color of the first item. No
First Item Details First Item Size Size of the first item. No
First Item Details First Product ID ID of the first product. No
First Item Details First Product Name Name of the first product. No
First Item Details Selected Dimension First Item 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 Category name of the level 1 second item. No
Second Item Details Second Category Name Level 2 Category name of the level 2 second item. No
Second Item Details Second Category Name Level 3 Category name of the level 3 second item. No
Second Item Details Second Category Name Level 4 Category name of the level 4 second item. No
Second Item Details Second Category Name Level 5 Category name of the level 5 second item. No
Second Item Details Second Item Brand Brand of the second item. No
Second Item Details Second Item Color Color of the second item. No
Second Item Details Second Item Size Size of the second item. No
Second Item Details Second Product ID ID of the second product. No
Second Item Details Second Product Name Name of the second product. No
Second Item Details Selected Dimension Second Item Second item of the selected dimension. The value of this field is determined by the value set in the Analysis By Dimension filter. No

Product

Dimension Name Description Pre-calculated field?
Product Brand Brand name for product No
Product ID 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 City for sales organization. No
Geography Sales Org Country Country for sales organization. No
Geography Sales Org State State for sales organization. No
Geography Sales Org Zip Code Zip Code for sales organization. No
- Sales Channel Channel for sales organization. ‘Digital’ includes any online stores, ‘Physical’ includes any offline stores, ‘Warehouse’ typically refers to warehousing locations, if any, and ‘Other’ refers to channels that don’t fall into earlier categories which could include call centers. No
- Sales Org ID Unique identifier for sales organization No
- Sales Org Name Name for sales organization No
- Sales Org Type Type of sales organization, an optional level of categorization below 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

Transaction

Sub-Group Dimension Name Description Pre-calculated field?
Customer Transaction Sequence Customer Transaction Sequence 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, etc. 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 will 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 Flag to indicate if a customer was a New customer vs 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 Bucketed version of [Customer Transaction Sequence] for transaction Yes
Days since Customer First Transaction Days since Customer First Transaction 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 Bucketed version of [Days since Customer First Transaction] for transaction Yes
Last Marketing Touch - Online Last Marketing Touch - Online 1 Last of all online marketing touches responsible for 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
Last Marketing Touch - Online Last Marketing Touch - Online 2 Last of all online marketing touches responsible for 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
Last Marketing Touch - Online Last Marketing Touch - Online 3 Last of all online marketing touches responsible for 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
Last Marketing Touch - Online Last Marketing Touch - Online 4 Last of all online marketing touches responsible for 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
Last Marketing Touch - Online Last Marketing Touch - Online 5 Last of all online marketing touches responsible for 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
Transaction Date Date Date of the transaction made by customer. No
Transaction Date Day of Month Day of the month of the transaction made by customer. No
Transaction Date Day of Week Day of the week of the transaction made by customer. No
Transaction Date Month Month when the transaction was made by customer. No
Transaction Date Month Name Name of the month when the transaction was made by customer. No
Transaction Date Quarter Quarter when the transaction was made by customer. No
Transaction Date Quarter of Year Quarter of the year when the transaction was made by customer. No
Transaction Date Week of Year Week of the year when the transaction was made by customer. No
Transaction Date Year Year when the transaction was made by customer. No
- Transaction Date - Is Before YTD (Yes / No) 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 could 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 would accurately compare measures sliced by [Transaction Date] in a YoY fashion. No
Transaction Fiscal Date Transaction Fiscal Month Num Fiscal month number of customer’s transaction date. No
Transaction Fiscal Date Transaction Fiscal Quarter of Year Fiscal quarter of the year of customer’s transaction date. No
Transaction Fiscal Date Transaction Fiscal Week of Year Fiscal week of the year of customer’s transaction date. No
Transaction Fiscal Date Transaction Fiscal Year Fiscal calendar year of customer’s transaction date. No
Transaction Line Date Date Date of the transaction made by customer at the line level. No
Transaction Line Date Day of Month Day of the month of the transaction made by customer at the line level. No
Transaction Line Date Day of Week Day of the week of the transaction made by customer at the line level. No
Transaction Line Date Month Month when the transaction was made by customer at the line level. No
Transaction Line Date Month Name Name of the month when the transaction was made by customer at the line level. No
Transaction Line Date Quarter Quarter when the transaction was made by customer at the line level. No
Transaction Line Date Quarter of Year Quarter of the year when the transaction was made by customer at the line level. No
Transaction Line Date Week of Year Week of the year when the transaction was made by customer at the line level. No
Transaction Line Date Year Year when the transaction was made by customer at the line level. No
- Transaction Line Date - Is Before YTD (Yes / No) 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 could 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 would accurately compare measures sliced by [Transaction Line Date] in a YoY fashion. No
Transaction Line Fiscal Date Transaction Line Fiscal Month Num Fiscal month number of customer’s transaction line date. No
Transaction Line Fiscal Date Transaction Line Fiscal Quarter of Year Fiscal quarter of the year of customer’s transaction line date. No
Transaction Line Fiscal Date Transaction Line Fiscal Week of Year Fiscal week of the year of customer’s transaction line date. No
Transaction Line Fiscal Date Transaction Line Fiscal Year Fiscal calendar year of customer’s transaction line date. No
- Transaction Line SubType Subtype of transaction line, possible values are ‘Demand’, ‘Canceled’, ‘Shipped’, ‘Returned’. No
- Transaction Line Type Type of transaction line, default value is ‘Purchase’. Please reach out to your CDP CSM for descriptions if you have custom transaction line types. No