--- title: "Audience rules" date: "2024-07-19T08:47:33+00:00" summary: "Discover audience rules and attributes to refine your customer segments. Learn about customer data, preferences, purchase history, email activity, web behavior, and campaign engagement to create targeted marketing campaigns." image: type: "page" url: "/customer-data-platform/audience-rules" id: "a984d731-f9ae-44af-85a8-a2da21596daf" --- The following page lists the audience rules and their attributes: Customer attributes ------------------- Customer attributes are filters directly associated with your customer’s name, address, age, birth month, and gender. Customer Data Platform (CDP) receives and integrates data from source systems, and then cleanses and populates it as customer attributes to enhance audience refinement. The following table lists the customer attributes: **Name** **Description** **Address Type - Residential or Business** Specifies whether the address is a business address or a residential address. Parcel delivery to residential addresses is more expensive than to business addresses. You can use the return value of this function to filter out the address, or determine the best carrier for the delivery. This is applicable to the United States only. The following are the values: * `R`: Residential address * `B`: Business address * `U`: No address or address unknown by the USPS **Address Certified** Specifies whether the address is certified by the CDP Data Quality Engine. It provides transparency by showing how CDP uses the USPS Coding Accuracy Support System (CASS) to certify addresses. This certification reinforces the accuracy of any addresses processed by the filter. The field adds more reliability of physical addresses as compared to the default addresses. The following are the values: * `True`: The address is CASS certified. * `False`: The address is not CASS certified and is likely invalid. * `Unknown`: There is either no address or the address is not in the US or Canada. CDP only supports these countries. **DPV Confirmed** Specifies whether the address is Delivery Point Validation-confirmed. CDP gets the DPV confirmation for US addresses from USPS. The following are the values: * `Y`: The address is DPV-confirmed. * `N`: The address is not DPV-confirmed. **Age** Specifies the age of the customers. CDP receives this data from the standard customer feed. **Behavior-based cluster** Specifies the behavior-based cluster signifies the cluster to which the customers belong. This is a part of the machine learning models based on past behavior. The default value is `Unknown`. The following are the values: * `Discount shoppers` * `Core buyers` * `High-value customers` **Birth month** Specifies the birth month of the customer. CDP receives this data from the standard customer feed in numeric values. The following are the values: * `1` * `2` * `3` * `4` * `5` * `6` * `7` * `8` * `9` * `10` * `11` * `12` * `Unidentified`: Invalid data * `Unknown`: Missing data **Birth year** Specifies the birth year of the customer. CDP receives this data from the standard customer feed. For example, `1988`, `2005`, and `1975`. **City** Specifies the city where the customer lives. CDP receives this data from the standard customer feed. The default value is `Unknown`. For example, `Huntsville`, `Omaha`, and `St Simons Is`. **Closest store** Specifies the closest store to your customer. The default value is `Unknown`. For example, `Inkanta Hacienda Santa Barbara`, `Hakata Hankyu`, `Maison Mode Chengdu`, and `DFS`. **Country** Specifies the country where the customers live. CDP receives this data from the standard customer feed. The default value is `Unknown`. For example, the `USA`, `Canada`, `France`, and others. **Distance to closest store** Specifies the distance, in miles, between the customer and the store closest to where they live. This filter is only available for US, Canada and UK addresses. For example, `23.5`, `40.3`, and `5`. **Distance to closest store - Group** This is the same as Distance to closest store, grouped into ranges for ease of use. The default value is `Unknown`. This filter is only available for US, Canada and UK addresses. For example, `20-30`, `30-40`, and `50+`. **Distance to a store** Specifies the distance to a store for a customer living within \[N\] miles of store \[ABC\]. For example, select all customers with distance to store \[San Francisco store\] is less than \[20\] miles. You can use this information to send them an email about an event going on in a specific store. The following are the limitations: * CDP supports a distance of up to 40 miles. Most retailers do not consider distances more than 40 miles because customers avoid traveling long distances. * CDP computes the distance to the 20 closest stores for each customer, so if there are more than 20 stores within a 40 miles radius around a given customer, CDP only computes the distance to the closest 20 stores. * This filter is only available for US, Canada and UK addresses. For example, a `Minneapolis store` and `15`. **Email domain** Specifies the domain of the customer’s email address. The default value is `Unknown`. For example: * `gmail.com` * `aol.com` * `yahoo.com` **Email status** Specifies the validity of the email address of your customer. The following are the values: * `V`: Verified email address. The email syntax is valid, the domain is good or known, and this email address is not a known spam trap. * `U`: Unverified email address. The email syntax is valid but the domain is bad or unknown. * `X`: Invalid email address. The syntax is invalid. **Gender** Specifies the gender of the customer. CDP receives this data from the standard customer feed or [Data Hygiene](/customer-data-platform/data-quality-resolution/validation-and-standardization). The default value is `Unknown`. The following are the values: * `Female` * `Male` * `Unknown` **Customer Status** Specifies whether a customer is a buyer or non-buyer. Customers who have ever made a purchase are considered buyers. The following are the values: * `Buyers` * `NonBuyer` **State** Specifies the state in which the customer lives. The default value is `Unknown`. For example, `MI`, `AL`, and `GA`. **Zip Code** Specifies the zip code in which the customer lives. The default value is `Unknown`. For example, `17921`, `03773`, and `75001`. **NCOA - Last Check Date** Specifies the date on which the record was last processed through National Change of Address (NCOA). **NCOA - Move Type** Specifies the type of move that is registered with the USPS NCOA database. The following are the values: * `B`: Business (matched by company name) * `F`: Family Match (matched by last name) * `I`: Individual (matched by first and last name)  * `Unidentified`: Unidentified mastercustomer (represented by mastercustomer ID of -1) * `Unknown`: Missing data **NCOA - Move Date** Specifies the date at which the move is registered with the USPS NCOA database. The format is `YYYYMM`. For example, `201401` is a move that was registered in January 2014. The actual move can happen earlier or later; the NCOA database cannot provide that information. **NCOA - Match flag** Specifies the NCOA processing returns this match flag. The following are the values: * `M`: Moved * `G`: PO Box closed * `K`: Moved and forwarding address is unavailable * `F`: Moved to a foreign country * `Unidentified`: Unidentified mastercustomer (represented by mastercustomer ID of -1) * `Unknown`: Missing information Customer preferences -------------------- The following table lists the customer preferences that you use to refine your audience based on email campaigns opt-out, primary brand, direct mail, and others. **Name** **Description** **Call Opt-out** You can select the customers that are opted-in or opted-out of your call campaigns. The following are the values: * `Y`: Customers explicitly opted-out * `N`: Customers explicitly opted-in * `Unknown`: When no explicit value of call opt-in or opt-out is available, CDP displays the `NULL` value as `Unknown`. **Direct Mail Opt-out** You can select the customers that are opt-in or opt-out of your direct mail campaigns. The following are the values: * `Y`: Customers explicitly opted-out * `N`: Customers explicitly opted-in * `Unknown`: When no explicit value of direct mail opt-in or opt-out is available, CDP displays the `NULL` value as `Unknown`. **Email Opt-out** You can select the customers that are opted-in or opted-out of your email campaigns. The following are the values: * `Y`: Customers explicitly opted-out * `N`: Customers explicitly opted-in * `Unknown`: When no explicit value of email opt-in or opt-out is available, CDP displays the `NULL` value as `Unknown`. **Primary brand** The brand that the customers have purchased the most in their entire lifetime. The default value is `Unknown`. For example, The North Face, Patagonia, and Dakine. **Primary device type** The device that the customers have used the most for purchases in their entire lifetime. The default value is `Unknown`. For example, `Computer`, `Mobile`, and `Unidentified`. **Primary Physical Store** The store through which the customers have made the most of the purchases in their entire lifetime. The default value is `Unknown`. For example, Store#1000, Store#1001, and Store#1002. **Primary Physical Store ID** The unique identifier of the physical store at which the customers have made most of the purchases in their entire lifetime. The default value is `Unknown`. For example, 1000, 1001, and 1002. **Primary Sales Channel** The organization through which the customers have made most of the purchases in their entire lifetime. The default value is `Unknown`. The following are the values: * `Physical` * `Unidentified`: Invalid data * `Unknown`: Missing data **Text Opt-out** You can select the customers that are opted-in or opted-out of your text or SMS campaigns. The following are the values: * `Y`: Customers explicitly opted-out * `N`: Customers explicitly opted-in * `Unknown`: When no explicit value of direct mail opt-in or opt-out is available, CDP displays the `NULL` value as `Unknown`. Email activity -------------- The following table lists the email activity that you use to refine your audience based on last email opened or clicked, email sent count in the last 30 days, and others. **Name** **Description** **Email Last Click Date** The date when the customers last clicked an email sent from a campaign. **Email Last Open Date** The date when the customers last opened one of the email campaigns. **Email Last Send Date** The date of the last email that you sent to the customers. **Email Click Count - 31-60 Days** The number of clicks on email campaign links between the last 31 and 60 days. **Email Click Count - 31-60 Days - Group** The number of clicks on email campaign links between the last 31 and 60 days, grouped into buckets for easier use. For example, 1, 2, 10, or 10+. **Email Click Count - Last 30 Days** The number of clicks on email campaign links in the last 30 days. **Email Click Count - Last 30 Days - Group** The number of clicks on email campaign links in the last 30 days, grouped into buckets for easier use. For example, 1, 2, 10, or 10+. **Email Open Count - Last 30 Days** The number of emails opened by the customers in the last 30 days. **Email Open Count - 31-60 Days** The number of emails opened by the customers between the last 31 and 60 days. **Email Open Count - Last 30 Days - Group** The number of emails opened by the customers in the last 30 days, grouped into buckets for easier use. **Email Open Count - 31-60 Days - Group** The number of emails opened by the customers between the last 31 and 60 days, grouped into buckets for easier use. **Email Sent Count - 31-60 Days** The number of emails sent to the customers in the last 31 and 60 days. **Email Sent Count - 31-60 Days - Group** The number of emails sent to the customers between the last 31 and 60 days, grouped into buckets for easier use. **Email Sent Count - Last 30 Days** The number of emails sent to the customers in the last 30 days. **Email Sent Count - Last 30 Days - Group** The number of emails sent to the customers in the last 30 days, grouped into buckets for easier use. **EU Extensions who performed the email event** The number of email events the customers have performed. Event activity -------------- The following table lists the event activity that you use to refine your audience based on email click, sms click, push click, browsed, app open, and others: **Name** **Description** **Performed a number of events** Customers who executed a specific interaction for a specific number of times in a time frame. The following are the values: * `emailBounce` * `emailClicked` * `emailOpen` * `emailSend` * `emailUnsubscribe` * `login` * `productBrowsed` * `categoryBrowsed` * `cartUpdated` * `checkout` * `onsiteSearch` * `logout` * `cartUpdated` **Performed an event** Customers who executed a specific interaction that is not a transaction in a specified time frame. For example, opened or clicked an email, browsed any product, the customers were sent any direct mail piece, or any other custom interaction that is not a transaction in a specified time frame. The following are the values: * `emailBounce` * `emailClicked` * `emailOpen` * `emailSend` * `emailUnsubscribe` * `login` * `productBrowsed` * `categoryBrowsed` * `cartUpdated` * `checkout` * `onsiteSearch` * `logout` * `cartUpdated` **Performed an event with a marketing campaign** Customers who interacted with a specific marketing campaign. You must select an event for the marketing campaign rule. Then you must add the refinement from the **Campaigns** refinement section. For example, the customers were sent a direct mail piece and opened or clicked an email. **Performed an event with a marketing message** Customers who interacted with a specific marketing message. You must select an event for the marketing message rule. Then you must add the refinement from the **Message** refinement section. This filter includes a subset of features of the **Performed an event with a marketing campaign** filter. For example, the customers were sent a direct mail piece, and opened or clicked an email. **Performed an event with a product** Customers who interacted with a specific product. For example, browsed a product on your website. **Performed an event with a product category** Customers who interacted with a specific product category. For example, browsed the Women’s products page on your website. This is not equivalent to interacting with a product within a product category. For example, browsed a Women’s product. Purchase activity ----------------- The following table lists the purchase activity that you use to refine your audience based on transaction placed, product purchased, Average Order Value (AOV), revenue decile, and others: **Name** **Description** **Placed a transaction** Customers who made a transaction in a specific time frame. You can add [refinements](/customer-data-platform/actions/campaigns/adding-content#refinements) to this rule. For example, made a transaction after 08/01/23 where Average Order Value (AOV) is lower than $50. **Placed a number of transactions** Same as **Placed a transaction**, you can select the number of transactions that the customer made in the selected time frame. You can add refinements to this rule. For example, made more than five transactions in the last 30 days. **Places first transaction within refinements** Customers who made their first transaction within specific conditions. For example, first retail transaction, first Men’s transaction, first purchase within brand X. The following steps explains how this rule works: 1. CDP evaluates the refinements. 2. CDP ranks the transactions in sequence. 3. CDP applies the input time frame to determine whether the first transaction within the specified conditions occurred within the time frame. **Places last transaction within refinements** Customers who made their last transaction within specific conditions. For example, last retail transaction, last women’s transaction, last purchase within brand X. The following steps explains how this rule works: 1. CDP evaluates the refinements. 2. CDP ranks the transactions in sequence. 3. CDP applies the input time frame to determine whether the last transaction within the specified conditions occurred within the time frame. **Purchased a product** Customers who purchased a product in a specific time frame. You can add refinements to this rule. For example, a customer purchased a product before 08/01/23 where ‘Organization Name’ equal to “San Francisco Store” and ‘Transaction Item Revenue’ is greater than $100. **Purchase a number of products** Same as **Purchased a product**, you can select the number of products that the customer purchased in the selected time frame. You can add refinements to this rule. For example, a customer purchased five products in the last 30 days. **Spent a certain amount in a time frame** Selects customers based on the amount of money they spent within a specific time frame. You can add refinements to this rule. For example, a customer spent more than $300 in the last 30 days where a subtype is Shipped or Returned. **Spent a certain amount in a transaction** Selects customers based on the amount of money they spent within a single transaction in a specific time frame. You can add refinements to this rule. For example, a customer spent more than $500 in a single transaction in the last 30 days where Organization Type is Physical. **Purchased a product in an organization (hierarchy)** Same as **Purchased a product**, you can add refinement by the organization’s hierarchy. This is for brands whose stores are organized within a hierarchy. **Purchased a product using a message** Customers who placed a transaction using a specific message in a specified time frame. **Purchased a product using a message in an organization (hierarchy)** Same as **Purchased a product using a message**, you can add refinement by the organization's hierarchy. This is for brands whose stores are organized within a hierarchy. **Purchased a product using a promotion** Customers who purchased a product using a promotion, such as a coupon, in a specified time frame. You can add a **Promotion** refinement such as Discount Amount, Expiration Date, or Discount Percentage. This rule filters audiences based on promotion attributes. With this rule, you can track the effectiveness of promotions and measure the revenue they generate. Additionally, you can clearly distinguish between email and direct mail data within CDP. Buying statistics ----------------- The following table lists the buying statistics that you use to refine your audience based on the transactions, revenue, product category, sales channel and other criteria: **Name** **Description** **Average Annual Transactions** The number of transactions that the customer made per year on average in their lifetime. If the customer is a non-buyer or a one-time buyer, Customer Data Platform (CDP) assigns the default value as _\-99_. **Average Annual Transactions - Group** The number of transactions that the customer made per year on average in their lifetime, grouped into ranges for easier use. **Average Discount Rate** The average rate of discount that the customer used in their lifetime. The following are the values: * `0%` * `0-5%` * `5-10%` * `>30%` * `Non Buyer` **Average Order Value - Group** The average value of the transactions that the customer made in their lifetime. The following are the values: * `25-50` * `200-300` * `300-400` * `Non Buyer` **Channel Mix - 13-24 Months** The mix of digital and physical sales channels from which the customer purchased between the last 13 and 24 months. The following are the values: * `Unidentified`: Invalid data * `Other Channel Buyer` * `Non Buyer` **Channel Mix - Last 12 Months** The mix of sales channels from which the customer purchased in the last 12 months. The following are the values: * `Unidentified`: Invalid data * `Other Channel Buyer` * `Non Buyer` **Channel Mix - Lifetime** The mix of sales channels from which the customer purchased in their lifetime. The following are the values: * `Unidentified`: Invalid data * `Other Channel Buyer` * `Non Buyer` **Discount - Lifetime** The total amount of the discount that the customer used in their lifetime. **First Transaction Date** The date of the first transaction of the customer. **First Transaction Date - Digital** The date of the first transaction of the customer in the digital sales channel. **First Transaction Date - Physical** The date of the first transaction of the customer in the physical sales channel. **First Transaction Revenue** The revenue generated from the first transaction of the customer. **First Transaction Revenue Group** The revenue generated from the first transaction of the customer, grouped into ranges for easier use. The following are the values: * `50-75` * `75-100` * `100-150` * `200-300` **First Transaction Sales Channel** The transaction channel from which the customer made the first purchase. The default value is `Unknown`. **Last Transaction Date** The date of the most recent transaction of the customer. **Discount - Lifetime** The total amount of the discount that the customer used in their lifetime. **Last Transaction Date - Digital** The date of the last transaction of the customer in the digital sales channel. **Last Transaction Date - Physical** The date of the last transaction of the customer in the physical sales channel. **Last Transaction Interval Group** The number of days between the last and penultimate (second to last) transactions that the customer made, grouped into ranges for easier use. The following are the values: * `One Time Buyer` * `3-6 Months` * `7-12 Months` * `Non Buyer` **Last Transaction Sales Channel** The transaction channel from which the customer made the last purchase. The default value is `Unknown`. **Months Since First Transaction** The number of months since the first transaction of the customer. **Months Since First Transaction - Group** The number of months since the first transaction of the customer, grouped into ranges for easier use. **Months Since Last Transaction** The number of months since the last transaction of the customer. **Months Since Last Transaction - Group** The number of months since the last transaction of the customer, grouped into ranges for easier use. **Product Category Count** The total number of distinct product categories that the customer purchased in their lifetime. **Product Category Count - Group** The total number of distinct product categories that the customer purchased in their lifetime, grouped into ranges for easier use. **Product Count** The total number of distinct products that the customer purchased in their lifetime. **Product Count - Group** The total number of distinct products that the customer purchased in their lifetime, grouped into ranges for easier use. **Revenue - 13-24 Months** The total revenue generated from the transactions that the customer made between the last 13 and 24 months. **Revenue - 13-24 Months - Group** The total revenue generated from the transactions that the customer made between the last 13 and 24 months, grouped in ranges for easier use. The following are the values: * `0` * `0-100` * `100-200` * `Unidentified`: Invalid data **Revenue - Last 12 Months** The total revenue generated from the transactions that the customer made in the last 12 months. **Revenue - Last 12 Months - Group** The total revenue generated from the transactions that the customer made in the last 12 months, grouped into ranges for easier use. The following are the values: * `0` * `0-100` * `100-200` * `200-300` * `300-400` * `400-500` * `500-600` * `500-600` * `600-700` * `700-800` * `800-900` * `900-1000` * `1000-1500` * `1500-2000` * `2000-3000` * `3000+` * `NA`: High volume buyer with more than 10000 transactions * `Unidentified`: Invalid data **Revenue - Lifetime** The total revenue generated from the transactions that the customer made in the lifetime. **Revenue - Lifetime - Group** The total revenue generated from the transactions that the customer made in their lifetime, grouped into ranges for easier use. **Revenue Decile - 13-24 Months** The total revenue generated from the transactions that the customer made between the last 13 and 24 months, grouped by deciles. Decile 1 are the highest spenders and decile 10 are the lowest spenders. Deciles represent each of ten equal groups into which customers are divided according to their purchasing behavior. **Revenue Decile - Last 12 Months** The total revenue generated from the transactions that the customer made in the last 12 months, grouped by deciles. Decile 1 are the highest spenders and decile 10 are the lowest spenders. **Revenue Segment - 13-24 Months** This is an efficient way to use ‘Revenue decile’. The following are the values: * `1- High Value`: The top 10% of customers in terms of revenue in a specific time frame * `2- Medium Value`: The 80% average customers that are not in the top or bottom 10% in terms of revenue in a specific time frame * `3- Low Value`: The bottom 10% of customers in terms of revenue in a specific time frame * `Unidentified`: Invalid data * `Non Buyer` **Revenue Segment - Last 12 Month** This is an easy way to use ‘Revenue decile’. The following are the values: * `1- High Value`: The top 10% of customers in terms of revenue in a specific time frame * `2- Medium Value`: The 80% average customers that are not in the top or bottom 10% in terms of revenue in a specific time frame * `3- Low Value`: The bottom 10% of customers in terms of revenue in a specific time frame * `Unidentified`: Invalid data * `Non Buyer` **Revenue Trend Segment** A segmentation representing the progression of revenue for the customers between 13-24 months ago and the last 12 months. It characterizes whether they spent less (Downward), the same (Stable), or more (Upward) in the last 12 months than in the last 13-24 months. The following are the values: * `New Buyer`: Customers who became buyers in the last 12 months * `Reactivated`: Customers who 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 who are buyers who did not make a purchase in the last 24 months, but made a purchase before 24 months ago * `Lapsed`: Customers who are buyers who made a purchase in the last 13-24 months, but did not make a purchase in the last 12 months **Sales Channel Count** The total number of organizations or transaction channels from which the customer purchased in their lifetime. **Second to Last Transaction Date** The date of the penultimate (second to last) customer transaction. **Transaction Count - 13-24 Months** The total number of transactions that the customer made between the last 13 and 24 months. **Transaction Count - 13-24 Months - Group** The total number of transactions that the customer made between the last 13 and 24 months, grouped into ranges for easier use. **Transaction Count - Last 12 Months** The total number of transactions that the customer made in the last 12 months. **Transaction Count - Last 12 Months - Group** The total number of transactions that the customer made in the last 12 months, grouped into ranges for easier use. **Transaction Count - Lifetime** The total number of transactions that the customer made in their lifetime. **Transaction Count - Lifetime - Group** The total number of transactions that the customer made in their lifetime, grouped into ranges for easier use. **Transaction Count with Discount** The total number of transactions that the customer made in their lifetime, with a discount amount greater than $0. Coupon activity --------------- The following table lists the coupon activity that you can use to refine your audience when creating a campaign: **Name** **Description** **Received a coupon** Customers who received a coupon in a promotion. Campaign activity ----------------- The following table lists the campaign activities that you can use to configure the audience of a campaign: **Name** **Description** **Were included in a CDP Campaign+ audience** Customers who were previously included in a Customer Data Platform (CDP) campaign audience. This filter references the data stored in `AudienceHistory`. **Were included in a CDP legacy Campaign** Customers who were previously included in a [CDP legacy campaign](/resource-archive/campaigns). This filter references the data stored in `CampaignHistory`. **Were included in a CDP legacy Campaign and variant** Customers who were previously included in a CDP legacy campaign and variant. This filter references the data stored in `CampaignHistory`. Web activity ------------ The following table lists the web activity that you use to refine your audience based on browsing a product, date of first web visit, date of last web visit, web visit count in the last 30 days, and others. **Name** **Description** **Browsed a product** Customers who have viewed any product on your website. **Abandoned a cart** Customers who have added one or more products to their cart, after their last transaction, and are not active on the web within the last 30 minutes. **Browsed a product with name** Customers who have viewed a particular product on your website. **Days Since First Web Visit - Group** The number of days since your customer first visited your website, grouped into ranges for easier use. **Days Since Last Web Visit - Group** The number of days since your customer last visited your website, grouped into ranges for easier use. **Web First Visit Date** The date when the customer first visited your website. **Web Last Visit Date** The date when the customer last visited your website. **Web Visit Count - 31-60 Days** The number of web page visits between the last 31 and 60 days. **Web Visit Count - 31-60 Days - Group** The number of web page visits between the last 31 and 60 days, grouped into ranges for easier use. **Web Visit Count - Last 30 Days** The number of web page visits in the last 30 days. **Web Visit Count - Last 30 Days - Group** The number of web page visits in the last 30 days, grouped into ranges for easier use.