Ready-to-use intelligent attributes
Oracle Unity comes with a set of ready-to-use intelligent attributes. If needed, you can manage these attributes. To view how attribute values are created in JSON from the Intelligent attributes page, click View formula for the attribute.
List of ready-to-use intelligent attributes
Review the set of ready-to-use intelligent attributes.
| Attribute name | Data object | Status | How attribute values are created |
|---|---|---|---|
| Age Group | Customer | Active | Attribute is based on banded values for Age |
| Average Order Value | Order Item | Active | total_spent/total_purchase_count |
| Average Purchased Item Value | Order Item | Active | Attribute is based on banded values for Average Order Value |
| Click Rate | Event | Inactive | total_click_messageID/total_sent_messageID |
| Days Since Last Clicked | Event | Inactive | datediff( current_date, last_click_event) |
| Days Since Last Email Click | Event | Inactive | days_since_last_clicked_event |
| Days Since Last Email Open | Event | Inactive | Attribute is based on banded values for days_since_last_opened_event |
| Days Since Last Opened Event | Event | Inactive | datediff( current_date, last_open_event) |
| Days Since Last Order | Order Item | Active | Attribute is based on banded values for days_since_last_purchase |
| Days Since Last Purchase | Order Item | Active | datediff(current_date , last_purchase_date) |
| Days Since Last Sent Event | Event | Inactive | max(sent_eventDate) |
| Email Click Rate | Event | Inactive | total_email_clicked_messageID/total_email_sent_messageID |
| Email Open Rate | Event | Inactive | total_email_opened_messageID/total_email_sent_messageID |
| First Purchase Date | Order Item | Active |
MIN(purchase_date) Purchase means orders with subtype = 'shipped' |
| Last Bounced Event Date | Event | Inactive | max(bounced_eventDate) |
| Last Clicked Event Date | Event | Inactive | datediff( current_date, last_click_event) |
| Last Email Opened Event Date | Event | Inactive | Attribute is based on banded values for days_since_last_opened_event |
| Last Opened Event Date | Event | Inactive | max(opened_eventDate) |
| Last Purchase Date | Order Item | Active | MAX(purchase_date) |
| Last Push Opened Event Date | Event | Inactive | Attribute is based on banded values for last_push_opened_eventdate |
| Last Sent Event Date | Event | Inactive | max(sent_eventDate) |
| Last SMS Opened Event Date | Event | Inactive | Attribute is based on banded values for last_sms_opened_eventdate |
| Life Time Value | Order Item | Active | Attribute provides banded values |
| Months Since Last Clicked Event | Event | Inactive | months_between( current_date, last_clicked_event) |
| Months Since Last Delivered Event | Event | Inactive | months_between( current_date, last_delivered_event) |
| Months Since Last Opened Event | Event | Inactive | months_between( current_date, last_open_event) |
| Months Since Last Purchase | Order Item | Active | months_between(current_date , last_purchase_date) |
| Most Popular Day | Order Item | Active | dayofweek is derived column in DSV |
| Most Popular Hour | Order Item | Inactive | orderHour |
| Most Popular Month | Order Item | Active | orderMonth |
| Most Popular Response Day | Event | Inactive | Most popular day of open events |
| Most Popular Response Hour | Event | Inactive | Most popular hour of open events |
| Numbers of Orders | Event | Active | total_order_count |
| Open Rate | Event | Inactive | total_opened_messageID/total_sent_messageID |
| Push Click Rate | Event | Inactive | total_push_clicked_messageID/total_push_sent_messageID |
| Push Open Rate | Event | Inactive | total_push_opened_messageID/total_push_sent_messageID |
| SMS Click Rate | Event | Inactive | total_sms_clicked_messageID/total_sms_sent_messageID |
| SMS Open Rate | Event | Inactive | total_sms_opened_messageID/total_sms_sent_messageID |
| Top Product | Order Item | Active | top_product |
| Top Product Category | Order Item | Active | top_product_category |
| Total Clicked Count | Event | Inactive | count(distinct click_messageid) |
| Total Delivered Count | Event | Inactive | count(distinct Delivered_messageid) |
| Total Email Clicks | Event | Inactive | Attribute is based on banded values for total_clicked_count |
| Total Email Opens | Event | Inactive | Attribute is based on banded values for total_opened_count |
| Total Email Sends | Event | Inactive | Attribute is based on banded values for total_delivered_count |
| Total Opened Count | Event | Inactive | count(distinct opened_messageid) |
| Total Order Count | Order Item | Active | count(distinct demand_orderid) |
| Total Purchase Count | Order Item | Active |
count(distinct purchase_orderid) count(distinct orderid) where subtype = 'shipped' |
| Total Return Count | Order Item | Active |
count( distinct return_orderid) count(distinct orderid) where subtype = 'return' OR 'cancel' |
| Total Returns Amount | Order Item | Active |
sum(return_extendedprice) sum(extendedprice) where subtype = 'return' |
| Total Spent Amount | Order Item | Active | sum(Spent_ExtendedPrice) |
Use cases for ready-to-use intelligent attributes
Review the following use cases for using ready-to-use intelligent attributes.
| Use case | Description | Segment criteria | Category | Intelligent attribute |
|---|---|---|---|---|
| Use case one | You want to create a segment of customers that are in a specific age range so that you can target them with age-suitable products. | Customers between the ages of 35 and 44 | Customer profile | Customer age group |
| Use case two | You want to create a segment of customers that are in the market for apparel because you want to run a winter apparel campaign. | The most frequently purchased product category for the customer is apparel | Purchase behavior | Most frequently purchased product category |
| Use case three | You want to create a segment of customers that have ordered a specific amount of products recently because you want to target active customers. | The average order value of the customer over the past 90 days is over $50. | Purchase behavior | Average order value (AOV) with a lookback window of 90 days |
| Use case four | You want to create a segment of high-value customers because you assume you will have a high chance of success with those customers. | The lifetime value of the customer is high | Purchase behavior | Lifetime value (LTV) with bands so that you can select the High band |
| Use case five | You want to create a segment of recent actively engaged customers because you assume you have a high chance of converting them. | The customer's recency score for email click is high | Engagement | Email click recency (an objective score to evaluate how recent a customer opens their email) |