What are the basic concepts of the Quick Audience V4 version? - Quick Audience

This topic describes the basic concepts of the Quick Audience V4 version.

Term	Description
The workspace to which the deployment belongs.	Referred to as Workspace. Quick Audience allows you to create multiple spaces. Data is isolated between spaces, which usually belong to different segments of brands or departments. Users must join a workspace and become a Workspace Member to perform business operations related to the brand or department.
Computing engine	The Computing Source is the database that stores the underlying data tables of the analysis objects and is ID Mapping by the computing source to complete user identification and omni-channel data integration. The same MaxCompute database can be added to different spaces as the computing source.
Analysis source	After ID mapping is completed in the computing source, the raw data is imported to the Analysis Source for subsequent analysis and marketing. You can add a Hologres database, an AnalyticDB for MySQL database, 3.0 a Hologres database, or an AnalyticDB for MySQL database.
Data Sources	A data source is a generic term for Computing Source and Analysis Source.
The user ID.	Referred to as ID. An ID can indicate the identity of a user. The user table to be imported must contain at least one ID field. For more information about the ID field, see Quick Audience Import Data Table Requirements. The ID can be used for marketing, push, and more. User IDs are divided into four categories: User ID: essentially represents a user. You may need to enter a user ID when you register a third-party platform or an enterprise account, such as a mobile phone number or email address. Device ID: the ID of the electronic device. It is usually collected through the application tracking, such as IMEI, IDFA, IMSI, OAID, and MAC address. It is not bound to the user. Enterprise ID: the ID generated by the business system of the enterprise for the user, for example, the membership ID of the CRM of the enterprise. Three-party platform ID: the ID of the user in the three-party platform, such as UnionID, OpenID, Taobao ID, Taobao nickname, Alipay ID, and Weibo ID. After the user ID is added to Quick Audience, a unique identifier (QAID) is generated by the ID Mapping. This way, user identification and data pull can be implemented across source channels and ID types.
QAID	A QAID is a unique ID provided by Quick Audience. The system assigns a unique ID to each user. When data tables are imported from a computing source to Quick Audience, or when Quick Audience receives the reported event data, the system performs ID Mapping, identifies users based on their IDs (identifies the same ID as the same user and performs deduplication), and then assigns a QAID to each user. In subsequent operations such as user analysis, audience filtering, and marketing delivery, the QAID will be used as the unique identifier of the user. If you need to use or push data of other ID types and tags, all ID fields and data of the user are matched based on the QAID.
The tag information.	Tags are classified into the following categories: Imported tags: The User Tags records a series of dimension characteristics of users. When you import Quick Audience to Quick Audience, you can configure tag aliases for fields to label users. When you use tags later, the corresponding fields are essentially used. Custom tags: You can customize tag rules based on imported data. This way, you can tag users who meet the tag rules. You can customize the following tags: Preference Tags: The dimension feature with the largest number of users or the largest number of users is used as the label value based on the data in the User Behavior Table Data Requirements and Order Details Data Requirements. For example, the user's preferred shopping window, price range, channel, theme, and category, and the highest price of a single item purchased by the user. Loyalty Tags: The tag value is based on the data in the User Behavior Table Data Requirements and Details Data Requirements. The tag value is based on the time of the last or first user behavior, the number of days to the current day, or the cumulative number of days. For example, the last purchase /access time or the number of days since the current day, and the cumulative consumption /active days. Purchasing Power Tags: The label value is based on the User Behavior Table Data Requirements and Details Data Requirements. For example, you can enter the following values: Cumulative Purchase Amount /Number of Purchases /Number of Orders, Average /Maximum /Minimum Order Amount in the last year. User Stage Tags: filters users based on the user hierarchy logic that you need. This method is similar to audience Audience Filtering. Users that meet different conditions are filtered and labeled with different hierarchical labels. For example, users whose total purchase amount is greater than or equal to 1000 and who like delicious food are filtered out and labeled with marketing priority 1. Users whose total purchase amount is between 100 and 999 and belong to group A are filtered out and labeled with marketing priority 2. The Tag List also includes User Attributes
The user attribute.	User Attributes specifies the basic information system of a user. The actual value is obtained from the imported user tag table. If the table field is mapped to a user attribute, the value of the user attribute is used. When used later, only the mapped user attributes are displayed, and the original labels are no longer displayed. User attributes and ID Mapping are used together to help you build a basic user information system across source channels and provide the basis for user profiles.
User profile	A user profile is a tagged user model that is abstracted from information such as user attributes, tags, preferences, behavior records, purchase records, and marketing records. On the User 360 page, the information about users who have been identified by ID Mapping is displayed in a centralized manner, and a convenient labeling function is provided to facilitate clue analysis and after-sales follow-up.
RFM model	The RFM Model measures user value by using the following metrics: R consumption interval (Recency), F consumption frequency (Frequency), and M consumption amount (Monetary). Quick Audience provides an RFM model based on Order Details Data Requirements and Order Summary Data Requirements. You can use this model to analyze RFM data and filter people.
AIPL model	The AIPL Model is a means of dividing user-brand-related behavior into intimacy stages to measure user value. Among them: A brand awareness (Awareness), I brand interest (Interest), P brand purchase (Purchase), L brand loyalty (Loyalty). And in different time periods, the user's related behavior is different, the intimacy phase may be transformed. The AIPL model of Quick Audience is created based on the Quick Audience Import Data Table Requirements. It can be used for AIPL user analysis, AIPL flow analysis, and audience filtering.
People	Audience in Quick Audience is a collection of QAIDs of multiple users. Unlike full users, a audience can be generated after being filtered from full users to achieve a specific purpose or to meet a specific condition. audience creation methods include: filtering users who meet specified conditions from imported data tables (that is, audience filtering), uploading user ID lists, using existing groups to calculate the intersection, combination, and difference to generate new groups, and filtering required users from user analysis, text messages, emails, push marketing results, and analysis reports. audiences can be used for insight analysis, marketing sending, and pushing to Data Bank, Dharma Disk, or Kafka.
Push	Send certain data to other channels for storage and use in other channels. Audience Push: You can push data to Brand Data Bank, Damengpan, Alibaba Cloud Kafka, or open-source Kafka. You can also push data to the social interaction, retail CRM, and media delivery modules. AIPL Model Push: The model can be pushed to the brand data bank.
Automated Marketing	Automated Marketing is an intelligent product that is automatically executed by the system after a differentiated marketing strategy is formulated by using a drag-and-drop canvas configuration tool.
Behavioral Event	Event for short. Defines certain actions of users in a specified channel as events. The channel reports real-time event data in a specified format for automated marketing. These include a class of order events that focus on the behavior of users related to the purchase of goods. Event data can be stored in Analysis Source. Common behavior events can be saved as user behavior tables, and order events can be saved as order details tables. These tables can be used to filter people, generate RFM/AIPL models, and customize tags.