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A target level is the entity that a marketer wants to count. Target levels are usually customer types such as individuals, businesses, or households. However, in special circumstances a target level might also represent other entities such as bank accounts, opportunities, or assets.
To support counting, the metadata definition for a target level specifies a column in the database table that uniquely identifies the target such as Customer-ID, Account-ID, or Household-ID. Target levels can be combined in a segment. For example, a segment might be created that counts the number of contacts who live in households that satisfy a certain criteria.
A segmentation catalog is an Oracle BI subject area (presentation catalog) that is enabled for segmentation. The segmentation catalog provides a set of dimensions and fact measures that can be used to create segment criteria. The Marketing module user interface combines the counts across segmentation catalogs using the KEEP/ADD/EXCLUDE operators. A segmentation catalog must contain mappings from only one physical star or snowflake schema.
Complex segmentation criteria evaluated against a large database can take significant time to execute. Initially, marketing users might be satisfied with an approximate count so that they can execute counts more quickly, and then adjust the criteria until they obtain more precise counts.
To facilitate quick counts, the Marketing Server can execute segmentation criteria against a sampled subset of the target-level data. Sampling works on the principle that if the segmentation criteria are applied to the sampled subset of customers, and then subsequently to each of the stars accessed by the segment criteria, the final count is a good approximation of the actual (100 percent) counts and executes more quickly.
Sampling is defined by creating a subset table for a target-level dimension table that contains the same set of columns, but only a percentage of the data. For every sampling factor a database table needs to be created. Each sampling definition includes a percentage value to indicate the sampling factor. Every target level can have many sampling factors (and corresponding sampled dimension tables) to provide multiple levels of sampling.
When you enable sampling, the Marketing Server continues to generate the same logical SQL. However, the BI Server generates physical SQL that queries against the configured sample tables using dynamic table names. For the dimension table (such as the Contact dimension table) that contains all target-level IDs, a dynamic table name is associated with the target-level table. If the value of the dynamic table name session variable is set to the sampled table, then all queries executed in that session that include the customer dimension table are executed against the sampled table. The session variable is automatically set to the appropriate sampling table, depending on the sampling factor chosen in the user interface for all counting tasks.
Make sure the sampled table contains a true random sample of the customers. The choice of the randomization method is determined by the business users and administrators. The technique chosen here dramatically affects the degree of accuracy of the sampled counts.
Conforming dimensions can be used when a star might include a dimension with the target-level ID. A conforming dimension links a fact that contains target-level IDs to a fact that does not contain target-level IDs by navigating along a dimension that is shared by both fact tables.
For example, a bank might track service requests at the bank-account level only and the Service Request star does not include the customer dimension. To be able to count the number of contacts who have filed a certain number of service requests, a conforming dimension is required. In this case, the conforming dimension is the Bank Account dimension, because it is a dimension shared by both the Service Request star and another star containing the Bank Account dimension, such as the Customer Profile star. To evaluate this count, the Marketing Server determines the bank accounts that satisfy the service request criteria, and then finds all customers who join to those bank accounts using a subquery. For more information, see Setting Up Conforming Dimension Links.
Use List catalogs to create vendor files for campaign fulfillment or files for loading a campaign with appropriate targets. An Oracle BI Subject Area is a list catalog in the Presentation layer of the Oracle BI Administration Tool that is enabled for list format design (list generation). The list catalog provides a set of columns that can be used to generate the content in a list file or used to filter the results of the list file. Because not all presentation catalogs are appropriate for use as a list catalog, enabling a list catalog requires the following configuration:
A qualified list item (QLI) is an entity that is evaluated against segment criteria so that the information related to that entity can be exported in a list file. A QLI can be of type Primary or Secondary. A primary qualified list item is the presentation column that maps to the dimension key that is being counted for a target level such as Contact-ID for the contact target level. A secondary qualified list item is primarily created for list exports. Use a QLI to restrict the list based on the logic used in the segmentation criteria.
For example, you might have a segment containing all customers who have a vehicle lease expiring in less than two months. You plan to create a list for this segment and Vehicle ID is one of the list columns. If you do not create a secondary QLI, the list contains vehicles that the customers in the segment own and it does not matter if the lease expires in less than two months. If you create a secondary QLI on the Vehicle-ID, the list contains only vehicles with leases expiring in less than two months (qualified) from the segment.
For more information, see Setting Up Marketing Qualified List Items.
Segmentation criteria blocks that count target-level identifiers can be used frequently. For example, an email marketer can always exclude contacts with no email address or those that have explicitly refused to receive emails. Instead of evaluating this set of contacts repeatedly in every segment, the marketer might create a single criteria block using this criterion. Caching such a criteria block saves the list of target-level identifiers in a table. When you reuse this criterion across segments that you create, the cache is used and time-consuming database query operations are minimized, improving throughput.
The set of tables that contain the cache information, the mappings of those tables in the Administration Tool, and assigning cache table schema to specific target levels, constitute the cache related metadata.
The resulting set of target-level identifiers of complex segmentation criteria can be saved permanently (until explicitly deleted). The saved result set can be used in other segments but more importantly it can be used to keep track of which targets joined and left a segment. This kind of an analysis helps marketers understand the dynamic behavior of the customer base. The target-level identifiers are stored in a table. The set of tables that contain the saved result set information, the mappings of those tables in the Administration Tool and the assigning of saved result set schema to specific target levels, all constitute the related metadata.
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