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SVP is the process of modeling projected sales of a product at one or more accounts within an upcoming period of time. Companies establish sales volume plans to forecast or plan the quantity of products they will sell to these accounts during the SVP period. The SVP Aggregate process models this complex account-category-product-period hierarchy and allows for the allocation and aggregation of data through this hierarchy.
The SVP Aggregate action uses the account-category links created by an administrator to construct the account-category-product hierarchy, and associates the imported source data with the appropriate SVP nodes. SVP nodes create records that represent the combination of accounts and category-products.
The SVP Aggregate action is an ongoing SVP task. Each time the action is run after the initial Aggregate action, a search is performed down the established account-category-product-period hierarchy structure and any new or missing nodes are created. The data is then reaggregated up through the account-category-product-period hierarchy.
The execution of the SVP Aggregate action is a resource-intensive task, as it is dependent upon the complexities of your account-category-product-period hierarchy, as well as your source data set. The appropriate planning of this process is a key component to using the SVP module.
NOTE: The SVP Aggregate action does not aggregate across period types, so data changes made to data stored in weeks will not aggregate up into the monthly periods, nor will changes made at the annual level allocate down throughout the periods that make up the year. Data may be aggregated, however, through the use of Siebel Analytics for reporting purposes. It is recommended that data be stored in the standard periods of either weeks or months.
The Aggregate action is the most resource-intensive action in the SVP module. To maximize performance, you should run Aggregate for select period ranges only. Also, you should keep the intervals small and run Aggregate only as frequently as dictated by your business requirements. For example, if you are storing volume data by week, it is preferable to run the aggregation at the end of each week, and then only for the week just completed.
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