Bookshelf v7.5.3: Custom Measures

Custom Measures are derived metrics that are built by using formulas and calculations based on other measures. They may include logical functions such as IsPositive, IsZero, and Not, and mathematical functions such as Absolute, Random, and Sign. Custom measures can also be aggregated to various targeting levels of a customer hierarchy using aggregate functions such as Sum, Count, Average, Minimum, or Maximum.

The Custom measure detail view includes the Custom measure form at top, and the measure-builder panes, which contains Available Measures, and Expression Builder elements. In addition, you can use the optional custom measure Aggregation view and Restriction view to refine the data set to which the custom expression applies.

Custom measures can be aggregated to each targeting level within the customer hierarchy. If you select a customer hierarchy and targeting level for a custom measure, Siebel Marketing assumes that you intend to aggregate the measure, and requires an aggregation function before you can save the custom measure.

When you specify the level, you are indicating that data from the level below should be aggregated up to the level selected. For example, if a customer has multiple bank accounts, calculating a Total Balance (sum) across accounts would be a custom measure for the targeting level.

The choice of aggregation levels determines what measures are available to use in the measure you are building. If you select a secondary targeting level as your aggregation level, you can only work with measures at that level and below. Measures aggregated at higher levels are not available.

Restrictions imposed using the custom measure Restriction view are flagged with a check mark in the Restrictions check box after you save the restriction. Measure restrictions are useful if you want to see list measure output that is computed at a certain hierarchical level, a specific bucket definition, and so on without restricting the counts of a targeted segment or the entire stage. For example, you might want to know the average dollars that were spent in the last quarter of 2000 but see this data for customers that spent money in the four quarters of 2000.

Table 34 provides examples of some uncomplicated custom measures and expressions.

Table 34. Custom Measure Expressions
Custom Measure Name	Base Measure Type	Targeting Level	Aggregation Function	Expression
Average Opening Account Balance	Bound: Opening Account Balance	Customers	Avg	Opening Account Balance / 12
Maximum Opening Account Balance	Bound: Opening Account Balance	Customers	Max	Opening Account Balance
Minimum Opening Account Balance	Bound: Opening Account Balance	Customers	Min	Opening Account Balance
Number of ATM Transactions	Bound: Transaction Amount	Account	Count	Transaction Value
Number of ATM Transactions by Customer	Custom: Number of ATM Transactions	Customers	Sum	Number of ATM Transactions
Number of Orders Per Month	Bound: Annual Orders	Account	Sum	Annual Orders/12