Create metrics
A metric is a measurement. In SQL and database structures, a metric is like a stored query over a variable period.
In Commerce Intelligence, you can use metrics to create charts. For example, the metric revenue is the total number of orders. The metric average customer revenue per order is what the average customer spends per order.
When used in reports, metrics can be analyzed over a specified time period and filtered or segmented by different categories. Consider analyzing average customer revenue grouped by gender - in this case, average customer revenue per order is the metric and gender is the grouping.
Defining the Metric
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To create a metric, click Data > Metrics.
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Click Create New Metric.
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From the dropdown, click the table that includes the native or calculated column you want to use for your metric.
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Name your metric.
Adobe recommends a name that, at a glance, tells you what the metric is. For example:
Average Order Revenue. -
The next step is to define what your metric does. Using the dropdown menus, define the metric’s operation, the
operationcolumn, and adatedimension:-
Choose an operation:
Count- This operation counts the number of rows in a data tableMax- Max returns the maximum value of a specific data columnMin- Min returns the minimum value of a specific data columnSum- This operation sums the values of a specific data columnAverage- This operation calculates the average of the data column valuesCount Distinct Value- This counts the unique number of values in a specific data columnMedian- This operation calculates the median of the data column valuesFirst and Third Quartiles- These operations calculate the 25th and 75th percentiles of the data column values, respectivelyTenth and Ninetieth Percentiles- These operations calculate the 10th and 90th percentiles of the data column values, respectively
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Choose a column to perform the operation on. For example, if you wanted to find your total revenue, you would perform a sum operation on the
order totalcolumn.If you are editing an existing metric, you can also change the metric’s operational table in this section.
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Choose a date dimension that can be used to trend the metric. For example,
order date.
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Adding Filters
The Filter section allows you to create a filter or apply a saved filter set to your metric.
For the average order revenue metric, you would not want to include any test orders that might have been done while setting up your store - this would give us an inaccurate result. Can apply a filter set to remove those orders from the data set. After the filter is created, it will apply to all charts built using this metric.
The Filter Logic section is where you can further define how a metric should behave.
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A] or [B]” allows any data that satisfy the filters [A] OR [B] - “[
A] and [B]” only allows data that satisfies both filters [A] and [B] - “([
A] and [B]) OR [C]” only allows data that either satisfy both filters [A] and [B], or satisfies filter [C] alone
Adding Dimensions
The Dimensions section shows all available data dimensions for filtering or grouping; by default, all available data columns are listed as dimensions. Continuing the example, if you wanted to segment your revenue by referral source, you can do that here.
In addition to listing all available data columns as dimensions, Commerce Intelligence guesses at which columns are groupable. To segment or group data on reports, columns must be marked as groupable.
Finishing Up
In addition to defining how your metric behaves, you can also set permission levels in the User Rights section. While Admin users have access to all metrics, you have to indicate the users who can use this metric by checking the box next to the appropriate group.
If you are editing an existing metric, you can view a list of charts (and who owns them) that use this metric in the Dependent Charts section.
Changes are saved automatically and your metric is now good to go.