Using Normalized Reports

Normalization Sets

To use the normalization features in the Emplifi Agent reports, you first need a set of data to use in performing the normalization calculations.

In the Normalized Reporting Overview, an example was given that compared the number of complaints for a product to the number of actual units sold. Emplifi Agent has access to all the complaint information and numbers. What it does not normally have access to is the number of units sold. This information needs to be obtained from somewhere else (typically from an external POS, ERP, or accounting system) and imported into the Emplifi Agent database before you can run a normalized report.

Creating Normalization Sets

Normalized data is imported into what is called a Normalization Set. Before data can be imported, you must first define the normalization set in Application Setup. For information, see Normalization Set Maintenance.

Importing Normalization Data

Normalization data is imported using a utility called the Normalization Data Import.

Time Periods

When you import or create normalization sets, you have the option of including dates or time periods with your data.

If you use time periods, you should be aware that normalized reporting only works with custom time frames. The reason for this is that you need a discreet value to assign to the data, which is not possible with standard time frames.

Using time periods is not a necessary requirement to use normalized reporting, and in some cases, it may make the task more difficult. You can use normalized reporting without time periods; for example, compare last month's complaints by store based on average monthly sales volume (as opposed to comparing against last month's actual sales volume, which might be difficult data to obtain).

Normalized Frequency Report

Once you have imported the data necessary for comparison, you can use the normalization features built in to the Frequency Report and Chart Report. Using these features allows you to see both the standard frequency counts and the normalized data counts. Comparing the counts allows you to more accurately identify true trouble spots in your data.