Category Variability
By analyzing store variability, you can determine if it is worth creating store clusters for the selected categories in the selected location. Three sections are displayed.
A grid is displayed for the selected categories and the sales contribution for a selected location.
Table 4-11 Category Variability
| Property | Description |
|---|---|
|
Categories |
A list of the selected categories that are used for store variability analysis. |
|
Variability |
The relative standard deviation of the stores in the category. A larger value for the standard deviation indicates greater store variability for the category. Such a category is a possible candidate for store clustering. |
|
Index to average |
For a selected location, an indication of how the store performs compared to the all store base. A value close to 1 indicates that the selected location is similar to the all store base. If the value is lower or higher, it indicates that the sales averages for the stores in the selected location are different from the all store base and that you should consider creating store clusters for the selected location. |
|
Average store retail |
Average store retail $ for the category for the selected location. |
|
Average store unit |
Average store units for the category for the selected location. |
|
Positive/negative index to average |
The difference in value for the index to average for the all store base to selected location. For example, a value of 1-index to average < 1 or a value of index to average -1 > 1. |
A graph is displayed for the index to average. This shows how the selected location performs compared to the all store base if the average sales metric is below, above, or the same when compared to the all store base average. A red color indicates a value below the all store base average. A blue color indicates a value above the all store base average.
A graph for standard deviation is displayed. This shows the standard deviation for the selected category. If the store value is greater than two standard deviations, then store clustering should be considered for the selected merchandise because the stores sales variability is sufficient.
