Clustering Criteria

The following clustering criteria (which are also called "Cluster by") are the defaults:

Consumer Profile

Cluster stores based on the similarities in the customer profile mix whose members shop in the stores or trading areas. These clusters form the basis for additional analysis that can provide an understanding of which customers shop in which stores and how they shop. Information from market research firms such as the Nielsen Corporation can help retailers develop customer profiles. Such information can be provided via a data interface.

Location Attributes

Cluster stores based on how shopping behavior varies by store attribute. In combination with the profile mix, this provides an understanding of demographic details such as income level, ethnicity, education, household size, and family characteristics. Such knowledge can help the retailer to make assortment and pricing decisions. By analyzing cluster composition and studying business intelligence, the retailer can make informed decisions based on shopper demographics.

Product Attributes

Store share is generated based on product attributes. The store clusters produced can be used in an assortment. In this type of cluster, stores with a similar share of sales for one or more attributes are grouped together. For example, for the product coffee, stores can be differentiated by the sales patterns for premium, standard, and niche brands. The percentage of each store contribution is calculated using Sales Retail $ for each product attribute value to the total sales retail for the category or subcategory in a specified location. Product attributes can only be configured at the category or subcategory level.

Performance Criteria

Cluster stores based on the historical sales metrics by performance at various merchandise levels. Determine how shopping behavior varies by category. This information can be helpful in identifying low, medium, and high-volume stores that all have similar sales patterns.

Mixed Criteria

Mixed criteria combine discrete and continuous attributes together. This allows a retailer to cluster stores using attributes from all the first four listed cluster criteria at the same time.