be done within each context once the purchase pattern profiles are created 920 which represent expected customer spend for a customer within the group across a set of products or product groups the profile benchmark statistics 925 are calculated by using the customer product spend proportion for the core customers the affinity relationship among product that provides the probability of products being purchased together for the product groups 930 is calculated using a technique such as affinity analysis and is used in determining the purchase pattern profile for non core customers non core customers are customers who do not fit the profile of an ideal customer as defined by the user 935 the purchase pattern profiles for non core customers 930 are determined based on the customer product spend proportion normalized for product affinity rather than actual customer product spend proportion of the non core customers once the purchase pattern profile is calculated then the cluster product group benchmark statistics along with the customer cluster information is output 940 fig1 is a flow diagram representing an embodiment of the purchase pattern profile process 1000 the core customers 1010 using their purchase profile are clustered to create purchase pattern profiles that are representative of the different customer groups based on their purchase patterns various clustering techniques such as affinity propagation centroid models hierarchical clustering and graph search algorithms are employed to create the purchase pattern profiles the affinity relationship between the product groups 1015 being analyzed is calculated using known methods like market basket analysis the purchase profile of the.