Patent ID: 11900395
Assignee: NCR VOYIX CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A method, comprising:
providing executable instructions to a processor causing the processor to perform operations comprising:
deriving a multidimensional space for items of a product catalogue based on transactions associated with a given enterprise and determining dimensions of the multidimensional space based on a total number of unique item codes detected in the product catalogue;
generating item vectors for each item defining positions of that item within the multidimensional space, and expressing each item vector as a collection of coordinates plotted within the dimensions of the multidimensional space, wherein each item vector plotted within the multidimensional space represents affinities between the corresponding item vector to remaining ones of the item vectors plotted within the multidimensional space;
receiving a segmentation request for a given period of time;
obtaining transaction histories for customers within the given period of time;
for each unique customer:
acquiring historical customer-specific transactions from the transaction histories;
identifying item codes associated with transaction items identified in the historical customer-specific transactions;
assigning specific item vectors associated with the item codes per transaction;
summing the specific item vectors per transaction creating a transaction vector for each transaction for the corresponding customer;
normalizing the transaction vectors as normalized item vectors; and
summing the normalized item vectors associated with all the historical customer-specific transactions from the transaction histories and producing an aggregate consumer-item vector for the corresponding customer, and plotting the aggregate consumer-item vector within the multidimensional space, wherein each aggregate consumer-item vector represents the corresponding customer's transaction vectors, each transaction vector for the corresponding customer represents given items purchased by the corresponding customer in a given transaction;
processing a clustering algorithm against the aggregate consumer-item vectors based on plotted aggregated consumer-item vectors within the multidimensional space;
receiving clusters of the customers as output from the clustering algorithm based on calculated distances within the multidimensional space between each aggregate consumer-item vector; and
providing the clusters as customer segments to a promotion engine or a loyalty system by identifying, determining and creating the customer segments based on data derived relationships from each customer's corresponding transaction history in response to segmentation requests requesting target customers in the customer segments for delivering customer promotions and times for delivering the customer promotions to each of the target customers identified in each of the customer segments, wherein providing further includes providing each customer's corresponding aggregate consumer-item vector as a numerical and mathematical context for evaluating the corresponding customer's transaction history; and
processing the method as a Software as a Service to the promotion engine or the loyalty system from a cloud and through an Application Programming Interface and providing the customer segments based on dynamic data-driven analysis as data-driven segmentation for the customers defined in the customer segments and not in predefined rules managed by the promotion engine or the loyalty system, wherein no particular segment is predefined and wherein no rules are processed to assign a particular customer to a particular customer segment, and wherein the customer segments change dynamically as changes are updated to the transaction histories for each customer.