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System and method for segmenting customers with mixed attribute types using target clustering method

A technology of target attribute and cluster analysis, applied in the direction of transmission system, marketing, data processing application, etc., can solve heavy and unexpected problems

Active Publication Date: 2022-02-11
ORACLE INT CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

This process is often onerous and undesirable for business users

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  • System and method for segmenting customers with mixed attribute types using target clustering method
  • System and method for segmenting customers with mixed attribute types using target clustering method
  • System and method for segmenting customers with mixed attribute types using target clustering method

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Embodiment Construction

[0021] Computerized systems, methods, and other embodiments are disclosed that convert both categorical and numerical attribute types into numerical attributes of the same scale using a specified target attribute (eg, sales). Embodiments implement any clustering algorithm (eg, K-means) compatible with numerical data to efficiently identify clusters. Target attributes help in deriving business-driven segments. Sales or number of sales are readily available datasets that can be used as target attributes.

[0022] According to one embodiment, the computing device is configured to analyze and convert numeric and categorical attribute types into the same comparable numerical dimension such that these attribute types can be consumed by many clustering algorithms (e.g., available for input to many clustering algorithms). class algorithm). Sales data are used to calculate weights for attribute values, which enables clustering algorithms to behave like classification algorithms witho...

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Abstract

Systems, methods, and other embodiments configured to segment customers using mixed attribute types are disclosed. In one embodiment, a computerized data structure is read. The computerized data structure has numerical demographic attribute data, categorical demographic attribute data, and target attribute data associated with the customer and stored in the computerized memory. The numerical demographic attribute data and the categorical demographic attribute data are converted to the same numerical scale based at least in part on the target attribute data to form consistent attribute data having an index compatible with performing a cluster analysis on the consistent attribute data Format. Cluster analysis is performed on the consistent attribute data to generate segmented customer data representing segments of customers. Segmented customer data can be used to control at least one enterprise function performed by the computerized management system.

Description

Background technique [0001] Customer segmentation is the practice of dividing customers into groupings that share similar marketing-relevant characteristics, such as gender, age, education level, or spending habits. Retailers base customer segmentation on the idea that each customer has different needs and that customers can be better served by identifying and targeting groups with similar preferences. [0002] Clustering analysis is a statistical technique used to classify a set of observations into mutually exclusive groupings. Various algorithms exist to perform cluster analysis, and these algorithms differ significantly in their cluster construction process and in their efficiency. Cluster analysis can be used as a tool to identify customer segments with similar purchasing behavior to earn additional revenue from customers. For example, the results of satisfaction surveys based on 1 to 10 points regarding different aspects of the customer shopping experience can be clust...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06Q10/08G06Q30/02
CPCG06Q10/06G06Q10/087G06Q30/0204H04L51/02G06Q30/0203H04L51/08
Inventor M·H·哈简
Owner ORACLE INT CORP
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