Data mining method and data mining device

Abstract

The invention discloses a data mining method and a data mining device, and relates to the field of big data. The method comprises the steps of determining multiple influencing factors of user brand viscosity, determining a factor loading matrix of the multiple influencing factors through factor analysis, determining the influence of each common factor on user brand viscosity according to the factor loading matrix and the multiple influencing factors, and calculating the weighted sum of the influence of the common factors on user brand viscosity to determine user brand viscosity. The complexity of big data analysis is reduced, and user brand viscosity can be analyzed more efficiently.

Description

Technical field [0001] The invention relates to the field of big data, in particular to a data mining method and device. Background technique [0002] Brand viscosity means that consumers have shown biased rather than random behavioral responses to a certain brand in their purchasing decisions. The formation of brand viscosity is not entirely dependent on product quality, popularity, brand association and communication, but is also closely related to the characteristics of consumers themselves, such as consumers’ product experience. Improving brand viscosity is of great significance to the survival and development of e-commerce companies. The value of brand viscosity to companies is also reflected in expanding market share, reducing marketing costs, increasing profits, attracting new customers, improving sales channel expansion capabilities and improving Competition threshold and other aspects. [0003] The prior art usually uses factors such as repeated purchases of brands and p...

AI Technical Summary

Problems solved by technology

High analytical complexity when measured with a large number of factors

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