Advertisement service user value evaluation method and device, equipment and storage medium

Abstract

The invention discloses an advertisement service user value evaluation method, device and equipment and a storage medium, and the method comprises the following steps: obtaining an association index of a user and an advertisement service, and building an index vector through the association index; calculating the association index by utilizing an analytic hierarchy process to obtain correspondingweight vector of the association index; and calculating a user value score according to the index vector and the weight vector. In combination with an actual scene of an advertisement information flowservice, a traditional RFM model is improved, L and P factors are added, a new model is constructed, and meanwhile, AHP empowerment is combined, so that a model result is more accurate and closely close to a service scene and conforms to a service rule, the user value subdivision is more accurate, and a layering effect is better; and the one-sided shortcomings of the traditional RFM model are scientifically improved, and meanwhile, the personal decision error is also improved by the AHP weighting, and the model score is more comprehensively calculated, so that the service decision is more scientific.

Description

technical field [0001] The present invention relates to the technical field of advertising data processing, in particular to a method, device, equipment and storage medium for evaluating user value of advertising services. Background technique [0002] In the current technology, the information flow advertising business needs to stratify users to facilitate the marketing and strategy formulation of operators. The commonly used model is the RFM model, which measures the current user value through Rencency (last consumption), Frequency (consumption frequency), and Monetary (consumption amount). However, this model cannot be well applied to information flow advertising business scenarios, and cannot fully reflect the value characteristics of users. The existing method assigns weights to each indicator, and the effect of individual setting is more obvious. Due to the limited understanding of the business or the deviation of decision-making, the distribution of weights may be in...

AI Technical Summary

Problems solved by technology

However, this model cannot be well applied to information flow advertising business scenarios, and cannot fully reflect the value characteristics of users

Existing methods assign weights to each indicator, and the effect of personal formulation is more obvious. Due to limited personal understanding of the business or decision-making deviations, the distribution of weights may be inaccurate and scientific, which is not conducive to accurately estimating the value of advertising business users

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