Customer value analysis method and device, and storage medium

Abstract

The invention discloses a customer value analysis method and device, and a storage medium. The method comprises the following steps: extracting data source data to form a data set, wherein the data set comprises a historical data set obtained by selective extraction and an incremental data set obtained by newly added data extraction; performing data exploration and preprocessing on the data set toenable the data set to meet a data format required by model establishment; establishing an LRFMCT model according to the preprocessed data set, wherein the LRFMCT model comprises six indexes, L is the customer relationship length, R is the latest consumption time interval, F is the consumption frequency, M is the consumption amount, C is the average discount coefficient, and T is the average residence time; performing customer grouping based on the LRFMCT model, and performing feature analysis on each customer group to obtain different customer values. According to the method, the defects ofa traditional RFM model are overcome, feature analysis is carried out on each customer group, customers with different values are identified, customized services are provided, and precise operation isrealized.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a method, device and storage medium for customer value analysis. Background technique [0002] In the face of fierce market competition, most enterprises will launch a series of preferential marketing plans to attract more customers. The focus of enterprise marketing has changed from products to customers, and customer relationship management has become the core issue of enterprises. The core issue of customer relationship management is customer classification. Through customer classification, customer groups are subdivided, low-value customers and high-value customers are distinguished, and personalized services are provided to different customer groups. Enterprises can reasonably allocate limited resources to Customers of different values, realize precise operation, in order to obtain the maximum conversion rate and achieve the goal of maximizing profits. Accurate customer class...

AI Technical Summary

Problems solved by technology

[0003] In customer classification, the RFM model is a classic classification model. The model uses the three core indicators in the general transaction link - recent consumption interval (Recency), consumption frequency (Frequency), consumption amount (Monetary) to segment customers Groups, so as to analyze the customer value of different groups, but only limited to the above three indicators, it is difficult to find potential high-value customers among new customers

Moreover, the selection of indicators for classification by this model is relatively broad, and the classification is not accurate enough

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