Abstract

Customer lifetime value is one of the most important tasks for enterprises to maintain customer relationships. However, due to the limitations of using a single data mining method, the measurement of customer lifetime value under the condition of noncontractual relationship has always been a research difficulty. This paper focuses on customer value measurement and customer segmentation based on customer lifecycle value theory, and carries out customer value measurement and customer segmentation research from the perspective of customer value, and constructs customer segmentation model. This paper first conducts feature engineering, such as data selection, data preprocessing, data transformation, and knowledge discovery, and then conducts customer value segmentation based on machine learning algorithms and customer relationship management analysis models and builds a customer value segmentation identification model under the condition of noncontractual relationship. Finally, empirical analysis is carried out with the real customer transaction data of the actual online shopping platform, which verifies the validity and applicability of the customer segmentation method and value calculation method proposed in this paper.

Introduction

Since the 1980s, customer relationship management (CRM) has increasingly become the focus of academia and industry. Facts have proven that maintaining good relationships with specific customers can increase corporate profits and increase the significant advantages of enterprises in market competition [1]. Enterprises can classify customer categories based on customer consumption behavior, customer lifetime value and other information, identify customer value, and apply it to CRM [2]. Scientific and effective research on customer lifetime value is the primary task of customer relationship management, which is of great significance to enterprise marketing plans and strategies. Scholars have confirmed that effective customer lifetime value segmentation research can bring more profits to enterprises [3]. However, due to the inadequacy of previous research on customer data processing and the limitation of single data mining methods, it is difficult to fully mine customer behavior information. The accurate measurement of customer life cycle value under non contractual relationships has always been a research difficulty. Through a literature review, the current calculation of customer lifetime value under noncontractual relationships mainly includes Pareto/NBD and other probability models [4,5], VAR, and other time series models, as well as machine learning algorithms [6]. Especially in recent years, with the wide application of Big Data in business intelligence and analytics, companies can collect a large amount of personal transaction data from customers at low cost. These can be analyzed using an array of methods including: neural network models, decision tree models, random forests, generalized additive models (GAMs), multiple adaptive regression splines (MARS), classification and regression trees (CART), support vector machines (SVM) and other machine learning algorithms. These methods are useful for the application of customer lifetime value calculation.

Conclusion and outlook

This paper studies the connotation of customer lifetime value under the noncontractual relationship from two aspects of customer segmentation and calculation methods, and constructs the customer segmentation mode under the noncontractual relationship and the customer value segmentation recognition model based on machine learning algorithm. This paper uses feature engineering to process the data, and comprehensively applies RFM model, machine learning algorithm and other methods to ensure the rationality and feasibility of the research results. The main innovation points of this paper are as follows: (1) Define the measurement method of customer value, build a customer segmentation model based on customer value under the noncontractual relationship, mine the characteristics of segmented customer groups, and enrich the existing research on customer value segmentation; (2) In the preprocessing stage of customer consumption behavior information, feature engineering is used to map the original data space to the new feature vector space through the data preprocessing and transformation process, so that in the new feature space, the machine learning model can better mine the feature vector information and improve the classification results of the model; (3) The traditional RFM model and machine learning algorithms are combined to expand the research on customer segmentation and customer lifetime value under the condition of noncontractual relationship.