Customer Portrait for Metrology Institutions Based on the Machine Learning Clustering Algorithm and the RFM Model

Abstract

With the increasing intensity of competition in the current metrology testing market, building customer portrait is an effective way for metrology institutions to improve service levels to customers. This paper is based on the basic business data of a certain metrology institution. First, recency, frequency, monetary value model (RFM model), which is widely applied in customer relationship management, is improved. Further, it is combined with the business features of the metrology institution and used to build data feature engineering, which is closely related to the business data of the metrology institution and can reflect the data situation. Then, the data are analyzed through correlation test, standardized by Z-score, and clustered with three clustering algorithms, namely K-Means, DBSCAN, and AGNES, which are in SKLEARN database based on Python. After that, the clustering results are compared. In the clustering process, the elbow method and method for traversing the silhouette coefficient are used to determine the optimal value of the clustering algorithm. Finally, with the analysis of clustering results, the customers’ features of the metrology institution are signed and the customer portrait is built, which provides data analysis methods, tools and decision basis for the metrology institution to offer better services.