Research and application of time series algorithms in centralized purchasing data
This work provides incremental improvements for companies like COSCO to optimize marketing strategies based on customer lifecycle stages.
The paper tackled the problem of analyzing centralized purchasing data by clustering time series transaction data and modeling cluster centroids, achieving a 12-period prediction for each category using ARIMA.
Based on the online transaction data of COSCO group's centralized procurement platform, this paper studies the clustering method of time series type data. The different methods of similarity calculation, different clustering methods with different K values are analysed, and the best clustering method suitable for centralized purchasing data is determined. The company list under the corresponding cluster is obtained. The time series motif discovery algorithm is used to model the centroid of each cluster. Through ARIMA method, we also made 12 periods of prediction for the centroid of each category. This paper constructs a matrix of "Customer Lifecycle Theory - Five Elements of Marketing ", and puts forward corresponding marketing suggestions for customers at different life cycle stages.