Through the correlation analysis, cluster analysis and other data mining of logistics data, it can realize the functions of logistics customer relationship analysis, commodity correlation analysis, logistics market information cluster analysis, etc., and provide effective analysis and decision-making for the operation and development of smart logistics.

(1) Smart Logistics Customer Relationship Mining

Aiming at the existing massive customer and commodity data, how to apply data mining technology to analyze its related relationship and internal law, and provide the correct decision-making basis for the development of logistics enterprises is the core of smart logistics under the background of big data. Faced with a huge amount of customer information, logistics providers are trying to retain old customers while also exploring potential new customers. The current information system is obsolete for customer management and does not attract and motivate customers. Therefore, the traditional method does not meet the demand. However, data mining technology with the ability to process massive data and self-learning combined with logistics information systems can provide strong support for logistics companies.

(2) Analysis of the relationship between smart logistics products

Reasonable storage of goods is of great significance for the utilization of storage capacity and the efficiency of storage and handling sorting. In the big data environment, for logistics centers with large volume of goods and fast shipping frequency, commodity storage means work efficiency and efficiency, and data mining technology is used to analyze the relationship between massive goods information, and then arrange it reasonably. Shelves can effectively improve sorting efficiency and help companies develop marketing strategies.

(3) Cluster analysis of intelligent logistics market information

After the product enters the market, it will not maintain the highest sales forever. Generally speaking, over time, products will comply with the pattern of sales changes, and go through four stages, namely, introduction period, growth period, maturity period and recession period, and at each stage, product production requirements and physical distribution strategies different. In the big data environment, for the huge and ever-changing logistics market, using data mining technology to cluster the logistics market data can effectively help logistics companies to avoid risks and make reasonable decisions.

Reprinted from the network