<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The traditional retail industry be affected of the rapid development of modern e-commerce industry. To improve the shopping mall member portrait has become an effective way for operators to refine management and give full play to the value of members. We hope that the member system analysis based on data mining can provide reliable basis for establishing stable member relationship and planning promotional activities. In this paper, based on the real data provided by large department stores, adopting software such as MATLAB and SPSS, using probability statistics, data crawler and other related knowledge to description of a member of a large department store. Firstly, through the relationship models between members’ consumption characteristics model, it is concluded that the profit contribution of members to shopping malls is higher than that of non-members. Through the members’ purchasing power model, it is concluded that different strategies can be adopted for different levels of members. Through the members’ life cycle and state division model, we got the value of the member’s life cycle is about 799, and the state division threshold is 4.5594. Through the members’ preferences and commodity joint rate, we got that promotional activities play a leading role in the activation of members. Finally, according to the preferences of members, combined with business profits, we can choose the following three kinds of goods to promote: 30768d8b, d55deeb5, 6feea3f5.</p>
</abstract>

The traditional retail industry be affected of the rapid development of modern e-commerce industry. To improve the shopping mall member portrait has become an effective way for operators to refine management and give full play to the value of members. We hope that the member system analysis based on data mining can provide reliable basis for establishing stable member relationship and planning promotional activities. In this paper, based on the real data provided by large department stores, adopting software such as MATLAB and SPSS, using probability statistics, data crawler and other related knowledge to description of a member of a large department store. Firstly, through the relationship models between members’ consumption characteristics model, it is concluded that the profit contribution of members to shopping malls is higher than that of non-members. Through the members’ purchasing power model, it is concluded that different strategies can be adopted for different levels of members. Through the members’ life cycle and state division model, we got the value of the member’s life cycle is about 799, and the state division threshold is 4.5594. Through the members’ preferences and commodity joint rate, we got that promotional activities play a leading role in the activation of members. Finally, according to the preferences of members, combined with business profits, we can choose the following three kinds of goods to promote: 30768d8b, d55deeb5, 6feea3f5.

Mathematical model for portraits of members in large-scale department stores

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Mathematical model for portraits of members in large-scale department stores"}

The traditional retail industry be affected of the rapid development of modern e-commerce industry. To improve the shopping mall member portrait has become an...

Mathematical model for portraits of members in large-scale department stores

Online veröffentlicht: 16. Juni 2023

Seitenbereich: 2303 - 2318

Eingereicht: 31. Jan. 2023

Akzeptiert: 04. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.1.00412

Schlüsselwörter
Probability statistics, data crawler, curve fitting, outlier selection

© 2023 Yanxi Xiong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mathematical model for portraits of members in large-scale department stores

Online veröffentlicht: 16. Juni 2023

Seitenbereich: 2303 - 2318

Eingereicht: 31. Jan. 2023

Akzeptiert: 04. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.1.00412

SchlüsselwörterProbability statistics, data crawler, curve fitting, outlier selection

© 2023 Yanxi Xiong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Probability statistics, data crawler, curve fitting, outlier selection