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

<p>In the era of e-commerce economy, how to mine and study the different values of customers is an essential factor affecting the development of e-commerce enterprises. Based on this, this paper uses a clustering algorithm to mine and study the value of e-commerce customers. The research firstly constructs RFM e-commerce customer value identification framework, designs e-commerce customer value evaluation indexes and calculates the weights of the value indexes, and then uses K-means clustering algorithm and classical artificial bee colony algorithm to improve the Calculation, and completes e-commerce customer refinement based on the value of the user. The results show that the type of e-commerce customer value based on the RFMC model can be divided into eight types, based on which the e-commerce customer value is further subdivided into four types by using the clustering algorithm, namely, core customers (13.86%), supportive customers (45.38%), habitual customers (21.08%) and risky customers (19.68%). Based on the clustering algorithm, the customer value was classified into type 0 customers (low value customers), type 1 customers (high value customers), and type 2 customers (medium value customers).</p>
</abstract>

In the era of e-commerce economy, how to mine and study the different values of customers is an essential factor affecting the development of e-commerce enterprises. Based on this, this paper uses a clustering algorithm to mine and study the value of e-commerce customers. The research firstly constructs RFM e-commerce customer value identification framework, designs e-commerce customer value evaluation indexes and calculates the weights of the value indexes, and then uses K-means clustering algorithm and classical artificial bee colony algorithm to improve the Calculation, and completes e-commerce customer refinement based on the value of the user. The results show that the type of e-commerce customer value based on the RFMC model can be divided into eight types, based on which the e-commerce customer value is further subdivided into four types by using the clustering algorithm, namely, core customers (13.86%), supportive customers (45.38%), habitual customers (21.08%) and risky customers (19.68%). Based on the clustering algorithm, the customer value was classified into type 0 customers (low value customers), type 1 customers (high value customers), and type 2 customers (medium value customers).

Research on e-commerce customer value mining based on K-means clustering algorithm

Applied Mathematics and Nonlinear Sciences

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

In the era of e-commerce economy, how to mine and study the different values of customers is an essential factor affecting the development of e-commerce...

Research on e-commerce customer value mining based on K-means clustering algorithm

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 17. Apr. 2024

Akzeptiert: 22. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1046

Schlüsselwörter
Clustering algorithm, Factor analysis, Artificial bee colony algorithm, E-commerce customer value

© 2024 Lidong Wang, published by Sciendo

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

Research on e-commerce customer value mining based on K-means clustering algorithm

Online veröffentlicht: 03. Mai 2024

Seitenbereich: -

Eingereicht: 17. Apr. 2024

Akzeptiert: 22. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1046

SchlüsselwörterClustering algorithm, Factor analysis, Artificial bee colony algorithm, E-commerce customer value

© 2024 Lidong Wang, published by Sciendo

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

Schlüsselwörter
Clustering algorithm, Factor analysis, Artificial bee colony algorithm, E-commerce customer value