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<p>The development of mobile Internet promotes the updating of cross-border e-commerce models, and the precision marketing realized by relying on big data technology better meets the all-around demand of users for content, socialization, and transactions. The article establishes a cross-border e-commerce marketing process on the basis of STP marketing management and builds a cross-border e-commerce precision marketing model by combining the STP marketing model. The user behavior characteristics of cross-border e-commerce users are extracted based on the RFM model, and the user behavior model is established by combining the user’s interest in purchasing goods. Then, the K-Means clustering algorithm is used to process the subgroups of cross-border e-commerce customer samples so as to construct a precise portrait of users. The cross-border e-commerce enterprise Z is selected as the research object, and the impact of precision marketing strategy on its user growth, merchandise sales, click-to-purchase conversion rate, and marketing optimization effect is analyzed. The number of effective users grew from 10,516 in 2020 to 16,804 in 2022, and the click-to-purchase conversion rate of products improved by 20%~46%, and different types of customers have various degrees of improvement under the precision marketing strategy. Based on big data technology, cross-border e-commerce users can be accurately portrayed, marketing products can be provided to users with more accuracy, and cross-border e-commerce enterprises can effectively enhance their marketing capabilities.</p>
</abstract>

The development of mobile Internet promotes the updating of cross-border e-commerce models, and the precision marketing realized by relying on big data technology better meets the all-around demand of users for content, socialization, and transactions. The article establishes a cross-border e-commerce marketing process on the basis of STP marketing management and builds a cross-border e-commerce precision marketing model by combining the STP marketing model. The user behavior characteristics of cross-border e-commerce users are extracted based on the RFM model, and the user behavior model is established by combining the user’s interest in purchasing goods. Then, the K-Means clustering algorithm is used to process the subgroups of cross-border e-commerce customer samples so as to construct a precise portrait of users. The cross-border e-commerce enterprise Z is selected as the research object, and the impact of precision marketing strategy on its user growth, merchandise sales, click-to-purchase conversion rate, and marketing optimization effect is analyzed. The number of effective users grew from 10,516 in 2020 to 16,804 in 2022, and the click-to-purchase conversion rate of products improved by 20%~46%, and different types of customers have various degrees of improvement under the precision marketing strategy. Based on big data technology, cross-border e-commerce users can be accurately portrayed, marketing products can be provided to users with more accuracy, and cross-border e-commerce enterprises can effectively enhance their marketing capabilities.

Research and optimization of cross-border e-commerce marketing mode based on big data technology

Applied Mathematics and Nonlinear Sciences

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

The development of mobile Internet promotes the updating of cross-border e-commerce models, and the precision marketing realized by relying on big data...

Research and optimization of cross-border e-commerce marketing mode based on big data technology

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 30 mar 2024

Przyjęty: 18 cze 2024

DOI: https://doi.org/10.2478/amns-2024-1953

Słowa kluczowe
RFM model, User behavior model, K-Means clustering algorithm, Precision marketing, Cross-border e-commerce

© 2024 Yuanyuan Jiang et al., published by Sciendo

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

Research and optimization of cross-border e-commerce marketing mode based on big data technology

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 30 mar 2024

Przyjęty: 18 cze 2024

DOI: https://doi.org/10.2478/amns-2024-1953

Słowa kluczoweRFM model, User behavior model, K-Means clustering algorithm, Precision marketing, Cross-border e-commerce

© 2024 Yuanyuan Jiang et al., published by Sciendo

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

Słowa kluczowe
RFM model, User behavior model, K-Means clustering algorithm, Precision marketing, Cross-border e-commerce