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

<p>This study employs SWOT and PEST analyses to examine the sports market by identifying internal strengths and weaknesses alongside external opportunities and threats. It integrates these findings with contextual environmental factors—political, economic, social, and technological—to strategically plan marketing approaches. The paper further explores the implementation of personalized recommendation algorithms to analyze sports marketing data and customer value trends, establishing dual promotional strategies for products. This entails comprehensive data analysis concerning the accuracy, coverage, and novelty of personalized product recommendations. The empirical results highlight significant growth in China’s sports market, which expanded from 0.74 million participants in 2014 to 133 million in 2023. Notably, in 2023, the per capita economic contribution in Beijing soared to 99,986 yuan, reflecting the burgeoning potential of the sports market in response to economic development. The study identifies robust national policy support as a critical competitive advantage, scoring 13 points. At the same time, the level of competition and external impacts from other domestic and international industries pose significant threats, receiving a threat assessment score of 12. Additionally, it was found that residents of first-tier cities exhibit a stronger preference for sports, suggesting targeted pricing strategies for sports products around 1,000 RMB to align with market dynamics.</p>
</abstract>

This study employs SWOT and PEST analyses to examine the sports market by identifying internal strengths and weaknesses alongside external opportunities and threats. It integrates these findings with contextual environmental factors—political, economic, social, and technological—to strategically plan marketing approaches. The paper further explores the implementation of personalized recommendation algorithms to analyze sports marketing data and customer value trends, establishing dual promotional strategies for products. This entails comprehensive data analysis concerning the accuracy, coverage, and novelty of personalized product recommendations. The empirical results highlight significant growth in China’s sports market, which expanded from 0.74 million participants in 2014 to 133 million in 2023. Notably, in 2023, the per capita economic contribution in Beijing soared to 99,986 yuan, reflecting the burgeoning potential of the sports market in response to economic development. The study identifies robust national policy support as a critical competitive advantage, scoring 13 points. At the same time, the level of competition and external impacts from other domestic and international industries pose significant threats, receiving a threat assessment score of 12. Additionally, it was found that residents of first-tier cities exhibit a stronger preference for sports, suggesting targeted pricing strategies for sports products around 1,000 RMB to align with market dynamics.

Research on Sports Marketing and Personalized Recommendation Algorithms for Precise Targeting and Promotion Strategies for Target Groups

Anhui Institute of International Business

Applied Mathematics and Nonlinear Sciences

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

This study employs SWOT and PEST analyses to examine the sports market by identifying internal strengths and weaknesses alongside external opportunities and...

Research on Sports Marketing and Personalized Recommendation Algorithms for Precise Targeting and Promotion Strategies for Target Groups

Publicado en línea: 05 jul 2024

Páginas: -

Recibido: 18 mar 2024

Aceptado: 01 jun 2024

DOI: https://doi.org/10.2478/amns-2024-1694

Palabras clave
Swot, Pest analysis, Personalized recommendation algorithm, Marketing strategy

© 2024 Zhenhua Wang., published by Sciendo

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

Research on Sports Marketing and Personalized Recommendation Algorithms for Precise Targeting and Promotion Strategies for Target Groups

Publicado en línea: 05 jul 2024

Páginas: -

Recibido: 18 mar 2024

Aceptado: 01 jun 2024

DOI: https://doi.org/10.2478/amns-2024-1694

Palabras claveSwot, Pest analysis, Personalized recommendation algorithm, Marketing strategy

© 2024 Zhenhua Wang., published by Sciendo

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

Palabras clave
Swot, Pest analysis, Personalized recommendation algorithm, Marketing strategy