Enhancing Sales Performance in Henan’s Media Industry through the Synergy of Digital Marketing and AI
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Abstract
This study examines how digital marketing and artificial intelligence (AI) can improve sales performance in the media industry of Henan Province, China. As the market undergoes rapid digital transformation, understanding the role of these technologies in driving sales is crucial for maintaining competitiveness. The research explores three main areas: the impact of digital marketing and AI on sales performance, employee perceptions of these technologies, and the interaction between digital marketing and AI in enhancing sales outcomes. Data was gathered from 240 participants using a structured questionnaire distributed via JotForm and popular Chinese social media platforms. The demographic analysis revealed that 74.2% of respondents were female, with 48.3% aged between 30-39 years. Various statistical methods, including normality tests, descriptive analysis, T-tests, and bivariate correlation, were applied to ensure the reliability and validity of the data. The normality test indicated well-distributed data, while descriptive analysis showed high mean scores, reflecting a strong understanding of the survey items. The T-test found no significant difference in the impact of digital marketing and AI on sales performance, suggesting equal contributions of both technologies. The bivariate correlation revealed a moderate positive relationship between digital marketing and AI, highlighting their complementary roles in boosting sales. A Cronbach’s Alpha value of 0.94 confirmed the high internal consistency of the data. The study concludes that integrating digital marketing and AI into a unified strategy is critical for media organizations to optimize their sales performance. It underscores the need for innovation and healthy competition within media networks to drive digital transformation. Future research should investigate the relationship between innovative marketing strategies and sales performance in non-Western contexts.