Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies.

In this article, we aim to explore the relationship between sustainable marketing and supply chain management (SCM) under the background of metaverse technology to realize the sustainable development of enterprises. First, this study deeply studies the influence of metaverse technology on sustainable marketing strategy from the theoretical level. Second, it deeply discusses the integration of digital transformation and sustainable development in SCM. Finally, this study implements a collaborative driving model of sustainable marketing and SCM supported by metaverse. By designing and analyzing the questionnaire on the sustainable performance of enterprises, it is found that SCM, cooperation with customers, investment recovery, sustainable marketing, R&D and design, production, and manufacturing have a significant positive influence on the sustainable performance of enterprises (p<0.01). In addition, the distribution and retail in sustainable marketing negatively impact the sustainable performance of enterprises, and the standardization coefficient is −0.225 (p<0.05). These research results emphasize the importance of sustainable marketing and SCM, which jointly promote enterprises to achieve sustainable performance, and ultimately provide valuable practical guidance for building a sustainable digital economy and contribute to collaborative optimization in enterprise engineering.

In order to explore the internal connection between information sharing and investment performance in the venture capital network community, this study took environmental-governance start-ups as the research object and used the 2009–2020 environmental-social enterprise start-up venture capital investment events as a data sample. The successful exit rate of the venture capital portfolio and the successful listing rate of investment ventures were used as the measurement criteria. Combined with regression analysis, the relationship between information sharing and investment performance in the venture capital network community was analyzed in detail. Research shows that there are differences between the ways of information sharing in the venture capital network communities. In the regression results, all coefficients are less than 0.01. There is a positive correlation between information sharing and investment performance in the venture capital network community. With the increase in enterprise characteristic variables, the degree of enterprise risk information sharing is getting higher and higher. This ultimately leads to more and more frequent corporate investment performance and a higher possibility of acquisition. Among them, the degree of information sharing in the venture capital network community is relatively high, and venture capital companies that are supported by corporate venture capital institutions will benefit even more from listed capital. Not only was the analysis of the relationship between finance and investment in the venture capital network community pointed out in this research, but also the investment development of entrepreneurial enterprises was also provided with feasible suggestions.

E-commerce has developed rapidly, and product promotion refers to how e-commerce promotes consumers' consumption activities. The demand and computational complexity in the decision-making process are urgent problems to be solved to optimize dynamic pricing decisions of the e-commerce product lines. Therefore, a Q-learning algorithm model based on the neural network is proposed on the premise of multimodal emotion information recognition and analysis, and the dynamic pricing problem of the product line is studied. The results show that a multi-modal fusion model is established through the multi-modal fusion of speech emotion recognition and image emotion recognition to classify consumers' emotions. Then, they are used as auxiliary materials for understanding and analyzing the market demand. The long short-term memory (LSTM) classifier performs excellent image feature extraction. The accuracy rate is 3.92%-6.74% higher than that of other similar classifiers, and the accuracy rate of the image single-feature optimal model is 9.32% higher than that of the speech single-feature model.

Service providers are increasingly employing eco-positioning strategies to promote sustainable products. However, the effectiveness of these strategies in driving service adoption, particularly among consumers with varying levels of inertia, remains unexplored. Drawing on the elaboration likelihood model, this research investigates the differential effects of eco-positioning on consumers with high and low inertia. In five studies, including a large-scale study of U.S. households' energy consumption (N = 15,568) and four experimental studies (N = 1,078), we document a consistent inertia utility in green service adoption ($7.69 in the field, $7.44 in the lab), demonstrating that eco-positioning is more effective in increasing service adoption intentions among high-inertia consumers than low-inertia consumers. Through experiments and topic modelling (BERTopic, LDA), we find that eco-positioning more effectively elicits a warm glow and reduces high-inertia consumers' service concerns, while the sustainability liability effect has a stronger negative impact on low-inertia consumers. Through a novel discrete choice model, we quantify that a 3.12% claimed reduction in emissions is equivalent to a $1 incentive in motivating high-inertia consumers. These findings provide actionable guidance for service providers and policymakers in tailoring eco-positioning strategies to consumers' inertia levels and leveraging quantitative benchmarks to optimise resource allocation between environmental and monetary incentives.

This research was conducted for the purpose of exploring the role of venture capital (VC) based on the heterogeneity of human capital in the process of transforming innovation capabilities of enterprise into enterprise benefits and providing a reference for further research on enterprise performance development. In this study, 399 listed companies that obtained VC before 2018 from the China VC database and Wind China financial database were selected as research objects, and relevant data of patent number, return on equity, Tobin’s Q ratio, and research and development (R&D) investment ratio of each enterprise were obtained. Enterprise innovation ability was introduced to construct the relationship model of human-capital VC, enterprise innovation ability, and enterprise benefit, and the relationship between human-capital VC, enterprise innovation ability, and enterprise benefit was analyzed by the multiple-regression model. The results show that the number of patents of invested enterprises has an extremely significant positive correlation with the human capital index (P < 0.001), and a significant positive correlation with the education level of personnel, the proportion of engineering professionals (P < 0.05). The return on equity and Tobin’s Q ratio of enterprises have extremely significant positive correlations with the number of patents of invested enterprises (P < 0.001) and have a significant positive correlation with the number of patents of the invested enterprises × education level of the personnel, and the number of patents of the invested enterprises × proportion of engineering professionals (P < 0.05). All in all, the education level of the entrepreneur’ s VC human capital and the proportion of engineering professionals can effectively improve the innovation ability of the enterprise, thus indirectly playing a value-added role in the improvement of enterprise benefits. The invested enterprises will also face the problems of scale diseconomy and financing constraint when their profitability is enhanced, so they need to optimize their own business strategies.