1. Introduction

In the digital era, the landscape of consumer behavior has been significantly reshaped by the pervasive influence of social media platforms. These platforms have transformed from simple communication tools into influential marketing channels capable of shaping consumer buying decisions. The integration of social media into marketing strategies has led to the emergence of a new paradigm where consumer engagement and brand interaction are closely intertwined. This study aims to delve into the role of social media marketing strategies in influencing consumer purchasing decisions through the lens of sentiment analysis, a field that has not been fully explored in the context of consumer behavior.

Research advancements in the intersection of social media and consumer behavior have been substantial. For instance, a study by Smith et al. highlighted the impact of social media engagement on brand loyalty, while another by Johnson focused on the role of user-generated content in shaping consumer attitudes [1-2]. However, a critical review of the literature reveals a gap in understanding how emotional responses, as captured by sentiment analysis, mediate the relationship between social media marketing and consumer purchasing decisions. Though there is an increasing amount of research on how social media affects consumer behavior, sentiment analysis has not been fully leveraged in this area, indicating the necessity for more in-depth exploration. According to Lee, the integration of sentiment analysis in social media marketing could provide deeper insights into consumer emotions and their subsequent actions [3].

The methodology adopted in this research is a combination of literature review and case study analysis. This paper focuses on understanding how social media marketing strategies, particularly those that leverage sentiment analysis, can elicit emotional responses from consumers, thereby influencing their purchasing decisions. It is scoped within the realms of social media platforms and their marketing tactics, centering the emotional underpinnings of consumer behavior. The significance of this research lies in its potential to contribute to the broader understanding of consumer behavior in the digital age. By examining the interplay between social media marketing and consumer emotions, it offers insights that can inform marketing strategies and enhance the effectiveness of social media campaigns. Moreover, it provides a framework for future research to build upon, potentially leading to more nuanced and emotionally intelligent marketing practices that can better cater to the needs and desires of consumers in a socially connected world.

2. Literature Review

2.1. Social Media Marketing

Social media marketing (SMM) refers to the use of social media platforms to promote products, services, or brands and to engage with customers. The concept of SMM is rooted in the ability of social media to facilitate interactions and build relationships with consumers [4]. Studies such as those by Aral et al. and Lamberton highlight the importance of social media as a marketing tool, emphasizing its role in creating value co-creation and engagement opportunities [5]. SMM strategies often involve content marketing, influencer partnerships, and targeted advertising, which leverage the viral potential of user-generated content and the trust associated with peer recommendations [6]. The impact of SMM on consumer behavior is substantial, as it can shape brand perceptions, influence purchase intentions, and foster loyalty.

2.2. Consumer Buying Decision

Theoretical models of consumer buying decisions have evolved to incorporate the digital landscape, particularly the role of social media. Traditional models like AIDMA (Awareness, Interest, Desire, Memory, Action) have been adapted to the digital age, giving rise to models like AISAS (Attention, Interest, Search, Action, Share), which acknowledge the importance of online search and the sharing of information post-purchase [7]. These models underscore the influence of social media in shaping consumer decisions, from initial awareness to the final purchase and beyond. The emotional aspect of consumer behavior is also critical, with psychology-based models like the Elaboration Likelihood Model suggesting that affective pathways can significantly impact persuasion and decision-making [8]. Social media environments amplify these emotional influences, as consumers are exposed to affective content and social cues that guide their purchasing behaviors.

2.3. Emotional Analysis and Psychological Perspectives

Emotional analysis, or sentiment analysis, involves the use of computational techniques to identify and extract subjective information from text, such as consumer reviews and social media posts [9]. This technique is gaining popularity in consumer behavior research to understand the feelings associated with how consumers engage with brands and products. Tools like natural language processing and machine learning algorithms are employed to analyze vast amounts of data, providing insights into consumer satisfaction, preferences, and behavioral intentions [10]. From a psychological perspective, researchers like Shiv and Fedorikhin have explored how emotions can mediate the relationship between product attributes and purchase behavior, especially in social media where emotional contagion and influence are prevalent [11].

2.4. Critical Analysis of Relevant Literature

Despite the extensive research on social media marketing and consumer behavior, there are still gaps that warrant further investigation. A significant gap is the underexplored impact of personalized social media recommendations on consumer behavior. Moreover, the long-term effects of social media engagement on brand loyalty and consumer well-being have not been extensively studied. There is also a recognized need for more comprehensive models that can capture the dynamic and multifaceted nature of consumer interactions with social media, especially with the emergence of new platforms and technologies. Future research should focus on filling these gaps to enhance our understanding of the intricate relationship between social media marketing and consumer behavior.

3. The influences of Xiaohongshu on consumer behavior

3.1. “Zhongcao” model: The operational model of Xiaohongshu

Xiaohongshu is a social e-commerce platform primarily focused on user-shared shopping experiences. It has gained widespread attention for its unique “Zhongcao” approach, which means “recommend” or “plant the idea in one’s mind” and serves as an effective content marketing strategy. The platform boasts nearly 300 million monthly active users, especially popular among young people, with a user base mainly consisting of women aged 18-35. Users can share shopping insights, product recommendations, and lifestyle content, covering various fields such as beauty, fashion, travel, and food. In addition to posting shopping insights and recommendations related to products and sharing actual photos and videos of the products, users can also interact with others through likes, comments, shares, and collections.

Xiaohongshu’s marketing strategy includes leveraging the spread of UGC (User-Generated Content), collaborating with KOLs (Key Opinion Leaders) for brand content marketing, and reaching strategic cooperation with multiple brands. UGC refers to users sharing their original content with others through internet platforms. As a new form of internet usage, when combined with commerce, it has given birth to a brand-new marketing model. Xiaohongshu leverages UGC as a business strategy to attract consumers with shared interests. The platform encourages users to autonomously share and promote content, which in turn draws a substantial following to Xiaohongshu. Research by Yuan Xin and Hu Yinhao shows that UGC has a positive impact on consumers’ willingness to purchase and recommend. These contents, through functional, emotional, and entertaining outputs and expressions, become an important source of information affecting consumers’ brand behavior [12].

Social media’s growth and consumer desire for personalized shopping have propelled “Zhongcao” content marketing as a new tool. This method offers professional product analysis to solve consumer issues and boost purchase intent. KOLs, which were first proposed in The People’s Choice, influence consumer attitudes and actions with their expertise and credibility. In 2018, Xiaohongshu transitioned from an e-commerce community to an algorithmic media platform, leveraging personalization through algorithms. It built a content ecosystem for users to explore topics of interest, where KOLs share authentic experiences, reviews, and analyses, earning consumer trust and influencing buying decisions. Xiaohongshu’s model enables users to follow KOL recommendations, gain product insights, and provide feedback, sparking a viral word-of-mouth effect. KOL-generated content attracts a vast user base, serving as a shopping guidance platform. This model strengthens brand-consumer engagement, allowing for quick adaptation to consumer needs, enhancing brand affinity, and increasing purchase likelihood.

These strategies leverage emotional connections to sway consumer sentiment. They involve sharing real user experiences to spread product information and resonate with others, as exemplified by Xiaohongshu’s “Share Your Life” slogan. Unlike Weibo and WeChat, Xiaohongshu offers a lighter social experience with fewer constraints and pressures, termed “light socializing.” This approach fosters a stable user base and sustainable engagement. Users can boost post credibility and appeal through likes and comments, spurring purchase intent. Social interactions allow users to share purchase experiences, learn about product use, and develop matching skills, which emotionally resonate and drive purchasing desire. Social media exposure to others’ experiences and evaluations can lead to a sense of social identity, prompting purchasing behavior to meet social needs, enhance image, and integrate into social groups. “Zhongcao” content marketing, facilitated by social media and user interactions, creates a socialized information dissemination pattern that enhances trust and affinity in products or services, increasing the likelihood of purchases.

3.2. Emotional Mode of Xiaohongshu

Xiaohongshu is also a prime example of a social platform filled with intimate emotional flow. Seventy percent of Xiaohongshu users are female, and they refer to each other as “sisters.” With the help of algorithmic recommendations, strangers from all over the world are connected by a sisterly bond, caring for, encouraging, and warming each other. In fact, this specific platform operation strategy is encoded into the algorithm, and today, users’ emotional experiences on digital platforms feel closely related to algorithms, using emotions to encourage continuous platform participation [13]. On Xiaohongshu, content and bloggers emphasizing altruistic values are given push traffic, and the platform creates a genuine, equal, friendly, and positive community atmosphere through algorithmic intermediation, providing users with a pleasant platform experience. The emotional dimension of users’ algorithmic imagination leads to the formation of an emotional network based on emotional practice. Dean points out that in digital platforms, invisible affective networks are the expressive form of the driving force behind the cyclical movements of manufacturing, uploading, forwarding, storing, and commenting. This continuous accumulation and interweaving of digital expression layers’ production and reproduction make the traces of emotions still exist for a long time after the event, thereby maintaining the connection and binding relationship of networked publics, generating “a sense of community without community [14].”

4. Discussion

4.1. Advantages of Xiaohongshu Marketing Strategy

Xiaohongshu’s marketing strategy boasts several distinct advantages that contribute to its success as a community-driven e-commerce platform. One of the most significant is the high user engagement and retention rate, which is attributed to its predominantly young user base that is both highly active and eager to share and exchange. The platform’s content quality is also a standout feature, with both UGC and PGC (Professional-Generated Content) being of high standards, attracting significant attention and interaction. This is further amplified by Xiaohongshu’s strong community culture and the prevalence of “Zhongcao,” which effectively stimulates users’ desire to purchase.

Moreover, the platform’s ability to conduct targeted promotions based on user interests and geographical locations enhances the conversion rate. Combining this with robust data analysis capabilities, Xiaohongshu can provide brands with data support to formulate more scientific and precise marketing strategies. This data-driven approach allows for continuous optimization of marketing tactics, leading to improved marketing effectiveness and return on investment.

4.2. Limitations of Xiaohongshu Marketing Strategy

Despite its advantages, Xiaohongshu’s marketing strategy also faces certain limitations. The platform is characterized by intense competition among brands, with numerous players vying for user attention and market share within each niche. Additionally, the platform’s strict content regulations require brands to invest more effort and time in content creation to ensure compliance with community standards and values.

Another limitation is the demographic and geographic constraints of Xiaohongshu’s user base, which is predominantly young females concentrated in first-tier cities and developed regions. This poses challenges for brands looking to expand into other age groups or less developed areas. Furthermore, the high cost of advertising on Xiaohongshu can be a significant burden for small and medium-sized brands, given the competitive landscape and the fierce competition for popular resources.

Lastly, the platform’s marketing approach is somewhat singular, relying heavily on content marketing and KOL collaborations. This over-reliance can limit brand exposure and user growth potential, as it does not leverage a diversified marketing strategy that could reach a broader audience and cater to different consumer segments.

4.3. Applicability to Other Social Media Platforms

The insights gained from Xiaohongshu’s marketing strategy can be applied to other social media platforms to enhance their marketing efforts. Content marketing, with a focus on creating and sharing high-quality content, is paramount to attracting and maintaining user attention. Building a positive community atmosphere that encourages user interaction and content sharing is also crucial.

Precision marketing, utilizing data analysis to accurately target users, can improve marketing effectiveness across platforms. Additionally, leveraging long-tail traffic through strategic keyword and content planning can extend the lifecycle of content and enhance its reach. By applying these strategies, brands can increase their visibility and influence on various social media platforms, achieving better marketing outcomes.

5. Conclusion

This study has demonstrated the significant role of social media marketing strategies, particularly those that evoke emotional responses, in influencing consumer purchasing decisions. The case study of Xiaohongshu, a leading social e-commerce platform in China, has shown that high user engagement, quality content, and strong community culture are key drivers of consumer interest and action. The platform’s targeted promotions and robust data analysis capabilities have been identified as enhancing the effectiveness of marketing strategies and improving return on investment. The study underscores the importance of emotionally engaging marketing strategies in social media, emphasizing the need for practices that resonate with consumers on a deeper emotional level. However, this study has certain shortcoming, for example, it lacks data analysis to deeply investigate the influence of social media on consumer behavior. In future studies, the author will investigate more platforms and employ statistical methods.