Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 313 https://internationalpubls.com The Influence of Social Media Algorithms on Consumer Buying Behaviour Dr.S. Prabha Arockia Joans1, Dr.R.Marie Sheila2 1Assistant Professor of Commerce, Cross College, (Autonomous), Tiruchirappalli-620 002 2Assistant Professor of Commerce, Holy Holy Cross College, (Autonomous), Tiruchirappalli-620 002 Article History: Received: 20-09-2024 Revised: 02-11-2024 Accepted: 20-11-2024 Abstract: Social media algorithms significantly shape 72% of consumer purchasing choices by customizing content to align with user interests. This research investigates the connection between engagement metrics—such as likes, comments, and shares—and consumer purchases, employing structured questionnaires and sophisticated statistical methods like regression analysis and Spearman's rank correlation, analyzing responses from 224 participants. The findings indicate a notable link between personalized content and buying behavior, with algorithms enhancing engagement by adjusting content visibility. Importantly, over half of consumers believe that personalized advertisements directly affect their purchasing decisions, while higher user engagement boosts brand trust. The research emphasizes the necessity for digital marketers to align their content strategies with algorithmic trends to enhance visibility, engagement, and sales, especially as around 54% of users acknowledge the strong influence of algorithms on their content. This study highlights the imperative for marketers to stay proactive in adapting to algorithmic changes to optimize their digital marketing initiatives. Keywords: Social media algorithms, consumer buying behaviour, digital marketing, content , user engagement, quantitative research, statistical analysis, marketing strategies, algorithmic trends, content optimization. 1. INTRODUCTION In 2023, it was estimated that over 4.76 billion individuals globally utilized social media, constituting nearly 60% of the world’s population. Platforms like Facebook, Instagram, and TikTok have experienced remarkable growth, with Facebook alone boasting 2.9 billion monthly active users[1][3]. According to Statista, 71% of consumers are more inclined to make purchases based on social media referrals, while 49% rely on influencer recommendations to guide their buying choices. Additionally, 62% of millennials indicate that social media plays a vital role in cultivating brand loyalty. Given that personalized advertisements drive 31% of users to explore products, it is evident that algorithms increasingly shape purchasing behavior. Research shows that algorithm-driven content personalization can elevate engagement rates by up to 15%, underscoring the substantial impact these algorithms have on consumer decisions[2]. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 314 https://internationalpubls.com Social media algorithms serve as powerful influencers, determining which posts appear in users’ feeds, the advertisements they encounter, and the sequence in which content is displayed. This dissertation explores the intricate relationship between the structural mechanisms of social media algorithms and consumer behavior concerning purchasing decisions. The aim is to furnish valuable insights for businesses navigating the complex digital marketing landscape by examining the various ways algorithms affect content exposure, user engagement, and consumer purchasing choices [4]. The contemporary era is marked by widespread digital connectivity and abundant information. Social media algorithms have emerged as influential gatekeepers, shaping the content users encounter and engage with, ultimately affecting their purchasing decisions. As social media becomes a primary avenue for communication and content dissemination, understanding the intricate mechanisms underlying these algorithms is essential. These elements are pivotal, actively creating the digital environments where consumers interact with products, services, and brands. Gaining insight into the impact of these algorithms on the digital marketplace and their effect on consumer purchasing behavior is vital for companies aiming to make informed marketing strategy decisions[5][7]. This research embarks on a comprehensive quest to discern the influence of social media algorithms on consumer purchasing behavior. The study aspires to deliver a thorough, evidence-based analysis of how algorithmic content curation affects user engagement with brands, products, and services, leveraging empirical research and an in-depth examination of the underlying dynamics involved. As we proceed with this investigation, it is crucial to consider the broader implications of these findings beyond the realm of digital technology, potentially affecting the strategies, methodologies, and success of businesses within the ever-evolving marketplace [6]. This project aims to enhance our understanding of the complex relationship between technology and consumer decision-making, leading to a more profound grasp of the digital factors that influence purchasing behavior. The subsequent chapters will clarify the mechanics of social media algorithms, explore the interactive dynamics between algorithms and consumers, and ultimately provide valuable insights to help organizations navigate this disruptive landscape more effectively [7][9]. 2. NEED FOR THE STUDY The swift ascent of social media platforms has revolutionized the digital marketing domain, fundamentally changing how businesses and individuals engage. Central to this transformation are intricate algorithms that curate and regulate the content users encounter, significantly influencing consumer perceptions and preferences. Despite extensive research on algorithmic impacts on engagement and content curation, a notable gap remains in understanding how these algorithms directly affect consumer purchasing behaviour [8]. This study aims to address this gap by investigating the specific ways social media algorithms influence consumer decision-making [9]. The research focuses on analyzing the correlation between algorithm-driven content curation and its substantial impact on consumer purchasing choices. By examining how algorithms determine content exposure and shape user engagement, this study seeks to provide actionable insights for businesses and marketers. Understanding these dynamics is essential as companies navigate the evolving landscape of consumer behaviour in the digital age, leveraging algorithmic trends to enhance their Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 315 https://internationalpubls.com marketing strategies and drive purchasing decisions based on user engagement metrics, including click-through rates, likes, comments, and shares, while examining how these metrics affect consumer perceptions and, consequently, their buying behaviour [10]. The primary objective of this study is to clarify the intricate relationship between social media algorithms and consumer purchasing patterns. The aim is to offer insights on critical issues such as how users respond to algorithmically curated content, the significance of content quality, and the tangible impacts on purchasing decisions in the physical marketplace [11]. The findings of this study hold significant implications for organizations, marketers, and social media platforms that must contend with the continually evolving landscape of consumer behaviour in the digital age. 3. SCOPE OF THE STUDY The primary goal of this research is to investigate the intricate correlation between social media algorithms and consumer purchasing behaviour. This includes understanding how algorithms influence metrics related to user engagement and content exposure, as well as the subsequent effects on consumer perceptions and purchasing behaviour. To achieve this, a quantitative research design will be employed, involving the electronic distribution of surveys and questionnaires to a diverse sample of social media users across various platforms. The study will explore the impact of social media on users' purchasing decisions, their engagement with brands and products, and their exposure to algorithmically curated content. A wide range of data will be collected concerning these factors. Geographically, this research aims to gather data from an extensive and varied group of social media users, ensuring that the sample represents diverse demographics and usage patterns. 4. OBJECTIVES OF THE STUDY The primary objectives of this investigation are as follows: • To analyze the effect of click-through rates, likes, comments, shares, and algorithm-generated engagement metrics on consumer purchasing behaviour. • To understand the interaction between social media algorithms and consumer behavior concerning purchasing decisions. • To investigate the influence of social media algorithms on content visibility across various platforms and its effects on consumer purchasing behaviour. • To examine the role of user-generated content and reviews in shaping consumer purchasing behaviour on social media platforms. 5. REVIEW OF LITERATURE The connection between social media platforms like Instagram and consumer purchasing behaviour has been explored. Their study involving a small sample of 50 participants underscored how influencers and targeted advertising significantly shape purchasing decisions, illustrating Instagram's impact on daily engagement and overall consumer satisfaction. Utilizing the percentage method and Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 316 https://internationalpubls.com Garrett ranking, their research reinforced the importance of social media in influencing consumer purchases [12]. In a mixed-method study investigated the effects of social media influencers on consumer behavior. They discovered that credibility—assessed through factors like expertise, trustworthiness, and attractiveness—plays a crucial role in influencing purchase intentions and brand loyalty. Their findings offer critical insights for marketers seeking effective collaborations with influencers to foster positive consumer behaviour [13]. Another study examined the effects of social media advertising on consumer behavior. Their research highlighted the efficacy of targeted advertising and engagement strategies in boosting brand recognition and driving purchases. By emphasizing the impact of social media exposure on consumer perceptions, their study provided practical strategies for businesses to enhance their digital advertising initiatives [14]. In a unique context, explored the effects of social media marketing on consumer purchasing habits in Nepal. Their research highlighted the significant role of social influence and content marketing in guiding buying decisions in developing countries, emphasizing the need for further research in emerging markets where social media marketing dynamics may differ due to cultural and technological factors [15][24]. One study in 2023 critically examined the influence of social media and mobile devices on consumer purchasing decisions. Using a quantitative approach, it explored how consumers utilize social media for information gathering and purchasing. The research focused on the correlation between social media engagement and the development of brand trust, demonstrating that proactive customer engagement on platforms fosters consumer trust and significantly affects purchase behaviour [16]. Analyzed consumer interactions with grocery apps in Iran and Hungary, employing advanced techniques like multi-layer perceptrons and Gaussian mixture models. Their study identified behavioral patterns and segmented users based on demographics such as age, income, and location, allowing grocery apps to tailor marketing strategies to enhance engagement and optimize user experiences [17][18]. Social media's influence on each stage of the consumer decision-making process has also been studied. Their research examined how social media affects information retrieval, product evaluation, and post-purchase behavior, focusing on reviews, endorsements, and post-purchase engagement. Their findings highlight the essential role of social media in shaping consumer choices[19][23]. In the realm of social network marketing, employed unsupervised machine learning to analyze consumer behavior on Facebook Marketplace. By categorizing consumers based on browsing habits and engagement with marketing content, their study demonstrated the effectiveness of targeted strategies in influencing purchases, emphasizing the necessity of tailoring approaches to specific consumer segments [20][21]. Another study by investigated the impact of startup technology innovations and CRM systems on consumer purchasing behavior. Utilizing advanced models like Support Vector Machines and Structural Equation Modeling, the research concluded that CRM technologies enhance customer Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 317 https://internationalpubls.com engagement and satisfaction, allowing businesses to co-create value with consumers and improve purchasing decisions [22]. RESEARCH GAP Existing literature has significantly enhanced our understanding of social media algorithms and their effects on users. However, a critical knowledge gap remains regarding the complex mechanisms through which these algorithms influence various stages of the consumer decision-making process. While previous studies have examined related areas, such as the impact of algorithms on user engagement and their role in content curation, there is a lack of comprehensive analysis focusing on their effects on consumer purchasing behaviour. To address this gap, the present study investigates the intricate relationship between social media algorithms and consumer purchasing decisions. Gaining a deeper understanding of this connection can yield valuable insights for social media platforms, businesses, and marketers. RESEARCH METHODOLOGY This study employed a quantitative research approach to examine the influence of social media algorithms on consumer buying behavior. Data were collected through structured questionnaires from 224 active social media users who regularly engage with platforms like Instagram, Facebook, and Twitter. The questionnaires were carefully designed to assess user engagement metrics— including likes, shares, comments, and click-through rates—while also capturing the participants’ purchasing behaviour as influenced by social media content. This method enabled a thorough analysis of how algorithmic content curation impacts consumer decisions. Various statistical techniques were employed to analyze the data. Spearman’s rank order correlation was used to determine the strength and direction of relationships between user engagement metrics and purchasing behaviour, considering the non-parametric nature of the data. Additionally, regression analysis was conducted to quantify the impact of engagement metrics and algorithmic exposure on consumer buying behaviour, helping to identify key factors that significantly influence purchasing decisions. Descriptive statistics were utilized to summarize trends in user behaviour and content visibility, providing a detailed understanding of how social media algorithms affect consumer engagement and purchasing outcomes. HYPOTHESIS & VARIABLES The research explores four hypotheses. The first null hypothesis (H0) asserts that click-through rates, likes, comments, shares, and other algorithm-generated user engagement metrics do not significantly influence consumer purchasing behaviour. The second null hypothesis (H0) posits that the interaction between social media algorithms and consumer behaviour does not significantly impact purchasing decisions. The third null hypothesis (H0) suggests that social media algorithms have no significant effect on content visibility across multiple platforms and do not influence consumer purchasing behaviour. Finally, the fourth null hypothesis (H0) claims that user-generated content and reviews on social media platforms have no significant impact on consumer purchasing behaviour. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 318 https://internationalpubls.com SAMPLING SIZE & TECHNIQUE To analyze the impact of social media algorithms on consumer buying behavior, this study used a quantitative research methodology. Convenience sampling, a non-probability sampling method, was employed due to its practicality in reaching a readily accessible subset of the target population. While a sample size of 384 is often considered standard for quantitative research, this study was limited to 224 respondents due to time constraints and availability, which still constitutes a robust sample size for the research objectives. SOURCES & METHODS OF DATA COLLECTION Primary data for this study were collected through a carefully crafted questionnaire aimed at uncovering specific ways social media algorithms impact consumer buying behaviour. The questionnaire explored key areas relevant to the study's goals, including users’ interactions with algorithmically curated content, the credibility of such content, and its influence on purchasing decisions. It included a mix of Likert scale and multiple-choice questions to capture the depth and breadth of consumer experiences and perceptions. Respondents were selected using a stratified random sampling method to ensure a representative cross-section of social media users across various demographics and platforms. This methodological approach enabled the collection of rich insights directly from individuals, facilitating a detailed analysis of the complex relationship between social media algorithms and consumer behavior. The integration of primary data with secondary sources from academic journals and industry reports enriched the research, providing a comprehensive perspective essential for addressing the research objectives and hypotheses. STATISTICAL TOOLS USED FOR ANALYSIS Reliability Test: Cronbach's alpha was employed to measure the internal consistency of the survey instrument, yielding a value of 0.951, indicating high reliability, well above the acceptable threshold of 0.7. Normality Test: A normality test indicated that the dependent variable, purchasing decision, was not normally distributed. Consequently, non-parametric tests were utilized for subsequent analyses. Spearman’s Rank Correlation: This test assessed the strength and direction of relationships between variables. Statistically significant correlations were identified, notably between user engagement and perceptions of algorithmic influence, underscoring the role of social media algorithms in shaping consumer behavior. Regression Analysis: Regression models were employed to explore relationships between independent variables (e.g., engagement metrics, algorithmic exposure) and consumer buying behavior. The analysis confirmed that algorithm-generated engagement metrics and content visibility significantly impact purchasing decisions. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 319 https://internationalpubls.com Level of Significance: A significance level of 5% (α = 0.05) was adopted for statistical analyses, indicating that results with p-values below 0.05 were considered statistically significant. This threshold, common in social science and business research, reflects a 95% confidence in the findings, suggesting that observed patterns are likely genuine rather than random occurrences. The choice of the 5% level balances the risk of false positives with a degree of conservatism, enhancing the credibility of the findings related to factors influencing consumer purchasing decisions. DATA ANALYSIS & INTERPRETATION Reliability Test Table 1 Reliability Test Note: Output taken from SPSS The reliability statistics table presents a Cronbach's Alpha of 0.951 for a scale comprising 22 items. Cronbach's Alpha is a measure of internal consistency, which assesses how closely, related a set of items are as a group. An alpha of 0.951 is exceptionally high, indicating excellent internal consistency among the items in the scale. This suggests that the items are measuring a single underlying construct very reliably. A Cronbach's Alpha value exceeding 0.9 is typically considered to show excellent reliability, and in this case, it implies that the questionnaire or test is highly consistent and that the scale items are very likely to be correlated with each other. With 22 items contributing to this high level of reliability, the scale used to measure the construct is robust and can be considered dependable for further analysis. This high reliability is critical for ensuring that any conclusions drawn from the data are based on a sound measurement tool. Normality Test The tests of normality, specifically the Kolmogorov-Smirnov and Shapiro-Wilk tests, have been applied to the variable "Purchasing Decision." Both tests aim to determine if a dataset is normally distributed. The Kolmogorov-Smirnov test yields a statistic of 0.355 and the Shapiro-Wilk test gives a statistic of 0.635, both with a significance (p-value) of less than 0.001. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 320 https://internationalpubls.com Table 2 Tests of Normality Note: Output taken from SPSS The significance levels being less than 0.001 for both tests suggest that the data for "Purchasing Decision" deviate significantly from a normal distribution. In other words, we reject the null hypothesis for both tests, which posits that the data is normally distributed. This has implications for statistical analysis, as many parametric tests assume normality of the data. Therefore, when analysing the "Purchasing Decision" variable, it may be more appropriate to use non-parametric statistical methods that do not assume normal distribution. The results of these normality tests are essential for guiding the correct choice of subsequent statistical analyses to ensure accurate and valid results. Descriptive Statistics Table 3 Descriptive Statistics Note: Output taken from SPSS Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 321 https://internationalpubls.com The statistical analysis of the demographic data reveals that the average participant age falls between 18-34 years, with a distribution skewing toward younger individuals and a peak higher than that of a normal distribution, suggesting that younger respondents were more common. Males have a slight advantage with a small variation in gender distribution, and the males have a larger concentration. There is a high level of education among respondents, with most having at least some college or associate's degree, with the distribution leaning towards higher education levels. Students and employed individuals are mixed in the sample, with a broad range of occupations represented. Several demographic groups in the sample show a high degree of diversity, including a younger age group and a single marital status that is more prevalent. Hypothesis Testing Hypothesis 1 To study the effect of click-through rates, likes, comments, shares, and algorithm-generated engagement metrics on consumer purchasing behaviour. Table 4 Regression Model Summary (H1) Table 5 ANOVA (H1) The regression analysis aimed to explore the effect of various online engagement metrics on consumer purchasing behaviour. With an R value of 0.336, the model indicates a moderate positive relationship between the engagement metrics and purchasing behaviour. The R Square value of 0.113 suggests that around 11.3% of the variance in purchasing behaviour is explained by the metrics included in the model, such as click-through rates, likes, comments, shares, and algorithm-generated engagement. The Adjusted R Square value of 0.092 accounts for the number of predictors, slightly reducing the percentage of variance explained, which implies other factors not included in the model may also impact purchasing behaviour. The standard error of estimate is 0.477, indicating the average distance that the data points are from the fitted regression line. According to the ANOVA table, the model is statistically significant (F = Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 322 https://internationalpubls.com 5.544, p < .001), affirming the predictors do have a significant effect on purchasing decisions, and the likelihood of the observed relationship being due to chance is very low. Hypothesis 2 To understand the influence of the interaction between social media algorithms and consumer behaviour on purchasing decisions. Table 6 Regression Model Summary (H2) Table 7 ANOVA (H2) The regression analysis aimed to explore the effect between social media algorithms and consumer behaviour on purchasing decisions. An R value of 0.299 indicates a weak positive correlation between the predictors and the purchasing decisions. The R Square value of 0.089 suggests that only about 8.9% of the variability in purchasing decisions can be accounted for by the factors in the model, which include unplanned purchases, the influence of personalised ads, and trust in friends and family. The Adjusted R Square, at 0.077, provides a slightly lower estimate that adjusts for the number of predictors, implying that the model’s explanatory power is modest. The Standard Error of the Estimate is 0.481, reflecting the average distance between the observed data points and the model's predicted values. The ANOVA results show a statistically significant model with an F value of 7.188 and a p-value of less than 0.001, suggesting that the relationship observed is unlikely to be due to random chance hypothesis 3 To investigate the influence of social media algorithms on content visibility across multiple platforms on consumer purchasing behaviour. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 323 https://internationalpubls.com Table 8 Correlations (H3) The correlation table presents the Spearman's rho correlation coefficients to investigate the relationships between various factors related to social media algorithms and content visibility, and their influence on consumer purchasing behaviour. The table indicates strong positive correlations between algorithm influence on content and engagement frequency (ρ = 0.523, p <.001), content alignment (ρ = 0.654, p < .001), content diversity (ρ = 0.344, p < .001), sponsored content frequency (ρ = 0.691, p < .001), and content changes (ρ = 0.636, p < .001). Similarly, engagement frequency shows strong positive correlations with content alignment (ρ = 0.610), sponsored content frequency (ρ = 0.555), and content changes (ρ = 0.535), all significant at p <.001. These results suggest that as the alignment, diversity, and changes in content increase, so does the frequency of engagement, which is likely influenced by the algorithm. Moreover, the high correlation between sponsored content frequency and algorithm influence suggests that algorithms significantly affect the visibility of sponsored . Hypothesis 4 To examine the role of user-generated content and reviews in influencing consumer purchasing behaviour on social media platforms. Table 9 Regression Model Summary (H4) Table 10 ANOVA (H4) Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 324 https://internationalpubls.com The linear regression analysis investigated the influence of user-generated content and reviews on consumer purchasing behaviour on social media platforms. The analysis yielded an R value of 0.572, indicating a moderate to strong positive correlation between the independent variables and the dependent variable, purchasing decisions. The R Square value of 0.328 signifies that approximately 32.8% of the variance in purchasing behaviour can be attributed to the model's predictors, which include trust in user reviews, following influencers, content quality importance, and user-generated influence. The Adjusted R Square of 0.315 accounts for the number of predictors in the model and slightly lowers the percentage of explained variance, suggesting a good fit but also indicating that other factors may play a role in consumer behaviour. The Standard Error of the Estimate is 0.414, denoting the average deviation of the observed values from the predicted ones. The ANOVA results indicate a significant regression model with an F value of 26.677 and a p-value less than 0.001, confirming that the model's predictors have a statistically significant effect on purchasing decisions. FINDINGS The exploration of demographic preferences and behaviors offers a detailed understanding of how various demographic factors, such as age, gender, and socioeconomic status, interact with social media to shape consumer behavior. Notably, younger individuals, particularly those aged 18 to 34, exhibit significantly higher engagement with social media platforms, utilizing these channels for information gathering and online shopping. The research indicates that gender influences preferences in product categories, with women leaning more towards fashion and beauty items, while men tend to favor electronics and gadgets. Additionally, urban residents from middle-income backgrounds are identified as the primary demographic engaging with social media, suggesting that marketers should adopt targeted strategies to enhance engagement and drive sales through customized content. The study underscores the essential role of engagement metrics (likes, comments, shares) in influencing user perceptions of brands and products on social media. Higher engagement rates correlate with increased visibility and enhanced brand trust, directly affecting users’ purchasing decisions. This correlation emphasizes the need for brands to create engaging and interactive content to build user trust and interest, indicating that those who effectively utilize these metrics can significantly bolster their market presence and consumer base.Moreover, this research highlights the substantial impact of social media algorithms on consumer purchasing decisions. By personalizing the content users encounter, these algorithms can subtly steer buying choices, positioning themselves as powerful tools within the digital marketing framework. The findings reveal that algorithms prioritizing content based on user preferences and behaviors can significantly boost engagement with branded content and stimulate sales, highlighting the necessity for marketers to understand and adeptly navigate these mechanisms. The analysis further explores how social media algorithms dictate content visibility, showing that these mechanisms prioritize user-engaging content, thereby shaping what appears in users' feeds. This selective visibility can greatly influence consumer exposure to different brands and products, impacting their awareness and perceptions. Understanding and optimizing for these algorithms can help brands enhance their content's reach and effectiveness. User-generated content, such as reviews and testimonials, is shown to play a crucial role in shaping consumer behaviour. The study finds that consumers place greater trust in user-generated content compared to traditional advertising, with peer opinions significantly influencing purchasing decisions. This trust underscores Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 3s (2025) 325 https://internationalpubls.com the importance for brands to encourage and utilize user-generated content as a means of building credibility and fostering community engagement. SUGGESTIONS E-commerce platforms can go beyond basic product recommendations by employing deep learning to analyze customer behaviour over time, enabling predictions of future needs and offering personalized discounts and promotions. Such personalization not only boosts sales but also enhances customer loyalty by making shoppers feel uniquely valued. Platforms should also incorporate features that allow customers to easily share their purchases and reviews on social media directly from the e-commerce site. Highlighting user-generated content, such as unboxing videos or practical product demonstrations, on product pages can significantly sway purchasing decisions by providing authentic endorsements. Integrating inventory management and customer service across channels is essential, allowing customers to transition seamlessly between social media, mobile apps, and web platforms without losing their shopping carts or order history. This approach prioritizes a cohesive shopping experience across various touch points. Advanced AI can be utilized to analyze customer inquiries in real-time, offering personalized shopping advice akin to a digital shopping assistant. Implementing predictive service features, such as anticipating customer issues before they occur, can distinguish a platform in terms of customer experience. Adopting blockchain or other secure technologies for data storage and transactions can enhance security. Conducting regular audits and issuing transparency reports can reassure customers about their data safety, transforming privacy into a competitive advantage. CONCLUSION This dissertation synthesizes key findings and their implications for understanding the effects of social media algorithms, engagement metrics, and user-generated content on consumer behaviour. It has been established that social media platforms, through their algorithms, significantly influence purchasing decisions by personalizing content exposure for users. This personalization not only boosts user engagement but also drives buying behavior by showcasing content that aligns with individual preferences. Demographic factors were found to greatly affect how different groups engage with social media, underscoring the necessity for targeted marketing strategies. Engagement metrics such as likes, shares, and comments serve as indicators of content popularity and trustworthiness, directly influencing user perceptions of brands and products. Furthermore, the visibility of content on social media—dictated by algorithms—is vital for ensuring users see the most relevant content, thereby shaping their purchasing decisions. User-generated content, including reviews and recommendations, emerged as a potent influencer of consumer behavior, providing authenticity and trust that significantly impacts buying choices. The study highlights the importance of leveraging such content to build a trustworthy relationship between brands and consumers. Recommendations for social media platforms include improving transparency regarding algorithms and promoting user engagement through diverse, high-quality content. For academic inquiry and future research, this study identifies areas for further exploration, such as the nuanced effects of algorithms across different consumer segments and the long-term impacts of engagement with user- generated content on brand loyalty. 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