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Article

Directed Consumer-Generated Content (DCGC) for Social Media Marketing: Analyzing Performance Metrics from a Field Experiment in the Publishing Industry

by
Eleni Ntousi
,
Chris Lazaris
*,
Pavlina Katiaj
and
Anastasios Koukopoulos
ELTRUN, E-Business Research Center, Department of Management Science & Technology, School of Business, Athens University of Economics and Business, 104 34 Athens, Greece
*
Author to whom correspondence should be addressed.
Systems 2025, 13(2), 124; https://doi.org/10.3390/systems13020124
Submission received: 10 January 2025 / Revised: 10 February 2025 / Accepted: 14 February 2025 / Published: 17 February 2025
(This article belongs to the Special Issue Complex Systems for E-Commerce and Business Management)
Browse Figure
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Abstract

:
This study examines the efficacy of a novel form of consumer-generated content (CGC) digital advertising, termed “directed” consumer-generated content (DCGC), in comparison to traditional brand-created social media advertisements. The analysis focuses on performance metrics and return on ad spend (ROAS). Data were gathered from social media campaigns incorporating both DCGC and non-CGC through a field experiment, followed by a rigorous statistical analysis to identify the most effective advertising strategies. Findings indicate that DCGC typically results in significantly higher conversion rates, increased conversions, and superior ROAS. Overall, DCGC advertisements demonstrate enhanced performance relative to non-CGC campaigns, suggesting that they represent a more strategic allocation of a brand’s marketing resources, particularly when the primary objective is to drive sales and achieve elevated conversion rates. This research contributes to the academic discourse and practical implementation of social media advertising by highlighting the advantages of DCGC as a cost-effective and efficient advertising approach for brands.

1. Introduction

The digital age has fundamentally reshaped the mechanisms of communication, social engagement, information acquisition, consumer behavior, and thought processes, reflecting a significant evolution in human interaction and cognition [1].
In the context of the rapidly shifting e-commerce landscape, brands are increasingly adopting innovative strategies to engage consumers and advertise their offerings, whether they are products, services, or the brand itself. Social media platforms, such as Meta, have revolutionized the ways in which individuals interact with content and how companies communicate and foster relationships with their customers. These platforms are characterized by high interactivity, multi-dimensionality, two-way communication [2], and even provide three essential elements that promote brand-customer co-creation: networks, relations, and interactions [3]. Thus, social media provides fertile ground for brands to explore innovative approaches to advertising and marketing their products. A unifying characteristic of these emerging platforms is their reliance on content generated by end-users, which serves as the foundation of their engagement and growth strategies [1]. Consequently, user-generated content (UGC) has emerged as a critical resource for marketers, evolving over the past few years into a cornerstone of many brands’ online marketing strategies [4]. Prominent e-commerce companies, such as SHEIN, exemplify this trend, leveraging UGC to amplify their reach, foster consumer trust, and drive engagement through authentic and relatable content shared by their customers [5].
User-generated content (UGC), created by ordinary users instead of professional content creators, has gained significant popularity due to its perceived authenticity and relatability [6]. In fact, user-generated content (UGC) is widely regarded as a powerful marketing tool, facilitating interaction between brands and their customers by leveraging its perceived reliability and credibility among consumers [7].
Recent research highlights the growing significance of user-generated content (UGC) and its impact on consumer engagement and purchasing decisions [8]. Studies have shown that UGC can lead to higher purchase intentions compared to traditional brand-created advertisements, as consumers perceive content generated by other users as more authentic and trustworthy [9]. Furthermore, consumer-generated branding is emerging as a powerful strategy for marketers, as empirical findings suggest that user-generated brand-related content fosters greater consumer engagement and strengthens brand perception more effectively than firm-generated advertising [10].
Additionally, research indicates that firm-created and user-generated social media content significantly influence purchase intentions, with UGC often outperforming traditional marketing efforts due to its perceived credibility and relatability [11]. A meta-analysis on the effects of social media content further supports these findings, demonstrating that owned and earned media positively impact consumer engagement and sales, particularly when brands integrate user-generated elements into their digital strategies [12]. Moreover, studies on digital platform marketing reveal that UGC influences consumer emotions and purchase intentions, reinforcing the argument that authenticity and peer endorsement are key drivers of marketing success [13].
A distinct category of user-generated content (UGC) is consumer-generated content (CGC) that specifically refers to content voluntarily created by consumers about a brand, its products, or its services [14]. Consumer-generated content (CGC) is a more specific form of user-generated content (UGC) that comes directly from consumers and offers meaningful insights into their experiences with a brand’s products or services. In comparison, UGC encompasses a broader range of content created by anyone online, which may not always have a direct connection to the brand or serve a specific purpose. While CGC is more focused than general UGC, it is still produced independently of professional guidance, resulting in content with varying levels of quality [15].
As brands are trying to leverage the authentic nature of CGC, which seems to offer several significant positive results for brands [16], without completely relinquishing control over the content shared, a new marketing tactic has arisen. That is a new type of CGC guided by brands. No specific definition of this emerging trend is yet available, but it has recently been referred to as “Directed Consumer Generated Content” [17].
Simultaneously, researchers and practitioners note that many companies continue to face significant challenges in effectively leveraging social media strategies to enhance digital customer engagement rates [18]. This remains a persistent issue for numerous organizations. Businesses are increasingly focused on maximizing the return on their investments by implementing well-optimized marketing strategies and allocating resources toward advertisements that drive strong performance and yield high conversion rates. It is noteworthy that, according to WARC’s latest research, Global Advertising Trends [19], social media has emerged as the world’s largest advertising channel in terms of investment. Projections indicate that it will reach an estimated USD 247.3 billion by 2024. Meta is the leader in this case, with its platforms accounting for 63% of global social media spending. In fact, Meta’s revenue this year from advertising alone is expected to reach USD 155.65 billion [20]. These substantial figures, coupled with the dynamic advancements in digital marketing, have motivated a deeper exploration of the role and impact of consumer-generated content as a strategic marketing tool.
To date, the impact of consumer-generated content (CGC), particularly this emerging form of directed CGC, on key performance metrics such as click-through rates, conversion rates, overall conversions, and return on ad spend (ROAS), remains insufficiently examined. This is especially evident when compared to the extensive research dedicated to traditional social media advertising strategies. Therefore, this paper aims to explore the efficacy and effectiveness of DCGC compared to traditional social media advertising in terms of performance and return on ad spend (ROAS) on social media platforms, and specifically on Meta.
To evaluate the effectiveness of DCGC (direct consumer generated content) compared to traditional online advertising in a social network, an example of an online bookstore is used. Therefore, this research proposes the following hypotheses:
H1: 
DCGC leads to higher click-through rates compared to traditional social media advertising content.
H2: 
DCGC leads to higher conversion rates compared to traditional social media advertising content.
H3: 
DCGC yields a higher return on ad spend (ROAS) compared to traditional social media advertising content.
This study focuses on campaigns and data derived exclusively from the publishing industry, specifically concerning the promotion of books. From a theoretical perspective, one of the research objectives is to evaluate how the publishing industry, as part of the broader e-commerce ecosystem, employs social media and consumer-generated content (CGC) to enhance marketing efforts. The study investigates the extent to which online platforms are leveraged to promote books and explores the unique characteristics and influence of CGC on platforms such as Meta within the context of e-commerce and book marketing.
In addition, the research aims to quantify and analyze performance metrics to better understand user interactions with various types of content. A key focus is determining whether CGC fosters higher purchase intention and leads to increased conversions. This analysis also includes a comparison of campaign performance across the two social media platforms to identify potential differences. The primary goal of this research is to establish which marketing approach yields superior performance outcomes and a higher return on investment (ROI), thereby providing practical recommendations for marketers within the e-commerce and publishing sectors.
The methodology employed in this study involves a comparative analysis of performance metrics, such as click-through rates (CTR), conversions, conversion rates, and return on ad spend (ROAS), across two distinct groups: campaigns incorporating directed consumer-generated content (DCGC) and campaigns utilizing traditional, brand-crafted social media advertising strategies. Descriptive statistical methods were applied to analyze the entirety of the dataset, while Z-Tests and Mann–Whitney U tests were conducted to assess significant differences in performance metrics between the two groups.
In summary, this study seeks to offer actionable insights for practitioners aiming to optimize their e-commerce digital marketing strategies through the use of consumer-generated content. The findings are intended to guide marketing professionals in improving the performance of social media advertising campaigns and maximizing the effectiveness of their investments.

2. Theoretical Background

2.1. Meta Social Media Platform

Meta (encompassing platforms like Facebook and Instagram) is a pivotal player in the digital marketing landscape, offering unique environments for content creation, distribution, and engagement. These platforms enable the exchange of information and facilitate interactions between users, brands, and communities [21]. Platforms such as Facebook, Instagram, Twitter, TikTok, and YouTube have established global online communities that facilitate the exchange of user- and consumer-generated content related to products, services, and companies. Their impact on consumer behavior and marketing strategies has been profound, transforming the ways in which businesses and consumers interact online. From a business perspective, these platforms serve as critical tools for fostering interactions between brands and their customers, thereby strengthening customer-brand relationships and contributing to the creation of experiences that influence purchasing decisions [22].
Meta’s platforms have been instrumental in fostering global connectivity, enabling users to share content in various formats, including text, images, and videos. From a business perspective, Meta offers sophisticated advertising tools, with advanced targeting capabilities that allow brands to reach precise demographics based on user behavior, preferences, and interactions [23]. This capability makes Meta particularly effective for implementing tailored marketing strategies, as it enables businesses to craft personalized marketing messages, increasing the likelihood of consumer engagement and conversion [24].
In 2023, Meta generated USD 134.9 billion in revenue, with 98.5% derived from its Family of Apps, highlighting the centrality of advertising in its operations [20]. Facebook and Instagram serve as key platforms for marketers, providing brands with unparalleled reach and audience segmentation tools. These tools are critical for executing both branded content and consumer-generated content (CGC) campaigns.
Meta’s platforms are designed to foster relationships and interactions within established social networks. Consequently, content dissemination is often influenced by existing connections, making branded content more likely to circulate within a brand’s immediate audience [21]. However, Meta’s advertising infrastructure mitigates this limitation by allowing brands to amplify their content through paid promotions, which can extend its reach to new and highly targeted demographics [15]. This precision targeting is especially effective for branded content, where maintaining control over the brand message is a priority [24].
The effectiveness of CGC is supported by academic research. Studies have shown that CGC enhances brand trust, engagement, and authenticity, making it a powerful tool in modern marketing strategies [25,26]. Schivinski and Dabrowski [15] found that consumer-generated social media communication positively influences brand equity components, such as brand awareness and brand image. On Meta’s platforms, CGC can also gain traction through user interactions like shares and comments, but its reach is often more contained within social circles unless amplified through paid promotion.
Despite their advantages, social media platforms pose challenges for CGC campaigns. For example, Meta’s reliance on user networks can limit the organic spread of CGC. Another significant challenge associated with CGC, in social media platforms, is its uncontrolled nature, which makes brands vulnerable to the spread of negative information. Unlike traditional brand-created content, CGC is highly susceptible to public sentiment shifts, and even a single viral negative review can disproportionately impact consumer trust and brand perception. Research has shown that negative electronic word-of-mouth (eWOM) tends to spread faster than positive content due to its higher emotional intensity [27]. This phenomenon aligns with the Negativity Bias Theory, which posits that individuals pay more attention to and recall negative information more than positive experiences [28]. Furthermore, within the framework of Social Influence Theory [29], consumer behavior in social media settings is often shaped by compliance, identification, and internalization processes [30]. Consumers may comply with peer recommendations due to social pressure, identify with influencers who share similar values, or internalize shared opinions as their own [29]. This suggests that opinion leaders and influencers play a critical role in shaping CGC impact—both positive and negative. For instance, influencer endorsements can amplify CGC’s effectiveness, but negative influencer commentary can quickly erode trust, even without objective justification. Brands must carefully consider these platform dynamics when designing campaigns, aligning their strategies with each platform’s strengths to maximize engagement and impact.

2.2. Social Media Advertising

Social media platforms have revolutionized how brands interact with their customers, providing innovative tools to engage existing audiences, attract new ones, and implement diverse marketing strategies to foster strong relationships [31]. These platforms have become integral to modern marketing, enabling brands to craft personalized messages and deliver them to highly targeted audiences. Alalwan et al. [23] identified key marketing practices facilitated by social media, such as advertising, electronic word-of-mouth (eWOM), branding, and customer relationship management (CRM). Of these, advertising has garnered the most attention from both academics and practitioners due to its centrality in achieving marketing objectives [23]. Businesses invest billions annually in social media advertising, with platforms like Meta emerging as leaders in enabling campaigns that are both scalable and highly interactive [19].
Paid social media advertising is a distinctive and essential tool that sets digital platforms apart from traditional advertising channels. Lovett and Staelin [32] classified social media into three types of media: paid, earned, and owned. Paid media involves advertisements that brands purchase on platforms like Meta. Earned media encompasses organic publicity generated by user interactions, such as shares and comments, while owned media includes brand-controlled content such as posts on official profiles. Social media advertising, as defined in this paper, refers specifically to paid campaigns on platforms such as Facebook and Instagram. These campaigns fall into two distinct categories based on their content: branded content created directly by companies or in collaboration with agencies, and consumer-generated content (CGC), which is created voluntarily by consumers and later incorporated into advertising campaigns.
Paid advertising’s effectiveness lies in its advanced targeting capabilities, which allow advertisers to segment audiences based on demographic, geographic, and behavioral data. Platforms such as Meta leverage vast user data to offer granular targeting tools that enable brands to reach audiences with precision [23]. For instance, Meta’s platforms, including Facebook and Instagram, allow marketers to tailor ads to specific consumer segments, optimizing both the reach and relevance of branded content [33]. This capacity for targeting increases the likelihood of engagement and conversion, as users are more likely to respond to messages that align closely with their interests and preferences.
The type of content used in paid campaigns—branded content or CGC—significantly influences audience engagement and campaign outcomes. Branded content is crafted to align with a company’s marketing objectives and brand identity, offering full control over the messaging. This ensures consistency in the portrayal of brand values, making branded content especially effective for campaigns targeting specific demographics [34]. However, branded content often lacks the perceived authenticity of CGC, which is created by consumers based on their personal experiences with a brand. CGC is widely regarded as more credible and relatable, as it reflects genuine user perspectives rather than polished, company-driven narratives [15]. As a result, CGC often fosters higher levels of trust and emotional connection, making it particularly effective in enhancing brand loyalty and driving purchase intentions [35,36].
Despite their respective advantages, both CGC and branded content present unique challenges. CGC requires brands to relinquish some control over their messaging, which can lead to inconsistencies in how brand values are perceived [34]. Additionally, CGC campaigns often rely on user participation, which can be unpredictable in terms of both quality and scale. Brands must weigh these trade-offs carefully when designing campaigns, aligning their choice of content type with their overall objectives and the unique characteristics of the platform they are using.

2.3. User-Generated Content (UGC) and Consumer-Generated Content (CGC)

User-generated content (UGC) is a multifaceted and extensively studied concept in the academic literature [24]. It has been defined in various ways by both researchers and practitioners across multiple disciplines [37]. Although a universally accepted definition of UGC does not exist, the Organization for Economic Co-operation and Development (OECD) defines UGC based on three core characteristics: “(i) content made publicly available over the Internet, (ii) reflecting a certain amount of creative effort, and (iii) created outside of professional routines and practices” [38].
UGC refers to “media content that is created or produced by the general public rather than by paid professionals and is primarily distributed on the Internet” [39]. It can include content created by fans, hobbyists, or anyone using a product or service. Fundamentally, UGC refers to digital material created voluntarily by users who may or may not be customers of the associated brand. The concept of user-generated content (UGC) was first introduced in the academic literature in 2001 by Figallo and Rhine [40] and Crawford [41]. In recent years, user-generated content (UGC) has experienced significant growth, driven by the rise in social media platforms. UGC has become ubiquitous, encompassing a wide range of content such as user-shared images, videos, comments, and blog posts across diverse digital platforms. In his study, Luca [1] categorizes the various roles played by users and other participants within UGC platforms. Two primary roles are identified: contributors, who create and share content, and consumers, who engage with and utilize this content. A distinctive characteristic of UGC platforms is that users often perform both roles simultaneously, acting as both contributors and consumers. In addition to these primary roles, Luca identifies three additional categories of participants. The first are advertisers, typically companies or organizations seeking to reach and influence platform users. The second group includes bystanders, which may consist of individuals or entities that are indirectly involved, such as those featured in or affected by the content. Finally, there are designers who are responsible for defining the platform’s rules, policies, and structural framework.
O’Hern and Kahle [42] argue that user-generated content (UGC) represents a significant shift in power dynamics, transferring greater control from companies to consumers. In their study, they propose a typology of UGC that classifies it into four distinct forms based on two primary dimensions. The first dimension, referred to as the “User Contribution Objective”, relates to the purpose behind a user’s content creation. For instance, users may produce content aimed at sharing their opinions or promoting a particular product or brand, categorized under “Product Promotion”. Alternatively, they may focus on contributing to the improvement or development of a new product, which falls under the category of “Product Innovation”. The second dimension examines the “Direction of UGC Knowledge Flows”, which describes how information is shared and with whom. Certain types of UGC are intended primarily for other platform users, characterized by customer-to-customer (C2C) knowledge flows. In contrast, some UGC is specifically designed to convey information directly to a company or organization, representing customer-to-business (C2B) knowledge flows. Based on this typology, the authors identify four distinct types of user-generated content (UGC): Informing, Pioneering, Co-Communicating, and Co-Creating. This study focuses primarily on the Informing type, which refers to UGC created with the intent of promoting a specific brand or product and is primarily directed toward other consumers.
It is crucial to differentiate between user-generated content (UGC) and consumer-generated content (CGC), despite the two terms frequently being used interchangeably in the literature. CGC represents a more specific subset of UGC. As its name implies, CGC refers to content created and shared by consumers who have directly used a company’s product or service, thereby providing feedback that is both relevant and actionable. CGC is often described as “brand-related UGC” [14], as it specifically pertains to a brand and its products or services. However, it is important to note that CGC is generated voluntarily by consumers, without influence or direction from the brand itself [14]. Consumer-generated content (CGC) is often confused with electronic word-of-mouth (eWOM). Although the two concepts are closely related and sometimes used interchangeably, they are distinct [14]. CGC encompasses a broader range of content compared to eWOM, which is specifically defined as “any positive or negative statement made by potential, actual, or former customers about a product or company, made available to a multitude of people and institutions via the Internet” [43].
Consumer-generated content (CGC) has emerged as a critical topic in contemporary marketing discourse. Studies emphasize its importance in modern marketing strategies due to its ability to enhance brand perception, loyalty, and consumer trust. For example, Schivinski and Dabrowski [15] found that both consumer-generated and firm-created content on social media significantly influence customers’ brand perceptions. However, CGC offers an additional advantage by positively affecting brand loyalty and perceived brand quality. Similarly, Demba et al. [35] demonstrated that user-generated content, including CGC, fosters brand trust and strengthens purchase intentions, highlighting its value in building strong customer relationships. Business media outlets, such as Forbes [44], also commend CGC for its effectiveness as a subtle yet impactful marketing tool. CGC integrates seamlessly with other social media content, giving brands the opportunity to leverage the creativity and influence of their customers. By doing so, brands can generate content that appears spontaneous and authentic. In contrast, traditional advertisements are often meticulously crafted and polished, which can make them less relatable to audiences. This authenticity associated with CGC has been identified as a key factor in its effectiveness, both in terms of cost-efficiency and marketing outcomes [45].
Studies have highlighted the positive effects and outcomes of incorporating consumer-generated content (CGC) into marketing strategies. For instance, Mayrhofer et al. [9] found that CGC generates higher purchase intentions compared to disclosed advertisements and brand-created content. The researchers further argue that CGC, as an inbound marketing strategy, integrates more naturally with other social media content [46]. By integrating seamlessly into the user experience without creating disruptions, CGC is regarded as a more effective and engaging marketing strategy. Schivinski and Dabrowski [15] demonstrated that while both consumer-generated content (CGC) and firm-created content on social media significantly influence customers’ brand perceptions, CGC exerts an additional positive impact on brand loyalty and perceived brand quality. In their research, Rosario and Dias [21] found that consumers are more likely to believe and be influenced in their purchasing decisions by recommendations and reviews from their peers than company-crafted promotional messages. According to Malthouse et al. [47], CGC positively influences consumers’ brand engagement, while Kim and Johnson [48] argue that it also has a favorable influence on a consumer’s purchase intention.
CGC is increasingly becoming a strategic priority for many brands as they seek to enhance the value they deliver to their customers [49]. Despite its potential benefits, some companies remain hesitant to integrate collaborative consumer-generated content (CGC) into their marketing strategies. This reluctance primarily stems from the need to relinquish a degree of control over brand messaging and the interpretation of brand meaning [50].
A new form of consumer-generated content (CGC) has emerged, enabling firms to harness the advantages of CGC while retaining partial control over their brand communication by providing direction in the content creation process. This emerging form of consumer-generated content (CGC) has recently been referred as “directed consumer-generated content” [17]. Directed consumer-generated content (DCGC) preserves the authenticity and creativity inherent in traditional CGC while aligning the content with the brand’s strategic marketing objectives. This form of CGC can be likened to “sponsored” user-generated content (UGC), wherein firms collaborate with influencers or opinion leaders to promote their brand [51]. The key distinction lies in the fact that directed consumer-generated content (DCGC) is primarily produced by lesser-known creators or individuals with a small following. As a result, its effectiveness does not rely on the creators’ influence as public personas but rather on the blend of CGC’s inherent authenticity and the strategic guidance provided by the brand.
Recent research has delved into the effectiveness of brand-directed versus consumer-generated content. A study by [52] found that brand-directed content can sometimes lack the authenticity perceived in consumer-generated content, potentially affecting consumer engagement. This highlights the importance of authenticity in DCGC initiatives. Furthermore, the perception of authenticity in sponsored UGC has been scrutinized. Research by [53] indicates that sponsorship can diminish perceived authenticity, leading to reduced consumer engagement. This suggests that DCGC campaigns must carefully balance brand direction with genuine consumer expression to maintain credibility.
For the purposes of this research, the term directed consumer-generated content (DCGC) will be used to describe the campaigns analyzed in this study, which can be categorized as a form of “firm-directed” CGC. This classification is based on the method of content creation employed in these campaigns, as well as the fact that the content is promoted through paid advertisements.
Consumer-generated content (CGC) appears to provide an efficient, cost-effective, and timely communication channel between companies and their customers. When effectively managed, CGC has the capacity to shape consumer perceptions of a brand and significantly influence purchasing decisions.

3. Methodology

3.1. Research Design

To compare the effectiveness of consumer-generated content versus traditional brand-created social media advertising, this study uses a field experimental design utilizing the Meta platform. A total of 26 DCGC campaigns were created and delivered on the platform. Additionally, 41 “traditional” Meta campaigns were executed at different times and aimed at different audiences, using a between-subjects design. To ensure that different types of campaigns were presented to distinct participants, the conventional campaigns were configured to exclude the audience that had previously been exposed to the DCGC campaigns. The same exclusion rule was applied in reverse. The DCGC campaigns were run during the first two weeks of May, followed by the traditional campaigns during the subsequent two weeks. For both campaign types, a custom audience was used that excluded anyone who had engaged with the account. This ensured that all users were in the awareness stage and that no participant saw both types of ads. Regarding temporal factors, there was no seasonality in May, and both campaigns ran back-to-back, minimizing time-based variations. Additionally, all campaigns promoted the same book, eliminating any bestseller effect. No audience segmentation was applied; we used broad targeting for all ads.

3.2. Data Collection

Throughout the field experiment, we cooperated with a professional digital marketing agency called Viral Passion, based in Athens, Greece, with expertise in designing, executing, and analyzing social media marketing campaigns. The agency provided metrics and results from the paid advertising campaigns that ran on Meta. The campaigns were developed for a single client, a publishing house, and consequently, their content focused on books. In total, 67 campaigns were analyzed, all targeting the Greek market, ensuring a consistent and structured approach to measuring the effectiveness of DCGC and traditional advertising strategies (See Supplementary Materials).
The publishing industry has observed a significant transition from conventional advertising methods to an increased emphasis on digital marketing strategies. This shift can be attributed to the increased use of social media, and the potential they offer for creating book-friendly communities. Such communities can be located in Facebook groups, Instagram book clubs [54].
The products advertised belonged to the same category, allowing for straight comparison. Possible confounding factors, such as content quality variations, were mitigated by maintaining a uniform structure across campaigns. The same product (one book) was promoted in all ads, and a consistent advertising approach was used, ensuring that any differences in performance were due to the ad format rather than variations in product appeal. Therefore, all campaigns, both DCGC and traditional, promoted the same specific book (same SKU). This ensured that any differences in performance metrics (CTR, conversions, ROAS) were due to the ad creative rather than variations in product appeal, price, or demand. Additionally, no audience segmentation was applied, meaning both campaign types targeted the same broad audience. Some of the campaigns were firm-created “traditional” paid advertisements, while the rest were “directed” consumer-generated content that was boosted by the advertising company as paid campaigns. The brand-crafted ads consisted of photos or videos that the company had created, the content of which was solely based on the product that was advertised in each case. Every campaign included a call-to-action to click on the link provided and purchase the book that was promoted. The campaigns were boosted by the company’s Meta accounts.
The methodology employed by the advertising agency for developing consumer-generated campaigns is intricate and is outlined as follows. The advertising company has created a pool of about 1000 creators. These creators are social media platform users who do not have a large number of followers and, therefore, cannot be considered influencers. Initially, the advertising agency and the client collaborate to establish the campaign and its strategic objectives. After identifying targets and selecting products for the campaign, the advertising company chooses seven creators who best align with the brand and product and presents these options to the client along with their costs. From a selection of seven creators, the customer must choose five. These chosen creators receive the product for free along with a brief outlining the content creation requirements. Each creator is then asked to make one video that includes three different introductions, each lasting the first three seconds. This results in three videos featuring different intros but the same main content.
The three variations are then evaluated through a brief advertising A/B test, conducted with minimal expenditure. The A/B test conducted to select the final DCGC video was a preliminary test and not part of our core study. The selection was carried out by the agency’s experts rather than through a formal statistical analysis. This step was purely to determine which video would be used in the main campaign and did not influence the comparative analysis between DCGC and traditional advertising. The A/B testing process for selecting the final DCGC video was conducted by the marketing agency. However, the specific selection criteria for creators were determined by the agency experts, and potential biases in this process were not systematically analyzed in this study. The video that performs best in the A/B test is the one the creator will be asked to publish on their platform. This video is promoted on Meta through the customer’s account. Videos that demonstrate a strong Return on Advertising Spend (ROAS) are retained for future advertising, while those that underperform are discontinued from advertising efforts. Regarding success metrics, CTR, conversion rate, and ROAS were used as key performance indicators, defined based on standard industry practices. These metrics were selected for their direct relevance to assessing ad effectiveness, with CTR measuring engagement, conversion rate indicating purchase intent, and ROAS evaluating financial efficiency. The analysis focused on statistical comparisons between DCGC and traditional campaigns, controlling for exposure and audience consistency.
It is essential to recognize that creators receive compensation for their services; however, the remuneration is relatively modest due to their limited online presence. As a result, the pricing for this type of content is significantly lower, particularly when juxtaposed with similar collaborations involving well-known influencers. In the DCGC campaigns, the cost structure was consistent across all creators. Each creator was compensated with a fixed payment ranging from EUR 40 to EUR 60 per video. Additionally, all creators received the same product free of charge, ensuring uniformity in promotional material. Compared to collaborations with well-known influencers, which typically involve significantly higher fees, this approach provided a cost-effective alternative for content creation. While these costs were a component of the overall advertising budget, their impact on ROAS remained minimal compared to the total ad spend, as the primary expense in Meta advertising comes from campaign promotion rather than content production.
The DCGC campaigns used in this study specifically featured videos where the creators reviewed and promoted a book, urging viewers to click the provided link to purchase the product.
After collecting the aforementioned campaign data from the advertising company, the data were cleaned, duplicates were removed, and all the campaigns were combined in one file, keeping only the variables and metrics that were available for all four types of campaigns, and which would be used in the statistical analysis.

4. Analysis of Results

As already previously analyzed, the aim of this study is to investigate the efficacy of DCGC in relation to several performance metrics. For the purposes of this study, paid advertising campaigns on social media were analyzed, utilizing key performance metrics such as click-through rate (CTR), conversions, conversion rate, and return on advertising spend (ROAS). These metrics focus on customers’ actual interactions with the ads and clearly provide an insight into customers’ purchase intentions and actual purchase actions.
It is important to provide a definition for each of these four metrics manipulated in this study:
  • Click-through rate (CTR) is an important and widely used metric in the realm of performance marketing. Essentially, it shows the ratio of users who click on the call-to-action to the total number of users who viewed the content. It is calculated using the following formula [55]:
CTR = (Clicks/Impressions) × 100,
  • Conversion rate (CR) measures the percentage of users that complete a desired action after clicking on an advertisement. The desired action could be completing a contact form, subscribing to a newsletter, or making a purchase. As a metric, CR directly links an ad and the desired outcome [56]. It is calculated with the following formula:
CR = (Conversions/Clicks) × 100,
  • Conversions show the number of users that not only click on the content, but also complete the desired action, which in this case is a purchase. This metric is a good indicator of how effective the content is when it comes to achieving the desired marketing objectives.
  • Return on ad spend (ROAS) measures the revenue generated for every dollar spent on advertising. It is a clear indicator of the level of revenue generation and profitability of the campaign. In this study, it was calculated using the following formula:
ROAS = Total Complete Payment Value/(Total Initiate Checkout × Cost per Initiate Checkout),
In other words, the revenue attributed to the ad campaign was divided by the cost of that campaign. Meta provides ROAS directly at the campaign reports.

4.1. Analytical Techniques

SPSS was used to perform the statistical analysis of the data. More specifically, descriptive statistics, such as means, medians, and standard deviations, were executed for all the available data in order to provide an overview of insights on the performance of each campaign.
CTR and conversion rate data exhibit characteristics consistent with a binomial distribution, where a value of 1 signifies a click or conversion, and a value of 0 indicates its absence. For the purpose of hypothesis testing, a Z-test is applicable, as we are evaluating proportions. Rather than calculating the CTR for each individual campaign (N = 67), we consolidate the data for all impressions and clicks across each category—traditional ads and DCGC ads—resulting in two aggregated datasets. Here, N traditional represents the total impressions for traditional ads, while N DCGC denotes the total impressions for DCGC ads, rather than the number of campaigns (67). Consequently, the mean CTR for traditional ads is determined by dividing the total clicks on all traditional ads by the total impressions for traditional ads. Similarly, the mean CTR for DCGC ads is calculated by dividing the total clicks on DCGC ads by the total impressions of DCGC ads. The conversion rate (CR) is computed in a parallel manner, where N traditional refers to the total clicks on traditional ads and N DCGC refers to the total clicks on DCGC ads.
As far as ROAS is concerned, the data were not normally distributed, therefore non-parametric independent samples tests, and more specifically the Mann–Whitney U Test, also known as Wilcoxon Rank-Sum Test, were employed to compare the means of ROAS between the two groups and consequently help in determining whether the differences observed have statistical significance. This technique was chosen as it provides a simple and effective way for comparing two groups on a single metric when the data are not normally distributed.

4.2. Results

Descriptive statistics for the campaigns on Meta are presented in Table 1. These include data for four key performance metrics: click-through rate (CTR), conversions, conversion rate, and return on ad spend (ROAS).
The following tables present the statistical results of the analysis for Meta campaigns, comparing directed consumer-generated content (DCGC) with traditional advertising. Table 2 and Table 3 provide descriptive statistics and the ranking data for the performance metrics analyzed, while Table 4 and Table 5 outline the test statistics, highlighting the significance levels for each hypothesis.
To assess whether DCGC leads to higher click-through rates and conversion rates compared to traditional social media advertising content, we conducted one-tailed Z-tests. For the CTR (Destination) comparison, we obtained a Z-score of −17.9985 with a p-value < 0.01. Since the Z-score is negative and the p-value is far below the α = 0.05 threshold, we reject the alternative hypothesis (H1) in favor of the null hypothesis. This suggests that DCGC leads to significantly lower click-through rates compared to traditional social media advertising content.
In the case of conversion rate, the Z-test resulted in a Z-score of 23.2051 with a p-value ≈ 2.02 × 10−119. Since p < 0.01, we reject the null hypothesis (H0). This result provides strong statistical evidence that DCGC leads to higher conversion rates than traditional social media advertising content. Moreover, as far as ROAS is concerned (p < 0.01), DCGC campaigns also overperform non-CGC ones.
Based on the descriptive statistics, several interesting observations can be made. DCGC campaigns lead in conversions, conversion rate, and ROAS. However, in terms of CTR, non-CGC campaigns seem to perform slightly better. Notably, although “traditional” Meta ads achieve the highest CTR, they result in the fewest conversions, as well as the lowest conversion rate and ROAS.
To conclude the analysis, the hypotheses tested in this study provide a structured understanding of the comparative performance of directed consumer-generated content (DCGC) versus traditional advertising approaches. The results highlight the potential of DCGC in optimizing critical performance metrics, though not all hypotheses were supported equally. The outcomes of each hypothesis are summarized in Figure 1 and Table 6.
The analysis did not support the hypothesis that DCGC campaigns lead to higher click-through rates (CTR) compared to traditional campaigns. Traditional campaigns on Meta achieved statistically higher CTR values, reflecting the platform’s algorithmic optimization for professional and polished advertisements. This preference aligns with the broader appeal of traditional ads among Meta’s diverse user demographics, particularly older audiences accustomed to brand-crafted content. However, it is important to note that a higher CTR alone does not necessarily translate to better overall campaign outcomes.
In contrast, the data strongly supported the hypothesis that DCGC campaigns lead to more conversions. On Meta, DCGC significantly outperformed traditional campaigns in this metric. These results underscore the power of authentic and relatable content, which resonates more effectively with audiences and compels them to take desired actions, such as completing purchases. Finally, the hypothesis that DCGC campaigns yield higher return on ad spend (ROAS) was supported. DCGC campaigns on Meta consistently delivered better financial returns than traditional campaigns, showcasing their potential as a cost-effective approach to achieving performance-driven goals. These results position DCGC as a valuable tool for marketers seeking higher ROI through innovative and consumer-centered advertising strategies.

5. Discussion

This research aimed to evaluate whether directed consumer-generated content (DCGC) campaigns are more effective than traditional brand-crafted campaigns in social media advertising. Businesses continue to allocate increasing resources toward social media advertising in search of innovative and impactful strategies to engage audiences. DCGC, a relatively new advertising approach, combines the authenticity of user-generated content with strategic direction provided by brands. This study contributes to the growing body of knowledge by examining the effectiveness of DCGC campaigns analyzing 67 campaigns on Meta. Metrics such as click-through rate (CTR), conversions, conversion rate, and return on ad spend (ROAS) were statistically evaluated to assess performance.
The findings of this research provide valuable insights for both academics and practitioners. Hypotheses H2 and H3 were supported by the results, indicating that DCGC campaigns significantly outperformed traditional campaigns on Meta in terms of conversions, conversion rate, and ROAS. These results suggest that consumers respond more positively to content created by other users rather than by brands. In turn, this aligns with the notion that content created by users is perceived as more authentic and relatable [7]. Research has already shown that people’s purchasing decisions are more likely to be affected by peer-to-peer recommendations than firm-crafted marketing campaigns [21,57]. The success of DCGC campaigns on Meta highlights their potential as a cost-effective and persuasive marketing tool, particularly in industries where trust and community-driven recommendations, such as the publishing industry, play a critical role. It is important to acknowledge that reasons that may favor DCGS ads versus traditional ads could be video style, message framing, and emotional appeal. Further research is needed to determine the underlying factors.
However, the results also revealed that hypothesis H1 was not supported, as traditional campaigns on Meta achieved significantly higher CTR compared to DCGC campaigns. The mean CTR for traditional campaigns was higher, and the statistical analysis confirmed this difference. This could be attributed to Meta’s algorithmic structure, which is optimized for highly polished, professional ads that align with established advertising norms. Traditional ads may also benefit from Meta’s larger and more diverse user base, as the platform has over 2.96 billion monthly active users on Facebook alone [58]. Furthermore, these results indicate that while DCGC may reduce click-through rates, it is associated with a higher conversion rate. This suggests that although users may engage less frequently with DCGC ads, those who do interact are more likely to complete the desired actions, which in this case, is a purchase. This could be explained by the fact that DCGC ads are more detailed and informative than conventional ones, preventing customer clicks that would not lead to conversions. In other words, traditional ads lead to more clicks since consumers need to click the ads to find more product content.
While DCGC improves engagement and ROAS, its reliance on user participation may create challenges in content consistency and scalability. Brands must carefully manage creator selection and maintain quality control to ensure effectiveness across campaigns. Beyond publishing, DCGC can be applied in industries where authenticity and peer influence drive consumer decisions, such as fashion, beauty, and tech. Future studies should explore its effectiveness in diverse markets.
To address scalability, brands can develop structured creator networks, implement content guidelines, and use AI-driven analytics to optimize performance across larger campaigns. These strategies can help marketers integrate DCGC effectively while mitigating challenges. Additionally, the professional design of traditional ads may appeal more to Meta’s broader demographic, including older users who are accustomed to brand-crafted content. Nonetheless, higher CTR does not guarantee better overall outcomes, as DCGC campaigns demonstrated superior performance in conversions, conversion rate, and ROAS.
Finally, it is essential to note that this research was conducted within the context of the publishing industry, with all campaigns focused on book advertising. The industry’s reliance on trust and peer-driven recommendations aligns well with the strengths of DCGC. Positive peer reviews and electronic word-of-mouth (eWOM) have long been recognized as effective marketing tools for books, as demonstrated in earlier research by Chevalier and Mayzlin [59]. The success of DCGC campaigns on Meta reinforces the idea that content perceived as authentic and community-driven resonates strongly with publishing audiences.

6. Implications, Limitations and Future Research

6.1. Theoretical Implications

This research contributes to the growing body of literature on digital and social media marketing, and more particularly on the topic of consumer-generated content in social media advertising.
This study offers a deep dive into the concept of consumer-generated content, what it entails, and especially the ways in which it can be incorporated into brands’ marketing strategy in order to create top-performing ads that yield the best possible results. More particularly, this research focuses on a new form of CGC, a sponsored brand-related type of CGC that is initiated and financed by marketers [60]. This type of content could also be described as directed customer-generated content (DCGC) [17]. At the same time, it offers an extended understanding of the available ways and metrics for measuring the performance of social media advertising, as well as the varying effects that different types of ads can have across social media platforms.
While prior research has established the significance of social proof in consumer trust and engagement—particularly through user-generated content (UGC) [15]—this study extends the theoretical framework by examining how digitally curated consumer-generated content (DCGC) operates as a structured mechanism of social influence. Specifically, this research demonstrates that DCGC campaigns amplify the effects of social proof by strategically presenting consumer-created content in a way that enhances perceived authenticity while maintaining brand coherence. Unlike organic UGC, which emerges spontaneously, DCGC represents a hybrid form of influence that blends the credibility of peer-generated content with the strategic intent of brand messaging. This study thus refines Kelman’s theory by illustrating how digital platforms facilitate a nuanced interplay between authenticity and curation, leading to a more systematic application of social influence principles in digital marketing. By integrating DCGC into the theoretical discourse, this research offers new insights into the evolving dynamics of social influence in online consumer decision-making.
Through the review of literature, it was found that CGC has a significant impact on brand trust and authenticity, as customers are more inclined to trust recommendations from their peers than brand-crafted messages [61]. The comparison between DCGC and non-CGC ads that was conducted also highlights the importance of authenticity in digital marketing and especially in brands’ communication on social media, by revealing a shift in effective advertising paradigms. The better-performing DCGC, which emphasizes and utilizes relatability and spontaneity, challenges conventional advertising theories, which usually put an emphasis on control and consistency over the advertising content. CGC forces brands to hand over to consumers part of that control [62]. On the contrary, DCGC offers an option somewhere in the middle. It retains the authentic nature of CGC, without forcing brands to completely relinquish control. The findings of this research support new views that promote more consumer-driven marketing tactics, such as DCGC, by showing that these types of ads actually perform better, leading to more purchases and better financial outcomes for companies.

6.2. Practical Implications

From a practitioner’s point of view, multiple insights can be extracted from this study to help professionals, brands, and businesses optimize their social media advertising strategies.
It has already been made clear from the results of this research that brands should consider incorporating more DCGC ads in their marketing strategies, especially if they are interested in running performance campaigns, where the main objective is to achieve sales and commercial success. Understanding the superiority of DCGC in terms of ROAS can help practitioners in effectively allocating their resources across the different available advertising options. By investing in this type of “directed” CGC, brands may yield better results and higher returns, thereby achieving greater financial performance. Overall, it seems that DCGC can be a cost-effective, yet impactful way for brands to advertise their products on social media.
The study also confirms that DCGC is a cost-effective strategy for increasing ROAS and conversions, offering practical value for marketers. However, to provide more actionable insights, future versions of the paper should include specific recommendations on designing effective DCGC campaigns. For instance, brands can enhance performance by ensuring that content aligns with audience preferences, leveraging storytelling techniques, and collaborating with micro-creators who have strong engagement within niche communities [63]. Additionally, optimizing ad placement and tailoring messaging to platform-specific user behaviors can further improve campaign outcomes.
Brands can also take advantage of already existing communities on social media, and mimic the content that users are sharing and consuming so as to leverage their DCGC ad’s potential. This is essentially what the advertising company did in cooperation with their client for the DCGC campaigns included in this study. They used the format of book reviews, which is already widely common in book communities, such as book related Facebook groups, and created advertising campaigns in the same form.
Lastly, it is important to remember that no single strategy is a panacea. The dynamic and ever-changing nature of social media and digital advertising requires brands and marketers to stay agile and adaptable in response to changes in the external environment and shifts in technological advancements, trends, and consumer behaviors.

6.3. Limitations and Future Research Directions

Undoubtedly, there are limitations to this study that should be considered. Firstly, the sample size of the data was relatively small (67 campaigns). All of the campaigns came from the publishing sector and were about books, which allowed for a fair comparison of the ads, but future studies should extend and test these hypotheses in other industries and sectors, ideally with a larger dataset. Also, the data manipulated refer exclusively to the Greek market. Additionally, the study’s focus on the Greek market limits generalizability. Replicating the experiment in larger, more competitive industries (e.g., fashion, technology, consumer electronics) would provide broader insights. Longitudinal studies could track DCGC’s long-term impact on brand loyalty and customer retention, offering a deeper understanding of its sustained effectiveness over time.
The study’s focus on the Greek market and the publishing industry presents certain limitations. Cultural attitudes toward consumer-generated content and industry-specific factors, such as the nature of book purchasing decisions, may impact the generalizability of the results. Future studies should test DCGC’s effectiveness across diverse cultural and industrial contexts to determine whether findings hold in markets with different consumer behaviors and advertising norms. Additionally, as social media algorithms evolve, prioritizing new content formats (e.g., short-form video, interactive posts) may shape the future relevance of DCGC strategies. Understanding how these trends influence consumer engagement will be crucial for marketers adapting to digital advertising shifts.
Moreover, this research did not take into account the qualitative elements of the advertisements’ content or the quality of DCGC. While the study shows that DCGC outperforms brand-crafted content, it does not account for variations within DCGC itself. For example, high-quality DCGC might perform better than lower-quality DCGC. Researchers should therefore try to establish which content characteristics (e.g., format, length, tone of voice, etc.) make social media advertising campaigns more effective, expanding beyond the simple distinction between DCGC and non-CGC ads. While the preliminary A/B test was conducted solely to select the final DCGC video, future research could explore the specific characteristics that make certain videos perform better than others. Factors such as message tone, visual aesthetics, and creator presentation style may influence engagement metrics like CTR, CPC, and Average View Time. A systematic analysis of these elements could provide valuable insights into optimizing DCGC for higher effectiveness in social media advertising.
DCGC enhances engagement and ROAS; however, its dependence on user participation presents challenges in scalability and content consistency. Future research should explore industry-wide applicability and long-term impact. Marketers can optimize DCGC strategies by standardizing content guidelines and leveraging AI for creator selection and performance tracking. Strengthening these aspects will improve the study’s practical relevance and academic contribution. Lastly, the available literature on this type of “directed” customer-generated content is quite limited and should be further explored and studied by the academic community in order to establish a more comprehensive theoretical framework.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/systems13020124/s1, Table S1: Detailed Data For Each Advertising Campaign.

Author Contributions

Conceptualization, E.N. and C.L.; methodology, E.N and C.L.; formal analysis, E.N. and C.L.; writing—original draft preparation, E.N., C.L., P.K. and A.K.; writing—review and editing, E.N., C.L., P.K. and A.K.; supervision, C.L.; project administration, C.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors would like to express their gratitude to the marketing agency Viral Passion (https://www.viralpassion.gr) (accessed on 16 February 2025) for their invaluable support and insights throughout this research. Also, they would like to thank Assoc. Emmanouil Zachariadis for his constructive comments regarding statistical analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

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Systems 13 00124 g001
Figure 1. Hypotheses and metrics graphical summary.
Figure 1. Hypotheses and metrics graphical summary.
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Table 1. Descriptive statistics for Meta campaigns.
Table 1. Descriptive statistics for Meta campaigns.
Campaign TypeMetricMeanMedianSt. Deviation
Meta DCGCCTR25.41922.38910.249
Conversions5.041.511.8
Conversion rate0.04430.01610.0756
ROAS70.33834.90896.404
Meta “Simple”CTR2.95627.6880.94
Conversions0.6304.061
Conversion rate0.00200.0129
ROAS0.481803.085
Table 2. Ranks table for Meta campaigns.
Table 2. Ranks table for Meta campaigns.
Campaign TypeNMean RankSum of Ranks
CTR (Destination)Meta DCGC2627.62718.00
Meta simple4138.051560.00
Total67
ConversionsMeta DCGC2645.081172.00
Meta simple4126.981106.00
Total67
Conversion rateMeta A DCGC2645.191175.00
Meta simple4126.901103.00
Total67
ROASMeta DCGC2645.191175.00
Meta simple4126.901103.00
Total67
Table 3. Aggregated descriptive statistics for impressions, clicks, CTR, and CR means.
Table 3. Aggregated descriptive statistics for impressions, clicks, CTR, and CR means.
ImpressionsClicksCTR MeanConversion Rate Mean
META simple685,41121,0020.03060.0012
META DCGC176,05439740.02260.0330
Total861,46524,9760.02900.0063
Table 4. Test statistics for CTR and CR.
Table 4. Test statistics for CTR and CR.
CTRConversion Rate
Standard Error0.00040.0014
Z-statistic−17.9985232.051
p-value0.00000.0000
Z-critical value (one-tailed)1.6451.645
Table 5. Test statistics for ROAS.
Table 5. Test statistics for ROAS.
ROAS
Mann–Whitney U242.000
Wilcoxon W1103.000
Asymp. Sig. (2-tailed)0.000
Grouping variable: campaign type.
Table 6. Summary of hypotheses and outcomes.
Table 6. Summary of hypotheses and outcomes.
HypothesisStatementSupport
H1DCGC leads to higher click-through rates compared to traditional social media advertising content.Not Supported
H2DCGC leads to higher conversion rates compared to traditional social media advertising content.Supported
H3DCGC yields a higher return on ad spend (ROAS) compared to traditional social media advertising content.Supported
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MDPI and ACS Style

Ntousi, E.; Lazaris, C.; Katiaj, P.; Koukopoulos, A. Directed Consumer-Generated Content (DCGC) for Social Media Marketing: Analyzing Performance Metrics from a Field Experiment in the Publishing Industry. Systems 2025, 13, 124. https://doi.org/10.3390/systems13020124

AMA Style

Ntousi E, Lazaris C, Katiaj P, Koukopoulos A. Directed Consumer-Generated Content (DCGC) for Social Media Marketing: Analyzing Performance Metrics from a Field Experiment in the Publishing Industry. Systems. 2025; 13(2):124. https://doi.org/10.3390/systems13020124

Chicago/Turabian Style

Ntousi, Eleni, Chris Lazaris, Pavlina Katiaj, and Anastasios Koukopoulos. 2025. "Directed Consumer-Generated Content (DCGC) for Social Media Marketing: Analyzing Performance Metrics from a Field Experiment in the Publishing Industry" Systems 13, no. 2: 124. https://doi.org/10.3390/systems13020124

APA Style

Ntousi, E., Lazaris, C., Katiaj, P., & Koukopoulos, A. (2025). Directed Consumer-Generated Content (DCGC) for Social Media Marketing: Analyzing Performance Metrics from a Field Experiment in the Publishing Industry. Systems, 13(2), 124. https://doi.org/10.3390/systems13020124

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