Örebro University
School of Humanities, Education, and Social Sciences Media and Communications
Manufactured Authenticity: How Beauty Brands Use
Consumers' Content to Communicate Branding Messages
Social Analysis, Second Cycle
Independent Project, 30 credits, 2020
Author: Meagan Nouis
Abstract
While beauty brands are often known to set industry trends, the consumers pave the way for branding communications on social media. Companies have adapted their marketing strategies to build interactivity into their branding outreach. Therefore, this study answers the question, “How
do beauty brands utilize consumer posts to convey branding messages?” To answer this, a
content analysis was performed using two sets of data: brand posts (n=314) from July 2019 and January 2020, and consumer posts (n=100) which tagged the beauty brands. Using consumer culture theory, the study examines several themes, including branding messages, consumer engagement, and brand authenticity. Results reveal that beauty brands typically use consumer-produced content to convey experiential or user-centered branding messages, while company-produced content most often includes informative and emotional messages. Further discussed is the inclusion of Calls-to-Action (CTAs) which brands use to encourage user engagement. This study found a significant correlation between posts with CTAs and increased numbers in Likes and comments; however, these numbers are often misleading and represent manufactured engagement. At the same time, users were found to engage more with the brands when incentives or self-promotion opportunities were available.
Keywords: branding messages, digital self-branding, Instagram marketing, consumer engagement, content analysis
Table of Contents
1. Introduction 4
2. Literature Review 6
2.1. Key Concepts 6
2.2. Social Media Influencers (SMIs) and General Users 7
2.3. Social Media Platforms 9
2.4. Consumer-Branding 9
2.5. Consumer Engagement 11
2.6. Literature Review Summary 11
3. Research Question and Hypotheses 12
4. Methodology 15
4.1. Research through Content Analysis 15
4.2. Criteria and Design 16
4.3. Consumer Posts 17
4.4. Codebooks and Coding Process 18
4.5. Pilot Study 20
4.6. Ethical Considerations 20
5. Results 21
5.1. Data Adjustments for Outliers 21
5.2. Brand-created Content vs. User-created Content 21
5.3. Post Elements 22
5.4. Engagement 23
5.5. Branding Messages 25
6. Discussion and Implications 28
6.1. Branding Messages 28
6.2. Engagement and Authenticity 29
6.3. Self-Branding and “Justification” 32
7. Study Limitations and Solutions 32
7.1. Original study 33
7.2. The Solution 34
7.3. Limitations with Engagement 34
7.4. Intercoder Reliability 35
8. Conclusion 36
References 38
1. Introduction
With the world becoming more digitally connected than ever before, companies are quickly evolving their marketing strategies to reach global audiences. Having an online presence has become a marketing standard for any business, as it allows consumers the opportunities to connect with and discover the brand and its products. This business-customer relationship is achieved through social media and social media marketing. “Social media” refers to a plethora of digital platforms where user-generated content is produced and shared with others. Although Facebook, YouTube, and Twitter have consistently dominated as the top social networks (Kallas, 2020), other platforms are quickly catching up, along with entirely new digital communications strategies tailored to fit these platforms and their users. Instagram has exploded into popularity in recent years, as has the term “influencer,” which refers to an online personality who has gained a massive following from sharing digital content (Lou and Yuan, 2019).
With traditional online marketing methods continuously losing impact due to ad-blockers and specialty web browser plug-ins (De Veirman, Cauberghe and Hudders, 2017; De Veirman and Hudders, 2020; Pöyry et al., 2019), businesses are using new tactics to reach audiences and build brand recognition. Social Media Influencers (SMIs) have become ubiquitous across all forms of social media. SMIs are “regular” people who have achieved a successful online profile, and companies have been quick to recognize the potential for advertisement opportunities through sponsored posts and endorsements. A staggering 94% of marketing campaigns which collaborated with an influencer were reported effective (Ahmad, 2018, cited in Lou and Yuan, 2019). This suggests that influencer marketing will continue to play a large part in marketing strategies.
Existing research has consistently proven the effectiveness of celebrity endorsements due to their ability to convey meaning and identity. Pöyry et al. (2019) provides a cohesive
description: “The culturally relevant symbolic meanings first reside in the celebrity, and, through the endorsement, they transfer to a product, and from the product to the consumer” (p. 337). We are in a new age of “celebrity” endorsements because social media influencers are not traditional celebrities. SMIs have attained followings through their own content-generation, thus their method and style of sharing a sponsored post online is perceived as less “commercial” to their followers (Pöyry et al., 2019). That being said, SMIs have their very own symbolic meanings
which become familiar to their followers. While influencer marketing could be seen as disadvantageously narrow in consumer reach, most research explains the value in using niche SMIs to promote relatable products (Neal, 2018). Influencers come from all professional and cultural backgrounds. Therefore, companies which choose to collaborate with SMIs should consider their primary audience and which influencers would be most congruent for the sponsored product.
Because influencer marketing has quickly become front and center for online businesses, it comes with an immense amount of unexplored research questions. Voorveld (2019) offers a helpful overview of existing research in social media marketing and brand communications; research in this area is increasing exponentially, but Voorveld et al. (2018) identifies the majority of studies focused either on Facebook by itself or “social media platforms” as a whole. While Facebook is understandably the most studied platform--due to being one of the first social media platforms and attaining global popularity--Voorveld (2019) suggests new research should break away from Facebook as newer platforms are gaining traction. Likewise, limited research exists about how consumers play a role in branding communications. Because brands are using SMIs in their marketing efforts, branding methods have shifted and become more casual and low-effort.
Brands often face the challenge of delivering consistency in their messages and content across different media outlets. SMIs are a recent addition to online marketing, and hence, an extra obstacle to overcome when achieving coherent branding. Because of this, many questions arise around the topic of branding communications and how messages are delivered from consumers versus the messages shared by the company itself. Analysis of language use, photo elements, calls-to-action, hashtags, emotional elements, and more are necessary to better
understand regular consumers and their role in brand communications. As companies continue to navigate online communications strategies, it is important to understand how branding messages are changing and what that means when growing a following on social media. With all of this in mind, and by following Voorveld’s recommendations, this paper centralizes around the social media platform Instagram and how branding messages change between company-produced content and consumer-produced content. More specifically, the aim of this paper is to answer the research question: “How do beauty brands utilize consumer posts to convey branding
To answer this question, a literature review was conducted to identify existing research in this field. The literature review points to key concepts surrounding online branding, consumer behavior, and company-audience engagement. The literature review also identifies possible research gaps and where further research is necessary. This transitions into the present study including the methodologies, statistical results, and study implications. Finally, the paper ends with my concluding thoughts and suggestions for future research.
2. Literature Review
Before presenting my own research, it is important to introduce existing literature as a reference point. This not only identifies commonalities within previous findings, but it also includes a comprehensive look at the theories and methodologies used when conducting research in media and communications--specifically online branding and consumer behaviors. Thus, the results from the literature review offer relevant guidance for the present study.
2.1. Key Concepts
With social media and online marketing constantly changing and developing, the existing research appears to be somewhat scattered and incohesive (Sundermann and Raabe, 2019; Voorveld, 2019). Certain communicative themes have emerged within marketing and social media research, which will be acknowledged in this paper; however, many concepts remain unexplored. Therefore, the current study shall attempt to expand on these themes and subsequently contribute to the existing body of research within media and communications.
While online advertising has been utilized by companies since the beginning of the Internet, “social media” completely changed how brands view and navigate online resources. Research has proven this as the marketing messages themselves have shifted over time from product-centered to more informative and personalized (Ashley and Tuten, 2014; Shen and Bissell, 2013). This is fitting given that “social media” is exactly that--social. Companies have adapted to the interconnectedness of social media platforms, and rightfully so, as companies face the ever-growing amount of ad-blockers installed by users (De Veirman, Cauberghe and
Hudders, 2017; De Veirman and Hudders, 2020; Pöyry et al., 2019), preventing organically sponsored ads from reaching larger audiences. Additionally, social media have led to companies
losing significant control over their branding communications (Rokka and Canniford, 2016). This is due to the abundance of review websites and the freedom for users to share opinions and criticisms on social media platforms. Thus, the emergence of electronic word-of-mouth or “eWOM” has become a target branding strategy for marketers (De Veirman and Hudders, 2020; Loureiro, Serra and Guerreiro, 2019; Sung, Kim and Choi, 2017; Voorveld, 2019). Companies have leveraged their audiences or “regular consumers” as a way of brand communication, and even more recently, “social media influencers” (SMIs).
2.2. Social Media Influencers (SMIs) and General Users
Social media influencers are the new spokespeople of brand campaigns. They have become ubiquitous across social media in recent years, and marketing companies continue to increase their budgets on SMI endorsements (Schouten, Janssen and Verspaget, 2019; Stubb, Nyström and Colliander, 2019; Voorveld, 2019). “Social media influencers” have been defined differently (Klassen et al., 2018; Lou and Yuan, 2019; Sundermann and Raabe, 2019), but they are commonly understood and accepted as individuals who have gained a significant following through social media platforms from sharing original content (De Veirman, Cauberghe and Hudders, 2017). Studies have observed that most of the successful SMIs encompass attributes of authenticity, relatability, and likeability (De Veirman and Hudders, 2020; Neal, 2018; Schouten, Janssen and Verspaget, 2019). While several articles claim SMIs to have “expertise” in various lifestyle areas, such as interior design, fashion, fitness, etc. (De Veirman, Cauberghe and
Hudders, 2017; Lou and Yuan, 2019; Sundermann and Raabe, 2019), not every SMI fits into the “guru” category. Many influencers gain followings for their personalities, photography styles, or entertaining content disregarding their professional backgrounds. “Influencers” can have
anywhere from a few thousand to several millions of followers across different platforms. While no exact number of followers defines an influencer, research generally concludes SMIs are people who create their own content and have the ability to actually influence consumer behavior (De Veirman, Cauberghe and Hudders, 2017; Klassen et al., 2018; Neal, 2018).
Many aspects of influencer marketing crossover into the much-researched topic of celebrity endorsements. Using a celebrity figure has proven highly successful in branding efforts for decades (Lou and Yuan, 2019; Schouten, Janssen and Verspaget, 2019; Sundermann and
Raabe, 2019). Thus, it is no surprise that popular online personalities produce similar marketing effects as traditional celebrities. SMIs, however, are a modern-day phenomenon; they make their impact by maintaining the “social” aspect of social media, which in turn, drives higher brand attitudes than traditional celebrities (De Veirman and Hudders, 2020; Schouten, Janssen and Verspaget, 2019). Companies today utilize SMIs as a way to reach target demographics,
especially younger audiences who use social media platforms more often than older generations (De Veirman and Hudders, 2020; Lou and Yuan, 2019; Sundermann and Raabe, 2019). Reaching these different target audiences has become much easier and more efficient for companies, whereas before, traditional celebrity endorsements could reach larger audiences but were more general and not targeted towards niche demographics.
Of course, many traditional celebrities have a social media presence and could also fall under the category of “influencer.” However, in terms of social media marketing, research has explored the differences of sponsored posts between traditional celebrities versus social media influencers, and results have shown a much higher brand attitude towards SMIs and their
affiliated endorsement (Schouten, Janssen and Verspaget, 2019). Several conclusions have been drawn about the power of SMI endorsements--one of them being their perceived authenticity (De Veirman and Hudders, 2020; Neal, 2018). Research has found consumers tend to be resistant towards celebrity endorsements because they believe celebrities have ulterior motives (Neal, 2018). In other words, celebrities will endorse products “just for the money.” When followers see sponsored posts from SMIs, however, they tend to trust SMIs’ intentions or understand the need for SMIs to accept endorsements in order to continue creating content for their followers (Stubb, Nyström and Colliander, 2019).
Influencer endorsements also communicate a better sense of relatability compared to celebrities (De Veirman and Hudders, 2020; Neal, 2018). SMIs emulate “regularity” and are active in sharing their personal lives, which is largely part of the appeal for others to “follow” them on social media. Meanwhile, traditional celebrities attain a certain “untouchable” trait about them that disconnects them from their mass of fans. SMIs thrive because of their seemingly “regular lives” and the connectedness between them and their followers, and marketers use this to expand their branding outreach.
The discussion of SMIs and influencer marketing is important to consider when looking at online branding strategies. Not only are companies using SMIs more frequently, but general
consumers are working to achieve “influencer status.” Therefore, general users with low follower counts are mimicking SMIs as a technique to grow their own followings.
2.3. Social Media Platforms
The majority of communications research dedicated to social media marketing focuses on either Facebook exclusively, or an umbrella of “social media platforms” for their measurements (Sundermann and Raabe, 2019; Voorveld et al., 2018; Voorveld, 2019). This leaves large gaps for other platforms to be studied more closely, which this paper achieves. Voorveld (2019) explicitly recommends Instagram to be more thoroughly researched as its immense popularity has led to entirely new marketing approaches. Instagram is a social media platform used for sharing photos or video clips accompanied with captions and “hashtags.” Each post has its own comments section where users can leave direct replies and “tag” other users. Comments sections can be publicly viewed, but the platform does include other features where private messages can be sent between users.
Previous social media research has attempted to measure the level of interactivity between businesses and their audiences; for example, studies which observed Facebook
engagement have recorded the numbers of Likes, shares, and comments received on public posts and used these quantitative values as a determinator for “outreach success” (Grigsby, 2020; Shen and Bissell, 2013). Instagram has similar Like and comment features (and is owned by
Facebook); however, the exact numbers of Likes and comments can be misleading, and the number of shares is hidden information to public users.
2.4. Consumer-Branding
Online communication strategies are evolving and consequently being perceived differently by consumers. In their research paper, Rokka and Canniford (2016) explain the evolution of branding messages and how consumers actively contribute to the production of “brand assemblages.” The idea stems from consumers acting as participants in a brand’s identity--most recently by posting original content, such as selfies, which incorporate a particular brand, and thus the brand’s meaning is molded through “cultural transfer” (Shen and Bissell, 2013).
Social media have consistently been a digital hub for self-expression opportunities. For some users, their personal identities are shaped through brand-identities; therefore, many people choose to incorporate brands into their own shared images, referred to as “brand-selfies” by Sung, Kim and Choi (2017). Their article goes on to explain the existence of brand-selfies as an extension of the brands’ own identities. As a result, brands lose a bit of control over their
branding messages. Similarly, after conducting a visual content analysis, Rokka and Canniford (2016) concluded that “selfies are becoming a nodal point at which official brand assemblages and consumer microcelebrity assemblages intersect. This intersection potentially undermines stable symbolic and material properties of heritage brands through heterotopian selfie practices” (ibid, p.1806).
Brand-selfies are prevalent on platforms such as Instagram. While companies could face unstable branding messages, many of them have utilized these brand-selfies within their own strategic communications practices. This largely explains why “influencer marketing” has skyrocketed into popularity in recent years. Companies have quickly realized the impact
audiences have in regards to electronic word-of-mouth, and as a result, companies have adapted their marketing methods to utilize these user-generated marketing opportunities. As mentioned earlier, SMIs have become increasingly popular across the Internet on numerous social media platforms because of their ability to generate brand loyalty; both the company and the influencer benefit from sponsorships. Generally, sponsored posts on Instagram are in line with the
influencer’s lifestyle, and most likely the lifestyles of the followers, which makes influencer marketing a prime tactic to reach target audiences (Lou and Yuan, 2019).
While influencers are compensated for their brand-selfies, many “regular” users are voluntarily active (whether they are aware of it or not) in the participation of brand marketing. Sometimes regular users purposefully create misleading posts which appear sponsored by certain brands in order to build their own followings (Grigsby, 2020). This brings an interesting dynamic when evaluating branding messages. Prior to social media, “self-branding” was virtually non-existent. With the evolution of influencers, however, many individuals have actively worked to build their own followings as a way of eventually gaining legitimate sponsors and advertising compensation (Grigsby, 2020). When posting photos to Instagram, users have the ability to “tag” other accounts indicating that the tagged accounts are somehow relevant to the post. This
specifically beauty companies--visitors can view the tagged posts on the brands’ profiles. Although the posts are presumably related to the brand itself, many times users will tag multiple brands, sometimes dozens, which are all within the same industry or lifestyle (Rokka and Canniford, 2016). Users use this technique for exposure of their own content. The more brands they include, the more chances of others seeing these posts when visiting the brand accounts’ pages. Because there are no restrictions for who can tag or be tagged, all posts, regardless of their relevance, can appear in company accounts’ profiles.
2.5. Consumer Engagement
Previous studies have analyzed the levels of engagement between consumers and brands. Most of these studies, however, only reviewed Facebook pages for business accounts. Shen and Bissell (2013) performed a content analysis of six beauty brands and examined how they retained “brand loyalty” among consumers. From their findings, they determined companies which had higher posting frequency, and which shared posts containing interactive questions for their audiences received the most attention--thus, receiving the highest scores of brand awareness and loyalty according to their study conclusions. Similarly, in a netnography approach, brands which showed higher social media engagement on Instagram were determined to have “online success” (Loureiro, Serra and Guerreiro, 2019). In both of these studies, quantitative data were used to measure empirical concepts, suggesting that further research methods are needed in this area.
The differences in interactivity levels could be a result of several different elements. First, branding pages are more inclined to build relationships with consumers as part of their marketing strategies. Higher levels of interaction strengthen the “emotional bond” between consumers and the companies (Ashley and Tuten, 2014). Further, brand pages also offer incentives through hosting contests and “calls-to-action” (CTAs). One example is the push for followers to Like, comment, and share certain posts to qualify for gift cards or free products. There are currently no known studies which measure engagement levels when CTAs are present. Consequently, the current paper addresses this.
Social media platforms have allowed increased opportunities for companies to better connect with their target consumers, both through the use of their own branding accounts and through influencers who have successfully generated their own audiences. Companies have shifted from traditional celebrity endorsements to social media influencers, and their branding practices overall have become more “casual.” Furthermore, consumers have historically looked to pop culture, celebrities, and brand identities when developing their personal identities. Therefore, when consumers choose to include brands in their social media posts, branding messages and “assemblages” become unstable.
In addition to branding messages, many studies have examined companies’ abilities to drive user-engagement. While most of the existing research relies on quantitative data when measuring engagement, further research is required to better understand not only how many Likes, comments, and shares there are, but also understand the intrinsic value and tonality of these comments.
Additionally, research heavily focused on social media “content” versus the actual
message of the content (Voorveld, 2019, p.18). While several studies used experimental methods
and content analyses, the majority of research relied on survey methods. Consequently, the existing themes and methodologies found within prior research guide the framework for the present study.
3. Research Question and Hypotheses
As stated earlier, the purpose of this thesis is to answer the question, “How do beauty brands utilize consumer posts to convey branding messages?” To the best of my knowledge, there are no existing studies which directly compare the content and messages between original brand content and audience content which has tagged the brand in their posts. This is an
important question to answer because influencers and “regular consumers” create the posts themselves (Stubb, Nyström and Colliander, 2019), leaving brands vulnerable to inconsistent marketing messages. Based on what we know of brand advertising, product messages have transitioned from transactional to informative (Ashley and Tuten, 2014; Shen and Bissell, 2013). Brands create more product-centered content, while the consumer participates in brand visibility.
Thus, when comparing the two message motives between brands and consumers on Instagram, we can expect the first hypothesis as such:
H1: Company-produced content will convey informative branding messages, while consumer-produced content will contain user-centered messages.
A common goal of marketing campaigns is outreach and user-engagement (Ashley and Tuten, 2014; Klassen et al., 2018; Neal, 2018; Shen and Bissell, 2013). This can be measured in several ways, most commonly through quantitatively documenting the number of comments, shares, and Likes received on each post.
Voorveld et al. (2018) observed consumer engagement more thoroughly and discovered a significant correlation between engagement and the selected social media platform. Their
research included a survey method using a dichotomy approach (e.g. questions asked the users whether they did or did not experience a certain event). Users who frequented eight unique social media platforms were surveyed, and the results confirmed that advertisements on each platform were perceived differently depending on how the platform was typically used and how the advertisements interfered with users’ experiences. These results are relevant for marketing and communications research. Voorveld et al. (2018) concluded that platform-type was critical in determining consumer behavior and that not all advertising messages were created equal. Although Instagram was one of their observed platforms, the study was conducted in 2015, and today’s results may be much different; thus, further investigation is necessary.
Several studies have also identified successful strategies to increase consumer engagement. In Ashley and Tuten’s (2014) content analysis, they discovered brands which posted most frequently also had the highest number of followers on various social media
platforms. Likewise, content which encouraged consumer engagement, such as posing questions, hosting competitions, or other calls-to-action, generated higher user-interactivity. Similar
findings appeared in another study which specifically reviewed company pages on Instagram. Brands such as H&M were scored as having higher “online success” due to their high frequency of posting, while Nike scored lowest (Loureiro, Serra and Guerreiro, 2019). Frequency was not the only factor, as their analysis also revealed the content of H&M’s posts were more “passive” in nature and meant as a means of entertainment. This is reflected in the following excerpt: “Use and gratifications theory suggests social media participants are likely to desire entertainment and
informativeness, but perhaps entertainment is a stronger motivator of engagement with top brands than informativeness” (Luo, 2002, cited in Ashley and Tuten, 2014, p. 24).
User engagement is often accepted as a high indicator of branding “success.” Therefore, the present study shall consider aspects of engagement between brands and consumers through the use of Instagram. The following hypothesis is stated as:
H2: A positive correlation will exist between user-engagement and posts which directly encourage users to comment and Like through the inclusion of Calls-to-Action.
A blurred line exists between “regular consumers” and social media influencers. This is because “influencers” are, in fact, regular consumers who have achieved a large following and subsequent sponsorships for their online posts. Nowadays, users in every corner of the Internet are seeking ways to build an audience of their own because of the money-making potential. Regular users are incorporating new strategies into their personal posts to expand their
reachability, such as creating “fake ads” (Grigsby, 2020) or tagging company pages which are not necessarily included in the post itself (Rokka and Canniford, 2016). This suggests that users are more focused on building their own online presence than on engaging with the brands themselves. With this, the third set of hypotheses read:
H3a: Consumers tag and mention multiple beauty brands to increase their own reachability.
H3b: Consumers focus more on self-branding rather than the brands themselves.
Many consumers are striving to reach “influencer” status. However, as mentioned in the literature review, there is no exact threshold or qualifications to achieve the label of social media influencer. As a result, these different labels between consumers, influencers, and audience are somewhat interchangeable. For the purposes of the present study, the focus lies on company branding messages versus non-company branding messages; therefore, “influencer” content will be grouped into the more general labels of regular consumer and/or audience content.
4. Methodology
The following section provides the methodology used in the present study. This includes both the logistical aspects, as well as the reasons behind choosing these methods. Further details are provided about testing reliability and finally, ethical considerations.
4.1. Research through Content Analysis
From the results of my literature review, I recognized an overwhelming amount of research was derived from surveys or manipulated simulations of marketing advertisements. While this data can be useful in understanding consumer behavior and attitudes towards brands, other research methods have been neglected. Therefore, for the purposes of this report, I have chosen to conduct a content analysis following the guidelines of Neuendorf (2002).
There are several reasons why content analyses are a widely used research method in the communications and media sphere. Content analyses take the form of a quantitative method which closely resembles a scientific method approach (Neuendorf, 2002). Through intricate planning, coding, and systematic protocol, the results should be objective and point to direct causal relationships. This method, just as the scientific method, relies on “generalizability,” meaning that randomness in samples can accurately represent a larger message set (Neuendorf, 2002, p. 12).
A content analysis approach for this thesis is appropriate for answering questions regarding company versus consumer posts on Instagram. By sampling and coding posts from each group, we can observe patterns and compare differences both in qualitative and quantitative data. The beauty industry has one of the largest presences on Instagram, so by performing a content analysis on a smaller sample of posts, we can draw conclusions representative to the beauty industry as a whole.
Prior to conducting my own content analysis, I have generated an a priori design, as recommended by Neuendorf (2002). This design ensures the research protocol is clearly laid out ahead of time, including “all decisions on variables, their measurement, and coding rules” (ibid, p.11). These criteria are presented in the following section.
4.2. Criteria and Design
The majority of Instagram users are female (Schouten, Janssen and Verspaget, 2019). Consequently, Instagram has drawn in brands which cater to female audiences, especially in the fashion, beauty, and lifestyle categories (ibid.). Therefore, this study will focus on beauty brands and their communication practices on Instagram.
Based on previous research designs (Ashley and Tuten, 2013; Loureiro, Serra and Guerreiro, 2019; Neal, 2018; Shen and Bissell, 2013), I have chosen five beauty brands using stratified random sampling. This sampling method allows for representation from different groups, which ultimately translates to the beauty industry as a whole. The selected brands represent a range of popularity and product affordability, and they all primarily sell makeup and skincare products.
The first brand I chose was Caia Cosmetics, which is a Swedish makeup brand. Because of the high price point [e.g. Bibbz Signature Eye Palette with 12 shade options costs $65 USD (Ögonskuggor, 2020)] , Caia is considered a luxury brand. Although the company has only existed since 2018, it is quickly gaining international attention (cf. Englund, 2019). Caia was founded by Swedish personality Bianca Ingrosso, who is a well-known celebrity and influencer among the Swedish population. At the time of this study, the Caia Instagram page had around 120,000 followers.
The second brand is E.L.F. Cosmetics--an American brand known for its cruelty-free makeup and skincare products (Metrus, 2020). E.L.F. falls into the affordable to mid-range category and is considered a drugstore brand. To give a price reference, their Rose Gold Eye Shadow Palette contains 10 different shades and costs $26 USD. With a large international reach, the brand has 5.6 million followers on Instagram as of August 2020.
Next is Maybelline. As one of the oldest beauty brands to exist today, Maybelline has consistently offered affordable makeup across the world. Founded in 1915 (Wischhover, 2015), Maybelline has navigated through over a century of marketing strategies and consumer reach and continues to be a powerhouse in the makeup industry. Their follower count is over 10 million. Although Maybelline has used celebrity campaigns for many years, the company also endorses SMIs on social media. As a price comparison, their 16-shades Nudes of New York Palette is $13.99 USD, and their products are in the low-cost category.
The fourth brand selected was Huda Beauty. Similar to Caia Cosmetics, “Huda” (for short) was founded by influencer and celebrity makeup artist Huda Kattan in 2013 (Sorvino, 2018). Huda has become one of the top makeup brands (ibid.) among other luxury cosmetic companies. Their NUDE Obsessions Eye Shadow Palette costs $37 but has only nine shades instead of 12 compared to Caia. Because Kattan is Iraqi-American, many of her followers are middle eastern, and oftentimes Kattan’s content reflects her cultural background. There are two different Instagram accounts related to Huda Beauty: one of them has the same name
(@hudabeauty) with a massive following of 47.6 million. However, this account is focused on Kattan herself and not the company. Therefore, the account @hudabeautyshop was selected for analysis. They have a following of 6.7 million and post similar beauty content as the other selected brands.
Lastly, Pixi by Petra was selected as the fifth brand for analysis. Out of the five brands, Pixi is the only one to have built its company on skin care products first followed by the addition of makeup products (Tan, 2018). The price range fits into low to mid-range, and their products can be found in both drug stores and high-end cosmetic stores. For reference, the Eye Reflections Shadow Palette has 12 shades and costs $24 USD. The brand has an international reach, and their Instagram account has 1.6 million followers.
The five brands give a strong representation of the beauty industry as a whole. The brands cover a range of affordability (E.L.F. versus Huda), traditional and modern branding (Maybelline versus Caia), and product range (E.L.F versus Pixi). They all have Instagram accounts and have follower counts from 120,000 to over 10 million followers.
As suggested by Neuendorf (2002), the findings from this diverse sample will be applicable to the beauty industry as a whole. Brand posts were measured during two different time periods: the first two weeks of July 2019 and the first two weeks of January 2020. These timeframes were chosen because beauty and fashion trends typically reflect seasonal styles, so having two opposing seasons offer more balanced insights for analysis.
4.3. Consumer Posts
In addition to the five beauty brands, posts from consumers on Instagram were also recorded and analyzed. 20 consumer posts were randomly selected from each brand during the
month of July 2020. All tagged posts had an equal chance of selection. Any posts in a language other than English or Swedish were not considered and therefore replaced. There were 20 posts coded for each brand for a total of 100 consumer posts.
4.4. Codebooks and Coding Process
To answer the hypotheses, there are several areas which will be observed. First, the brands’ own Instagram accounts set the stage for branding expectations: which messages are shared, how the products are shown, which emotional elements are present, how they encourage engagement, and how their communication techniques are effective. Likewise, consumer content will be coded following similar criteria but with additional elements tailored specifically to general users, including the presence of additional brands and tags in their posts.
Because the study design required two sets of data (beauty brands vs. consumer posts), there were two similar but separate codebooks used, which can be viewed in full in Appendix 1. Both codebooks were divided into two sections: content characteristics and branding messages. The first section focused on the physical elements of the post, such as content type (i.e. photo, video, other), product visible, and person/consumer visible. These categories were based on Rokka and Canniford’s (2016) visual content analysis. By documenting the physical
characteristics of each post, we will be able to measure visual frequencies between each group. This will be beneficial in answering the first and second hypotheses. Several categories related to calls-to-action and promotional incentives were also included from Shen and Bissell’s (2013) content analysis. The presence of CTAs on brand posts will assist in answering questions about engagement for Hypothesis 2. One category unique to this paper was identifying whether or not the content was originally produced by the company or taken and shared (referred to as a “regram”) from another user. This category was coded exclusively on brand posts. Posts which are user-made are essential in identifying which branding messages are selected and shared with the brands’ millions of followers.
The second half of the codebook was related to branding messages as used by Ashley and Tuten (2014). This category identified informative, experiential, emotional, and user-centered messages. Both data sets were coded for branding messages. In doing so, the results will provide insight into Hypotheses 3a and 3b, which center around self-branding.
All coded elements used ratio data and were given numerical values during the coding process. A sample of coded elements are presented below in Figure 4.4.1:
Figure 4.4.1
Codebook Sample: Beauty Brands - Physical Characteristics*
Content type: 1=Photo 2=Video 3=Other
Advertised product in photo:
Product name is visible and/or product packaging is shown.
1=Yes 2=No
Person/consumer present:
Must show the face. Photos with only close-ups, including hands, arms, lips, cheeks, etc. are coded 3.
1=Influencer shown and identified
2=Only unknown person or persons in advertisement 3=No persons/consumers present
Call-to-Action used: 1=Like** 2=Comment* 3=Tag 4=Share 5=Follow 6=Visit (another account or external URL)
7=Other/None
*This includes verbiage such as “leave an emoji below” or asking users to “double-tap” the post. “Double-tap” is synonymous with “Like.” Any commands which have the intention to increase user engagement are categorized under this variable.
*Based on content analyses from Rokka and Canniford (2016) and Shen and Bissell (2013)
Codebook Sample: Branding Messages*** Informative content about brand or product:
Utility or functionality of the product/service, where to purchase products
1=Yes 2=No
Emotional
Psychological/social need--how it will make them feel, “your favorite product”
1=Yes 2=No
Experiential
Visually shows or explains how the consumer will experience sight, sound, taste, touch, smells. Includes makeup tutorials.
1=Yes 2=No
User-centered 1=Yes
The main focus is the SMI. If main focus is the brand/product, code 2. Examples: “you deserve it,” “you’re worth it.”
Self-branding
Does the user regularly post make-up photos?
1=Yes 2=No *** Based on Ashley and Tuten (2014, p.21)
4.5. Pilot Study
To ensure the codebook was sufficient for analysis, a smaller sample size was used as a pilot study. Each post was coded for a total of ten posts, and then the results were submitted to my advisor for review. Together we discussed the changes that should be made to the codebook before applying it to the full data set. With the improved codebook in place, the full datasets were ready to be coded for analysis.
4.6. Ethical Considerations
There were several important ethical considerations while designing and implementing the present study. As recommended by the American Sociological Association (ASA), their
Code of Ethics (1997) handbook provides an outline of expectations and practices when
conducting sociological research. In particular, the concept of confidentiality offers relevant discourse regarding the data collected from Instagram:
… [Sociologists] ensure the integrity of research and the open communication with research participants and to protect sensitive information obtained in research, teaching, practice, and service. When gathering confidential information, sociologists should take into account the long-term uses of the information, including its potential placement in public archives or the examination of the information by other researchers or
practitioners. (ibid., p.11)
Although all of the data gathered was publicly available to anyone with an Instagram account, any information which included “personal identifiers” was omitted herein. This was to ensure privacy and anonymity for the individuals whose content was used or shared personally or by the beauty brands. Brand-produced content did not need to be modified.
5. Results
In this section, the statistical data and results are presented. The discussion and implications of these results will follow thereafter in Section 6.
5.1. Data Adjustments for Outliers
After gathering all brand posts from the two time periods (July 2019 and January 2020), there were a total of 314 posts. All data were scanned for errors and outliers using SPSS
Statistics software. After running a series of descriptive analyses to determine “normalcy,” two outliers were identified and adjusted as recommended by Pallant (2016). One post from Huda Beauty had 37,823 comments and one E.L.F post had 29,709 comments. Both of these were replaced with the third highest count of 2,160. Further discussion of these outliers is found in Section 6.2: Brands and Authenticity.
5.2. Original Brand Content vs. Shared Consumer Content
All five brands shared (“regrammed”) images created by other users on their company accounts. Out of 314 posts, 207 (65.9%) posts were original content created by the brand, and 107 (34.1%) were photos uploaded and created by other Instagram users. Looking more closely at the five brands in particular, four of them (Caia, E.L.F., Maybelline, and Pixi) posted original content more often than user-produced content. However, Huda Beauty only shared 13 (26.5%) original images and was the only brand to use others’ images more often than their own. Figure 5.2.1 provides a visual representation of how many posts were original to the company. This leaves us with two sub-categories for further analysis: original brand content (OBC) versus shared consumer content (SCC). While all 314 posts came from the beauty brand accounts, 107 represent not only how consumers communicate branding messages, but which messages brands choose to use for their own branding strategies.
Figure 5.2.1
5.3. Post Elements
Posts were coded for physical characteristics, such as media type (e.g. photo, video, other), product visible, consumer/person visible, gender of consumer, and promotional elements. When considering the two sub-categories, brand-produced images included an advertised
product in 156 (75.4%) posts out of 207. Consumer-produced content had a product visible in 70 (65.4%) images. Conversely, Figure 5.3.1 shows consumer-produced content had a
consumer/person visible in the photo 56.1% of the time compared to only 31.4% of brand-produced images.
A chi-square test for independence (with Yates’ Continuity Correction) was used because of its ability to examine relationships between two categorical variables (Pallant, 2016). The test revealed a significant relationship between user-produced content and consumers visible, 𝑋!(1,
n=314) = 16.907, p < .000, phi = -.24. The negative phi value represents a negative correlation between variables. The results of a second chi-square test indicated there was no significant association between company-produced content and product visibility, 𝑋!(1, n=314) = 2.98, p =
Figure 5.3.1
5.4. Engagement
Only a few quantitative values were publicly available for this study. One of those values was the number of comments on each post. Because each beauty brand had a different number of followers, the average number of comments were also different. A visual comparison of the averages is available in Figure 5.4.1. As mentioned earlier, two outliers were adjusted. The number of Likes were also recorded for each post, which is shown in Figure 5.4.2.
Both of these quantifiable variables were used to help determine the level of engagement between the brands and their followers. Two independent-samples t-tests were performed
because of their ability to process continuous variables across different groups. For the two tests, the dependent variables were set to Number of Likes and Number of Comments, and the
grouping variable set to Brand-Produced Content. With equal variances not assumed for Number of Likes, there was a significant difference found between brand-produced content (M = 23.02, SD = 19.8) and consumer-produced content (M = 31.52, SD = 25.59; t (314) = -3.00, p = .003, two-tailed). A Cohen’s d effect size statistic revealed a medium difference between means (Cohen's d = (31.52 - 23.02) ⁄ 22.8789 = 0.372.) Conversely, there was no significant difference with equal variances assumed for Number of Comments: brand-produced content (M = 246.29,
SD = 357.01) and consumer-produced (M = 275.38, SD = 253.97; t (314) = -.750, p = .454, two-tailed). Because both tests produced negative t-values, this indicates the means were lower for consumer content (Gillespie, 2018).
Figure 5.4.1
One of the coding variables was “calls-to-action,” which included any verbiage asking users to physically Like, comment, or engage with the post. Again, an independent-samples t-test was used with the same continuous variables measured. A significant difference was found when brands included a “comment” CTA (M = 339.15, SD = 370.88) versus posts without a CTA (M = 217.45, SD = 294.91; t (314) = 2.883 [equal variances not assumed], p = .004, two-tailed). The effect size was medium: Cohen's d = (217.45 - 339.15) ⁄ 335.055685 = 0.363. Because there were only eight posts which were coded as having a CTA for “Like,” there was insufficient data to perform a correlation or significance analysis.
5.5. Branding Messages
On Instagram, users can tag people or companies in their posts. This is how beauty brands find which audience-posts to share on their company accounts. Other users can also view these posts by visiting the brand’s Instagram account and navigating to the “recently tagged” icon. For this part of the analysis, 20 “consumer posts” from each brand were coded. This group of data allows for direct comparison between brand posts, and more specifically, the photos which brands chose to “regram” as their own.
After running a frequencies analysis, some content elements proved to be drastically different between consumer posts and brand posts. For example, advertised products were shown 22% of the time in consumer posts, while brands posted images of their products 72% of the time. Conversely, a consumer/person was visible in 78% of consumer posts, while only 39.8% of brand posts featured consumers.
As for branding messages, brands were more likely to include informative content (52.5%) compared to consumer posts (14%). Likewise, “product testimonials” were more frequently included in brand posts (31.1%) than in consumer posts (13%). Content from consumers were coded more frequently as emotional, experiential, and user-centered. A visual representation of the branding messages can be seen in Figure 6.5.1:
Figure 6.5.1
Branding messages: Brand posts versus Consumer posts
Branding Message # of Brand Posts (n=314) Brand Post (Percentage) # of Consumer Posts (n=100) Consumer Post (Percentage) Informative 165 52.5% 14 14% Emotional 101 32.2% 43 43% Experiential 203 64.6% 75 75% User-Centered 203 64.6% 82 82% Social Cause 4 1.3% 1 1% Product Review 99 31.1% 13 13% Exclusivity 16 5.1% 5 5%
Branding messages: Beauty brands original content versus shared consumer-produced Branding Message Brand-Produced Posts (n=207) Brand Post (Percentage) Consumer-produced (n=107) Consumer Post (Percentage) Informative 112 54.1% 53 52.5% Emotional 64 30.9% 37 34.6% Experiential 124 59.9% 79 64.6% User-Centered 115 55.6% 88 82.2% Social Cause 4 1.9% 0 0.0% Product Review 65 31.4% 34 31.8% Exclusivity 11 5.3% 5 4.7%
As the tables above show, consumers typically include messages which are experiential and user-centered more than any other message type. This was even more apparent when brands chose to share consumer-produced content on their own accounts.
A chi-square test for independence revealed a significant difference between branding messages and whether the content was OBC or SCC. Experiential messages were significant, 𝑋!
(1, N = 314) = 5.39, p = .02. Likewise, user-centered messages were also significant, 𝑋! (1, N =
314) = 20.83, p < .000.
6. Discussion and Implications
As we know, the content analysis included two sets of data: the first set being beauty brand posts and the second set being consumer posts. After reviewing the beauty brand posts, two apparent sub-groups were identified, as 107 of 314 posts were, in fact, consumer posts which had been shared by the brand. For the purposes of this discussion, these sub-groups are identified as “original brand content” (OBC, n=207) and “shared consumer content” (SCC, n=107). This distinction is pertinent in the upcoming discussion when considering which posts brands selectively integrate into their public Instagram accounts.
6.1. Branding Messages
The primary focus of this study is to answer the question, “How do beauty brands utilize consumer posts to convey branding messages?” With this in mind, the results revealed several trends. Brands which borrowed or “regrammed” others’ posts most often chose images of consumers using the products and/or showcasing different makeup looks. Meanwhile, original brand content most frequently shared information about products, sales, or upcoming product releases. Hypothesis 1 is therefore confirmed: Company-produced content will convey
informative branding messages, while consumer-produced content will contain user-centered messages.
This is in line with previous literature as companies have transitioned their branding messages over time to reflect a more personable brand identity (cf. Ashley and Tuten, 2014; Shen and Bissell, 2013). The online culture on social media has required companies to adapt their approaches to communications strategies.As stated in Shen and Bissell’s article, “[brand’s have shifted] their focus from products to people and from information delivery to information exchange” (2013, p.646). The results of this study reveal that companies are approaching these message types with the consumers as “spokespeople.”
From a communications perspective, beauty brands are greatly benefiting from organic Word-of-Mouth (eWOM) generated by other Instagram users. Moreover, consumers are most
frequently sharing experiential content, which communicates ideas of brand functionality and authenticity. Brands reap the benefits of “free marketing” through eWOM but still carefully balance other branding messages which are more informative.
6.2. Engagement and Authenticity
The perception of authenticity is pertinent when building an online presence. As discussed in the literature review, authenticity is one of the most influential factors in brand attitudes among consumers. The results from this content analysis revealed several interesting tactics brands use to communicate authenticity which should be addressed.
One of the few measuring methods available for engagement was the number of Likes and comments received on each post. There are many reasons why the number of comments inaccurately reflects user-engagement, but this will be discussed later in the Section 7.3:
Limitations with Engagement. As the analysis results showed, content which was created by the
brand itself received more Likes than posts that were “regrammed” or created by other users. This is contradicting when considering the level of engagement from users who tag the brands. For example, brands support their consumers by sharing consumer-produced content on their own accounts, yet these consumer posts received fewer Likes (engagement) than organically produced content. Beauty brands are therefore compensating for these lower-engagement posts by uploading multiple times a day with an average mix of original content (65.9%) and consumer content (34.1%).
Furthermore, every brand post was coded for the inclusion of Calls-to-Action. Although the codebook included a number of categories, such as asking followers to “tag” others, share the post, or visit an external URL, the most common CTA was asking users to leave comments. The posts would most often say something along the lines of “comment your favorite summer lipstick shade” or ask users what their New Year's resolutions were. Some examples are shown in Figure 6.2.1.
A significant correlation was found between posts which included a CTA and the number of comments. This corresponds with previous research (Shen and Bissell, 2013), and also
confirms Hypothesis 2: A positive correlation will exist between user-engagement and posts
At first, this approach used by brands to increase engagement seemed effective in terms of quantitative data. However, after looking more closely at the comments themselves, I
uncovered several misleading assumptions about the level of engagement.
First, the number of comments shown represents the total number including both original comments from followers and any replies from the brand or other users. In other words, if a post shows as having 100 comments, this does not necessarily mean 100 different people left
comments or engaged with the post. After observing the different brands in this study, brands such as E.L.F. and Pixi were highly responsive to other comments and would oftentimes leave replies to further the engagement level with their followers. This means that posts which initially show 100 comments, hypothetically, could only have 50 unique users engaged with the post and the brand represented the other 50 comments. For the purposes of this research study, these misrepresented numbers are problematic for analysis. However, for the millions of followers of these brands, these numbers communicate different perceptions of community engagement and how likely others are to engage with posts.
Figure 6.2.1
Second, the inclusion of any CTA potentially generates manufactured engagement. When brands add certain Calls-to-Action for followers to Like and comment on posts, then the
followers are contributing to the physical “numbers” but not necessarily engaging with the brand itself or adding anything of intrinsic value. This was most apparent when coding and analyzing
Maybelline’s posts. Out of Maybelline’s 104 posts, 86 (82.7%) of them included at least one CTA--the majority being “comment” (55 posts, 52.8%). Maybelline frequently asked followers to leave certain “emojis” in the comments section. This approach from Maybelline is a low-effort attempt to generate engagement and is ultimately inauthentic. When comparing Maybelline to the other brands, Maybelline had the highest frequency of posts and the highest number of followers, which is in line with previous research (Ashley and Tuten, 2014). They also had the highest percentage of CTAs, yet when the posts and comments were further analyzed,
Maybelline rarely interacted with followers within the comments sections. On the surface Maybelline could be seen as a brand which promotes engagement with their followers or could even be considered to have “online success” as measured by Loureiro, Serra and Guerreiro (2019), but ultimately the brand practices low-effort communications and manufactures engagement.
Another important observation to discuss was the use of contests as an incentive for user-engagement. Out of the 314 posts, two of them (one from E.L.F and one from Huda) were contest opportunities for followers to participate in and enter for a chance to receive free
products from the brands. In both instances, the contest rules required users to follow a series of steps, such as Liking the post, following the brands’ Instagram accounts, and tagging other users in the comments. Both of these contest posts resulted in drastically higher numbers for both Likes and comments and were thus deemed as outliers for this study. For example, after altering the outliers to match the second highest value of 2160, the Number of Comments for E.L.F. had a Mean of 221.33 (Std. Dev. = 340.75); however, the post was originally recorded as having 29,709. Likewise, Huda had an altered Mean of 453.33 (Std. Dev. = 368.58) after replacing an outlier of 37,823 comments. Both situations indicated a massive surge in participation when a reward or incentive was involved, which was not surprising. What should be considered,
however, is how we perceive these engagement-incentives and what they mean when discussing brand authenticity.
Incentives are nothing new when it comes to marketing tactics and generating interest among consumers. However, brands should consider the implications of using incentives on Instagram. Brands which require engagement in the form of “following” certain accounts and commenting on the posts produces a false sense of engagement. One surprising observation was the number of followers the brand accounts had in comparison to the number of comments and
Likes received on their posts. Maybelline has the highest number of followers with over 10.4 million, yet their highest number of comments was only 1,440. One would expect a much higher number of comments based on the millions of followers which see these beauty brands’ posts every day. This could mean that many users are merely passively following brands and waiting for a chance to win a contest, receive a discount code, or be featured on their page. By asking users to follow and Like their posts, they receive artificial engagement rather than authentically connecting with their audiences.
6.3. Self-Branding and “Justification”
After observing and coding the 100 posts from consumers, Hypotheses 3a and 3b were both confirmed. First, H3a predicted that consumers tagged multiple brands to increase
reachability. The results found 81% of consumer posts tagged more than one beauty brand, and the content was not directly related to any one particular brand. This corresponds with the literature review and with Rokka and Canniford’s (2016) previous findings; consumers have historically used brands when shaping their own identities. Yet, in doing so the brand’s identity is also altered, and the brand assemblage becomes unstable (ibid.).
Hypothesis 3b was also confirmed after the majority of posts from consumers and influencers were “self-promoting,” rather than promoting the beauty brand itself. Even though these posts were retrieved from beauty brand accounts, the users were actively growing their own pages through showing makeup techniques, providing product reviews, or sparking inspiration for different makeup looks. Product visibility was low in consumer posts, but the brands would be listed in the post description when products were used to create the makeup look featured in the photo. Therefore, tagging multiple beauty brands was “justified.” This brings up an interesting concept around the idea of tagging “justification.” Users are, in fact, tagging relevant beauty brands in their shared posts even when the connection is not initially apparent. Future research could look at this method of self-branding and the justified use of audience reachability.
While developing the methods for this study, I encountered numerous limitations. The majority of obstacles were directly caused by user-accessibility on Instagram. In this section, I will explain my initial study methods, the reasons these methods failed, and the substituted solutions. Further, some limitations also exist within the methodology and intercoder reliability.
7.1. Original study
When originally designing this study, I focused on social media influencers and their effects on branding messages. I picked six beauty brands and two affiliated influencers who had been sponsored or featured by each brand.
In general, coding the beauty brands was successful and provided adequate data for the analysis. However, many problems arose when gathering data from the Influencers. One of the first problems I noticed was the frequency of posts. While brand pages were consistently uploading new content onto their Instagram pages, influencers were inconsistent. This posed a problem for the time frames set in place for the content analysis. Some influencers took “social media breaks,” and chose not to share anything for weeks at a time. As a comparison, brand pages would typically share between 14 and 50+ posts in a two-week timeframe, while some influencers only shared five or six posts in that same amount of time. This was a problem when acquiring enough information for the content analysis. Additionally, because of the limited number of posts, there was limited sponsored content, which was essential to answering the desired research questions.
To address these problems, I initially changed my method from specific influencers to gathering 20 sponsored posts from each brand regardless of influencer. This would allow me to directly compare branding messages between the company and the influencers. However, this method could not be implemented due to limitations of the Instagram platform. There is no viable way of searching and viewing sponsored posts associated with specific brands. Although influencers must legally disclose if their posts are sponsored, the disclosure methods are
inconsistent (De Veirman and Hudders, 2020). Some of them state the disclosure above the photo in a platform-generated statement, while many others include a generic hashtag #ad. The generic hashtag can be used on any post featuring any company, thus disabling the ability to find brand-specific advertisements.
Some of the beauty brands in the study did have specific hashtags when collaborating with influencers. For example, SMIs which promoted Maybelline products would include the hashtag #MaybellinePartner. This allowed the search feature on Instagram to generate relevant results. However, the inclusion of an influencer-specific hashtag was not available for all brands in this study.
7.2. The Solution
After considering all available options, I adjusted the study in ways which were practical yet productive in answering the research questions. Because the majority of issues were related to Influencer research, this was changed to be inclusive of all users regardless of status or follower-count on Instagram. The six beauty brands were reduced to five because Anastasia Beverly Hills did not allow public photos or tagged photos to be visible on their account. The remaining five brands kept the same data from before, plus a couple extra categories to be included in the coding process.
7.3. Limitations with Engagement
One of my goals when developing this study was to measure user-engagement on Instagram posts. The purpose was to see if engagement frequencies changed depending on whether or not the posts included certain elements or certain influencers. Many previous studies measured engagement based on the total number of Likes and comments received on posts, which was ultimately the same approach I used, as well. However, this method does not accurately represent the full extent of user-engagement.
In my original study design, I opted to analyze not just the quantitative data of Likes and comments, but also code the contents of comments themselves. This is because oftentimes posts receive comments that are spam or contain no “intrinsic value” as defined by Loureiro, Serra and Guerreiro (2019). Thus, studies which rely solely on the number of comments may falsely identify the “level of engagement.” Moreover, comments which are negative in nature can lead to harmful brand attitudes when publicly available for users to see.
Another reason why quantitative data is misleading when measuring the number of comments on Instagram posts is because the total number includes any replies from the brands or influencers themselves. For example, if a post shows 100 comments, that does not mean 100 different people “engaged” with the post. Instead, if the brand replied to any questions or other feedback, their replies are included in the total number of comments.
With all of these considerations in mind, I attempted to develop a coding method which more accurately represented the level of engagement between brands/influencers and their followers. Let me preemptively acknowledge this method had its own limitations and consequently could not be used in the final study results. Because the actual content of user comments was crucial in measuring engagement, I considered the following coding categories: intrinsic value, positive tonality, negative tonality, shareability (how often were comments “tagging” other users), and interactivity level between the brand and other users. Ideally, I would have coded each comment individually, but this proved to be unrealistic due to the sheer volume of comments (sometimes thousands of comments per post), as well as the limitations when navigating the Instagram platform. At the time of conducting this research, there is no feasible way of searching all comments or viewing all comments without manually loading about 20 comments at a time. I found that by simply scrolling through the comments, the type of feedback was typically easy to decipher, whether it was mostly spam, positive comments, or people asking questions. While I do think this method allowed for a general insight into user engagement, it was inadequate for producing scientific results. Therefore, I suggest future research uses a more thorough approach when measuring user engagement on social media.
7.4. Intercoder Reliability
When conducting qualitative research, it is important to produce reliable results,
especially when adding nominal values to intrinsic characteristics as done in a content analysis (cf. Spence and Lachlan, 2005). The most common method to ensure reliable results is through documented intercoder reliability. This is completed by having another individual code a sample of the data and verify that results are consistent between both parties. An intercoder reliability test was not performed during the course of this research study, and therefore, the results could vary or be interpreted differently. While my academic advisor did review the codebooks used,
this is not sufficient in determining reliability. Therefore, I recommend a reliability test being done in the future using the codebooks in Appendix 1.
8. Conclusion
Instagram has become a leading platform for digital communications between companies and general users alike. Previous research identified ways in which branding messages have been communicated through the decades. Not only have marketing practices changed from celebrity endorsements to social media influencers, but the branding messages themselves have shifted to more informative content. This is due to the consumer narrative online. Brands are letting consumers pave the way in branding communications. Because the platform allows users to tag others and to categorize their content with the use of hashtags, branding messages have become more unstable for beauty brands. Therefore, the purpose of this paper was to answer the question “How do beauty brands utilize consumer posts to convey branding messages?”
After conducting a content analysis of both beauty brand posts and consumer posts, brands are sharing consumer-generated content on their own Instagram accounts due to
consumer content most frequently conveying experiential and user-centered branding messages. Meanwhile, original content produced by the company tends to be more informative and have emotional branding messages. By sharing the user-made posts, beauty brands appear more interactive with their followers, and thus they emulate qualities of “online success.” User-made posts offer authentic testimonials for the brands, which ultimately build brand authenticity. However, after looking more closely, user-made content appears to have underlying motives of their own. Nearly all consumer posts were identified as “self-branding,” meaning that regular users were more interested in building their own followings than they were in engaging with the beauty brands. Instagram users would most often tag multiple beauty brands in one post as a way to reach wider visibility and perhaps lead to being featured on one of the beauty brands’
accounts.
With the surge in influencer culture, more and more users are attempting to build their own following in hopes of gaining credibility and company sponsorships. The present study revealed a mutual benefit between beauty brands and regular users; brands which featured others’ content would provide incentives for users to actively upload and tag the brands in future
