A Picture Is Worth a Thousand Words: Segmenting Consumers by Facebook Profile Images

⁎ Corresponding author.E-mail addresses: [email protected] (I. Vilnai-Yavetz),

[email protected] (S. Tifferet).

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Journal of Interactive Marketing 32 (2015) 53–69

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A Picture Is Worth a Thousand Words: Segmenting Consumersby Facebook Profile Images

Iris Vilnai-Yavetz⁎& Sigal Tifferet

Department of Business Administration, School of Economics and Business Administration, Ruppin Academic Center, Emek Hefer, 4025000, Israel

Abstract

Conventional segmentation efforts usually focus on verbal or behavioral data while ignoring visual cues, which play a significant role inimpression management. Drawing on theoretical work regarding motivations for impression management (need to belong and need for self-promotion), we propose that Facebook users differ from each other in the composition of visual elements they portray in their Facebook profilephotos (PPs), and thus can be segmented based on this composition. In this exploratory study we present a methodological proof of concept for thevisual segmentation of Facebook users. Using a randomly selected international sample of 500 Facebook accounts, we analyze data implicit in PPsand identify visual cues relevant to virtual impression management. Using these cues we segment users into types, and relate the types todemographics, Facebook usage, and brand engagement as reflected in the Facebook profile. At the theoretical level, the findings suggest that thecurrent accepted motivations for Facebook impression management (need to belong and need for self-promotion) should be expanded to include athird motivation, need for self-expression. At the practical level, the findings demonstrate the utility of visual segmentation, which can later beimplemented using computerized systems.© 2015 Direct Marketing Educational Foundation, Inc., dba Marketing EDGE. All rights reserved.

Keywords: Facebook; Impression management; Photo analysis; Cluster analysis; Segmentation; Social presence

Introduction

Facebook is one of the two most popular Web sites in theworld, second only to Google (Alexa Internet, Inc. 2013), witha reported 1.2 billion active users monthly and 725 milliondaily (Facebook 2013). Facebook is also the number onemobile app used in the United States, after passing the popularGoogle Maps in October 2012 (comScore 2013). As a result,Facebook is rapidly emerging as a platform for advertising andmarketing: indeed, the Facebook business model is based onadvertising income (Mourdoukoutas 2013). But while adver-tising indiscriminately to 725 million people is bound toproduce a certain number of matches (i.e., the right ad beingseen by the right person at the right time), it is not highlyefficient. Marketers therefore have a strong interest in finding

ways to segment Facebook users so as to target ads for goodsand services more effectively.

One of the key reasons people use Facebook is for purposesof impression management (Nadkarni and Hofmann 2012).Facebook users often employ verbal and nonverbal presenta-tion of preferred brands for this purpose (Chen, Fay, and Wang2011; Hollenbeck and Kaikati 2012; Labrecque, Markos, andMilne 2011; Smith, Fischer, and Yongjian 2012), offeringmarketers a convenient way to match users with products andservices they are likely to find appealing. Another rich sourceof information is the personal data that Facebook users post intheir profiles, including both demographic data and informationon the person's interests and activities. But Facebook profilesalso contain implicit data, such as photographs, which aremostly overlooked. Facebook users upload 350 million newimages every day (Henschen 2013), providing data thatmarketing researchers can use to understand impressionmanagement — and to design ways of targeting online adsmore effectively.

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54 I. Vilnai-Yavetz, S. Tifferet / Journal of Interactive Marketing 32 (2015) 53–69

In this study we have three aims. First, we suggest a methodfor segmenting social networking site (SNS) users based onvisual cues instead of self-reports, and we illustrate the method ona random international sample of Facebook users. Second, weidentify relevant visual cues present in virtual impressionmanagement. Third, we use these cues in order to identifydifferent user types, and to relate them to theoretical motivationsfor using SNSs. We do this by analyzing visual data depicted inFacebook profile pictures (PPs) and identifying the impressionmanagement tactics they reflect. We first isolate segments ofFacebook users based on the way they use PPs to create firstimpressions. We then examine whether the identified segmentsdiffer by their Facebook usage patterns, by their demographics,and by their brand engagement and preferred product categoriesas reflected in their Facebook pages.

Theoretical Background

Photos as Online Impression Management Tools

The brain processes visual data 60,000 times faster than it doestext (Parkinson 2012). It is unsurprising, then, that advances incomputing power and broadband capacity have been accompa-nied by a tremendous increase in the visual content uploaded tosocial networks (Lee 2014). Today, two-thirds of the content onsocial media consists of images (Citrix report 2014). Nearly halfof all Internet users have reposted a photo or video they havefound online, and more than half have posted a photo or videothat they have personally created (Duggan 2013). Tweets withimages receive 150% more retweets than those with text only(Cooper 2013). Indeed, surveys suggest that including images isthe most effective means of optimizing social media content (i.e.,increasing the number of shares, “likes”, and followers that result;Aragon 2014).

We suggest that profile pictures serve two interrelated purposesin SNS. First, according to social presence theory (Short,Williams, and Christie 1976), communication media differ in thelevel of social presence they enable, where social presence isdefined as the degree to which the interaction partners have a senseof personal human contact. Social presence is highest inface-to-face communication, and lowest in wholly text-basedcommunications, including the forms of electronic communica-tion normally employed by firms engaging in e-commerce (Gefenand Straub 2004). One way to increase social presence inelectronic communications is to add images such as photographs.Xu (2014), in a study of trust and credibility in the context ofonline consumer reviews, found that participants showed greateraffective trust in reviewers who included a profile photo, andunder some conditions photos were associated with greaterperceived credibility of online reviews. Xu (2014) explainedthese findings on the grounds that feelings of uncertainty ininterpersonal relationships make people uneasy (Berger andCalabrese 1975), and that profile photos reduce the feeling ofunpleasantness which that uncertainty generates. Accordingly, wesuggest that Facebook users similarly seek to increase their socialpresence and thereby reduce the uncertainty inherent in onlineinterpersonal relationships by representing themselves via a PP.

The second purpose served by PPs in social media, whichfollows directly from the first, is impression management. Inimpression management, people seek to construct an image ofthemselves based on their ideas about how others will interpretthat image (Leary and Kowalski 1990). First impressions areformed quickly, in a matter of moments, and as a result must bebased on limited information, such as external appearance(Burgoon, Guerrero, and Floyd 2010). The centrality of firstimpressions in evaluations leads people to employ variousimpression management tactics, including verbal and nonverbalcues (Moore, Hickson, and Stacks 2010). Verbal tactics include,for instance, information filtering or “pleasing the audience”(Baumeister 1989); nonverbal tactics include facial expressions,body language, and physical appearance (Fletcher 1989).

On the Internet, both social presence theory and impressionmanagement underlie the use of images and other visual cues,such as symbols, by individuals or entities that want to conveya particular message. For example, Vilnai-Yavetz and Tifferet(2013) found that top-ranked universities include more imagesin their Web sites than lower-rated academic institutions; andVilnai-Yavetz and Tifferet (2009) found that prospectivestudents relate the presence of images on a university site tobetter service. Koernig (2003) suggested that online images canbe analyzed systematically for the same communication tacticsthat have been demonstrated in printed ads (Berry and Clark1986). For instance, the qualities associated with an object canbe physically represented using cues such as colors, logos, orsymbols, while pictures, videos, etc. can encourage visitors tothe site to visualize the object.

Facebook Images as Impression Management Tools

Facebook users can express themselves through explicitdeclarations regarding their interests or favorite books, films, ormusic (Pempek, Yermolayeva, and Calvert 2009). Yet viewers ofFacebook profiles rely less on these explicit statements, and moreon implicit cues such as those found in posted images (Zhao,Grasmuck, and Martin 2008). For example, when participants inone study evaluated the personality of Facebook users whom theydid not already know, they based their impressions primarily on theusers' PPs (Ivcevic andAmbady 2012). The appeal of these imagescan even raise the response rate to friendship requests (Tifferet,Gaziel, and Baram 2012; Wang et al. 2010).

Lately, in addition to the PP, Facebook has enabled users toadd a cover photo as part of its new Timeline format (Smith2012). In the user's profile, the cover photo is a large,banner-style image that dominates the page, while the PP issmaller and less prominent. However, the PP is still the imagethat appears in Facebook friend requests and news feeds, and ittherefore continues to be the basis of others' first impressions.Thus we focus in the current analysis on the PP.

With Xu's (2014) findings in mind, we suggest that socialnetwork users use PPs to increase their social presence andreduce uncertainty in interaction partners by presenting aparticular image of themselves, an image chosen to convey aparticular impression about the user's identity. Specifically,users select profile photos whose visual elements reflect (for

55I. Vilnai-Yavetz, S. Tifferet / Journal of Interactive Marketing 32 (2015) 53–69

example) a given emotional expression, dress preference, orpreferred outdoor environment with the expectation thatviewers of the picture will feel they “know” the individualbetter because of these elements. The use of these impressionmanagement techniques thus reduces uncertainty in the viewer(Berger and Calabrese 1975) and increases the sense ofpsychological social presence (Gefen and Straub 2004) morethan would the mere presence of a photo (compare, forinstance, the information conveyed by passport photos).

Previous studies suggest two motives for Facebook use:self-promotion and belonging (Nadkarni and Hofmann 2012;Underwood, Kerlin, and Farrington-Flint 2011). We proposethat these two motives also direct the specific composition ofvisual elements that users choose for the purpose of non-verbalimpression management in their Facebook profile pictures. Wealso posit that Facebook users differ from each other in theirspecific motivations for Facebook impression management andin their specific composition of visual elements, and further,that they can be segmented based on this composition.

In the current study we analyze impression management inFacebook PPs based on a set of conceptual constructs representingdifferent aspects of impression management. The four constructschosen are expressions of emotion, status, activeness, and totallook.

Below we elaborate on each construct and our theoreticalrationale behind this choice, based on the two motives forFacebook use suggested in previous studies — namely,self-promotion and belonging (Nadkarni and Hofmann 2012;Underwood, Kerlin, and Farrington-Flint 2011).

Expressions of EmotionEmotional expressions are a time-honored means of impres-

sion management (Rafaeli and Sutton 1987). While people whoare in the grip of an emotion may have difficulty hiding ormasking their true feelings, individuals are also practiced atconveying particular emotional states whether these are accuraterepresentations of their true feelings or not (e.g., when asalesperson smiles at a customer). Emotions can be expressedin three main ways: verbal (what one says), nonverbal (e.g., eyecontact, smiling), and paraverbal (e.g., tone of voice, speakingloudly) (Oksenberg, Coleman, and Cannell 1986). Positiveemotions are easily expressed through nonverbal means, andparticularly by engaging the muscles of both the mouth and theeyes (Ekman, Davidson, and Friesen 1990). People may also tendto divert their eyes when experiencing negative emotions such assadness, or fear (Adams and Kleck 2005).

Feelings of belonging intrinsically elicit positive emotions(Baumeister and Leary 1995). Therefore, we suggest thatexpressions of positive emotions in Facebook photos, such assmiling and looking directly at the camera, reflect a need tobelong. To put it differently, Facebook users who are driven atleast partially by a need to belong are expected to choose PPsthat show such positive emotional expressions.

StatusIn today's society, impression management is often bound

up with people's aspirations for a given social status —

something that, in turn, may paradoxically reflect a need for bothbelonging (i.e., membership in a social group) and self-promotion.A key way in which people signal both their social group (need tobelong) and social status (need for self-promotion) is through theirclothing and the objects (accessories) they associate themselveswith (Johnson et al. 2008). With respect to objects, these may bestatus symbols such as cars (Belk 2004) or fashion accessories(Han, Nunes, and Drèze 2010). With respect to clothing, a formalstyle of dress is often associated with high status (Nellisen andMeijers 2011). For instance, in one study, restaurant clients inbusiness suits were served earlier in comparison to clients incasual dress (Stead and Zinkhan 1986). In another, participantsassociated formal business attire with authority and competence(Cardon and Okoro 2009). Andersen (2008) suggests thatindividuals in more formal clothing typically receive higher levelsof compliance compared to people who are informally dressed.

The notion of status has to be seen in context. Hirschman(2003), for example, points to “rugged individualism” as a “coreAmerican cultural value” expressed through the symbolism of“men, dogs, guns, and cars.”Within that frame of reference, statusmay be conveyed not by formal and expensive clothing andaccessories, but by the opposite (see the Method section below).Specifically, we argue that the need for self-promotion may bereflected in the degree to which profile photos portray theirsubjects in clothing or with objects that are associated with highstatus within their frame of reference. However, the need to belongmay be reflected in the degree of fit between the type of dressportrayed and the normative dress in the wearer's socialsurrounding (Bellezza, Gino, and Keinan 2014). As the currentstudy was not designed to analyze human networks, we considerthe clothing and objects pictured in PPs mainly in their role aspotential symbols of social status (need for self-promotion), butnot as a symbols of belonging to a social group.

ActivenessModern society glorifies activeness and adventurousness

(Underwood, Kerlin, and Farrington-Flint 2011). These traitsare most associated with pursuits categorized as extreme sports,such as snowboarding, windsurfing, or hang gliding. Butactiveness can be reflected by involvement in any type ofoutdoor activity. Here again we can benefit from Hirschman's(2003) notion of “rugged individualism,” where the world of“men, dogs, guns, and cars” finds expression in earthy outdooractivities such as hunting. We suggest that PPs which portraytheir subjects as active, outdoorsy, and adventurous reflect theneed for self-promotion.

Total LookThe fourth and final construct examined here is what we call

“total look,” by which we mean the total look of the photo, notthe subject. That is, our concern is not the subject's overallappearance (hair, clothing, accessories, etc.), but rather thegeneral design choices made by the user — for instance, thedegree to which the image is artistically processed versusnaturalistic, or compositional questions such as whether thesubject is shown alone or with other people (the specific criteriaexamined will be detailed in the Method section). The total


look construct may inform questions such as whether theindividual is aiming to present some ideal self (for instance, viaa heavily edited photo) rather than his or her actual self. Theissue of ideal versus actual selves on Facebook has been thesubject of recent research (Back et al. 2010; Hollenbeck andKaikati 2012). The total look of a profile image may alsosuggest whether the account holder is driven more by the needto belong (e.g., users whose PPs show them as part of a group)or by the need for self-promotion (e.g., users whose PPs showthem doing something unique and nonconformist [Bellezza,Gino, and Keinan 2014].)

Segmenting Users of Social Networking Sites

Identifying different groups of consumers has long beenrecognized as an important marketing tool. Segmentation systemshave been developed based on consumers' motivations anddemographics (e.g., Farrag, El Sayed, and Belk 2010), preferences(e.g., Jiang and Balasubramanian 2014), attitudes and beliefs (e.g.,Strizhakova, Coulter, and Price 2012), activities (e.g., Gilboa2009), shopping behavior (e.g., Wu and Chou 2011), service usagebehavior (e.g., Bose and Chen 2010), and lifestyles (e.g., Ye, Li,and Gu 2011). However, a review of the SNS literature in general,and the SNS impression management literature in particular,yields – to the best of our knowledge – only four SNSsegmentation studies (Alarcon-del-Amo, Lorenzo-Romero, andGomez-Borja 2011; Constantinidesa and Zinck Stagnoa 2011;DiMicco and Millen 2007; Underwood, Kerlin, and Farrington-Flint 2011). Moreover, all four of these studies have methodolog-ical weaknesses, the most important being the use of self-reports.For instance, Underwood, Kerlin, and Farrington-Flint (2011)employed cluster analysis (CA) to segment 113 Facebook usersbased on their self-reported scores on both self-promotion andcommunication-related behaviors. They identified three seg-ments: high broadcasters (interested mainly in self-promotion),high communicators (interested mainly in relationship mainte-nance), and a high interaction segment (interested in intensiveinteraction). Alarcon-del-Amo, Lorenzo-Romero, and Gomez-Borja (2011) used an online survey to question SNS users abouttheir SNS activities, experiences, and interaction patterns; theyidentified four segments: introverts, novel users, versatile users,and expert-communicators. Finally, Constantinidesa and ZinckStagnoa (2011) identified market segments among futurestudents in The Netherlands who reported their social mediahabits in a national survey. Unfortunately, while self-reports are auseful means of gathering data in the social sciences, the methodhas several pitfalls including the difficulty of remembering pastbehavior (Brewer 2000), social desirability bias (De Jong, Pieters,and Fox 2010), and the fact that some psychological behaviorsmay be unconscious (e.g., Dijksterhuis et al. 2005). In the lastcase, for example, a man might display a photo of himself with anexpensive sports car in the background without any self-awareness of his concern with status.

With the aim of collecting more objective data, DiMicco andMillen (2007) segmented Facebook users employed by IBMinto three behavioral types based on data collected from theirFacebook profiles: number of Facebook friends, interests, and

job information. However, their study was only an initial casestudy that employed preliminary exploration of only 68Facebook profiles in a non-representative sample. In fact,many samples are culturally biased, especially in light of thefact that 80% of Facebook users are from outside the U.S. andCanada (Facebook 2013).

Along with DiMicco and Millen (2007), other researchers havebegun to highlight the ways in which activity on social networksmay be superior to the demographic data and self-reported attitudestraditionally used to segment consumers. Hargittai (2007), whilenot offering a segmentation herself, suggested various usageparameters as worthy of future study. Bachrach et al. (2012)analyzed a large data set of 180,000 Facebook users for featuressuch as the size or density of friendship networks, the number ofimages uploaded, or the number of group memberships. They didnot segment the users for marketing purposes, but they showed thatthese parameters can be used successfully to predict certainpersonality traits.

Hill, Provost, and Volinsky (2006) followed another direction.They investigated rates at which target customers adopted anew telecommunications service, comparing 21 customersegments identified by the telecommunications firm. Theresearchers found that, in general, network neighbors (i.e., peoplewho had communicated directly over a social network with acurrent subscriber of the service) adopted the new service at ratesconsiderably higher than non-network neighbor target customers.Hill, Provost, and Volinsky (2006) concluded that analyzingcustomers based only on traditional attributes may miss potentialcustomers. These findings support our own argument thattraditional segmentation methods are at best a crude instrumentin identifying people likely to try a product or service, and helpmake the case for examining the value of user attributes that maybe derived from visual impression management.

We suggest that objective and quantitative yet innovative dataare a necessary input for supporting high quality marketingdecision-making in today's brave new world of social media.The current study aims to meet this objective by showing howFacebook PPs can be used to segment consumers based on theirimpression management tactics, thereby providing an innovativesource of information compared to traditional socio-demographicand self-reported data. We consider this study an importantexploratory initial stage. It presents a proof of concept that mayserve as a conceptual and methodological basis for future “BigData” analysis. Hence we start our study with the following threepropositions.

P1. Facebook users differ in the composition of visual elements(e.g., emotional expressions, style of dress, outdoor versusindoor environment) they use to present themselves in PPs.

P2. It is possible to identify separate groups of Facebook usersbased on their composition of visual elements.

P3. Human needs in general, and users' motives for Facebookimpression management in particular, are the source ofvariance between users. Therefore, the visual element compo-sition patterns shared by members in a group should relate totheir key motive for Facebook impression management.


Method

Scoring Tool and Data Collection

In order to identify different segments of Facebook users, webuilt a scoring tool that included variables related to, or reflecting,the conceptual constructs described above. After constructing aninitial version of the tool, we reviewed previous studies ofFacebook photos in order to identify possible additional variables.McAndrew and Jeong (2012) assessed Facebook impressionmanagement variables such as whether a picture includes objects,making faces at the camera, and graphic editing of photos. Steele,Evans, and Green (2009) examined characteristics such assmiling, location, eye coverings (e.g., sunglasses), and numberof people. Zhao, Grasmuck, and Martin (2008) discussedcharacteristics such as the presence versus absence of people andexpressions of affection in the context of how users employpictures in identity construction on Facebook. This processresulted in a 20-item checklist. Three independent coders thenused the checklist to separately code Facebook profile images.After discussing discrepancies, the coders constructed a finalchecklist comprised of 17 items, of which 10 related directly to the

Table 1Study variables and ICC coefficients.

No. Name Variable content

1 Subject Serial number2 ID Facebook ID (URL)3 Gender 0. Male

1. Female4 Relationship Status 0. Not committed (single, it's complica

open relationship, widowed, separated,1. Committed (in relationship, engaged

5 Year of birth Year6 Friends Number of friends7 Likes Number of likes8 Style 0. No profile photo/blank

1. Original photo/image2. Artistic design (black–white, drawin

9 Image 0. Non-human photo or image1. Human profile photo

10 Number of people Number of adults and kids11 Object 0. No Object

1. Object12 Smile 0. No Smile

1. Smile without teeth2. Smile with teeth

13 Eye_Cover 0. None or eyeglasses1. Sunglasses

14 Eye_Contact 0. Not looking at camera1. Looking at camera

15 Dress 0. Minimal (swimming suit, bare)1. Sportswear (running shorts)2. Weekday casual (T-shirt)3. Smart casual or branded attire4. Formal/semi-formal

16 Situation 0. Indoors1. Outdoors

17 Timeline 0. No1. Yes

Note: CI: confidence interval; ICC: IntraClass Correlation coefficient.

profile picture and 7 related to other information available fromthe profile (see Table 1).

With the final checklist established, an experienced ratercoded the profile pictures of 500 randomly selected Facebookusers in the fall of 2012 as part of a wider study assessingFacebook images (as described in Tifferet and Vilnai-Yavetz2014). Since the variables were based on objective measures(e.g., no smile, smile showing teeth, smile without teeth), therewas no need for additional independent coders. However, toassure coding accuracy of a single coder with these data, wearranged for independent coders to analyze a subsample of theFacebook profiles, and then tested for inter-rater reliability. Theinter-rater measures were satisfactory (see Table 1). Thisprocedure is described in detail below.

Research Sample

We collected a sample of 550 randomly selected Facebookprofiles using ImageCrashers software (http://www.imagecrashers.com/GRP.do). This software displays random profiles selectedfrom Facebook using FacebookGraphAPI. Of the 550 profiles, weexcluded 45 profiles that did not state the user's gender and another

Accounts assessed ICC(2,1) 95% CI

All – –All – –All 1.0 –

ted, in andivorced)

All .88 [.80, .93]

, married)All – –All 1.0 –All with Timeline 1.0 –All .68 [.57, .77]

g)With image .91 [.87, .94]

Human .94 [.91, .96]All .53 [.39, .65]

Only one person .71 [.55, .84]




Human .76 [.67, .84]

All – –

http://www.imagecrashers.com/GRP.do

http://www.imagecrashers.com/GRP.do


five that represented a company and not a personal user. The studysample therefore contained 500 Facebook profiles, of which 198(39.6%) belonged to females and 302 (60.4%) to males (seeAppendix A for an example of a PP).

To analyze the images, we first checked the full sample of 500PPs for the style of image and the presence of people and objects,where objects were defined as any inanimate item (a vehicle, amusical instrument, etc.). We then checked all the images thatincluded people (n = 420) for an outdoor setting and for numberof people. Last, we checked those images showing just oneperson (child or adult; n = 321) for sunglasses, eye contact (i.e.,whether or not the person was looking at the camera), smileintensity, and level of formality in dress. A flow chart of theanalysis process is shown in Fig. 1.

Inter-rater Reliability

Three independent coders were sent links to the same set of100 Facebook pages, sampled randomly from the original 500Facebook links. Due to a gap of a few months between the fulldata coding and this inter-rater reliability test, eight links wereunavailable. After receiving instructions for the coding, thethree coders coded the first five profiles separately, and thendiscussed issues of interpretation with the other coders and withthe researchers. Then they continued to code the rest of thesampled Facebook profiles independently.

A two-way, random-effects, single measure, absoluteIntraClass Correlation (ICC) coefficient (ICC(2,1) model) wasused to assess inter-rater reliability. The aim of this test isspecifically to see how accurate a single coder will be whencoding the data (Landers 2011). Point estimates of the ICCswere interpreted based on Fleiss (1986) as follows: excellent(.75–1), modest (.4–.74), and poor (0–.39). In general, thecoders showed excellent reliability for most variables (Table 1).For three variables – showing an object (ICC = .53), style of

Fig. 1. Sample structure.

the image (ICC = .68), and type of smile (ICC = .71) – theinter-rater reliability was modest.

Research Variables

Impression Management ConstructsThe research variables were divided into four segmentation

constructs representing different aspects of impression manage-ment in Facebook: total look, status, activeness, and expressionsof emotion.

Total look reflected the general design choices made by theaccount holder, and was defined by three items: the style of theimage, the decision to feature people or not, and the number ofpeople in the image. Style reflected the decision to present noimage (0), an unprocessed image (i.e., a realistic photo = 1), ora processed image (i.e., one that was graphically altered = 2).Presence of people reflected the decision to include (1) or notinclude (0) people in the image. Number of people showed thenumber of adults and children appearing in the photo on acontinuous scale of 0 and up.

Status displays were tested by the presence of objects and bythe formality of the individual's dress. Objects were recorded onlyif they were central in the photo (0 = no object, 1 = presence of anobject). Most of the objects displayed in the PP were electronicgadgets (37%) or vehicles (24%), both of which can serve as statussymbols (see, for instance, Belk 2004). In most cases the vehiclesshown were luxury vehicles (e.g., sports cars or heavy motorcy-cles). However, the study sampled users from around the world,including many less-affluent countries. In these countries, the mereownership of a vehicle or an electronic gadget may serve as a statussymbol, regardless of the brand. Dress style or formality of dresswas rated only in photos that featured as subject just one person(see research sample section). It was coded as 0 = minimal(swimming suit, bare torso), 1 = sportswear, 2 = weekday casual(jeans and T-shirt), 3 = smart casual or branded attire, or 4 =formal/semi-formal.

We rated activeness using an outdoor setting as a proxy. Weassessed the setting only for images that included at least oneperson and that showed the situational context — e.g., at homeor in an office for indoor photos (coded 0), or engaging insports or an identifiable outdoor activity for outdoor photos(coded 1). Photos that were discernibly taken either indoors oroutdoors but with no situational context (i.e., photos where thebackground was unidentifiable) were not rated.

We measured emotional expression only in photos with justone individual (see the Research Sample section) using thefollowing variables: eye contact (0 = not looking at the camera,1 = looking at the camera), smiling (0 = no smile, 1 = slightsmile with no teeth showing, 2 = full smile with teeth showing),and sunglasses (i.e., hiding emotional expression: 0 = eyeglassesor no eye cover, 1 = sunglasses). Smiling serves a social displayof emotion (Fridlund 1994; Kraut and Johnston 1979). Smilingwith bared teeth has a unique significance in humans and otherprimates in that it communicates submissive, nonaggressiveintentions (e.g., De Waal 1989; Sapolsky 2004) and functions toincrease affiliation (Parr and Waller 2006).


Facebook UsageIn addition to the image analysis data, for each account we

recorded the following data representing Facebook usage of theaccount owner: Number of Facebook friends; number of “likes”(i.e., the total number of times the user “liked” a commercialfirm or any other type of organization, political party, service,product, brand, or celebrity); adoption of the Timeline format;and disclosure of data in the public realm (for relationshipstatus, number of friends, number of likes, and year of birth).

DemographicsFrom the basic information in the Facebook profile we

recorded the user's reported gender and year of birth. Wecategorized the user's relationship status into not committed(0 = single, it's complicated, in an open relationship, widowed,separated, or divorced) and committed (1 = in a relationship,engaged, or married).

Data Analysis

SegmentationRinne and Swinyard (1995) divided market segmentation

methods into two main approaches. In the “a priori” or“management-driven” approach, segmenting variables are deter-mined in advance (e.g., Gonzalez-Benito 2002). This method ismore methodologically controlled and can utilize past knowledge,but may miss unfamiliar or creative aspects of the subject. In the“analysis-based” or “market-driven” approach, data are segmentedduring the data-analysis process, usually by cluster analysis (e.g.,Strizhakova, Coulter, and Price 2012). This approach is notdirected by preexisting assumptions and so is more open topotentially unexpected aspects of the subject. Given the innovativenature of Facebook, we employed the market-driven approach inorder to “listen to the data” and avoid a priori assumptions aboutthe behavior of Facebook users.

We conducted a cluster analysis (CA) aimed at differentiatingFacebook users based on how they present themselves in their PP.As described above, this is the first image of a Facebook accountowner normally seen by other users, and it therefore forms thebasis of others' first impressions. We used a two-step CA thatcombined Ward's hierarchical analysis method with a nonhierar-chical k-means clustering procedure in order to optimize thecluster solutions (Clatworthy et al. 2005). A two-step CA wasmost suitable for this study since it enables the analysis ofcategorical variables (e.g., the three smile categories). The sevenvariables relevant for PPs showing only one person (style ofphoto, presence of an object, dress style, situational context, smile,eye contact, sunglasses) – operationally representing the foursegmentation constructs – were analyzed together. The CA wasbased on the characteristics of the person depicted in the PP,presumably the user. For this reason, only those PPs showing justone person (n = 321, 64%) were analyzed at this stage.

In order to avoid a priori assumptions, we used the CAoption that sets the number of clusters based on the dataanalysis (instead of setting the number of clusters arbitrarily).For that purpose, hierarchical clustering was used to determinethe optimal number of clusters and to choose the most

appropriate solution based on changes in the agglomerationschedule, dendrogram, and cluster membership. The optimalnumber of clusters was determined using the point at which anoticeable increase in the clustering coefficient (agglomeration)occurs (Grove, Fisk, and Dorsch 1998). Then, a k-means CAwas performed to determine the significance of each of theseven variables to the formation of the clusters.

Facebook Usage and Demographics by SegmentOnce we had segmented the account holders based on their

PPs, we examinedwhether and how the segments differed in theirdemographics and Facebook usage using cross-tab and ANOVAanalyses. The demographic variables were gender, age, andrelationship status, and the usage variables were number offriends and likes, adoption of the Timeline format, and level ofdata disclosure.

Brand Engagement by SegmentWe applied Hollenbeck and Kaikati's (2012) qualitative

approach to identifying brand engagement in Facebook pages.For this analysis, we randomly sampled eight Facebook pages foreach of the five segments and examined whether and how thesegments differed in the brand names and product categoriesdisplayed in profile interests, likes, and uploaded photos.

Results

Segmentation Categories

Of the full sample of 500 profile images, 321 featured asingle person, presumably the account holder. We segmentedthese images based on their features (e.g., smile, eye contact,and dress style). The CA produced three clusters (agglomera-tion coefficient = 3.52), which we termed Aloof, Affectionate,and Go-getter.

Table 2 presents the distribution of each variable for the threesegments. As shown in the table and in Fig. 2, the three clustersare similar in size, with each comprising about a third of thesegmented accounts. The clusters significantly differed in dressstyle, situational context, smiling, eye contact, and sunglasses,but not in the processing of the image (style of image) or thepresence of objects.

We termed the first segment Aloof. These users' PPs showedthem wearing low-end dress (minimal or weekday casual) in anindoor setting (e.g., at home or in an office), and with very littleemotional expression (no smile or eye contact). For the mostpart, these individuals seemed to aim for an image ofthemselves as not needing to appear friendly or likeable, or toimpress others via their dress or surroundings.

We designated the second segment Affectionate. These usersdiffered from the first group in that their photos showed positiveemotions, making them seem friendly and open to communica-tion. All were smiling, either fully (53%) or partially (47%). Nonewore sunglasses, and almost all (94%) were looking at thecamera, as if making eye contact with the viewer.

The third segment was termedGo-getter. These users presentedthemselves as tough and high-status. All were photographed

Table 2Clusters of Facebook users based on profile photo design choices a (n = 321). b

Cluster name Aloof Affectionate Go-getter Chi2

Size of cluster 31.9% 34.1% 34.1%

Total lookStyle N.S.Unprocessed image 82.8% (−1.3) 88.7% (.4) 90.3% (.9)Processed image 17.2% (1.3) 11.3% (− .4) 9.7% (− .9)

StatusPresenting an object N.S.No 68.3% (−2.2) 79.0% (1.1) 79.0% (1.1)Yes 36.2% (2.2) 21.0% (−1.1) 21.0% (−1.1)

Formality of dress style 29.3 ⁎⁎⁎

Minimal 13.8% (3.4) 1.6% (−1.7) 1.6% (−1.7)Sportswear 0% (−1.4) 3.2% (.7) 3.2% (.7)Weekday casual 62.1% (2.6) 50.0% (.4) 32.3% (−3.0)Smart casual 19.0% (−2.6) 33.9% (.4) 41.9% (2.1)Semi-formal or formal 5.2% (−2.1) 11.3% (− .4) 21.0% (2.4)

ActivenessSituational context 121.6 ⁎⁎⁎

Indoors 100% (8.9) 58.1% (1.2) 0% (−10.0)Outdoors 0% (−8.9) 41.9% (−1.2) 100% (10.0)

Expression of emotionSmile 141.7 ⁎⁎⁎

No smile 93.1% (6.2) 0% (−12.0) 90.3% (5.9)Smile without teeth 6.9% (−3.2) 53.2% (7.7) 1.6% (−4.6)Smile with teeth 0% (−4.4) 46.8% (7.0) 8.1% (−2.6)

Eye contact 20.3 ⁎⁎⁎

No 39.7% (2.6) 6.5% (−4.5) 35.5% (1.9)Yes 60.3% (−2.6) 93.5% (4.5) 64.5% (−1.9)

Sunglasses (hiding eyes) 12.2 ⁎⁎

No 91.4% (.1) 100% (3.0) 82.3% (−3.1) ⁎Yes 8.6% (− .1) 0% (−3.0) 17.7% (3.1)

⁎ p b .05.⁎⁎ p b .01.⁎⁎⁎ p b .001.a Figures in parentheses are adjusted standardized residuals (ASRs). An |ASR| larger than 1.96 indicates that the number of cases in that cell is significantly

different from the expected if the null hypothesis were true (p b .05).b A sub-sample of photos with only one person in the profile photo.


outdoors, some engaging in outdoor activities such as hiking ordiving, and others driving. Of the three segments, they had thehighest rate of sunglasses use (18%). As with the Aloof segment,most users in the Go-getter group were shown not smiling (90%),and slightly more than a third (36%) did not look at the camera (i.e.,no eye contact). On average, the users in this group tended to dressmore formally than those in the other segments.

After completing the CA and identifying the three segments(Aloof, Affectionate, and Go-getter), we were left with 179 PPsthat were excluded from the CA since they showed either nopeople or more than one person. We divided these profiles intotwo additional segments based on the number of people shown(i.e., none or multiple). PPs showing two or more people (e.g.,family, friends; 19% of the full sample) were categorized as afourth segment, which we termed Sociable based on the mainattribute available about these users — the importance theyascribed to other people. PPs showing no people (i.e., displayingobjects or abstract graphics; 17%) were categorized as a fifthsegment. We termed this segment Cryptic because they provided

no physical information about their appearance, expressions, orclothing (see Fig. 2). In fact, personal testimonies show that someSNS users choose to minimize information disclosure in order to“create mystery” (Labrecque, Markos, and Milne 2011).

Facebook Usage Patterns and Demographic Characteristics byProfile Segment

Of the full sample, 54% disclosed data regarding theirrelationship status in their public profile, but only 10%disclosed their year of birth. Disclosure of Facebook usageparameters was more prevalent; 80% disclosed the number oftheir Facebook friends (M = 565 friends), and 79% disclosedtheir likes (M = 185 likes). Almost all users (95%) had adoptedthe Timeline format. No significant differences were foundbetween the five segments in Facebook usage patterns (numberof friends and likes, adoption of the Timeline format, and thedisclosure or non-disclosure of data for relationship status,friends, likes, and year of birth).

Fig. 2. Five segments of Facebook users based on profile photo design choices.


The results in Table 3 show significant differences between thefive segments in gender and in reported relationship status.Regarding gender, males comprised 61% of the full sample, butformed a significantly higher proportion of two segments,Go-getter and Cryptic, at 86% and 73% respectively, and asignificantly lower proportion of two others — Sociable andAffectionate, at 47% and 39%. With regard to relationship status,42% of the full sample reported being in a committed relationship,compared with a high of 53% in the Sociable group and a low of14% in the Aloof group. As less than 10% of the sample reportedtheir year of birth, we were not able to characterize the segmentsby age.

Brand Engagement by Segment

Application of Hollenbeck and Kaikati's (2012) qualitativeapproach to identifying brand engagement in Facebook pagesrevealed several differences between the segments. As can be seenin Table 4, profiles in the Aloof segment tend to feature brandedalcoholic beverages, sports teams and athletes, fast food, cars,movie stars known for action and adventure movies (e.g., VinDiesel, Bruce Lee), characters from such movies (e.g., Rambo,Spiderman), electronic devices, and other brands or namesassociated with a “masculine” lifestyle. Brands shown in theAffectionate segment tend to not only cluster in areas associated

Table 3Demographic characteristics of types of Facebook users based on profile photo desi

Demographics Overall sample Aloof Affectionate

GenderMale 61.4% 67.2% (1.0) 38.7% (−4.0)Female 38.6% 32.8% (−1.0) 61.3% (4.0)

Relationship statusNot committed 57.7% 86.2% (3.4) 54.1% (− .5)Committed 42.3% 13.8% (−3.4) 45.9% (.5)

⁎ p b .05.⁎⁎ p b .01.⁎⁎⁎ p b .001.a Figures in parentheses are adjusted standardized residuals (ASRs). An ASR large

from the expected if the null hypothesis were true (p b .05).

with shopping and retail experiences, such as beauty, fashion, andsweet foods and snacks, but also include exciting and high-statusvehicle brands such as BMW, Ferrari, Jaguar, and HarleyDavidson. Theoretical models (e.g., the FCB grid; Vaughn 1980)supported by empirical studies (e.g., Claeys, Swinnen, andAbeele 1995) suggest that brands in these product categories –fashion, snacks, luxury cars, etc. – are particularly associatedwith emotion, in that their purchase is driven by the urge to satisfyemotional needs such as ego gratification, social acceptance, andsensory stimulation. Put differently, these are “feel” products,whose consumption is based on affective information processingcentered around possibilities for self-enhancement, subjectivemeanings, and emotional impressions (as opposed to “think”products – kitchen appliances, food staples like bread, detergents,etc. – whose consumption is based on cognitive processingcentered around the product's functional performance). TheGo-getter can be characterized mainly by branded sports outfitsand shoes, fast food and snacks, soft drinks and beers, suggestinga lifestyle associated with young people who are well-adapted totoday's fast and demanding culture.

Given that Facebook is a digital platform, unsurprisingly,electronic apps and devices feature to some degree in all fivesegments. However, digital applications and electronic gamesare the focus of interest for brand engagement in the Crypticsegment. It appears that those users who choose not to show

gn choices a (n = 494).

Go-getter Sociable Cryptic Chi2

85.5% (4.3) 47.4% (−3.3) 73.3% (2.4) ⁎⁎ 41.9 ⁎⁎⁎

14.5% (−4.3) 52.6% (3.3) 26.7% (−2.4)

58.6% (.1) 47.5% (−2.0) 54.3% (− .4) 12.6 ⁎

41.4% (− .1) 52.5% (2.0) 45.7% (.4)

r than 1.96 indicates that the number of cases in that cell is significantly different

Table 4Brand engagement in Facebook profiles by PP segments.

Gender Country International brands

AloofM USA Bud Light, Walmart, iPhone, Mustang, Nike, Texas Rangers, Dallas CowboysM India FC Barcelona, CBF Brazil, YouTube, Android, Samsung, Hyundai, Teachers whisky, National Geographic, NASA, Rambo,

Spiderman, Scott Kelly (astronaut), Jet Li, Will Smith, Jason Statham, Vin Diesel, Neymar, Tom & JerryF USA McDonald's, Nesquik, Pizza Hut, Jelly Belly, Tic Tac, Pokemon, Batman, Tom & JerryF USA Herbalife, Miami Heat, Boston Celtics, San Antonio SpursM Brazil Lacoste, Heineken, Johnnie Walker, Tsunami (energy drink), Askov (vodka), Selvagem Comary (wine), Candy Crush, Sao Paulo FC,

Manchester City FC, Bayern Munchen FC, Chicago Bulls, Real Madrid, Will Smith, Captain America, Harry Potter, NeymarM USA Visa, Venus Williams, The Hobbit, Star Trek, Denzel Washington, Will Smith, Harry Potter, Jules Verne, J.K. Rowling, National

GeographicM Romania NoneF USA Purina, Victoria's Secret, Colorado Rockies, Kansas City Chiefs, Candy Crush, Bruce Lee, Venus Williams, Muhammad Ali, Jackie

Chan, Van Damme, Vin Diesel, Jason Statham, Paul Walker, Hugh Jackman, Rambo, Mickey Mouse, Donald Duck, Snoopy

AffectionateF USA Walmart, Nintendo Wii, Bud Light, Dove, Colgate, Quaker Oatmeal Squares, YouTube, FritoLay, Disney, Candy Crush, Adam

SandlerM India Levi's, KFC, Nokia, BBC News, Candy Crush, Lionel MessiM India Van Heusen, McDonald's, BMW, Ferrari, Jaguar, Reebok, Acer, Sony, Microsoft, Harry Potter, NASAM USA iPad, DropboxM India Disney, Harley Davidson, National Geographic, NeymarF Philippines Nescafe, Dell, Converse, Unilever, Louis Vuitton, BurberryF USA Activia, Disney, Unilever, Walmart, Amazon, Visa, Milky Way, Pillsbury, Acer, Lancôme, Tide, Duracell, Yoplait, Skippy, Lipton,

Olay, Sisley, Ghirardelli (Lindt Chocolate), Smashbox Cosmetics, Green Island (Rum), Angry BirdsF Mauritius Sprite, Sony, Smirnoff

Go-getterM Nigeria Copa Lagos Beach Soccer, Manchester City FCM Philippines Cadbury, Warner Brothers, Converse, Nestle, Ford, Instagram, Yahoo!, NBAM USA Nike, Diadora, Ford, Corona, BudweiserM USA Kellogg's, Dunkin Donuts, JCPenney, Pay Pal, Yahoo, ESPN, Walmart, Candy Crush, SEGA, X-Men, Los Angeles LakersM India NoneM Israel Waze (GPS), Tuborg, 7 Up, Carlsberg, Samsung, Schweppes, Candy Crush, Angelina JolieM Botswana Kaizer Chiefs FC, DBS (clothing), Will Smith, Jason Statham, Paul Walker, Oprah WinfreyM Rajasthan iPhone, Microsoft, Intel, WhatsApp

CrypticM Sudan NoneF Philippines Google Chrome, Gmail, Microsoft, Google, Mozilla Firefox, Hello KittyM N.A. CanonM Iran SuperMario, BBC News, National Geographic, Bruce LeeM UnitedArab Emirates LexusM Hong Kong 7-Eleven, Disney, iPhone6, Samsung, Olympus, iPad, Lego, M&M's, 20th Century Fox, Google Chrome, National Geographic,

Sony, Microsoft, Ferrari, BMW, CNN, Captain America, Spiderman, Superman, Batman, Power Rangers, The Hobbit, Harry Potter,Bruce Lee, Paul Walker

M USA Nintendo, Disney, Sony PlayStation, Amazon, SEGA, Pokemon, Digimon, Spiderman, Batman, Cartoon Network, Dragon Ball ZF Nigeria None

SociableM USA Netflix, FOX Sports, PlayStation, Hunger Games, Kobe BryantF USA Adidas, Michigan U, Budweiser, National Geographic, Vogue, Cleveland Browns, Cincinnati Bengals, Cincinnati Reds, Venus

(clothing), Shoeaholics, SnoopyM Bangladesh Pizza Hut, PC Gamer, Mitsubishi, Sims, KFC, Angelina Jolie, Nicole KidmanF USA Toys R Us, HondaM Romania Vans, M&M's, Quiksilver, Etnies, Ralph Lauren, National Geographic, Levi's, Adidas, DC (shoes), Coca Cola, Reebok, Salitos, The

SimpsonsF USA NoneF Nigeria Shell, Sterling, GlaxoSmithKlineF Malaysia Kit Kat, Nestle, Hershey's, Coca-Cola, Pepsi-Cola, Casio, Disney, iPhone, HushPuppies, Winnie the Pooh, Dora


themselves or other people in their photos do so not for a lackof digital literacy, though it is unclear whether their mainmotivation is a desire to remain private and unexposed to thepublic, or to communicate a sophisticated message. Finally,

brands featured in the Sociable segment relate mainly toproducts that fill social needs and impress others (fashion andbranded clothing) and products associated with hanging out(snacks and fast food).


Discussion and Conclusion

SNSs in general, and Facebook in particular, offer marketersaccess to millions of potential customers. But to makeadvertising on these platforms more effective, marketers needto find ways to target ads to the people who are most likely tobe interested in a particular product or service. We suggest thatthe images users post on Facebook or other social mediaplatforms for impression management purposes (Nadkarni andHofmann 2012) can provide valuable data on their attributes,interests, and attitudes — data that can be used to segmentaccount holders based on the marketer's particular needs.

The first aim of the current study was to suggest a method forsegmenting SNS users based on visual cues instead of self-reports,and to illustrate this method on a sample of Facebook users. Weachieved this by developing a segmentation of Facebook users onthe basis of the impression management decisions expressed intheir posted PPs. First, we documented five types of Facebookusers based on the way they convey a concern with status,activeness, and emotional expression in their Facebook PPs. Thesegmentation reflects psychological characteristics such associability, creativity, empathizing or systemizing tendencies,multidimensionality, emotional expressivity, status seeking, aneed to impress, and attitudes toward technology. Given that thesecharacteristics are implicitly embedded in the images posted byFacebook users, our segmentation avoids the pitfalls faced byprevious segmentations of Facebook users, which were based onself-reports (e.g., Alarcon-del-Amo, Lorenzo-Romero, andGomez-Borja 2011; Underwood, Kerlin, and Farrington-Flint2011). Although there are differences between SNSs in the type ofuser-generated content they present and the ways they present it(Smith, Fischer, and Yongjian 2012), the study results andsegmentation can be applied to any SNS that uses photos (e.g.,LinkedIn, Myspace).

Our second objective was to identify relevant visual cuespresent in virtual impression management. The segmentation ofthe PPs suggests that five different categories of Facebookusers (Aloof, Affectionate, Go-getter, Sociable, and Cryptic)can be distinguished based on only a few variables: the numberof people in the photo (none, one, or multiple), the setting(indoors or outdoors), emotional expressions (the presence orabsence of a smile; whether the subject is looking at thecamera), and the relative formality of the subject's dress. Thesefindings support the significance of PPs in general, and thespecific visual cues studied in particular, as reducing uncer-tainty (Berger and Calabrese 1975) and increasing socialpresence (Gefen and Straub 2004), and show that visual cueshave the potential to create interesting and useful segmenta-tions. The segmentation results support P1 and P2 regardinguser variation in visual impression management in Facebook,and the potential for segmentation.

The identification of the segments led to our third goal,namely identifying the factors that motivated each segment in itsSNS conduct. Using self-reports, previous studies suggested twomotives for Facebook use: the need to belong and the need forself-promotion (Nadkarni and Hofmann 2012; Underwood,Kerlin, and Farrington-Flint 2011). Based on their characteristics,

it appears that Affectionate and Sociable users are motivatedmainly by a need for belonging, andGo-getter users by a need forself-promotion.

The needs for belonging and self-promotion are very similarto two of the three higher-order needs in Maslow's (1954)hierarchy — Belonging and Esteem. (Maslow's lower-orderneeds relate to existence and are thus less expected to becommunicated in impression management.) Based on thecharacteristics of our identified types, we suggest adding athird Facebook motive to parallel Maslow's third higher-orderneed (self-actualization): the need for self-expression. This isthe desire to present yourself to the world as you are, ignoringsocial norms and prejudice. In the current study, this tendencywas apparent in Aloof users, who made no effort to present anideal image to the world, and indeed displayed images whichmight reveal unpopular characteristics.

It is debatable whether self-expression should be considered aseparate need (as suggested by Maslow's theory), or as encom-passed within another need. On the one hand, the need forself-expressionmay have specific characteristics that distinguish itfrom other needs. Hemetsberger (2005) described the motivationsof contributors to open-source projects on the Internet. He claimedthat their actions are motivated not by a desire for self-promotion,but rather by a need for self-realization — the wish to becomeone's own self. On the other hand, self-expression can be seen asserving various broader needs (e.g., Kenrick et al. 2010). Forinstance, Zinkhan et al. (1999) suggest that Internet users whocreate a personal Web site are motivated by a number of factors,including the need for “personal portrayal” or the need “tocommunicate different dimensions of your personality.” Yet intheir view, this need is one component of the need for affiliation,and does not stand alone.

Presumably, to some degree or another, people express all oftheir needs through their Facebook activity: belonging, self-promotion, and self-expression. However, our typology, likeMaslow's theory, suggests that one dominant need usuallyemerges. In this, our findings confirm our third proposition —namely, that human needs in general, and users' motives forFacebook impression management in particular, can be linked tothe use of visual elements in PPs. The findings also support theadded value of nonverbal communication (Burgoon, Guerrero,and Floyd 2010) over verbal communication for understandingimpression management.

Practical Implications

The results of the current study can be used to inform anumber of business practices: customer relationship manage-ment, customer recommendation networks, and, of course,customer segmentation. The results of our qualitative analysisshow that the segments differ in the type of brands, productcategories, and cultural icons they engage with in their Facebookprofiles. We found that the Aloof segment prefers “masculine”lifestyle brands, while the Affectionate segment prefers brandsthat seek to engage the emotions. The brands favored by theambitious Go-getters reflect the fast and demanding rhythms ofmodern life. The Cryptics seem to live in a virtual-digital world,


keeping busy with digital applications and electronic games. TheSociables, in contrast, are stimulated by social interactions, andfavor branded clothing and fast food fit for quick get-togethers.The match between Facebook segments and brands is notsurprising. Previous research has shown that Facebook users usebrands to express themselves (Hollenbeck and Kaikati 2012), andmay even identify themselves with a specific brand (Labrecque,Markos, and Milne 2011).

Segmenting users by their impression management patternscombined with their brand engagement preferences allowsmarketers to offer people in each segment deals and bargainsdesigned specifically to attract those individuals. For instance,the Sociable segment can be offered deals on products orservices that appeal to families or groups of friends, such asamusement parks, while the Go-getter segment can be targetedfor sports accessories. Followers of specific brands in the Aloofsegment can be targeted for premium benefits, while followersin the Affectionate segment can be targeted for consumerengagement programs and emotional customer experiences.

The information gleaned from the suggested segmentation canbe further combined with specific data on individual users, suchas whether the user has “liked” or displayed a particular brand, foreven more individually tailored marketing. Yet crucially, thesegmentation gives marketers a powerful sense of users'preferences even in the absence of specific information directlyprovided by the user. Hollenbeck and Kaikati (2012) found thatonly about 25% of Facebook users display their self-identitythrough the symbolic act of “liking” a brand, while McGrath(2015) found that 40% of Facebook users are passive users whoonly browse the site without posting or liking any content. TheFacebook profile photo, therefore, serves as a basic commondenominator for all users, in contrast to other data which requireshigher levels of involvement or activity.

This point is important in light of the increasing develop-ment of automated techniques for targeted marketing via BigData analyses. Facebook, for instance, enables the activation ofthird-party apps that are based on user data (Wang, Xu, andGrossklags 2011). Google analytics uses past online activity asa means of learning about the customer. Finally, SocialNetwork Analysis (SNA) is a mathematical-statistical approachwhich analyzes the flow of information from one participant tothe other, but cannot analyze the content of the information(van Aalst 2012).

The present findings offer practical value even in the era ofSNA and data mining. First, SNA and data and text mining fortarget marketing are still complicated techniques that are notoften used in business practice (Bonchi et al. 2011). This maybe especially true for small-sized businesses which make upapproximately 50% of the U.S. Gross Domestic Product (Kobe2012). A segmentation approach that focuses on profile imagesmay provide a more accessible managerial tool for such firms.

Second, in SNA, SNS users are characterized based on theirrelations with others (i.e., location and connections in the network),without taking their attributes (e.g., motives, emotions, prefer-ences) into account (Marin and Wellman 2011). Sentimentanalysis might add the required psychological insights to SNA,but this is text-based analysis with major limitations, such as

topic-specific interpretations (Thelwall and Buckley 2013).Moreover, text-based analysis in general is likely to become lessvaluable given the rapid rise in prominence of visual content inSNS (Lee 2014). Thus, marketers would do well to adopt tools andtechniques which can use this visual information for marketingpurposes.

Eventually, of course, visual data may be incorporated intoSNA as well (van Aalst 2012). Yet even then, while SNAs canserve as useful tools for segmentation, any additional tool thatcan increase their predictive power may translate into largecumulative earnings.

Our suggested segmentation has further practical implications.An important part of anymarketing communications strategy is thechoice of the message strategy, in general, and the type ofendorser, in particular. Huber et al. (2013) found that a “typicaluser” endorser can be an effective way to influence consumers'brand perceptions. They suggest that different groups, or segments,of people are differently affected by the same endorser, based onthe level of fit between the endorser and the consumer's mentalimage of typical brand users. Our Facebook profile photosegmentation provides a tool for better identifying the stereotypic“typical user” for each of the user segments. A high fit between theaverage user profile of a segment and the endorser can improve theeffectiveness of ads.

A final consideration involves the Buyer Persona concept.The Buyer Persona is “a semi-fictional representation of thefirm's ideal customer based on market research and real dataabout existing customers” (Kusinitz 2014); such data usuallyinclude gender, age, profession, education, financial situation,and buying motivations (Handy 2014). Unfortunately, manymarketers define their customers' personas based on intuitionand not facts, leading to errors in strategy and content creation(MacFarland 2014). The notion of the Buyer Persona suggestsuses for our Facebook profile photo segmentation even beyondtargeted marketing, in that it can help marketers characterizetheir ideal customers based on psychological, emotional, andlifestyle elements — thus creating “a picture of the persona.”MacFarland (2014) suggests that marketers should employ BigData technologies to expand the information on which theybase their customer personas. We believe that profile photoscan form a crucial source of such information.

The suggested extended theoretical framework of motivationsfor impression management through Facebook has importantpractical implications that we could not test in the current study.Specifically, we suggest that advertising appeals, sales tactics, andcustomer relationship management strategies can be adapted to thetype of user according to the dominant need directing his or herFacebook activity: belonging, self-promotion, or self-expression.Here we suggest applying Bacile, Ye, and Swilley's (2014)approach to consumer-contributed marketing communications, anarea that is attracting growing interest in the age of social media.For instance, users motivatedmainly by the need for belonging arebetter targeted with emotional appeals (e.g., a promise forhappiness if visiting a specific mall), users motivated chiefly bythe need for self-expression could be encouraged to offer newideas for product innovation, suggest improvements in serviceprocesses, or express their feelings toward a specific brand, while


users motivated by the need for self-promotion can be recruited topresent new products in their Facebook accounts, turning theminto public opinion leaders. Future research should try to validatethese suggested links between Facebook impressionmanagementmotivations and the attitudes and behaviors of users for thepurpose of consumer-contributed marketing. Future researchmight also employ reciprocity and assortativity analyses todetermine whether users in the same PP segment are morestrongly connected to other users in the same segment in otherways— e.g., whether they tend to “like” and friend others in theirsegment more than people in other segments. Such knowledgewould be valuable in designing consumer-contributed marketingstrategies.

Special Considerations and Future Research

The approach outlined in this paper raises a number ofspecial considerations, some of which offer scope for futureresearch. The first of these involves issues of privacy andethics in both research and marketing (see for exampleCrawford 2014). Is it acceptable to collect data about anaccount holder's emotional expressions, dress, and otherdetails available in photographs without obtaining explicitpermission from the user? In this regard, it can be argued thatimages uploaded to social media are intentionally chosen andpublicly presented as a means of impression management orpersonal branding (Labrecque, Markos, and Milne 2011).More important, Facebook requires that account holders'names and PPs be accessible to all other users, but sinceusers differ in the level of personal privacy they need orprefer (Such, Garcia-Fornes, and Botti 2013), Facebookallows them to restrict access to other verbal and non-verbalinformation via the site's privacy settings (Zhao, Grasmuck,and Martin 2008). Thus, PPs and any other visual data notspecifically restricted by the user can be equated to the visualinformation people convey during a visit to the mall, beach,or other public area — and, therefore, as equally legitimatesources of data for analysis, whether for research or marketingpurposes (Hurworth and Sweeney 1995).

The second consideration deals with the authenticity ofFacebook information. Do the features of the PP truly representthe Facebook user? Do a user's brand “likes” represent his brandengagement? Past studies have shown high congruency betweenthe psychological traits of individual Facebook users and theirpersonalities as perceived by observers (Gosling, Gaddis, andVazire 2007). Kosinski, Stillwell, and Graepel (2013) evensucceeded in predicting the demographics of Facebook usersthrough their “likes”. Moreover, some Facebook users considerauthenticity to be a successful self-branding strategy, expectedfrom oneself and others (Labrecque, Markos, and Milne 2011).Even so, we do not know if a given user really has a dog or likeshiking— all we can see are the data presented by the user as herpublic persona. On their own, brand “likes”, brand namementions, brand photo uploads, brand links, etc., may beweak-signal manifestations of actual brand engagement. Yet theaccumulated evidence about a Facebook user, composed of all

the above, may be a significant road map for marketers in theirattempt to reach their target market.

Future studies can further assess the authenticity of thesuggested segments. Labrecque, Markos, and Milne (2011)presented Facebook profiles of twelve users to others andasked for their impressions and evaluations. They thenshowed these impressions to the profile owners and askedthem to rate their accuracy. Using their methodology, one canvisually segment Facebook PPs and then ask the PP owners torate these evaluations. Experimentally, fictive Facebookprofiles can be created based on the design characteristics ofeach of the five segments. These Facebook profiles can thenbe evaluated by marketers and customers, allowing forcomparison between the impressions created by each profileand the current study findings about each segment's“persona”.

A third concern relates to stability over time. Some Facebookusers change their PPs based on the time of year (e.g., holidays)and current life events (e.g., birthdays, anniversaries). Personalbranding decisions also seem to change over time (Labrecque,Markos, and Milne 2011). These changes can reflect eithertransient shifts or more stable maturation processes. Futureresearch using a longitudinal approach can study the dynamics ofPP choices and change (frequency of changes, design congruencyover time, etc.). An analysis of the congruency between the PPand the cover photo of the user can further contribute to ourunderstanding of this process.

Beyond the ethics, validity and reliability of the proposedsegmentation process, there remains a fourth question — can itaccurately predict consumer behavior? Here we conducted somequalitative explorations showing that the segmentation doesindeed coincide with certain consumer tendencies. This, however,was only a preliminary trial, and further quantitative tests shouldfollow. Future research can use the visual segmentation applied inthis paper to segment Facebook PPs of known users who agree toparticipate in a survey and report their life style, purchasing habits,media consumption, level of exposure to advertising, and otherconsumer behavior information. Such data can link the PPsegmentation to crucial consumer behavior variables and increasepredictability.

A fifth issue concerns the practicalities of the work presentedhere. The current study was based on manual coding. Such codingis labor intensive and although relatively objective in nature (e.g.,counting the number of people in a photo, or coding a smile withor without teeth), it might be subject to bias in the interpretation ofsome variables, such as the formality of someone's clothing. Thisis especially relevant in an international sample where images ofpeople from different nations and cultures are rated. With thepassage of time, software is increasingly becoming able to processand analyze aspects of images (e.g. ImageJ, http://rsbweb.nih.gov/ij/), including faces (e.g., Luxand Face recognition software,http://www.luxand.com). Future research might seek to replicatethe current findings using such software, examining its efficacy insegmenting social media users based on their photographs.

Finally, the market segmentation approach we employ in thispaper was developed as a tool for targeting groups of consumerswhen mass communication was the main customer relations

http://rsbweb.nih.gov/ij/

http://rsbweb.nih.gov/ij/

http://www.luxand.com


instrument available. Some might argue that this approach isoutdated in the Big Data era, when marketers can directly targetads for specific merchandise to individuals based on their browsinghistory. As technological advances alter the nature of marketingchannels and consumers become increasingly sophisticated in theirshopping behavior, market segmentation schemes are growingever more refined, to the point where each consumer may beconsidered a “segment of one” (Dibb 2001) or “market of one”(Gilmore and Pine 2000).

While much work in this area remains to be done, wesuggest that the move from mass production and standardi-zation to mass customization and differentiation (Gilmoreand Pine 2000) does not obviate the value of photo analysis.Rather, advanced technologies for analyzing images andidentifying human features may allow photo analysis to beused either independently or in conjunction with otheravailable data (e.g., a person's browsing history) to improvethe identification of consumers as “segments of one”. Thechoices people make with regard to their Facebook PPsprovide significant information about their creativity, resis-tance to change, sociability, and more — information whichreflects the user's interests and personality. The current paperis an exploratory study that presents a proof of concept aimedat identifying conceptual segmentation constructs. Futureresearch should aim to link the image-based segmentationparadigm suggested in this paper with the “segment-of-one”approach, especially as photo analysis software becomesmore refined.

Appendix A. An Illustration of a Facebook Profile

Conclusion

Facebook reports 725 million daily active users (Facebook2013) and had a revenue of $2.02 billion for the third quarter of2013 (Mourdoukoutas 2013). As such, it is a goldmine foronline marketing. While traditional segmentation builds onexplicit data such as demographics, along with self-declarationsand posts, we attempted to segment Facebook users using theimplicit cues revealed through their PP. We used these implicitdata to segment users based on the way they convey status,activeness, and emotions in their PPs. The findings suggest apotential new way of segmenting social network users whichmay be useful for marketing managers who wish to use socialnetworks as a marketing tool.

Acknowledgments

We would like to thank Naama Bayev, Chen Salamania,Betty Greenberg, and Diane Polakow for their assistance in datacollection, and Meira Ben-Gad for language editing. We aregrateful for the helpful insights received from colleagues inacademia and industry. We thank Zvika Jerbi, Gadi Hayat,Daphne Raban, Benny Bornfeld, Tali Efrat, Hila Barda, AmitRechavi, Itay Tsamir, Sheizaf Rafaeli, and Yoel Asseraf as wellas two anonymous reviewers for their constructive comments.We are also grateful for the financial support of the RuppinAcademic Center (Research grant 22023_2014-15).


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Iris Vilnai-Yavetz received her PhD in Management from the Technion, Haifa,and her BA in Psychology and her MBA from the Hebrew University,Jerusalem. She is a senior lecturer of marketing at the Ruppin Academic Center,Israel. Before her graduate studies Iris held a senior managerial position in amarketing research and consulting firm. Her current research interests are in thearea of environmental psychology, with implications for consumer behavior,organizational psychology, services, and marketing. She has published inoutlets such as the Journal of Service Research, European Journal ofMarketing, Service Industries Journal, Service Business, Environment &Behavior, Computers in Human Behavior, Electronic Commerce Researchand Applications, and Organization Science.

Sigal Tifferet received her PhD in Psychology from the Hebrew University,Jerusalem. She is a senior lecturer at the Ruppin Academic Center. Sigal isinterested in the implementation of evolutionary theory to the behavior ofconsumers and families.

http://dx.doi.org/10.1080/09593969500000010

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