Influence of Social Networks in Target Marketing

Dept. of CIS - Senior Design 2009-2010

Aman Nalavadenalavade@seas.upenn.edu

Univ. of PennsylvaniaPhiladelphia, PA

Vanditha Srungavarapuvanditha@seas.upenn.edu

Univ. of PennsylvaniaPhiladelphia, PA

Lyle Ungarungar@seas.upenn.edu

Univ. of PennsylvaniaPhiladelphia, PA

Shawndra Hillshawndra@wharton.upenn.edu

Univ. of PennsylvaniaPhiladelphia, PA

ABSTRACTOnline social networks such as Facebook have shown a hugeincrease in the number of users. In this work, Facebook willbe used as a dataset to see how social networks can be usedto provide better target marketing strategies. In our work,a Facebook application was built and products were recom-mended by users to friends over their social networks. Theserecommendations are considered to be a form of advertise-ment. It was shown that: (1) Users are more likely to searchfor and recommend products with high brand equity, whereasacceptance of a recommendation depends upon complexity ofthe product that was recommended. (2) Genuine recommen-dations that are sent out after considering product character-istics such as newness, complexity, risk, and brand equity,have a higher acceptance rate as compared to random rec-ommendations or banner advertisements. This research isimportant because it will enable marketers to provide moreeffective marketing strategies by showing that product rec-ommendations play a vital role in increasing the product ac-ceptance rate. It also helps them evaluate the products thatdo and do not work well over social networks by looking atwhat product characteristics cause that product to be recom-mended.

1. INTRODUCTIONA social network is a network consisting of individuals

who are connected to other individuals through social linkssuch as friendship, professional or family links. Social net-works have always been of interest to researchers as theyencode how people interact with each other. Previously, so-cial networks were limited by proximity as forming bondswith people who were located further away required moreeffort than forming bonds with proximal people. With in-creased access to the Internet, social networks have becomemore prominent and diverse because people can now connectmore easily with less effort.

Although people can have a diverse set of connections, itis usually seen that many of these connections tend to be ho-mophilous [9]. Homophily is defined to be the tendency forindividuals to associate with people of similar interests anddemographics such as age, gender, hometown, etc [9]. Thisproperty of networks has been of interest to marketers be-cause similar people tend to have similar interests and thus

have similar purchase patterns. Marketers use this prop-erty to predict the product adoption probability, which is theprobability of a user’s friend considering purchase of a givenproduct assuming that the user recommends that product.Network-based marketing uses this concept of product adop-tion probability and provides a marketing technique wheresocial networks and the links between customers are usedto improve marketing and sales [8]. A link is defined as atie between two individuals based on one or more types ofinterdependencies such as friendship, kinship, professional,etc. Facebook is primarily a social network consisting offriends and family where users can add friends, send themmessages, write on their walls, and join applications [10]. InFacebook, a link is formed between two users if one acceptsthe “friend request” of the other [10]. Target marketing onthe other hand, does not use a social network and marketsa given product to a specific group of people who can bedefined possibly by age, gender, or race.

A recommendation is a form of advertisement where oneuser shares the product information with other users andsuggests that product to them. A random recommendationin this context is when a user randomly recommends a prod-uct to different friends, but those friends are not aware thatthe recommendations are random and thus are perceived tobe genuine by them. A genuine recommendation in contrastis when a user recommends a selective product to a specificfriend, after considering the friend’s probable interest in thatproduct. In this case, the product characteristics will playa role in determining which product is picked for the rec-ommendation. In a recommendation, the recommender orthe user suggests products to the recommendee or the user’sfriend. Click rate is defined as the probability that a userwill click on and accept a recommendation. This metric willbe used to test if recommendations provide a better form ofmarketing.

Product characteristics form an important role in deter-mining whether a product is recommended over a social net-work. The product characteristics that were taken into ac-count in the analysis are defined below: (1) Risk is definedas the monetary amount that the purchaser pays in orderto buy or use the product [2]. For example, purchasingelectronics has higher risk than purchasing groceries. (2)Complexity is the difficulty that the user faces while usingof the product and is defined as whether the user needs prior

knowledge of how the product works [2]. Automotive toolshave high complexity due to its need of specialized knowl-edge. (3) Involvement refers to the capital value of a good,where a high involvement good is purchased only after longand careful consideration such as a car or a laptop [2]. (4)Brand equity is a brand’s power derived from name recog-nition that it has earned over time, and thus gives highersales volume [2]. (5) Experience products are those whichare difficult to observe in advance but can be evaluated uponconsumption [2]. For example, music albums are experiencegoods whose value is determined only after listening to it.(6) Newness refers to how different the product is comparedto the already existing products [2].

Sociologists and marketers are interested in this work be-cause it looks at the interactions between social networksand products.This will enable them to understand aboutwhat products get recommended over social networks andthus help provide better marketing strategies. This will alsogive insights into how people behave over a network withrespect to products. Computer scientists plays a major inthis domain by building the application to collect large setsof data, store the data efficiently in a database, then ana-lyze the data and finally build a predictive model to providebetter marketing strategies.

The purpose of this work is to look at social networks andunderstand how they function and how they can be usedfor marketing. Social networks can provide a better market-ing tool when certain factors such as the characteristics of aproduct, the user’s demographic data and the user’s interestdata are considered. Using this additional data, the accep-tance of a recommendation can be increased significantly.The characteristics of a product like newness, risk, complex-ity, etc form an important part in determining whether theadvertisements for that product will be accepted and alsowhether the product will be recommended to other friends.

The project is essentially divided into two different sec-tions: Data acquisition where the data is collected by creat-ing a Facebook application, and data analysis which entailslooking at the data collected and building a model to pro-vide effective target marketing strategies.

2. RELATED WORK

2.1 HomophilySocial networks have been to found to display homophily.

Homophily in general can be based on a wide range of at-tributes like gender, age, and hobbies. Homophily has beenfound to exist in a vast majority of social networks and canbe used for target marketing.

The earliest studies of homophily looked at small socialgroups, where an observer could see the links between themembers [9]. The observations were based on behavioralcues as well as explicitly stated data from the individualsabout their close friends. The first evidence of homophilycame from informal ties between school children, college stu-dents and small urban neighborhoods. These initial studiesshowed homophily based on demographics such as age, sex,race and education [9].

During the 1970s and the 1980s, the study of homophilychanged due to the use of modern sample surveys. Recentresearch looks more at the organizational contexts of net-works. There are two kinds of homophily: status homophily

in which similarity is based on social status or one’s positionin society and value homophily where similarity is based onvalues, attitudes and beliefs and does not depend on socialstatus [9]. This work will focus mainly on value homophilyto figure out whether people tend to recommend products toother people who are homophilous with respect to their val-ues. Another important factor affecting homophily is prox-imity. With the use of the Internet, although proximity isnot crucial in forming ties, it has been found that residentialproximity is the single best predictor of how friendships areformed [9].

2.2 Tie Strength over Social NetworksIn addition to homophily, relationships need to be mea-

sured in terms of tie strength so that strong ties can bedistinguished from weak ties in order to better use socialnetworks to market products. Tie strength is measured byfour strength dimensions: (1) the amount of time spent to-gether, (2) the emotional intensity, (3) the intimacy and (4)the reciprocal services between the users [5]. A study [5]looked at a dataset of 2000 Facebook users and classifiedtheir friendships as strong or weak ties with an accuracyof 85%. This tie strength has been used for studying indi-viduals and organizations to figure out how social networkswork. Banks that form the right proportion of strong andweak ties with other firms get better financial deals. Weakties on the other hand also benefit certain groups of peoplesuch as job-seekers [5].

2.3 Network-Based MarketingThis use of links gives rise to network-based marketing.

There are three different modes of network based market-ing: explicit advocacy wherein individuals openly recom-mend certain products to their friends or acquaintances,implicit advocacy wherein individuals do not explicitly rec-ommend products but they do it implicitly through theiractions such as adopting the product. The third mode ofnetwork-based modeling is network targeting wherein thefirm targets the prior purchasers’ network based neighbors [8].A key assumption that network-based marketing makes isthat consumers propagate only positive information aboutthe products that they buy and no negative information [8].Using these assumptions, the firm will value a certain groupof people more highly as compared to the others as theyare the influencers. These influencers promote the productbetter than other groups of people [8]. Prospective targetsin this type of marketing are found based on demographicdata and customer relationship data. It was found that net-work neighbors, or consumers that are linked to a previouscustomer, usually adopt a service 3 to 5 times higher thanthe control groups that were not related in a network. Itwas also found that analyzing the network allowed firms toacquire new customers who otherwise would not have beenrecognized using the traditional marketing techniques [8].

2.4 Viral MarketingAnother interesting aspect of social networks is the abil-

ity to look at viral marketing. Viral marketing is definedas the use of pre-existing social networks to increase brandawareness and sales using self-replicating viral promotionssuch as video clips, images, interactive Flash games, etc [1].Given a model of a social network, there is a well-definedoptimization problem that chooses the set of customers to

market to, so as to maximize net profits. This has beenshown to be an NP-Hard problem but can be approximatedquite well [1]. With the use of online social networks, track-ing this problem becomes much easier as the interactionsbetween people are known. In addition, if one could segre-gate the users, a company could market to those people whoare more influential.

Companies are finding that traditional marketing is incrisis, because customers are increasingly becoming habitu-ated with the traditional means of advertisements and henceare not paying attention to them. In spite of this, somecompanies like Amazon and Google succeed with virtuallyno marketing because of word of mouth and network effectwherein the value of the product increases as more peopleuse it. A recent study found that positive word of mouthamong customers is by far the best predictor of a company’sgrowth [1]. The problem that these marketers now face isto figure out how to do this effectively and to understandhow viral marketing works. There are still many questionsregarding which products work well with this type of mar-keting. While this is known at a high level for some productsegments, many startup companies have failed because theyinvested heavily in creating network effects that never ma-terialized [1].

2.5 Product typeA target based marketing system predicts the content that

is relevant to a given person, and advertise only this in-formation to that person. To make those predictions, thesystems take in large amounts of data about the user andfilter it based on previous product interaction history [3].Another way to find data was to look at a user’s social net-work. A study that looked at the advantages of using socialnetworks found that by creating sub-groups of people basedon interaction data, they were able to increase the quality ofadvertising [3]. Starting with an entire graph of all the usersand then partitioning the users into groups, the researcherssaw that their algorithms predicted the library books usedby different groups. This algorithm performed quite wellas compared to randomly choosing books [3]. Although thestudy ended there,the valuable information that was foundabout social networks would enable us to better target prod-ucts to each of these sub groups. This study however lookedsolely at library books, and so the extrapolation is quitelimited in terms of which products work well in a social net-work. Also the study used only user interaction data, anddid not take into account demographic or basic profile infor-mation about the users. The combination of both the socialnetworks as well as user data could prove to be a betteradvertising system than the use of only network data.

Other studies have also looked into social networks usinga wide range of products. A study [6] that looked at movierecommendations saw that the use of networks allowed bet-ter recommendations. The algorithm traversed the graphand ranked the “closeness” of users and their friends andthen used that information to suggest how much a givenuser’s friend may be interested in a movie given that theuser already saw the movie. This study compared the re-sults with a simple person correlation recommender and sawmuch better results of product adoption [6]. With this study,the strength of links was derived based on the network itself,and this was a key input in determining the recommenda-tions [6]. This study however, did not look at user profile

data and did not create sub groups within the network it-self. The study did raise an interesting point about trying tomake accurate recommendations when the number of linksto a given person is small or close to zero [6]. This often cre-ated inaccurate results, and thus the degree of each node(thenumber of friends for each user) was considered to be an im-portant attribute to consider.

3. SYSTEM MODELThe primary data set for this work was collected using an

application that was created on Facebook. Considering thatFacebook has a social network, the information about users’demographics, interests and friends is readily available andeasily accessible [10] and thus it forms the data set for thiswork.

3.1 Data Acquisition

Figure 1: Outline of the data acquisition process

A Facebook application was built to collect the requireddata. Facebook allows developers to build custom appli-cations that can automatically collect the required demo-graphic and interest data from the user’s profile. This al-lows developers to ask other interesting questions such asto recommend a product, rate a product, etc. and not ex-plicitly ask questions about their demographics or interests.Although the benefits of using Facebook to collect this dataare clear, the more pressing issue was finding incentives forusers to actually use this application.

To overcome this problem, coupon codes were used insteadof products. Coupon codes are “codes” that users can applyto certain products in order to receive a financial discountor rebate. Coupons are based on products and have thesame product characteristics and also provide the user anincentive to use them, in the form of a discount. This enablesusers to gain monetary benefits by using the coupon, whileproviding information about the user’s preferences and thefriends to whom the user recommends the products to.

In order to provide users with quality coupons, a companycalled CouponCabin that had a number of online coupon

codes was contacted. These coupons were then categorizedbased on the Keller Kay Group’s [4] categorization, whichis a common classification used in the marketing commu-nity. Some of these categories include apparel, food anddining, home goods, sports, electronics etc. Each of thesechosen categories have unique product characteristics (expe-rience, risk, complexity, involvement, etc). Along with theseproduct characteristics, products are also categorized acrossdifferent brands, which play an important role in the spreadof products.

Users sign up for the Facebook application and their pro-file information is accessed in the background. Their pro-file information includes demographics, their interests, theireducational background, their work history, etc. Link infor-mation regarding the user’s friends is stored as well. Infor-mation about the recommendations (product, recommender,recommended users and specific link type between the users)are stored once a user recommends a coupon. In addition,how a user enters the application and the user’s movementsthrough out the application such as clicking on a specificcategory, searching for specific coupons, using of a coupon,rating of a coupon, and inviting of friends to use the appli-cation are stored. The flow of this back-end processing hasbeen shown in Figure 1. This information will be useful totest our hypotheses.

Figure 2: Outline of the user’s movement through-out the Facebook application

Once users access the application, they can browse throughthe different categories and select a category and view all thecoupons in the category. The coupons appear in a randomorder to prevent any influence on the user to pick only thefirst coupons that were presented on the screen. A usercan use the coupon by going to the merchants website, canrate or share the coupons. When a user shares a coupon,the coupon is posted on the friend’s wall and serves as arecommendation. A user also specifies the exact link type(friend, family, or colleague) that characterizes their rela-tionship with that Facebook friend. Finally, the user hasthe option of searching for coupons, stores and categories aswell as seeing the top coupons and the top “recommenders”.Figure 2 shows the user’s movement throughout the appli-cation.

3.2 Data AnalysisThe data from the data acquisition section was used to

Figure 3: Data analysis outline

understand how to market a product over a social network.The two main hypotheses that were tested are: (1) Usersare more likely to search for and recommend products withhigher brand equity, whereas acceptance of a recommenda-tion depends upon the complexity of the product that wasrecommended. (2) Genuine recommendations that considerproduct characteristics such as newness, complexity, risk,and brand equity have a higher acceptance rate than ran-dom recommendations or banner advertisements. The stepsused in the data analysis have been shown in Figure 3.

In order to test these hypotheses, homophily is a prerequi-site and needs to be tested. To show homophily, the rates of“sameness” among different demographic attributes like age,gender, religion, etc. and different interest attributes suchas favorite movies, books, music, etc. were calculated fora given user and his friends. Given that homophily exists,and that similar people have similar interests [9], it is nowpossible to suggest that possibly purchase patterns of thesehomophilous users could be similar.

Recommendation data is then considered to check if therewas increased homophily between a recommender and a rec-ommendee, than compared to a user and his friends. Prod-uct characteristics were considered along with the recom-mendation data to test if they influenced: (1) use of a couponby a user, (2) coupon recommendation by a recommender,(3) acceptance of a coupon recommendation by a recom-mendee.

Finally, the click rates of the recommendations posted tothe recommendee’s walls were calculated to check if there

was an increased click rate from: (1) banner advertisements,to (2) random recommendations, to (3) genuine recommen-dations.

Research has looked at specific products to see how theyspread over social networks and the results suggest that cer-tain specific products are spread more successfully acrossnetworks [9]. Given the productivity of these research re-sults, this work looks at a wide range of and more generalproduct categories and analyzes the product characteristicsthat cause a given product to be used and recommended bya given user. This will define how or if a social network canbe used as a marketing strategy tool depending upon theproduct. Looking at whether product characteristics influ-ence the acceptance of a recommendation helps understandwhether the recommendation was accepted solely based onthe user’s recommendation or if the type of the product alsoplayed a role. This allows marketers to decide which prod-ucts do and do not work well over a social networks.

4. SYSTEM IMPLEMENTATION

4.1 Data AcquisitionThe Facebook application was developed using the Face-

book Developer platform, PHP, MySQL, JavaScript andAJAX. The Facebook developer platform that was used con-sists of an API, a markup language, and a query language.The Facebook API allows access to the profile informationof the users who have signed up for the application. FBMLwhich is the Facebook Markup Language provided addi-tional graphical features while the Facebook Query Lan-guage or FQL provided analysis and retrieval of the Face-book data in an SQL-style interface. The data collected wasstored in a MySQL database which integrates well with PHPand is convenient to use along with a Facebook application.

The Facebook application that was created is called “the-couponbook”, which is an interactive application allowingusers to easily search for and use coupon codes while shar-ing them with their friends. The coupon codes are pulled infrom an XML file provided by the Coupon Cabin, a largeindependent company which runs its own coupon code web-site. When a user signs up for the application, the user’sdemographic data, interests and friend information is ac-cessed by the application automatically by using preexistingqueries provided by the FQL. This data was stored in aMySQL database and the main components were stored inthe structure shown by Figure 4.

Figure 4: Entity-Relationship diagram representingwhat data was stored for the application

As soon as the user enters the application, a screen showsup that allows him to pick a category and to look for specificcoupons in that category as shown by Figure 5. The user canalso search for coupons in different brands, stores, or cate-gories of their interest. The searching algorithm, parses thesearch terms and removes any basic articles such as ”the” or”a” or ”an”. It also considers singular and plural searches asequivalent by aggregating results from both these searches.Additionally, the search term is delimited by the space char-acter and searching occurs based on all the different stringsobtained from the search term. The results are displayedbased on a store match, a category match or a coupon de-scription match. The user can also look at (1) the couponsthat have been recommended the most (2) users who haverecommended the most number of coupons.

Figure 5: Initial Screen where user can pick a cate-gory

The user can select a category and see all the relevantcoupons in that category as shown by Figure 6. In this page,the user has an option of (1) using the coupon, (2) sharingthe coupon with his friends or (3) rating the coupon. Touse the coupon, the user can click on the coupon code anda new tab redirects him to the retailer’s website where thecoupon can be used. The user can rate the validity of thecoupon by clicking on the “Like” or “Dislike”. As stated inthe system model section, these coupons were presented in arandom order to prevent skewing of the data. The user alsohas the option of looking at only the top brands or lookingat all the coupons in that category.

The user can share a coupon with his Facebook friend byclicking on the share button as shown in Figure 6 and this isconsidered to be a“recommendation”as defined earlier. Thisinformation is tracked and stored in the database. The usercan select up to ten friends to share the coupon and mustselect the type of link (family, friend, or colleague) that theyshare with that Facebook friend as shown in Figure 7.

When a user shares a coupon with a friend, a wall postis created on that friend’s profile page as shown in Figure 8and a notification, that a recommendation is made, is sent tothem. A wall post is also made on the recommender’s wallstating that this user made a recommendation. This wallpost contains a link to the application that the friend can useto sign up for the application and to view the coupon code.

Figure 6: Looking at different coupons in the “Elec-tronics” category

Figure 7: Screen where a user can share a couponwith their friends

This allows the spread of the application, thus providingmore data than can be analyzed in the data analysis section.These wall posts are also tracked to see how a given user’sfriends respond to the recommendations. The user also hasa choice of commenting on the recommendation or seeingother coupons that are available in the application.

As the user moves through the application, their move-ments such as the category they clicked on, the friends withwhom they shared the coupon, etc. are tracked. All of thisdata was stored in a different tables in a MySQL database.Although this information could be essentially stored in dif-ferent text files, using databases was chosen to be a betteroption as they allow for easy storage using PHP and alsoeasy retrieval by using SQL queries.

Apart from looking at coupons and using coupons for one-self like in other websites, this application additionally al-lows: (1) sharing coupons with one’s friends, (2) friendsto see which coupons the user recommended to his otherfriends, thus letting them use the coupon as well. This ap-plication is also easy to find and access because it leveragesFacebook’s popularity [10].

Figure 8: Wall post on recommendee’s profile

4.2 Data AnalysisThe data that was collected in the data acquisition sec-

tion using the Facebook application was first tested for ho-mophily. The user and his link information (friends’ infor-mation) was the specific data that was used for this analysis.This information was stored in text files and was parsed tofind the different attributes of user’s demographic and in-terest data. The parser was implemented using JAVA. Theuser’s (1) demographic attributes such as gender, home city,college name, religion, etc. and (2) interests attributes suchas books, music, television shows, hobbies, etc. were com-pared individually with their friends’ attributes. To estab-lish a baseline, users were compared to all existing appli-cation users (regardless of whether they were connected bya link). This allowed an evaluation of whether homophilyamong a user and his friends in fact exists.

Then the recommendation data was analyzed to test forincreased homophily specifically to test whether a user’s rec-ommendees are more similar to a user across different at-tributes compared to the rest of the user’s friends. To im-plement this process, recommendation data was read fromthe MySQL database and the text files containing recom-mendees’ information were parsed using JAVA. This anal-ysis was done for each user and the results were averagedacross all of the different users.

To test the hypothesis that product characteristics playa significant role during the recommendation process, risk,complexity, involvement, brand equity, experience and new-ness were determined for each of the categories as shown inTable 1. Due to the presence of a large number of coupons,each with different product characteristics, coupons withina given category were grouped together to allow for eas-ier analysis. Each of the categories were assigned a ratingbetween 1 and 10 for each of these different product char-acteristics based on the kind of coupons that were presentin that category. These ratings were then standardized bycalculating a z-score to prevent skewing towards one of theextreme values. This regression testing was done to checkwhether these product characteristics were significant for thefollowing events: (1) The number of times coupons in a givencategory were used, (2) the number of times coupons in agiven category were recommended, and (3) the number oftimes coupon recommendations from a given category wereaccepted.

Finally, the hypothesis that click rate increases from ban-ner advertisements to random recommendations to genuine

Table 1: Ratings on a scale of 1-10 for each of thedifferent categories based on product characteristics

recommendations was tested by finding the values for eachone of these metrics. Click rates were calculated by dividingthe number of users who clicked on an a recommendation bythe number of recommendations that were made. The ban-ner advertisements’ click rate was considered to be the baselimit against which the latter two metrics were tested. Therandom recommendations’ click rate was found by havingusers send out random recommendations to all their friends.The click rate on these kinds of recommendations were thenmonitored to see how well people respond to these randomrecommendations from their friends where the recommenda-tion is thought to be genuine. In this case, the user makesno considerations as to whether the recommendee would likethe product. The genuine recommendations’ click rate wasfound by having users send out genuine recommendationsto their friends after considering their friend’s preferences.The acceptance of these recommendations was monitored totest whether these kinds of recommendations had a higherclick rate compared to the former two.

5. RESULTS

5.1 Data AcquisitionThe Facebook application was officially released to the

public on February 18th, 2010. The application currentlyhas 80 users who have signed up for the application viarecommendations from an application user, invites, or paidadvertisements through Facebook. These users have beenusing the application to: (a) look at coupons, (b) use thecoupons, (c) rate these coupons, (d) share the coupons withtheir friends and (e) search for coupons. The main obser-vations that were found from these users’ browsing historyare: (1) Most users signed up for the application through arecommendation that was posted on their wall as shown inTable 2.

Table 2: User application entry mode statistics

(2) The application was released to the public and by wordof mouth many college students started using the applica-tion, with students from the University of Pennsylvania be-ing the majority of the sample. The application was alsoadvertised on Facebook and it was seen that the users whosigned up through these advertisements were mainly usersover the age of 40 years. Figure 9 shows a frequency distri-bution of the ages of the application users.

Figure 9: Frequency of the age of the applicationusers

(3) It was seen that there were more female users thanmale users of the application. There were 59% female usersand 41% male users. This could be attributed to the factthat more of the coupons were dedicated to women’s inter-ests like apparel, flower’s and gifts, jewelry, etc. (4) Usersfirst select a category that matches their interests. Cate-gories such as women’s apparel, electronics and men’s ap-parel were clicked on the most as shown in Table 3.

Table 3: Category clicks statistics

(5) Users first look for coupons that interest them andthen only recommend those coupons to their friends. (6)Users do not tend to rate all the coupons they use. (7)

Users tend to search for coupons that interest them throughthe search box. Analyzing their search terms shows that thesearching algorithm needs to use a more sophisticated toollike stemming. Searching is a valuable functionality to haveas in this case where there are a large number of couponsand it is difficult to find the desired coupon easily.

5.2 Data Analysis

5.2.1 Homophily between users and their friends

Firstly, a given user’s demographic information was testedagainst all of his friends for homophily. The demographicattributes that were tested were: (a) last name, (b) gender,(c) birthday month, (d) birthday year, (e) home city, (f)home state, (g) home country, (h) religion, (i) current city,(j) current state, (k) current country, (l) college name, (m)college year, and (n) college concentration. As shown inFigure 10 users are more similar to their friends as comparedto random users for all of the demographic attributes.

Figure 10: Homophily seen in application users’ de-mographic data

Table 4: z-test to test for significance in user’s de-mographic data when n = 80

A z-test was conducted to test for significance and the re-sults are shown in Table 4. The p-value is significant (lessthan 0.05) for most of the demographic attributes exceptbirthday month, religion, and current country. Birthdaymonth was expected not to be significant as a given user

does not tend to make friends based on similarity of theirbirthday month. Similarly, religion also did not show a sig-nificant difference, but its significance was greater than thatof birthday month. Therefore, this shows that homophilydoes in fact exist among users and their friends when demo-graphic attributes are considered.

Next, a given user’s interest information was tested againstall of his friends for homophily. The interest attributes thatwere tested were: (a) hobbies, (b) books, (c) music, (d) tele-vision shows, and (e) movies. As shown in Figure 11, userswere more similar to their friends as compared to randomusers for all of the interest attributes.

Figure 11: Homophily seen in application users’ in-terest data

However, when a z-test was conducted to test for signifi-cant differences among the attributes, only television showsshowed a significant p-value (less than 0.05) as shown inTable 5. This could be because larger number of users listtelevision shows in their profile as compared to music andhobbies. Therefore, although users’ interest data showedhomophily, it was significantly lower as compared to the ho-mophily shown by the demographic data.

Table 5: z-test to test for significance in user’s in-terest data when n = 80

5.2.2 Homophily between users and their recommendedfriends

Recommendation data was then tested for homophily tocheck whether people recommended coupons to friends whowere more similar to them than their average friend. Dif-ferent demographic attributes such as gender, college name,college year, current country, current state, and current citywere considered. As shown earlier, there was more homophilyfor a friend than a random user. The recommendation datashows that this already existing homophily increased whenrecommended friends were considered as shown in Figure12.

Age was one of the demographic attributes that was con-sidered, but unlike other attributes, rather than testing for

Figure 12: Increased homophily from a friend to arecommended friend for different demographic at-tributes

similarity in ages, difference in ages was calculated. Thesame homophily principle that was shown for the other de-mographic attributes was seen in this case as well as shownin Figure 13.

Figure 13: Increased homophily from a friend to arecommended friend when age difference is consid-ered

The p-values were calculated for the different demographicattributes when user’s friends and recommended friends werecompared. The p-values were significant for only gender, col-lege year and current city as shown in Table 6. Although thegraphs seem to show a significant increase in homophily forcollege name, current state, current country, and age, due tothe large variance of the data as well as the small size of thedata set, there was no significance when regression testingwas done.

Users first search for coupons that interest them, and thenrecommend those coupons to their friends. Categories likeapparel, jewelry & watches, tools & automotives, health& beauty, etc., are gender biased, and hence when peoplesearch for coupons for themselves in these categories, theyalso recommend these coupons to the same gender. Gen-der is also an attribute that is listed most often on a user’sprofile and so a large enough dataset was available to showsignificance for gender.

Similar to the demographic attributes, interest attributesfor recommended friends were also considered as shown inFigure 14, there was increased homophily for hobbies and

Table 6: p-values shown for different demographicattributes when a user’s friends and recommendedfriends are compared

television shows, but slight decrease in homophily for music.When regression testing was conducted, it was found thatthe p-value was significant for only television shows as shownin Table 7. This is probably due to the fact that thereare a fewer number of television shows as compared to thenumber of available musical selections as well as the fact thatmore people tend to list television shows on their profilescompared to music and hobbies.

Figure 14: Increased homophily from a friend to arecommended friend for different interest attributes

Table 7: p-values shown for different interest at-tributes when a user’s friends and recommendedfriends are compared

5.2.3 Product Characteristics

Now that homophily was found between a user and hisrecommendees, product characteristics were tested for cor-relation with (a) the number of times a user clicked on agiven category, (b) the number of times a coupon in a givencategory was recommended by a user, and (c) the numberof times a coupon recommendation from a given categorywas accepted. A regression testing was conducted to checkfor significance among the different product characteristics

namely (1) risk, (2) complexity, (3) involvement, (4) brandequity, (5) experience, and (6) newness.

Three results were concluded: (a) Brand equity was theonly product characteristic that showed a significant p-valuewhen compared against the number of times a user clickedon a given category as shown in Table 8. Users only tendto search for coupons that they have heard of and accordingto the definition of brand equity, coupons in categories withhigh brand equity are marketed the most. Therefore, asexpected brand equity shows a significant correlation whenusers look for coupons. (b) Table 8 shows that brand equityis again the only product characteristic that shows a signif-icant p-value when compared against the number of timesa user recommends coupons in a given category. Users onlyrecommend coupons that they search for and from the ear-lier result, brand equity was the only product characteristicthat was correlated with the search for coupons. Therefore,brand equity is again the only product characteristic thatwas correlated with the number of times coupons in a cat-egory were recommended. (c) When acceptance of couponrecommendations was considered, complexity was the onlyproduct characteristic that was significant as seen in Ta-ble 8. The coefficient of the complexity variable was neg-ative showing that as complexity increased, users were lesslikely to accept the recommendation.

Table 8: p-value shown after regression testing forproduct characteristics

5.2.4 Click rates

Finally, the hypothesis that click rate increases from abanner advertisement to a random recommendation to agenuine recommendation was tested. Normally, products areadvertised through banner advertisements, which are dis-played on websites. These advertisements are often randomand not tailored to individual users. It is known that theclick rate for these kinds of advertisements is approximately0.5% [7]. The random recommendation click rate was foundto be approximately 3%, which is higher than that of thebanner advertisements. This shows that regardless of prod-uct preferences, the recommendations are accepted solelybased on the fact that a friend recommended it. The genuinerecommendation click rate was also calculated and it wasfound to be approximately 11% as shown in Table 9. Thisrate is much higher than that of random recommendationsand banner advertisements. This shows that apart from afriend recommending a product, when the recommendationis genuine (the user considers the friend’s product prefer-ences) the click rate increases drastically. Therefore lookingat these results, genuine recommendations are a better formof advertisements, especially over social networks.

Table 9: p-value shown after regression testing forproduct characteristics

6. FUTURE WORKThis Facebook application was built for the purpose of

finding better marketing strategies over social networks. Thecapabilities of this application allow for data collection tohelp track and understand users’ product preferences andwho they would recommend products to. The final resultsfrom this data collection can range from general statisticsabout products or users to predictive models of how theuser is expected to act. The main obstacle to finding theseresults are getting a large number of users to actively usethe application, and so as the number of users increase, sowill the accuracy of the results and possible models.

Some of the interesting questions that can be solved in thefuture with a sufficiently large dataset are: (1) The effect ofcreating a chain message over a social network. This wouldentail sending out recommendations where the sole purposeis to propagate a certain product by creating a chain in-centive hence increasing the ”word of mouth” of a certainproduct. (2) Understanding how link type factors in withrecommendations. (3) Calculate the link strength betweenfriends on a social network to test what kind of productrecommendations are sent over strong links and what aresent over weak links. 4) Creation of a model to predict whowould be most likely to purchase a certain good with certainproduct characteristics. Social networks have a unique prop-erty in that one can track user’s data as well as friends’ data,and thus perhaps knowing users’ shopping habits would helppredict what their friends would like. The world of networkmarketing has many possibilities and there are many ques-tions that can be asked. This application was built to beversatile and so with it, many other questions as mentionedabove can be solved.

7. CONCLUSIONEarlier research has been conducted to find better target

marketing strategies [2], but limited research has been doneusing social networks to see whether they improve productacceptance rate. Hill’s study [8] used social networks to showhomophily. Hence, using this homophily concept and socialnetworks, there was some untapped potential that marketerscould use to improve target marketing. This work aimed toshow that social networks can in fact be used to improvetarget marketing. Also, research has been done previouslybased on specific products like movies [6], but no researchhas been done on general product categories such as apparel,electronics, home & garden, etc. There was also limited re-search based on relationship between product characteristicsand social networks [8]. Therefore, this work looks at a widerange of product categories with different product character-istics and sees how coupons in each of these categories arerecommended over a social network.

The Facebook application was created to collect data about

users’ demographic information, interest information, friends’information and recommendations that they sent to theirfriends. This data helped show that: (1) Homophily in-creases between a user and (a) a random user to (b)a friendto (c) a recommended friend. Therefore a user is most sim-ilar to a recommended friend compared to all of his friends.From a marketers perspective, if a user likes a certain prod-uct, they can extrapolate that their friends will also prob-ably like that product and thus advertise the product tothese friends. (2) Users are more likely to search for and rec-ommend products with higher brand equity whereas prod-ucts with a lower complexity are more likely to be accepted.Therefore marketers should target products which have highbrand equity when they want a user to use or recommenda product to a friend. However when they want a recom-mendation to be accepted, lower complexity products workwell. (3) Genuine recommendations show a higher accep-tance rate as compared to random recommendations, whichshow a higher acceptance rate than banner advertisements.The best way that a marketer can get a product to be ac-cepted is by finding a user who is interested in the productand have that user recommend that product to his friendswhom he thinks might like the product.

However, the limitations of this work are: (1) The numberof users using the application is currently not large enoughand given enough time for the application to spread on Face-book, a sufficiently large dataset can be collected. (2) All ofthe different available coupons are not recommended. Usersfirst look for coupons that interest them, and then recom-mend only those coupons. Therefore data about all of thecoupons is not available. (3) Some stores sell a wide range ofproducts and it is not defined as to how these stores are clas-sified into different distinct categories. (4) Brand strengthcannot be completely calculated based on the number ofcoupons recommended from a given brand, as coupon’s mon-etary value influences it as well. After considering these lim-itations, there is some convincing evidence that shows thatsocial networks can in fact improve target marketing.

8. REFERENCES[1] Pedro Domingos. Mining social networks for viral

marketing. IEEE Interlligent Systems, 21(1):80–82,2005.

[2] Leila Hamzaoui Essoussir, and Dwight Merunka.Consumers’ product evaluations in emerging markets.International Marketing Review, 24(4):409–426, 2007.

[3] Wan-Shiou Yang et. al. Mining Social Networks forTargeted Advertising. IEEE, 2006. Proceedings of the39th Hawaii International Conference on SystemSciences.

[4] Ed Keller, Brad Fay, and Jon Berry. Leading theconversation: Influencers’ impact on word of mouthand the brand conversation. Keller Fay Group, 2007.

[5] Eric Gilbert and Karrie Karahalios. Predicting TieStrength With Social Media. ACM, 2009.

[6] Jennifer Golbeck and James Hendler. FilmTrust:Movie Recommendations using Trust in Web-basedSocial Networks, volume 1. IEEE, 2006.

[7] Justin Michie. Street Smart Internet Marketing.Performance Marketing Group, 2006.

[8] Shawndra Hill, Foster Provost and Chris Volinsky.Network-based marketing: Identifying likely adopters

via consumer networks. Statistical Science,21(2):256–276, 2006.

[9] Miller McPherson, Lynn Smith-Lovin, and James MCook. Birds of a feather: Homophily in socialnetworks. Annual Review of Sociology, 27(2):45–53,August 2001.

[10] Nicole B. Ellison,Charles Steinfield, and Cliff Lampe.The benefits of facebook friends: Social capital andcollege students’ use of online social network sites.Journal of Computer-Mediated Communication, 12(4),2007.