Electronic copy available at: http://ssrn.com/abstract=2307627

WORKING PAPER NO: 438

Market Segmentation of Facebook Users

Raj Dash Doctoral Student,

Marketing Indian Institute of Management Bangalore Bannerghatta Road, Bangalore – 5600 76

raj.dash@hotmail.com

Year of Publication-November 2013

Electronic copy available at: http://ssrn.com/abstract=2307627

Market Segmentation of Facebook Users

Raj Dash, IIM Bangalore

ABSTRACT

In this paper, I develop afresh a comprehensive approach to market segmentation of

Facebook users. The approach considers the implications of Facebook’s multi-sided

platform (MSP) based business model. Using cluster analysis of survey data, I develop a

basic 3 segments solution for the OSN - infrequent, frequent and engaged. Then I

extend the 3 segments solution to factor in responses to marketers and developers as

well. The resulting comprehensive SN analytical framework for the MSP can help

design market segments for all three parties/groups, i.e. Facebook, marketers, and

developers.

As an outcome of this research, I offer a specific set of propositions addressing (a) the

causal mechanism for response heterogeneity of Facebook users (b) interdependence of

services (OSN, marketer’s fare and developer’s fare) on the Facebook MSP (c) basis for

comprehensive market segmentation, and (d) the integral role of the low rate users in

the network. Future research may validate these propositions further.

Key Words

Market Segmentation, Facebook, Multi-sided Platform (MSP), Cluster Analysis,

Network Effects, Online Social Networking (OSN), Strategy, Marketers

INTRODUCTION

Market segmentation1 of Facebook users

2, as a topic of research, raises intriguing

questions in relation to the fact that Facebook is a multi-sided platform (MSP).

Research studies on this topic (Brandtzaeg & Heim, 2011; Bernoff, 2010; Lorenzo-

Romero & Alarcón-del-Amo, 2012; Lee & Sutherland, 2011; Foster & Francescucci,

2011 ) classify users of social media and specifically those of social networking sites

(SNSs) like Facebook into segments based on variables such as rate and type of use,

benefits sought, motivations and demographics and so on. But, current literature largely

assumes that the task of segmentation strategy for Facebook is limited to helping drive

1 Segmentation (Smith, 1956) is based upon developments in the demand side of the market and

represents a rational and more precise adjustment of product and marketing effort to consumer or user

requirements (italics added). In the language of the economist, segmentation is disaggregative in its

effects and tends to bring out recognition of several demand schedules where only one was recognized

before.

2 I use ‘user’ for ‘personal users’ as opposed to business users; namely affiliate marketers or businesses

and developers in addition to Facebook Co itself. Though I often use the term affiliate marketer or just

marketer, one may choose to call them businesses or affiliate business users because they are increasingly

doing activities such as social care, crowd-sourcing, and so on that go beyond marketing/advertising.

Electronic copy available at: http://ssrn.com/abstract=2307627

the use of online social networking service (OSN). It largely ignores the interests of

Facebook’s sources of revenue; mainly affiliate marketers, and partly affiliate

developers. The segmentation schemes for Facebook in these studies, therefore, are not

aligned with the overall strategy of the Company. The literature on strategy for multi-

sided platforms (MSP) (Hagiu et al, 2011; Rysman, 2009; Eisenmann, Parker, & Van

Alstyne, 2006; Evans & Schmalensee, 2005; Rochet & Tirole, 2006; Weyl, 2011)

addresses platform pricing and product strategies, and in fact, explains why Facebook

freely offers its OSN service to users, the side that has greater network effects. But

implications of MSP based business models for market segmentation strategy is yet to

be addressed directly. To address this gap in research, I model market segmentation as a

3 dimensional problem at the MSP level for Facebook to factor in the interdependent

interests of Facebook company, its affiliate marketers as a group, and developers as a

group. Making use of an empirical study in addition to secondary research, I develop a

comprehensive framework of analysis to aid the task of segmentation. Based on the

analysis of the study, I put forth a set of propositions addressing (a) the causal

mechanism for response heterogeneity – the prime basis for segmentation (b)

interdependence of services (OSN, marketer’s fare and developer’s fare) on the MSP (c)

basis for comprehensive segmentation, and (d) the integrality of the low rate users for

further validation through research.

Facebook’s MSP (Figure 1) has a diverse range of offerings for users today. For reasons

of tractability, these may be broadly categorised as (a) online social networking (OSN)

from Facebook company, (b) marketers’ fare3, and (c) developers’ fare. Facebook’s

business model (Hagiu & Wright, 2011; Enders, Hungenberg, Denker, & Mauch, 2008)

suggests that Facebook would benefit from identifying, growing and reaping from

segments that respond to marketers and developers also rather than segments that limit

themselves to use of OSN only.

Facebook’s MSP and Business Model

Figure 1

3 Marketers’ fare includes advertisements, business or brand pages, marketing posts/communications,

storefronts, social care, social shopping, crowd-sourcing etc. Developers’ fare is a wide range of games

and non-game apps relating to travel, music, health, books, education, fashion, entertainment etc.

Marketers Developers (apps/games)

Facebook MSP

Facebook Users

Facebook Co.

Electronic copy available at: http://ssrn.com/abstract=2307627

However, one must not overlook the fact that use of OSN makes possible the

interactions of marketers and developers with users4 and as a consequence, the earnings

for Facebook. Though many people use only the OSN offering on Facebook, and hardly

respond to marketers; i.e. .05% CTR for advertisements (Lee, Jarvinen & Sutherland,

2011) and developers; i.e. only 10% revenue in 2013; they still do enhance the value of

the Facebook platform and earnings on it through network effects (Eisenmann, Parker &

Van Alstyne, 2006). They help bring in, retain and engage other users who choose not

to limit themselves to using OSN only, and respond to marketers and developers.

Facebook’s current business model5 based on its MSP architecture shown in Figure 1

makes it primarily depend on advertising revenue from marketers today. 90% (2013

figures#) of revenue is from advertisements by marketers and 10% from developers in

the form of fees. Facebook provides a system that enables affiliate marketers to leverage

reach, relevance, social context, and engagement. Facebook users may choose to

interact with affiliate marketers by connecting to a marketer’s business page on

Facebook by ‘liking’ the brand or the marketer. The latter mode is often used by

marketers to drive engagement (Van Doorn, Lemon, Mittal, Nass, Pick, Pirner, &

Verhoef, 2010) through a range of activities. Many marketers are driving awareness and

engagement with their key brands of products/services and using Facebook as a

listening post. Facebook earns revenue for advertising and also for facilitating posts

from marketers to users who have ‘liked’ them on Facebook. Facebook makes money

from developers mainly through its share of 30% of value transacted between

developers and users. Most of the money from developers is based on purchase of

virtual goods and services that accompany the use of apps/games.

In sum, to segment Facebook users, I make a contention in this paper to recognise

differing demand schedules of users for the offerings of marketers and developers in

addition to that for OSN.

1 LITERATURE REVIEW

SNS like Facebook constitute a growing segment in the social media space. I review

five research studies that classify or segment Facebook/SNS/Social Media users below

in this section. This is followed by relevant details from research literature on MSPs.

1.1 Socialisers, debaters, lurkers, sporadics and actives

A very elaborate classification study (Brandtzaeg & Heim, 2011) of SNS users is based

on an analysis of the survey data from 5,233 respondents in Norway in four major SNS:

Facebook, Orkut, LinkedIn, and MySpace. Based on rate and type of use, the study

found five distinct user types: (a) socializers, (b) debaters, (c) lurkers, (d) sporadics and

(e) actives. The sporadic constituting 19% are occasional users with little initiative.

They seek information from time to time. Lurkers making up 27% are also passive users

contributing little content, but they use SNS for recreation. Socialisers at 25% incidence

are younger; have greater participation; follow up and comment on pictures of friends,

4 http://bmimatters.com/2012/04/10/understanding-facebook-business-model/

5 http://marketrealist.com/2014/01/facebook-revenue-advertising/

and even seek out new friends on SNS. Debaters at 11% participate as much as

socialisers, but are more intellectual in their pursuits. Actives at 18% have the widest

range of activities such as community events and publishing music or videos.

1.2 Introverts, versatile and expert users

A more recent segmentation study (Lorenzo-Romero et al, 2012) made use of latent

class segmentation technique to arrive at 3 segments that differ in rate, type, and

motives of use. Introverts (41%), with less than 50 friends, use SNS at most once a

week for less than an hour largely for email. Versatile users (47%) use several times a

week for a total of 1-5 hrs. They young and have a wide range of use. Keeping in touch

and entertainment are dominant motives. Expert users (only 12%) use it more than once

in a day for the widest range of activities on SNS including informing others about

brands or products they use. Dominant motives are making new friends, experiencing

the novelty of SNS, and pursuit of professional interests.

1.3 Social technographics ladder of social media savvy

Social Technographics (see Table 1 below) by Forrester Research (Bernoff, 2010)

profiles social media users into 7 types that overlap to an extent. Type of use and extent

of savvy are the basis of classification. The savviest users of social media at the top

rung create content and upload or publish it on the web. These 24% of creators contrast

with spectators who mainly consume content – read, listen or watch content but do not

create/upload/publish it. The other roles are as shown below.

Social Technographics (2010): Grouping Consumers by How They Participate

Groups % Activities that indicate group membership

(group member participates in at least one of the listed activities for the

group and groups do overlap)

1 Creators 24% publish a blog/own web pages, upload self-created video/audio/music,

write and post articles and stories

2 Conversationalists 33% update status on SNS like Facebook or microblog like Twitter

3 Critics 37% post ratings/reviews on products/services, comment on others’ blogs,

contribute to online forums or to wikis

4 Collectors 20% use RSS, vote for websites, add tags to web pages or photos

5 Joiners 59% Maintain profile on a SNS, visit SNS

6 Spectators 70% read blogs, online forums, ratings/reviews, tweets, listen to podcasts,

watch video from other users

7 Inactives 17% none of the above

Table 1 Source: Forrester Research

1.4 Middle of the road, social interactors, maximizers and information seekers

A study that addressed response to marketing stimuli (Lee et al, 2011) in some measure,

found five segments on the basis of general and feature uses of SNS as well as

motivation factors. The maximizer segment (26%) is the highest on rate of use and also

on all four gratifications sought - social interaction, entertainment, self-expression and

information seeking. A maximizer is likely to be a fan of a company and send out mass

messages. The social interactor (15%), information seeker (20%), middle-of- the-road

(32%) , and laggards (7%) were other segments. Motivations for using Facebook did not

appear to influence responses to advertising on Facebook. No significant difference in

advertising avoidance was found across the five segments (p >.05). Opinion leadership

showed no difference across four out of five segments though it did differ for the

laggards segment. Interactors and laggards were significantly lower on market

mavenism than the other segments. All five segments showed medium to high deal

proneness. Social interactors (15%) were found to be low on use of apps/games and

lowest on using coupons or promotions.

1.5 The MSP context

A multi-sided platform (MSP) may be defined (Hagiu & Wright, 2011) as an

organization that creates value primarily by enabling direct interactions between two or

more distinct types of affiliated customers. A MSP can have a range of business models

to choose from and strategy (Eisenmann et al, 2006) for the platform provider is a

function of the business model. The basic models for generating revenue in case of SNS

(Enders et al, 2008) are advertising, subscription and transaction. Facebook mainly uses

advertising and transaction. As a MSP, Facebook has two types of network effects

(Hagiu et al, 2011; Rysman, 2009; Eisenmann, 2006; Evans & Schmalensee, 2005);

same-side (also called direct) and cross-side (indirect) effects, each of which can be

positive or negative. A same-side effect, in which increasing the number of users on one

side of the network makes it either more or less valuable to users on the same side; and

a cross-side effect, in which increasing the number of users on one side of the network

makes it either more or less valuable to the users or players on another side (refer Figure

1). Same-side network effects are positive for SNS users but usually negative for

marketers and developers if competitors are added. Marketers of complementary

offerings may be added with positive effect for incumbent marketers. Usually, there are

positive cross-side network effects between users and marketers, and users and

developers. Increase in number of users benefits and attracts more marketers as well as

developers. Increase in developers has positive effect on users. The effect may be

negative when addition of marketers results in more advertisements. Often some users

benefit from adding a marketer whereas other users find it intrusive. Increasingly, the

negative effect is being minimized with more relevant targeting.

1.6 Implications of the review

It is evident from the preceding review that the segmentation solution can be made more

comprehensive by taking into account the MSP based business model of Facebook. The

study by Lee et al (Lee et al, 2011) observes that use of OSN on Facebook isn’t

necessarily accompanied by response to marketers and developers. A scheme that uses

only the response to OSN as the basis for segmentation and ignores that to marketers

and developers, therefore, is unlikely to suffice. Missing out on the important

dimensions of response to marketers and developers, it yields an inadequate solution.

For a comprehensive solution, we need to bring in response to marketers and developers

as basis variables in addition to response to OSN. This may call for new metrics that

separates low scale of response from high for marketers and developers.

2 MARKET SEGMENTATION FOR OSN: EMPIRICAL STUDY

In this section, I present the findings of an empirical study6 that helps develop the

approach to market segmentation for Facebook. This is a segmentation for the OSN

service (and not marketers’ or developers’ fare) to begin with. It is based on around 261

(50 pilot and 211 in final round) responses from current and past students of Indian

Institute of Management Bangalore, a leading business school in India. The study

gathers information on user characteristics, use and engagement.

2.1 Segments identified by two-step cluster analysis

Two Step Clustering procedure in SPSS that used 4 proposed basis variables (the SPSS

term is input features); in touch (feel out of touch in a while without Facebook), adult

(whether more appropriate for teenagers than for adults), f_24 (whether used Facebook

in last 24 hrs – these are frequent users who use it almost every day), and ps_1a

(whether likely to continue using if a monthly rent of $1 is charged for Facebook),

yielded a simple 3 segment solution (Figure 2) tabulated in section 2.2 below. The close

to 0.5 value for silhouette measure of cohesion and separation showed that the market

segmentation solution is satisfactory in terms of within group homogeneity and across

groups heterogeneity. Infrequent, frequent and engaged are the 3 segments.

3 Segments Solution/Framework for Facebook

Figure 2

6 Web link for survey questionnaire is at: https://qtrial.qualtrics.com/SE/?SID=SV_ahlxGCTWQEyk4mN

In case of difficulty in accessing, you may write to the author.

Frequency of use

Price insensitivity

Infrequent

(30%)

Engaged (21%)

Frequent (49%)

2.2 Segment Descriptions

3 Segment Solution for OSN

Segment S1

Engaged User (30%)

Segment S2

Frequent User (49%)

Segment S3

Infrequent User (21%)

Profile

Engaged users (S1) average 57

minutes a day on Facebook.

These are frequent users who

are lower than segment S2 and

S3 on price sensitivity and

higher (refer Tables A1 and A2

in appendix) on variables that

measure attitudinal and

behavioural engagement such as

(a) love Facebook (b) intend to

continue using it for a long time

(c) use it as a daily routine (d)

feel out of touch in a while

without Facebook, and (e)

Facebook use is not a low

priority activity. Importantly,

segment S1 is unlike S2 and S3

who are likely to discontinue

using it tomorrow if a rent of $1

per month is charged per

account.

Frequent users, averaging 44

minutes a day on Facebook,

are comparable to segment S1

on rate, range, proficiency and

priority of Facebook use, but

their use differs qualitatively

as well as in duration. They are

as responsive as engaged users

on Facebook on activities such

as like and playing games but

significantly less frequent on a

range of initiatives such as

status updates, use of album,

comment and chat. They score

lower than the engaged users

on most engagement variables

(see under S1 on the left). It is

likely that Facebook is not

integral to their basic social

activities such as keeping in

touch with friends, relations

and acquaintances.

With an average of 16 minutes of

use a day, Facebook is not a daily

habit for segment S3. They are

lower on frequency of use and

engagement. They have less than

half as many friends and groups

on Facebook compared to

segment S1 and S2. For them

social media and OSNs have

lower priority in a day’s activity.

Only a few of them access

Facebook on the mobile. A higher

proportion of segment S3 are

married, stay with family and are

older; factors that probably lower

the use of OSNs and Facebook .

Some of them may be infrequent

or lower in duration of use

because they use G + or LinkedIn

or Twitter longer than S1 and S2

(see Tables A1 and A2 in

appendix).

Frequency of use

Frequent use is a necessary but

insufficient condition to be an

engaged user. 37% of frequent

users constitute the engaged

users of segment S1.

63% of frequent users

constitute segment Y; these

frequent users are low in

engagement variables.

Around 58% of infrequent users

used it at least for 5 minutes in

last 1 week and 24% have done so

between a week to a month.

Engagement

For 29%, OSN is low priority

(activity), for 45% Facebook is

low priority, for 69% daily

routine, 81% have continuance

intention, 21% become anxious

without it, 53% feel out of touch

if they miss out on Facebook

For 42%, OSN is low priority,

for 50% Facebook is low

priority, for 63% daily routine,

66% have continuance

intention, 16% anxious

without it, 29% feel out of

touch in a while without it, and

For 69% OSN is low priority,

for 84% Facebook is low priority,

for 7% daily routine, 42% intend

to continue for long, 2% anxious

without it, 13% feel out of touch

in a while without it, and only

7% say they love Facebook.

for a while; and reportedly, 56%

love Facebook. All willing to

pay $1 a month.

28% love Facebook. None are

willing to pay $1 a month for

using Facebook.

Demographics

42% are married

young (85% are 35 or less)

52% stay alone (away from

family)

46% are married

young (83% are 35 or less)

54% stay alone

76% are married (sample % is 51)

old (60% are 35 or less)

34% stay alone

Subjective Norms/Beliefs

21% think Facebook is more for

teens than adults, 31% find it

trivial, for 48% it is more

stylized self-presentation than

authentic socializing, and 31%

are not comfortable exposing

thoughts online.

34% think it is more for teens

41% find it trivial

66% find it less authentic

49% uncomfortable opening

up online

47% think it is more for teens

49% think it is trivial/frivolous

80% view it as more stylized self-

presentation

60% not comfortable exposing

thoughts online

Behavioural

8 out of 10 use F mobile

interest in social media and

SNS is higher for segment S1

89% chat on internet

21% comment on blogs

66% play games on mobile

45% used LinkedIn in 24 hrs

Facebook gets 69% of time for

SNS (among F,L, G+ & T)

39% find it useful for

career/business

6 out of 10 use F mobile

larger share of SNS in time

pass for S1 and S2 than S3

82% e-chat

31% comment on blogs

51% play games on mobile

33% used LinkedIn in 24 hrs

Facebook gets 65% of SNS

time

24% find it so for

career/business

5 out of 10 use f mobile

priority of OSN and Facebook

falls from segment S1 to S2 to S3

47% e-chat

4% comment on blogs

36% play games on mobile

13% used LinkedIn in 24 hrs

Facebook gets 38% of SNS time,

LinkedIn & G+ score higher

16% find it so for career/business

Rate, range and proficiency of use

is remarkably lower for S3

Table 2

2.2 Evaluation of Segmentation Solution for OSN

The above empirical solution was found to be better than several other alternatives that

were considered (detailed analyses are available from the author). It meets the

requirement (Lilien & Rangaswamy, 2004; Frank, Massy & Wind, 1972; Wind & Bell,

2003) of an appropriate segmentation scheme (refer Tables A1 and A2 in ANNEXURE)

for the following reasons.

There is significant heterogeneity in extent and type of need across the segments

Though users are heterogeneous across groups, they do cluster into

homogeneous groups

These segments may respond differently to the firm’s new propositions or

stimulus as evident from the response to the price change question.

Using descriptor or access variables, it is possible to predict segment

membership with reasonable accuracy.

Response heterogeneity in relation to marketing stimulus is provided by the price

sensitivity measure. The segment structure analyses showed that such behavioural

segmentation based on frequency of use and level of engagement may be more useful

for OSN than segmenting on the basis of variables such as benefits or demographics or

psychographics.

3 MARKET SEGMENTATION IN THE MSP CONTEXT

The segmentation solution for Facebook’s OSN offering developed in section 2 based

on empirical analysis augments current research by incorporating a response variable in

the form of price sensitivity. Facebook may use it to drive use and engagement, design

and offer premium features and so on. However, since response to marketers and

developers is not factored in, segment profitability (also revenue and growth) cannot be

evaluated.

3.1 Extending the 3 segment solution for OSN to marketers and developers

I propose to extend the 3 segment solution or framework of engaged – frequent –

infrequent for OSN to marketers’ fare and developers’ fare as well. This is in order to

factor in the responses sought from other important quarters paving the way for a

comprehensive solution. Response of a user to marketers’ fare may be measured by the

user’s spend level in response to advertisements in a given period. For marketers who

advertise, users spending above a certain cut-off level but below a certain cut-off level

in engagement behaviour may be grouped as the frequent or high rate users. Users

scoring above cut-off levels in spend level as well as engagement behaviour may be

designated as engaged users of marketers’ fare. Spend level, however, is inappropriate

for marketers who do not advertise or sell on Facebook. Frequency of interaction with

marketers’ fare may replace spend level in such a case. One may choose between the

two measures depending on requirement. Engagement behaviour in relation to

marketers includes likes on brand pages and other content; recommendation to friends;

liking, commenting on or sharing of posts or ads; participation in events etc. A score

card may be designed keeping in mind the range of engagement behaviour possible. The

approach may be similar for identifying infrequent – frequent – engaged users of

developers’ fare or apps/games. Such an extension shall provide us with the building

blocks of a comprehensive segmentation solution.

3.2 The F cube segmentation framework

Extending the 3 segments framework to all 3 services; OSN, marketers’ fare and

developers’ fare, I arrive at a framework as shown in Figure 4 below. The resulting

27 Theoretical segments of F Cube

Figure 3

3x3x3 = 27 segments which may be represented by a Rubik’s cube (I refer to it as the F

cube segmentation framework for Facebook.) because it has the same number of

constituent small cubes (see Table 3 for enumeration). The segments for OSN are on the

vertical F axis of the cube (Figure 4). I designate the infrequent, frequent and engaged

segments as F1, F2 and F3 (equivalent to S1, S2 and S3 of 2.2) respectively with F1 at

the bottom layer and F3 at the top layer of the larger cube.

Each segment for OSN is further subdivided into 9 theoretically possible smaller

segments or cells. These 9 segments capture the variations in frequency of

interactions/response and level of engagement with marketers and developers. M1, M2

and M3 are respectively the infrequent (low rate of spend), frequent (high rate of spend)

and engaged levels of response/interaction with marketers; and D1, D2 and D3 are the

ascending levels of response/interaction with developers through apps/games.

One may note that the 3 axes in Figure 3 are used like those of a a Rubik's cube for

representational purpose. Basically, it shows the 27 possible combinations of responses

of a user to the 3 services - OSN, marketers and developers. On each axis, I show 3

categories of responses - infrequent, frequent, and engaged. Strictly speaking, the axes

for OSN, marketers and developers do not represent orthogonal dimensions. The axes

are not linearly scaled. An engaged user of OSN need not be less frequent than a

frequent user.

F

M

F1 M1 D1

F3 M3 D3

F3 M1 D1

F1 M1 D3

F1 M3 D1

F3 M1 D3

F3 D1 M3

D

The F Cube (or more generally SN) Segmentation Framework

Groups Combinations

or segments

Facebook

Co

Marketer Developer Size

Remarks Priority

F1 F1 M1 D1

F1 M1 D2

F1 M1 D3

F1 M2 D1

F1 M2 D2

F1 M2 D3

F1 M3 D1

F1 M3 D2

F1 M3 D3

Base layer of F cube

Combinations of F1

#

#

#

#

#

#

#

#

#

# low

interest

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

#

No.

of

users

inside

the

small

cubes

Cubes with

M2, M3,D2

and D3 are

likely to have

few users

Strategy may

be to migrate

F1 users to F2

Important for

network

effects

Least

for

current

profits

Informs

competiti

ve and

growth

strategies.

F3 F3 M1 D1

F3 M1 D2

F3 M1 D3

F3 M2 D1

F3 M2 D2

F3 M2 D3

F3 M3 D1*

F3 M3 D2*

F3 M3 D3*

Top layer of F cube

Combinations of F3

√

√

√

√

√

√

√

√

√

√ high

interest

#

#

#

√

√

√

√

√

√

#

√

√

#

√

√

#

√

√

do Top priority

Segments in

F3 are most

receptive to

Fremium

business

model (like

LinkedIn)

Highest

for

Facebook

least price

sensitive

to

Facebook

offerings

F2 F2 M1 D1

F2 M1 D2

F2 M1 D3

F2 M2 D1

F2 M2 D2

F2 M2 D3

F2 M3 D1*

F2 M3 D2*

F2 M3 D3*

Mid layer of F cube

Combinations of F2

√

√

√

√

√

√

√

√

√

#

#

#

√

√

√

√

√

√

#

√

√

#

√

√

#

√

√

do Medium

to top priority

F2 is strategic

because of its

sheer size –

clearly bigger

(Table 2) than

F3.

High for

Facebook

May be

highest

for

marketer

or

developer

in some

cells

Table 3 (segments s = 3; groups of business users n = 3 and S

N = 27 – F Cube in this case)

This extended framework is an improvement over the framework of Figure 2 that

ignored response to marketers and developers. Earlier, the groups were 3 - F1, F2 and

F3 (S1, S2 and S3 in 2.2). While, theoretically, there are 27, adequate homogeneity

across some of these segments may let us club them to design final segments. Empirical

data and analytics may guide the design of final segments with these 27 building blocks

and subsequent choice of segments for targeting. Depending on the purpose, one may

choose segments from the menu of 27 and even further sub-segment them before

evaluating.

The F cube segmentation framework can be more generally christened SN segments

framework for MSPs where s is the number of segments for each of the business players

(3 in this case) and n is the number of business players (also 3 in this case). This SN

segments framework (Table 3) can make it easy for individual marketers and developers

also to understand and constitute their market segments. For a newspaper for example,

there are two business users – newspaper owner and advertisers as a group. Readers are

the non-business users. Using 3 segments for both parties, we get 32 = 9 theoretical

segments – from N1A1 (N for newspaper and A for advertisers collectively), the least

lucrative to N3A3, the most lucrative. Here, of course, the business model is

subscription based for readers.

The approach may differ for specific developer or marketer on Facebook. For example,

the prime segment for a specific game developer, say Zynga, may be constituted by

segments with D3 and F2 or F3. For greater relevance D3 may be further divided into

D3g the games segment and D3a the none-game apps (such as for books and health)

segment. Similarly, while segmenting users in the served market for a specific marketer,

only relevant marketers, relevant activities (such as advertisements or business page

posts) need be considered as a subset of Mi.

If Facebook, for example, wants to release an advertisement campaign to persuade users

to do social shopping on Facebook, the segments to target may be the ones in F2 and F3

with M2 or M3, keeping in mind the cost of exposure. Mass marketers may prioritise

F2M3 and F2M2 segments ignoring the D levels. The ideal segment to grow for the

MSP is F3M3D3. F3M1D1 with growing numbers may make a strong case for the

fremium model. In case the proposition is to offer an advertisement free version of

Facebook for a price to users, this framework may help weigh the gains from the F3

segments against the opportunity loss from M2 and M3 segments because a part of F3

users would not be accessible for advertising. As mentioned in Table 3, users in F1

segments are important for network effects. They help bring in, retain and engage other

users (refer 1.5) who choose not to limit themselves to using OSN only, and respond to

marketers and developers.

4 DISCUSSIONS AND PROPOSITIONS

To recapitulate and advance our understanding of the segmentation strategy context for

Facebook, I develop a set of propositions based on the learning from this study. These

are defeasible inductive arguments that can further enhance our understanding when

new evidence accumulates. Mapping and hypothesising relationships that govern causal

mechanisms behind response and response heterogeneity may contribute to academic

research. Identifying conditions under which the propositions may hold well can prove

useful to the business users. Each set of propositions below is preceded by its line of

reasoning based on theory or empirical facts.

4.1 The sources of heterogeneity of Facebook users

Table 2 and Table A1 in the Annexure list the heterogeneity across the segments for

Facebook users. The heterogeneity in response variables such as rate of use, price

sensitivity, and associated level of engagement can be explained in terms of a range of

other variables reported in the same tables. The theory of planned behaviour (TPB)

(Ajzen, 1991) provides several important reasons for heterogeneity among users in

usage patterns. The empirical survey data particularly supports the role of attitude and

subjective norms (refer Table 2 and table A1 in Annexure) in determining levels of use

and engagement. Regression analysis (Table A3 in Annexure) of a range of variables

showed that the other important sources of heterogeneity are: (a) demographic, i.e.

marital status; (b) category (social media) attitude, i.e. keeping in touch and social

influence; (c) category behaviour, i.e. priority of online social networking in day’s

routine and blogging habits (commenting on blogs); and (c) competitive factors or

multi-homing (Evans, 2003), i.e. LinkedIn and Google +. Perhaps, those who are

married have less need for online socialising than those who are unmarried. Also,

greater need for privacy for the married may discourage use of Facebook. LinkedIn and

Google + compete with Facebook for the user’s time though Twitter doesn’t do so

significantly. It is worth noting that social technologies are still evolving and diffusing

in terms of extent and type of uses (Chui et al, 2012). Different people may take to

different set of services and features at a given time. This is another important yet

changing source of heterogeneity. A key variable that differentiates engaged user from

the frequent user and importantly explains differences in price sensitivity between them

is the love (Batra, Ahuvia and Bagozzi, 2012) variable (see Table A1 in Annexure and

online questionnaire). In the context of this Facebook study, it signifies emotional

attachment, separation distress, passionate desire to use, and willingness to invest

resources, i.e. time or money. Based on these theoretical underpinnings and related

interpretations of the empirical findings, I offer the following proposition to explain

sources of heterogeneity of Facebook users.

Proposition I

(a) Some of the key sources of heterogeneity in Facebook use are: (i) marital status

(ii) category (social media) attitude and behaviour, and (iii) use of competing

OSNs.

(b) Association of Facebook use with social influence and habitual use of Facebook

for keeping in touch give rise to differences in usage and engagement.

(c) An important source of heterogeneity is subjective norm: (i) whether perceived

as more appropriate for adolescents than adults, and (ii) whether perceived as a

frivolous rather than serious activity.

(d) Differences in level of engagement can be partly attributed to love of Facebook.

A larger statistical survey along with objective data from Facebook accounts of the

respondents can help validate these propositions conclusively. Heterogeneity in

response to marketers and developers calls for research investigation using database of

Facebook accounts. The empirical findings of this research are based a sample 261

Facebook users among current and past students of IIM Bangalore is not representative

of the overall population of Facebook users. Also, the survey did not measure response

to marketers and developers for two reasons. The pilot study showed low rates of use of

apps/games, purchase response to Facebook ads, and response to marketers’ pages in

the sample of study. To get statistically sizeable sub-samples of users who are frequent

and engaged in relation to marketers and developers a stratified sample (addressing the

27 sub-segments of Table 3) may be useful and efficient. Ideally, the sampling frame for

survey of users may be prepared based on Facebook profile page records of activity in

relation to OSN, marketers and developers. In relation to marketers’ and developers’

fare the differences among users from different countries is substantial. This is partly

because of differences in facilities and payment infrastructure on the Facebook platform

itself. To develop a market segmentation scheme, one may also add pan-regional

variables, i.e. US and Canada, Europe, South East Asia and so on.

The sources of heterogeneity in response to marketers may differ from those in case of

OSN. Online purchase behaviour is a patent source. Peer network externality (Lin & Lu,

2011) may have an important role; the more your peers interact with marketers on

Facebook, the more value you find in such interactions and do likewise. Online

openness to experience and self-express, attitude towards marketers/advertisers, and

creativity may count as other sources of heterogeneity. In case of developers’ fare, the

main source of heterogeneity may the gaming habit in case of games. For non-game

apps, social technology savvy may be a key source. Age and lifestyle may be other

important sources. Data from profile pages and activity logs of Facebook along with

survey responses may help evaluate propositions or hypotheses developed on these

lines.

4.2 Interdependence of services

The offerings on Facebook from the business users, i.e. Facebook Co, Marketers and

Developers may be viewed as 3 different offerings as depicted by the rows in Table 4

(this is a simplification of Table 3 earlier) below. Theoretically, seven possible

combinations of use of these three offerings are possible for Facebook users. S7 are

users who use all three services and are most valuable for the platform. S2, S3 and S4

are rare in occurrence or incidence, because, people primarily come to Facebook for the

OSN.

Interdependence of Services

S1 S2 S3 S4 S5 S6 S7

OSN √ √ √ √

Marketers √* √* √ √

Developers √* √* √ √ * signifies sparseness in number of users

Table 4

It is possible that there are a few users who use Facebook mostly to play some games or

use some apps or interact/transact with some marketers who are not conveniently

accessible elsewhere. However, such users are likely irregular and infrequent. Also,

they may not fully benefit from the interactions in the absence of an active friend circuit

on Facebook. OSN adds value to multi-player games, apps that peer network members

use, and interactions with marketers in varying degrees. Another reason why these

combinations are sparse in incidence is the fact that marketers, barring a few small ones,

are likely to have equivalent offerings on other channels. Developers also do multi-

homing. So, interest in marketer’s or developer’s fare per se doesn’t require the prospect

to interact with them on Facebook. When interest in OSN is lacking, they are less likely

to do so. Use of OSN is a pre-requisite though only a fraction of those who meet it

respond well to marketers’ and developers’ fare. Another strong argument for the

criticality of OSN use is peer or local network effects (Lin and Lu, 2011). More use of

OSN implies more friends on the Facebook platform which in turn would enhance

perceived value of services on the platform.

There have been many changes in the multiplex that is Facebook in recent times. New

services such as social care and features such as graph search integrate the platform

more and raise the scope for positive same side and cross side network effects. The

wider the range of offerings from a marketer, the more reasons a prospect has to connect

with the marketer on Facebook. While games are intrinsically different from activities

in relation to marketers on Facebook, the same cannot be said of apps that are not

games. For example, a user of Tripadvisor, a popular Facebook app, may be interested

in travel products and a user of a health care app may look for health care products from

marketers on Facebook and so on. Each developer may need to use its own analytics to

evaluate how the response it gets correlates with that for other developers, marketers

and the OSN fare.

The engaged segment tends to use Facebook significantly more than even the frequent

segment (see Table A1 in appendix) and this itself increases their exposure to

communications from and in relation to marketers and developers. The higher exposure

may result in higher likelihood of engagement activities such as sharing and

recommending. Perhaps, more important than the higher exposure is the more positive

social context an engaged user has to support activities on Facebook across the board.

A marketer’s range of offerings on Facebook to such a user benefits from the fact that

Facebook is a preferred platform for her. The argument is similar for developers. The

preceding discussion gives rise to the next set of propositions.

Proposition II

(a) Response to or interaction with (i) marketers and (ii) developers critically

depends on the extent of OSN use. However, OSN use is a necessary but

insufficient condition for it.

(b) Response to or interaction with marketers and that with developers are

positively affected by each other while controlling for OSN use.

(c) An engaged user of Facebook is more likely to respond to or interact with

marketers and developers than a frequent or infrequent user.

This set of propositions is best tested using activity logs of Facebook users. It would be

interesting to investigate all the non-OSN reasons why Facebook is preferred by some

users. Interdependence between marketers’ and developers’ fare, if any, can be

established from longitudinal data from Facebook logs. The stages a typical user goes

through from trial to adoption over time may be studied from historical records of users.

Activities in relation to OSN, marketers’ fare and developers’ fare and associations

among may be measured qualitatively and quantitatively to evaluate proposition II

above. Score cards may be designed for measuring responses to marketers’ fare. As

discussed in 3.2, pages of marketers liked; posts from marketers liked, commented upon

and shared; service apps from marketers used; advertisements clicked on; purchases

made via Facebook and so on may count towards response/engagement with marketers.

Rate of use of apps/games and associated spend levels may count towards

response/engagement with developers.

4.3 Key basis for segmentation

Sections 2 and 3 dealt with the development and extension of market segmentation

solution at length. The analysis underscored the need to include response or interaction

with marketers and developers as basis variables for market segmentation. A pertinent

question that may arise is why segment Facebook users when you can market one to

one. One to one marketing, barring a few cases, does not imply that all elements of

marketing mix are unique for each customer. New product development, for example,

can benefit from segment level standardization. There may be little point in driving a

new product development initiative according to disaggregated requirements of a billion

users found by one to one marketing. The level of disaggregation may vary according to

the element of marketing mix while keeping the segments sizeable. The SN

segmentation framework can guide the process of arriving at the one to one value

proposition and related marketing mix for each customer. Segmentation, for example,

may help sharpen behavioural targeting for advertisements. Information about the target

market segments can be incorporated effectively into strategies (Wind and Bell, 2003).

In case of Facebook Co, strategies may pertain to user recruitment advertising,

competitive positioning, new product development, pricing premium features, business

model innovation and public relations content. All these can be adjusted (Smith, 1956)

to take advantage of the heterogeneity for specific segments of interest.

To illustrate further, suppose, Facebook offers tomorrow premium features that it has

been experimenting with in recent years. It can use browsing behaviour data in

conjunction with profile, activities, and online transaction records to identify

appropriate market segments within the SN framework (F3 segments), and then

selectively offer suitable premium features with appropriate scheme. If a user often

clicks on ads on Facebook, it may not be a good idea to target ads on premium ad free

version of Facebook at the user. Similarly, privacy needs are different for different

groups of users, and so premium privacy features may be targeted selectively. Marketers

placing ads for products and services can be more selective with the help of the SN

segmentation scheme. A marketer however, need not limit herself to infrequent -

frequent – engaged as basis variables. She can sharpen targeting by using in conjunction

other information at her disposal. Besides, for a marketer, segmentation of Facebook

users has to be coordinated with the segmentation it does outside Facebook. An

important benefit of using this framework is that the marketer can distinguish among

segments such as F1M3, F2M3 and F3M3 (refer figure 4) and allocate advertising

dollars optimally. The sn segmentation framework provides the basic building blocks for

segmentation. Depending on contextual needs, one may add more basis variables.

Because there are more than a billion users and it is online, it is tractable to have many

segments. The preceding discussion lays down the ground for the next proposition.

Proposition III

A comprehensive market segmentation of Facebook users calls for basis variables that

measure response to not only OSN, but also marketers and developers. Extent of use

and engagement with OSN, marketers, and developers can be key basis variables on

which we may segment Facebook users. Such a segmentation scheme, with adaptable

metrics for basis variables, can guide strategies not only for the Facebook Co, but also

for individual marketers and developers.

The above proposition, along with the SN framework of section 3 (refer Figure 3 and

Table 3) may be evaluated on a statistical database of Facebook activity records of users

along with survey responses on lines similar to the reported empirical study.

4.4 Network effects and the long tail of low rate users

While high rate users of marketers’ and developers’ fare (M2, M3 and D2, D3) respond

to or interact with marketers and developers more often, the aggregate value low rate or

infrequent users (M1 and D1) bring to the platform need not be low. As discussed in

section 1.5, positive same side network effects (Eisenmann et al, 2006) is the

fundamental value driver associated with low rate or infrequent users. Take low rate

users off the platform, and many frequent users shall no longer remain frequent; some

will drop out too. The same argument goes for marketers and developers as well. Cross

side network effects also drive value. The low rate users raise the addressable market or

prospect size which triggers a rise in marketers and developers which in turn spurs the

numbers (Weyl, 2010) as well as rate of response of users. If you take low rate users off,

it will bring down cross side numbers as well. This is special about platform economics.

The base of Facebook users whose rate of response to marketers’ fare is low (M1 in

Table 3, say below the median rate) is likely to be much larger than those with higher

rates (M2 and M3). This may be implied (needs to be validated empirically) by the fact

that the company earns about half of its revenue (2013 figures7) from only two

countries, US and Canada, though they have less than 18%8 of global monthly active

users. The total value the low rate users contribute thanks to their numbers from all over

the world may eclipse the aggregate value from high rate users like in the case of

Google and eBay. Google, for instance, makes most of its money off small advertisers

(the long tail of advertising), and eBay is mostly tail as well - niche and one-off

products. Because of the long tail (Anderson, 2007) phenomenon, revenue and profits

from M1 may outweigh that from M2 and M3. The same argument applies in the case

of apps/games from developers. There are other reasons in favour of low rate users of

marketers’ and developers’ fare. Low rate (M1) users may be of greater interest to

marketers and developers who want to grow new markets. Most of today’s M1 may

grow to M2 or M3 tomorrow. As our earlier analysis in 2.2 and 4.1 showed, many users

in M1 segment may prefer competing SNS or social media services. Interestingly, there

are many marketers who mainly want to gather consumer insights (Barwise & Meehan,

2010) on Facebook. They do considerable market research of the inobtrusive as well as

obtrusive kind on Facebook. For such purposes, marketers may look for a portfolio of

segments cutting across M1, M2, M3, F1,F2, and F3. These factors lead to the

proposition outlined below.

7 http://venturebeat.com/2013/10/30/facebook-grows-per-user-revenue-in-every-single-global-territory/ 8 https://newsroom.fb.com/key-Facts

Proposition IV

(a) Low rate users of marketers’ fare on Facebook (combinations of M1 with F2

and F3 in Table 3) are of substantial value to marketers and therefore also to

the Facebook Co itself.

(b) Low rate users of developers’ fare on Facebook (combinations of D1 with F2

and F3 in Table 3) are of substantial value to marketers and developers and

therefore also to the Facebook Co itself.

(c) Low rate users of OSN (combinations of F1) are (i) of value to the Facebook

Company, and (ii) not of significant value to marketers and developers.

Long tail phenomenon, if any, may be verified by linking revenue and profits to users.

The value of each of the 27 segments in Table 3 may be evaluated from the point of

view of marketers, developers and the Facebook Co to examine the above propositions

well. Data on dollars/Rs transacted for the latest financial year may be used for

evaluating value of each segment of users to marketers and developers at aggregate

level. To evaluate network effects of F1 users, low rate users may be taken off by

deactivating their accounts in an experiment to study the effect on high rate users who

remain. We can also compare the Facebook usage of a family in which parents are

infrequent users and children are frequent users of OSN against that of another in which

parents are inactive or non-users whereas children are frequent users. Ceteris paribus,

the former is likely to yield more benefits to marketers and developers compared to the

latter.

4 CONCLUSION

The key issue I addressed in this paper, to reiterate, is the implications of Facebook’s

MSP based business model for the market segmentation strategy of itself and affiliate

business users of the platform; marketers and developers. The SN segmentation

framework, developed by extending the 3 segments solution found empirically, informs

segmentation strategy formulation and implementation. It helps recognize several

demand schedules for each service offering whereas the conventional solution misses

out on offerings other than OSN. The four propositions developed in section 4

encapsulate the learning and some of the implications of this study.

An important research agenda that emerges from this study is the need for developing

metrics for user’s scale of response and level of engagement with marketers and

developers. The task is easier for advertising response or purchases at storefronts but

difficult in case of response to other activities such as social care, interaction with brand

pages, crowd-sourcing. The nature and range of a marketer’s offering varies from one

marketer to another and score cards to measure non-advertising response or engagement

may have to be designed on a case by case basis. Judicious and near optimal choice of

market segments is possible only when user response to marketing stimuli is understood

well. As observed in 3.3 and 4, market experiments (including ex post) may be carried

out to understand market response at segment levels. Experiments may also help

evaluate same side and cross side network effects.

The issues of greater interest to academics, I addressed in some measure in section 4, is

the causal mechanisms that give rise to response heterogeneity for OSN. Response

heterogeneity in relation to marketers’ and developers’ fare needs to be unpacked

further. Patterns of network effects across various segments as enumerated in Table 3

and even at more granular level such as different types of services from marketers and

developers may be studied with longitudinal quantitative data on activities of Facebook

users from the logs.

The MSP based perspective on market segmentation I presented in this paper is not the

only one of importance. Another valuable perspective takes shape when we look at

users, marketers and developers as distinct, though interdependent, markets. How do we

go about segmenting marketers and developers for Facebook? Facebook has been

tweaking its business model for marketers in recent times. What innovations can we

expect in business models in SNS space tomorrow? How network effects may be

manipulated to advantage by a marketer or developer? Future research may answer

these and other related questions that arise from this study.

ACKNOWLEDGEMENTS

I thank Professor Srinivas Prakhya, Professor Avinash Mulky, Professor G. Shainesh, Professor

Mithileshwar Jha, and Professor Manaswini Bhalla whose valuable reviews and comments have helped

improve this study. I thank Professor Dinesh Kumar, Kaushank Khandwala, and Sunil S. at Data Centre

and Analytics Lab (IIM B) for facilitating me to present the initial draft of this paper in Business

Analytics and Intelligence Conference 2013 at IIM Bangalore. I also thank colleagues Praveen S. and

Tushar Tanwar with whom I had originally gathered the empirical data used in this paper as part of a

general purpose research project on social networking.

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ANNEXURE

Table A1: Metric Scaled Segment Descriptors:

Descriptors for Segment Engaged Users Frequent Users Infrequent Users

Metric (mean) measures

1 curious 3.61a

3.51a

2.82c

2 interesting

2.87a

2.69a

2.24c

3 time pass 3.96a

3.94a

3.44c

4 in touch 3.21a

2.77b

2.09c

5 love 3.61a

3.04b

2.53c

6 daily routine 3.76a

3.54a

2.04c

7 continue 3.97a

3.67a

3.09c

8 anxious 2.58a

2.3a

1.56c

9 useful for career/business 3.02a

2.86a

2.42c

10 open online 3.18a

2.8ac

2.58c

11 more for teens than for adults 2.58a

3.05bc

3.33c

12 trivial or frivolous 2.95a

3.28ac

3.53c

13 priority_o 3.11a

2.95a

2.33c

14 priority_f 2.84a

2.65a

1.80c

15 tp_general 19.07a

26.47b

22.24ab

16 SNS share of time pass (%) 20.25a

16.48ac

10.14c

17 share of time of F (%) 68.69a

64.97a

37.87c

18 share of time of L (%) 18.63a

20.0a

34.02c

19 F mobile visits (last 24 hrs) 4.03a

3.54a

.60b

20 # F groups 9.35a

7.81a

3.31b

21 # F friends 555a

491a

244b

22 F minutes a day (average) 56.97a

43.94b

15.71c

23 like 3.95a

3.67a

1.53c

24 status update 2.27a

1.71b

1.02c

25 comment 3.55a

2.88b

1.33c

26 follow 2.94a

2.38a

1.00b

27 album 2.21a

1.63b

1.11b

28 chat 3.03a

2.25b

.82c

29 apps/games 1.02a

.84ac

.44c

30 use rate (self rate) 5.16a

5.0a

2.13c

31 use range (self rate) 4.0a

3.63a

1.8c

32 use proficiency (self rate) 5.85a

5.19a

3.69c

33 authentic 2.56a

2.29ac

1.93c

34 t_mins (%) 7a

8a

16c

35 g_mins (%) 5a

6a

13c

# If two segments share a superscript a/b/c, difference is not statistically significant (α = .05). Else, the

difference is significant. In 33 and 34 above, segments X and Y are not different, but Y and Z are not

different in 33 and different in 34.

Table A2: Non-metric Scaled Segment Descriptors

Categorical (%) Engaged

Users

Frequent

Users

Infrequent

Users

1 d_m** (use f mobile) 77.4 63.5 46.7

2 blog_c** (comment on blogs) 21.0 30.8 4.4

3 l_24**(used LinkedIn in 24hrs) 45.2 32.7 13.3

4 m_games** (plays games on mobile) 66.1 51.0 35.6

5 e-chat*** 88.7 81.7 46.7

6 father on Fns

25.8 19.2 11.1

7 mother on Fns

21.0 10.6 15.6

8 spouse on Fns

37.1 44.2 55.6

9 partner on F* (overall 20.4%) 27.4 22.1 6.7

10 pro_acqns

80.6 75.0 66.7

11 femalens

(overall 21.3%) 12.9 22.1 31.1

12 marital*** (overall 51.2%) 41.9 46.2 75.6

13 stay alone* 51.6 53.8 31.1

14 age (>35)** 14.5 17.3 40.0

χ

2 significant at * α = .05 ** α = .01 *** α = .001

ns not significant

# The same segmentation analysis was also carried out with frequent user defined as those who claimed to

use Facebook daily. The difference was not substantive compared to the results in A1 and A2.

Table A3

OLS Regression of Rate of Facebook Use (Y)

with demographic, attitudinal and behavioural variables (X)

lm(formula = sqrt(u_rate) ~ s5 + t2 + adult + serious + factor(blog_c) + priority_osn +

g_mins + l_mins + factor(marital), data = osn)

Residuals

Min 1Q Median 3Q Max

-1.643 -0.382 0.068 0.403 1.520

Coefficients

Estimate SE t value Pr(>|t|)

Intercept .0946 .2366 .400 .6896

s5 .1657 .0451 3.673 .0003***

t2 .1143 .0415 2.756 .0064**

adult .1493 .0409 3.650 .0003***

serious .1023 .0460 2.227 .0270*

factor(blog_c)1 .3115 .1009 3.088 .0023**

priority_osn .1930 .0408 4.726 .0000***

g_mins -.0112 .0030 -3.744 .0002***

l_mins -.0068 .0020 -3.361 .0009***

factor(marital)2 -.2400 .0883 -2.713 .0072**

#Residual Standard Error: 0.6126 on df = 204

Multiple R-squared: 0.5189 Adj R-squared: 0.4976

F-statistic: 24.44 on 9 and 204 df, p-value: <2.2e-16

#Residual SE is very high if u_rate is used instead of sqrt(u_rate) for Y

s5: perceived benefit of social influence t2: usual means of keeping in touch

adult: perceived appropriateness for adults (as opposed to teenagers)

serious: extent to which it is not perceived as a trivial or frivolous activity

blog_c: whether comment on blogs (Yes = 1)

priority_osn: priority of OSN in a day’s activity

g_mins: share of google + in time for Facebook, LinkedIn, twitter and Google +