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Word-of-mouth marketing influenceon offline and online communications:Evidence from case study researchLars Groegera & Francis Buttleaa Macquarie Graduate School of Management, MacquarieUniversity, North RydeNSW2109, AustraliaPublished online: 10 Jun 2013.

To cite this article: Lars Groeger & Francis Buttle (2014) Word-of-mouth marketing influenceon offline and online communications: Evidence from case study research, Journal of MarketingCommunications, 20:1-2, 21-41, DOI: 10.1080/13527266.2013.797736

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Journal of Marketing Communications, 2014 Vol. 20, Nos. 1–2, 21–41, http://dx.doi.org/10.1080/13527266.2013.797736

Word-of-mouth marketing influence on offline and online communications: Evidence from case study research

Lars Groeger* and Francis Buttle1

Macquarie Graduate School of Management, Macquarie University, North Ryde, NSW 2109, Australia

This case study reports results from three research studies conducted over 12 weeks as part of a product seeding campaign. Partnering with a word-of-mouth marketing (WOMM) agency for this research, studies 1 and 2 report agency-conducted surveys of campaign participants’ online and offline word-of-mouth (WOM) behaviors. Study 3 deployed an innovative web-based methodology to map and visualize WOM communication patterns, to reveal how campaign-related conversations spread within and across offline friendship networks and the role played by tie strength in that process. We find that agency reports of WOMM campaign results overstate reach and understate frequency. Our results have implications for the measurement of reach and frequency of WOMM campaigns.

Keywords: word-of-mouth; word-of-mouth marketing; brand ambassador; Facebook; social network analysis

Introduction

Word-of-mouth marketing (WOMM) campaigns are an increasingly popular component

of the marketing communications mix, being referenced not only in the popular business

press (Rosen 2009; Sernovitz 2009) but also in contemporary marketing management

texts (Kotler, Keller and Burton 2009). Often, WOMM campaigns are associated with

influencer strategies in which products are placed with persons who are expected to use,

share, and talk about the product with their friends and family; these persons are known as

brand ambassadors, buzz agents, or product seeds. Armstrong and Kotler (2011, 170), for

example, note that an increasing number of businesses are applying ‘buzz marketing by

enlisting or even creating opinion leaders to serve as “brand ambassadors” who spread the

word about their products’. These products may be completely new-to-market or new to a

segment that is being targeted. Product sampling is not new (Holmes and Lett 1977; Jain,

Mahajan, and Muller 1995). Being a well-established sales promotion tool, it is widely

used to introduce new products and generate positive word-of-mouth (WOM) or buzz.

However, the association of product sampling with a dedicated WOMM campaign is a

relatively new phenomenon.

A brand ambassador campaign provides the context for our research, which explores

problematic issues of campaign effectiveness, in particular the measurement of WOMM

campaign reach and frequency. We present a case study of one particular WOMM

campaign undertaken as part of a product seeding strategy for a new product launch.

Overall, we report three studies. The first two were conducted by the agency and

investigated the online and offline WOM behaviors of campaign participants. The third

study was conducted by the authors of this paper and explores how the social-structural

*Corresponding author. Email: [email protected]

q 2013 Taylor & Francis

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22 L. Groeger and F. Buttle

characteristics of participants’ social networks influence the spread of the campaign

message within and across generations of conversation partners.

Literature review

WOM researchers, from the 1950s (Katz and Lazarsfeld 1955) to the late 1990s

(Sundaram, Mitra, and Webster 1998), focused on offline dyadic interactions, such as

how opinion leaders influence followers, ignoring the socio-structural context within

which these interactions take place. In this century, however, researchers have shifted their

interest to online environments, where WOM has become styled as word-of-web and

word-of-mouse (Breazeale 2009). People can now be connected in many electronically

enabled ways, including online social networks such as Facebook, Twitter and LinkedIn,

blogs, wikis, file-sharing services, chat rooms, and online communities.

The idea of motivating customers to spread pro-brand messaging within their social

networks is well established (Mancuso 1969), but the work by Reingen and Kernan (1986)

helped marketers understand the significant impact that social networks could have on

message dissemination. They called for research into the influence of social networks on

consumer behaviors and were critical of previous WOM research for ‘its failure to capture

the social-structural context within which such communication is embedded’ (Reingen

and Kernan 1986, 370). Twenty years later, Van Den Bulte and Wuyts (2009) again urged

researchers to develop an improved understanding of the location of individuals in social

networks as this could offer considerable insights into how WOM is disseminated.

Recently, marketing researchers have begun to explore the significance of consumer-

generated content in online environments. For example, Liu (2006) finds that the volume

of online WOM mentions, rather than positive or negative valence, best predicts box office

success for movies. Other studies have examined the effects of recommendation behaviors

from customers on Amazon.com and similar shopping websites (Chevalier and Mayzlin

2006), social networking platforms (Trusov, Bucklin, and Pauwels 2009), bulletin boards

(Huang 2010), chat rooms (Godes and Mayzlin 2004), and online communities

(Mathwick, Wiertz, and De Ruyter 2008; Zhu and Zhang 2010). Of these, only Trusov,

Bucklin, and Pauwels (2009) investigated the effects of social networks on message

dissemination.

Very few researchers have focused specifically on product sampling and WOMM

campaigns. Carl (2007) set out to understand what he calls the ‘generational relay’ of

WOMM messaging – the spread of WOM from the original WOMM campaign

participants (Generation Zero or Gen0) to their conversation partners (Generation One or

Gen1), and then from those Gen1 conversation partners to the next generation of

conversation partners (Gen2), and then to Gen3. In their later work, Carl, Libai, and Ding

(2008) analyzed inter-generational WOM relay data from a WOMM campaign for a low

involvement consumer good that had 5000 original Gen0 participants. Ahuja et al. (2007)

explored whether buzz agents feel any ethical tensions about exerting commercial

influence on their friends and family. Kozinets et al. (2010) studied an online WOMM

campaign in which consumers were seeded with a new technology device and encouraged

to stimulate WOM by writing about it in their personal blogs. Hinz et al. (2011) compared

four product seeding strategies in two small-scale field experiments and one real-life viral

marketing campaign, and found evidence that the best seeding strategies, those that focus

on seeding hubs (people with many ties) or bridges (people who connect two or more

otherwise disconnected clusters of people), can be eight times more successful than the

least successful ones.

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23 Journal of Marketing Communications

Problem definition

In conventional advertising research, reach and frequency are two commonly used

effectiveness measures (Katz 2010). Reach refers to the number of a defined target

audience (e.g., people or households) exposed at least once to a firm’s advertising message

in a defined period of time. Reach is broadly synonymous with ‘cumulative audience’

(AMA 2012). Frequency is the average number of times a member of that audience is

exposed to the message during the defined period of time.

Media planners may make assumptions about the number of times an audience

member needs to be exposed to a message in order for the desired cognitive, affective, or

behavioral outcome to be achieved. For example, low involvement products are often

advertised with high frequency. McDonald (1995, 1) suggests that media planners should

strive for ‘effective frequency’, the underlying concept of which he describes thus: ‘If

there is too little exposure, the advertising will fail to be noticed; on the other hand, if there

is too much, the recipient will be ‘saturated’ and the surplus will be redundant’. Writing in

pre-digital days, Krugman (1972) suggested that three exposures might be enough to

arouse action, while Kamin (1978) suggested that it was not unusual for a planner to

recommend a media mix that reaches over 90% of prospects an average of eight times

each. Cheong, de Gregorio, and Kim’s (2010, 403) contemporary survey of US advertising

agencies report that ‘traditional exposure-based criteria such as reach-and-frequency

distribution remain important [in the age of digital advertising] and often are used in

evaluations of offline media schedules’.

Thus, it is still accepted that target audiences may need to be exposed to an ad several

times for the message to have its desired effect (Katz 2010). The potential for multiple

exposures of an audience member to the campaign message, whether offline or online, is

often known or estimated from syndicated media usage research (Cheong, de Gregorio,

and Kim 2010). No such insight into frequency is currently available for WOMM

campaigns. Our interactions with our partner agency, Soup2 (http://www.thesoup.com.au),

and investigations of other WOMM agencies such as BzzAgent, SheSpeaks, Tremor, trnd,

and Vocalpoint indicate that frequency is usually not reported by the agencies that create

and run WOMM campaigns. In the absence of data about frequency, it is assumed that

each person reached in a WOMM campaign is a unique identity. Thus, the total number of

campaign-related conversations is used as a proxy for campaign reach.

Moreover, the integration of reach generated by WOMM campaigns with equivalent

data from associated offline and online marketing campaigns to provide a unified

assessment of the reach and frequency of an integrated multi-channel marketing campaign

can be very troublesome. Agencies currently have little understanding of the incremental

ad message exposures of persons reached by WOMM campaigns. Even if the offline and

online data were available and of good quality, integration of the data to provide a single

picture of campaign reach and frequency would be conceptually and technically difficult.

Measurement of the effectiveness of WOMM campaigns can be difficult,

inconvenient, and costly. Our WOMM agency partner measures WOMM campaign

reach by asking Gen0, the brand ambassadors, in an online survey how many people they

spoke to about the product or campaign. The ambassadors in turn invite the members of

Gen1 via email to also participate in the survey. In the absence of specifically identified

persons, researchers tend to rely on estimations of reach based on the number of reported

conversations, as outlined in Figure 1.

For example, if the product is given to 500 brand ambassadors (Gen0) and each reports

talking to an average of 10 people, Gen0-to-Gen1 reach is assumed to be 5000 (500 £ 10)

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10 conversations

4 conversations

500 Generation 0 (Gen0)

5,000 Generation 1 (Gen1)

20,000 Generation 2 (Gen2)

Figure 1. Example of reach estimation across multiple generations.

persons. If a sample of Gen1 is subsequently contacted and reports talking to a further four

people on average about the campaign, then Gen1-to-Gen2 reach is estimated at 20,000

(5000 £ 4) persons. Estimates of WOMM campaign reach therefore assume that each person spoken to is a unique person. However, there is clearly some possibility that these 20,000

persons are not unique individuals; in other words that there may be multiple exposures of

some persons (illustrated in light gray in Figure 1) to the campaign-related messaging.

Lazarsfeld and Merton (1954) introduced the term ‘homophily’ to refer to the tendency

of persons to affiliate with others who have similar attributes such as age, education, or

ethnicity. Whether that does happen in the WOMM context will depend on the social-

structural characteristics of the networks of Gen0 participants and subsequent generations.

If homophily is influential, then it may be inappropriate for agencies and clients to assume

that the total number of campaign-related conversations is the same as the campaign’s

reach. Homophily leads to the formation of relatively homogenous groups or network

clusters (McPherson, Smith-Lovin, and Cook 2001). Also, because the friends of any

single person are often friends with each other, it also is very likely that these friends also

speak to each other (Christakis and Fowler 2009). This property of network relationships is

called transitivity (see Figure 2).

If there is a friendship tie between person A and person B, and another between person

A and person C, then in a transitive network persons B and C will also be connected. This

idea is captured by the expression ‘friends of my friends are my friends’ (Davis 1970).

Strong friendship ties, characterized by high clustering, are more often transitive than

weak ties, and thus transitivity is often seen as evidence for the existence of strong ties

(Shi, Adamic, and Strauss 2007). In principle, therefore, if campaign-related WOM

diffuses along strong, transitive ties from Gen0 to Gen1, it is possible that when Gen1

passes on the message it will be to a Gen2 who could also be friends with Gen0.

A

B

C

?

Figure 2. Transitivity of ties.

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Figure 3 shows how transitivity could lead to multiple exposures. The left-hand side of

the figure shows that there are nine conversations in total between Gen0 and Gen1, and

Gen1 and Gen2. As shown on the right-hand side of the figure, the transitive ties between

Gen1 and Gen2 mean that only seven people are reached by the campaign. Both of

the people spoken to by person B (Gen1) are also spoken to by person C (also Gen1).

This illustrates that the true number of people reached by a WOMM campaign can only be

measured if conversations are associated with individuals embedded within a network of

friendship ties.

The purpose of our research is to present a case study of one particular WOMM

campaign in order to shed light on how agencies may report WOMM campaign reach, and

to subject agency practices and assumptions to critical reflection. Our research partner

joins us in striving to improve the quality of reporting not only of WOMM campaigns

per se, but of multi-channel integrated communication strategies that include WOMM campaigns. Our analysis of the social-structural attributes of WOMM campaign

participants’ social networks takes us outside the regular scope of agency-conducted

WOMM campaign measurement to identify unique conversation partners and to

differentiate explicitly between reach and frequency.

The case of Hahn White

The brand ambassador campaign we report and analyze supported the launch of a new

beer, Hahn White, into the Australian market. The Australian beer market is in the mature

stage of its life cycle. In the 5 years through to 2011–2012, industry value added fell by an

annualized rate of 0.7%, compared to an annualized rate of national GDP growth of 2.6%.

Reach based on conversations Nine conversations in total

Reach based on unique individuals within transitive network

Seven individuals exposed to message (Nine conversations)

Conversation

A B

C

A B

C

Gen 0 Gen 0

Figure 3. How transitivity may lead to multiple exposures.

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In those 5 years, new products were introduced both by the market leaders, Fosters and

Lion-Nathan, and by smaller boutique competitors (IBISWorld 2012). Our case study is a

Lion-Nathan innovation.

The WOMM campaign’s goal was to drive momentum for the adoption of Hahn

White during summer by creating tasting occasions, encouraging brand ambassadors

(the agency called them summer ambassadors) to talk to friends about Hahn White, and

increasing their propensity to purchase. The campaign was run by our WOMM agency

partner.

A total of 2000 members of the agency’s panel of potential brand ambassadors were

invited to an on-premise tasting. On the basis of their relative enjoyment of the beer and

their fit with the brand’s target market profile, 800 of the 2000 attendees were invited to

become summer ambassadors. The participants were aged 25–35, male and female, city

dwellers, drank beer at least fortnightly, and were organizers of social events for their

friendship networks. The timeline of the WOMM campaign and the research we report

here is outlined in Figure 4.

The summer ambassadors were sent two cases of the beer, coasters, and branded

glasses for two key dates, Christmas Day (25 December) and Australia Day (26 January).

They were expected and encouraged to incorporate the new beer into social get-togethers,

parties, and barbeques.

During the course of the WOMM campaign, three studies were conducted, all of which

were principally designed to develop a better understanding of campaign reach. We report

those studies below, and later reflect on what the results mean for agencies and their

clients. Studies 1 and 2 were conducted by our partner and follow the agency’s routine

research and reporting practices for all the campaigns they run. Study 3 is a non-agency

investigation conducted by authors of this article.

Research conducted by WOMM agency as Academic part of typical campaign management research

Brand Ambassador

Selection

Campaign Start Study 1 Study 2

Study 3 Facebook App

800 panel members selected to be summer ambassadors (Gen0)

Campaign set-up

Two cases of beer are sent to Gen0

Tasting and sharing events take place

Campaign Start

Online Survey

Gen0 report on the number of online and offline conversations since campaign start

4 weeks

Online Survey

Gen0 report on the number of additional offline conversations since study 1

Gen1 report on the number of offline conversations

Facebook app

Pass on function to invite Gen1

Collection of FB friendship network data from Gen0 and Gen1

Gen0 and Gen1 identify unique conversation partners and define the strength of their friendship tie

12 weeks

Figure 4. Campaign and data collection set-up.

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Study 1: first agency-conducted census survey of Gen0

One month after the campaign began, as is customary practice, the agency conducted an

online census survey of the summer ambassadors to investigate the reach of the campaign

and the WOM behaviors of participants; 630 of the 800 summer ambassadors responded

(78.8%). The agency did not investigate the possibility of non-response bias influencing

the results reported here. Results showed that an average of 16.6 people attended the

first sharing event and 15.4 people attended the second, resulting in 25,600 people

experiencing a Hahn White event. Summer ambassadors reported sharing Hahn White

bottles with 26.4 people on average each, resulting in 21,120 event-based tastings of Hahn

White as a direct result of the campaign. Because the agency did not collect identification

details of attendees, it had no idea how many individuals attended these events. Given the

influence of homophily and transitivity, it is possible that some attendees participated in

more than one event, meaning that the 25,600 attendees are not unique individuals.

In this first census survey, participants were asked about their offline campaign-related

conversations since the start of the campaign 4 weeks before. To reduce the complexity of

the recall task (Nelson-Field, Riebe, and Sharp 2012), participants were asked to think about

the social events during which they shared the product. Further, they were asked to think

about their family and friends and recollect whether they had discussed Hahn White with

them during the past 4 weeks. Summer ambassadors reported speaking offline to an average

of 44 people (Gen1). Another question asked whether any ambassador had mentioned Hahn

White online in any way. The question listed a variety of online communication channels

and asked Gen0 respondents to check the channels they had used: 43% reported using

email, 73% Facebook, and 6% Twitter; 11% had commented on forums or blogs, and 2%

mentioned the brand on their own blog. Clearly, since these percentages total more than

100%, some ambassadors had mentioned the product or campaign in more than one online

channel. Based on the agency’s experience of over 100 previous WOMM campaigns, they

estimated the total online campaign reach at 56,614 persons (Table 1), which is equivalent

to about 71 persons per ambassador. The agency does not know whether these are unique

persons or whether there is some degree of multiple exposures; for example, some Gen1

might receive emails from two different Gen0, or might see a Facebook item and receive a

Tweet. Equally, the agency has no insight into whether the offline Gen1 population is

different from the online Gen1 population. Given our earlier remarks about homophily and

Table 1. Agency’s estimation of online reach.

Percentage of participants communicating by each channel (n ¼ 630)

Average audience size for members of the Soup community

Estimated percent of online messages with receiver response (e.g., email opened, blog read)

Agency’s estimate of channel

Forums, message boards, Participant’s

Email Facebook Twitter blogs, etc. own blog

43 (271) 73 (459) 6 (38) 11 (69) 2 (13)

20 248 301 30 915/month

95 30 10 30 30

6536 43,450 1445 792 4392 reach (n ¼ 800)

Total online reach (n ¼ 800) 56,614

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transitivity, there is certainly a possibility of overlap between the two populations, meaning

that there is potential for multiple exposures to the campaign messaging from both online

and offline sources.

Table 1 shows the agency’s computation of online reach. For example, 271 of the

ambassadors reported using email to ‘talk’ about Hahn White. Internal studies conducted

by the agency led them to estimate that each email from a panel member reached about 20

Gen1. They also estimated that 95% of these emails are opened and read by the recipients

(Soup 2012). Assuming that the online communication behaviors of the 630 participants

are representative of all 800 ambassadors, email messaging about the brand or campaign

reached 6536 persons (800 £ 0.43 £ 20 £ 0.95). Applying the same computational logic to each online channel produces the numbers shown on the ‘channel reach’ row of the

table.

Study 2: second agency-conducted survey of Gen0

Following the agency’s routine campaign research and reporting practices, a second

agency-conducted survey was undertaken 12 weeks after the campaign began. This online

survey was only sent to the 630 participants in study 1. In total, 304 completed responses

were received (48% of the 630 who participated in study 1; 38% of the original 800

summer ambassadors). This survey found continued growth in offline campaign-related

conversations. Gen0 participants were first presented with their response from the study 1.

They were then asked if they had spoken to any additional people, clearly stating that they

should only report incremental conversations. Summer ambassadors reported having a

further 11 offline conversations, on average, resulting in a total of 55 offline Gen0-to-Gen1

conversations for each ambassador in 3 months. Ambassadors were invited to pass this

second online survey to the Gen1 to whom they had spoken. Gen0 forwarded the survey to

665 Gen1; 89 G1 clicked on it, 66 attempted the survey and 52 Gen1 respondents

completed the survey successfully, which represents a response rate for Gen1 of 7.5%.

These 52 Gen1 survey participants reported speaking on average to another 7.3 persons

about the campaign. In this particular campaign, Gen2 was not contacted, but previous

studies by the agency suggest that number of offline conversations drop by 50% from

Gen1 to Gen2 (Soup 2012). Thus, it is estimated that on average another 3.5 conversations

were held between Gen2 and Gen3. Figure 5 summarizes the number of total offline

conversations based on the agency’s routine campaign measurement practices. It shows

that the campaign reached an estimated 1,240,787 people offline over three generations.

The agency cannot tell from its research whether these are unique persons or whether some

of those reached were exposed more than once to campaign-related messaging. However,

55.4 conversations

7.3 conversations

800 Generation 0

44,320 Gen 0 to Gen 1 Conversations



3.5 conversations

1,240,787 Total number of Conversations

Figure 5. Estimate of offline reach 12 weeks after campaign start.

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once again, given the presence of homophily and transitivity, we believe there is a

possibility of multiple exposures.

Study 3: social network investigation

The third study steps outside the agency’s customary research and reporting processes and

uses an alternative approach, grounded on social network analysis (SNA), to estimate the

reach of the Hahn White WOMM campaign. Figure 4 illustrates how our investigation fits

into the timeline of research conducted for this campaign and summarizes how it differs

from Soup’s routine campaign measurement practices in terms of both methodology and

data collected. Invitations to participate in study 2 and study 3 were sent out at the same

time, because study 3 took place immediately after study 2.

Methodology

SNA is a sociological methodology that identifies an individual’s role in a group or

community and maps the network of connections between that individual and others

(Moreno 1934). One of SNA’s leading principles is ‘that dyadic relationships do not occur

in isolation, but rather form a complex structural pattern of connectivity and cleavage

beyond the dyad’ (Kilduff and Brass 2010, 317).

We used SNA to identify the nodes (friends) and friendship ties that make up the social

networks of campaign participants: we identified ego (participant), ego’s alters

(participant’s Facebook friends), alter–alter relationships (ego’s friends who are friends

with each other on Facebook), and the strength of the ties that connected these participants

in the WOMM campaign. By applying SNA to map the network connections of campaign

participants and then identifying specific conversation partners within this network, we

were able to link each campaign-related conversation to a unique individual. SNA has

generally been limited to examining small, well-bounded populations involving a small

number of snapshots of interaction patterns (Eagle, Pentland, and Lazer 2009). This is

mostly due to the inconvenience and high costs of capturing the names of an individual’s

friends and subsequently mapping their relationships through the usual data collection

methodologies of interview, survey, or observation. However, we apply SNA to this much

larger and potentially less tightly bounded network of WOMM campaign participants by

tapping into existing network data on Facebook.com.

Facebook

Facebook is a social networking phenomenon, with 845 million monthly active users by

the end of 2011 (Facebook 2012). Facebook allows users to download their own ‘ego’

friendship network and to learn which of these people are friends with each other. Hence,

Facebook provides a convenient and low-cost platform for exploring social-structural

network effects. For our research, participants granted us permission to access their ego

friendship networks, as enabled by Facebook for all their members. Although the

Facebook friendship network is a digital construct, we were particularly interested to know

who had talked to whom in real, ‘offline’ life about the Hahn White WOMM campaign.

We used Facebook social network data as a proxy for the offline social network(s)

within which the WOMM campaign is embedded. Clearly, it is only legitimate to use

Facebook data as a proxy if the online and offline social worlds of participants align.

Previous research suggests that Facebook users tend to interact online with people with

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whom they have already an offline relationship (Ellison, Steinfield, and Lampe 2007;

Lewis et al. 2008) and that, although online friends are not a precise mirror of offline

friendships, they are a reasonable proxy (Hogan 2008; Subrahmanyam et al. 2008). We

further tested this assumption in our own research.

Our results did confirm the alignment of the online and offline social networks of our

participants: 83% of all Gen0 and Gen1 participants in our research reported that at least

half of their regular offline social interaction partners were also their friends on Facebook.

Indeed, 51% stated that ‘most’ or ‘pretty much all’ of their offline friends were in their

online friendship network. Therefore, we are confident that our participants’ Facebook

friendship networks are a reasonable proxy of their offline social networks, though not a

precise mirror.

Data collection through Facebook app

Data about WOMM campaign participants’ social networks were collected using a

proprietary Facebook application (app) that was specially developed and custom built for

this research in partnership with the agency. The app invited respondents to identify the

members of their Facebook friendship network to whom they had talked about Hahn

White, and to report the strength of the ties that bind those friendships.

Each participant’s list of Facebook friends was automatically populated to the screen

by the app, and participants merely had to check boxes to indicate, firstly, that a campaign-

related conversation had taken place with a particular person and, secondly, to report tie

strength. Thus, we were able to model each participant’s friendship network and the

embedded communication ties that connect Facebook members. In addition, summer

ambassadors (Gen0) could also forward the survey onto members of their social network

(Gen1), who could complete it and in turn could send it on to Gen2 directly through

Facebook or via email. Each person was identified with a unique identification number.

This allowed us to protect the privacy and confidentiality of survey participants whilst

simultaneously building a network picture of WOMM campaign participants and their

conversational partners.

Three months after the launch campaign began, our WOMM agency partner sent an

email message to ambassadors inviting them to participate in our Facebook survey.

Participation was incentivized with a gift of two blocks of chocolate. Study 3 was

undertaken immediately after study 2 (see Figure 4). We hoped that study 2 would have

encouraged participants to reconnect with the campaign and bring campaign-related issues

to the front-of-mind.

Results from the Facebook app survey

NodeXL, an open-source template that can be applied to network data stored in Excel 2007

or 2010 workbooks, was used to understand, map, and visualize the social-structural

attributes of participants’ social networks.

We collected friendship networks of 304 ambassadors (Gen0) and 52 Gen1. Figure 6 is

a sample of one friendship network of a summer ambassador (Mr Green). The ambassador

(ego) is at the center of the network, and by definition connected to all other members

(alters) of the network, represented as black dots. SNA practitioners call these ‘nodes’.

Lines (known in SNA as ‘ties’) between the nodes represent friendships between the

summer ambassadors’ friends (alter–alter ties).

In Figure 7, we now integrate the offline conversations that Mr Green had with

members of his friendship network about the campaign. The solid arrows pointing away

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Facebook Friendship Network of Mr Green, Gen0

Gen0 Mr Green

Gen1 Mr Orange

Gen1 Ms Blue

Figure 6. Illustrative Gen0 friendship network (Mr Green).

from the central node represent conversations. Two conversation partners (Gen1) are

marked as Mr Orange and Ms Blue as they also participated in our research. Thus, they

accepted an invitation to complete our survey that had come from Mr Green and identified

their respective conversation partners (Gen2).

Participants identified a total of 5294 conversation partners and the strengths of ties

that bound those friendships. Although there are several multidimensional measures of

Facebook Friendship Network of Mr Green, Gen 0

Gen0 Mr Green

Gen1 Mr Orange

Gen1 Ms Blue

Mr Green (Gen0) to Gen1 Conversations

Figure 7. Mr Green’s conversations to Gen1 embedded within friendship network.

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tie-strength (Petróczi, Nepusz, and Baszo 2006), we used ‘closeness’ a single-item measure

of tie-strength. Marsden and Campbell (1984, 497) state that ‘closeness (the measure of the

emotional intensity of a tie) is the best indicator of tie strength’ and has often been used as

single indicator of tie-strength in previous research.3 Our selection of this single-item

measure was partly due our desire to minimize respondent burden. Our scale item asked

participants to rate the closeness of each friendship on a nine-point Likert-type scale

ranging from 1 ¼ barely know the person to 9 ¼ we are very close friends. We found that that the stronger the tie, the more likely it was to be activated for Hahn

White-related conversations: 70% of all offline Hahn White conversations travelled along

strong ties (points 7–9 on the scale) and 30% along weaker ties (points 1–6). This is

confirmation of Reingen and Kernan’s (1986) and Carl’s (2006) finding that strong ties are

the dominant conduits for referral behaviors.

We then investigated the number of common friends as a function of tie strength. Our

motivation for this was our belief that having a high number of common friends raises the

possibility of multiple exposures to the campaign message. If this were the case, there

would be a concomitant reduction in campaign reach, because the assumption that each

person spoken to is unique falls away. Our analysis showed that conversation partners who

reciprocally define their friendship tie as strong have on average 41 common friends (20%

of all Gen0’s friends are also friends with Gen1), whereas weaker-tied friendships have

only 8 common friends, or 4% friendship overlap. In SNA terms, a Gen1 whose friendship

tie is defined as strong has an average ‘degree’ of 41 as opposed to a weak tie Gen1 with an

average ‘degree’ of 8, degree being the number of connections to other nodes.

Figure 8 illustrates the friendship overlap using the example of Mr Green and two of

his conversations partners, Mr Orange and Ms Blue, who also provided us with their

network data. Mr Orange and Mr Green defined their friendship tie as strong. Thirty

percent of Mr Green and Mr Orange’s friends are friends with both of them. It is therefore

possible that Mr Green and Mr Orange both spoke about the campaign to their common

friends, thus limiting campaign reach in terms of unique individuals, whilst increasing the

likelihood that their common friends are exposed to the campaign message multiple times.

In contrast, Mr Green and Ms Blue defined their friendship tie as weak. They have only

3% of friends in common. However, this weak tie provides an opportunity for the

campaign to reach into new clusters of the social network, as there is a strong possibility

that Ms Blue could pass campaign-related information on to people who do not know and

have not spoken to Mr Green (see Figure 9). This would be confirmation of Granovetter’s

(1973, 1982) weak tie hypothesis, which suggests that while strong ties are more likely to

be associated with within-cluster influence, weak ties allow communication to flow

between otherwise disconnected clusters of nodes.

Figure 9 now further integrates the conversations of the Gen0, Mr Green, and two

participating Gen1 (Ms Blue and Mr Orange). Solid arrows pointing away from Mr

Orange and Ms Blue represent offline conversations that they had with members of their

friendship network (Gen2). Clearly, Mr Orange’s conversation partners are mostly located

within the cluster of common friends he shares with Mr Green. Ms Blue, however, reaches

out to members of her network that are not even connected to Mr Green. Ms Blue and Mr

Orange are also not directly connected.

Figure 10 looks in more detail now at the conversations that Mr Green and Mr Orange

initiated. Two of Mr Orange’s six conversation partners were also spoken to by Mr Green,

thereby reducing Mr Orange’s reach to four persons instead of six.

Through following the analysis of all generations’ social networks and conversational

behaviors, we found that 20.7% of all offline campaign-related conversations were

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Gen0 Mr Green

Gen1 Mr Orange

Gen1 Ms Blue

Facebook Friendship Network of Mr Orange, Gen1

Friendship Overlap Mr Green & Mr Orange

Friendship Overlap Ms Blue & Mr Orange

Facebook Friendship Network of Mr Blue, Gen1

Figure 8. Integrating Mr Orange’s and Ms Blue’s friendship network.

Facebook Friendship Network of Mr Orange, Gen1

Ms Blue to Gen2 Conversations

Mr Orange to Gen2 Conversations


Facebook Friendship Network of Mr Blue, Gen1

Figure 9. Ms Blue’s and Mr Orange’s conversations embedded.

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Multiple Exposures of Mr Green’s and Mr Orange’s common friends

Mr Orange to Gen2 Conversations


Figure 10. Multiple exposures of Mr Green’s and Mr Orange’s common friends.

Table 2. Total campaign reach and multiple exposures (mean).

Number of Number of unique Multiple Generation conversations individuals exposure (%)

Strong ties Weaker ties Total

Gen0 þ Gen1 þ Gen2 Gen0 þ Gen1 þ Gen2

969,873 270,878

1,240,751

731,085 252,893 983,978

24.6 6.6

20.7

instances of multiple exposures. By differentiating between the conversations along strong

and weaker ties, only 6.6% of conversations along weaker ties lead to multiple exposures,

as opposed to 24.6% along strong ties. Even when we remove the extreme cases from our

analysis and compute trimmed means, there is no change in the percentages of multiple

exposures (see Table 2).

Discussion

This case study describes three studies associated with a single WOMM campaign,

including two routine agency-conducted analyses of the offline and online reach of the

campaign (studies 1 and 2) and an independent investigation (study 3) that also

investigates campaign reach (and frequency) but using a different methodology, SNA.

We are able to draw several conclusions from this research. First, we consider the

results and implications of study 3 where we used SNA to assess campaign reach and

frequency. Our evidence finds that approximately 21% of offline conversations in the

Hahn White campaign were with persons who also were reached by another member of the

node’s social network. We conclude that agency estimates of WOMM campaign reach are

overestimated. Agencies err when they assume that every conversation is with a unique

person. Multiple exposures, it appears, are a product of the strong ties that exist between

members of social networks, and those ties, in turn, may be associated with homophily and

transitivity. These results provide support for Godes and Mayzlin’s (2009) assertion that

weak-tie acquaintances are important for the spread of WOM campaigns, because

although they are peripheral in a participant’s ego network, they are likely to be central in

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another social network. Where weak ties connect nodes, those conversational partners

have only on average 4% of friends in common as opposed to 20% for strong ties.

Study 1 revealed that each summer ambassador reached on average 71 persons (56,000

in total) through their online use of email, Twitter, Facebook, chat-rooms, and blogs. It is

certainly possible that Gen1 receivers of those messages forward, re-post, or re-tweet some

of them, thereby further extending online exposure to the message beyond Gen1.

However, the agency does not capture this data. Neither does the agency know whether the

56,000 persons who reached online are unique persons or whether some of them are

exposed to online messaging from more than one conversational partner or in more than

one channel.

The agency did collect some tantalizing evidence of overlap between the offline and

online worlds of campaign participants during study 2, in which participants were asked

open-ended questions about how they had used Hahn White. Alannah (aged 32) wrote:

For the sharing bottles, everyone I knew was all gathered out after Christmas through Australia day rush. So I just posted on my Facebook that the first 6 people to visit my house bringing a snack could have 2 beer glasses and 2 large bottles of beer. I had 8 people show up and we had a mellow night with music.

This remark suggests two things. First, this is evidence of an alignment of online and

offline social networks, with Facebook friends meeting face-to-face for the mellow

evening; second, this is evidence of multiple exposure to campaign-related messaging –

first online and then at Alannah’s home. Study 1 also found that summer ambassadors

(Gen0) spoke to about 35,000 people offline about Hahn White (average < 44 persons) during the 4 weeks after the campaign start. Again, the agency does not know whether

these are unique persons. It is certainly possible, given homophily and transitivity, that

some of these 35,000 persons are also represented in the 56,000 persons that were ‘spoken’

to online. Study 2 further explored offline reach with a further 11 persons being spoken to

by each summer ambassador, making a total of about 55 on average or about 44,000 across

the community of summer ambassadors over the course of three months. Yet again, the

agency does not know whether these are unique persons, because the survey methodology

only allowed for reporting of gross numbers, not specific identified people.

The Gen0 survey in agency-conducted study 2 could also be forwarded to Gen1 and

Gen2. Data obtained from these cohorts, sensitized by historical data sourced from our

agency partner, suggest that the campaign reached an estimated 1,240,787 people offline

over three generations (Figure 4). The agency does not know whether these are unique

persons. Evidence from study 3 suggests that approximately 21% of offline WOMM-

messaging are multiple exposures. This implies that true offline reach to unique

individuals may be 980,000 (i.e., 1,240,747 £ 0.79), subject to there being no significant non-response bias. Regardless of the accuracy of these estimates, it is clear from these data

that WOMM campaigns reach out beyond the initial cohort of Gen0 participants and

expose a large number of people to the messaging across several generations.

While we explore the reach and frequency of one particular WOMM campaign, some

critics may suggest that these measures, adopted from advertising, are inappropriate for

assessing the performance of WOMM campaigns. In recent years, agency practitioners

and academics have begun to conceptualize and operationalize customer engagement

(Verhoef, Reinartz, and Krafft 2010; Hollebeek 2011). Hollebeek (2011, 565) suggests

that three themes capture the complexity of customer-brand engagement: immersion,

passion, and activation. These themes ‘represent the degree to which a customer is

prepared to exert relevant cognitive, emotional and behavioral resources in specific

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interactions with a focal brand’. Gen0 participants in our focal campaign certainly spoke

favorably about the brand and shared it with friends at social events, thus devoting

emotional and behavioral resources to the campaign, so we do accept that there is merit in

these observations. However, our partner agency and others such as trnd and Bzzagent

continue to present campaign reach as one of the key performance indicators of a

campaign rather than engagement. US-based agency ChatThreads (http://www.

chatthreads.com) uses a proprietary methodology to calculate a ‘net conversation value’

for each WOMM campaign they monitor. This metric goes beyond campaign reach and

combines a customer lifetime value model with a WOM referral value model. While

ChatThreads uses an adjustment factor for ‘social network overlap’ (Cuppari et al. 2010,

12), the agency does not publish how this is calculated or whether the structure of

participants’ friendship network is considered.

We also note that our Facebook app methodology was a success. We deployed an

innovative approach to collect offline friendship data, leveraging existing Facebook

friendship networks at low cost and high convenience for both researcher and participant.

This approach enabled us to apply SNA within the context of a WOMM campaign

producing insights that refine our understanding of WOMM campaign reach. We do not

claim that this methodology is suitable for all WOMM research projects. The

demographics of the Hahn White summer ambassadors and their Facebook use may

make them particularly suitable.

Managerial implications

The WOMM industry is still in its infancy. This research shows that the industry needs to

develop indicators of reach and frequency that are as robust as those used in other media

channels. The difficulty lies in tracking the flow of WOM and thus potential overlap, not

just in offline and online contexts independently, but between these two contexts.

We are not yet in a position to assess the degree of conversation overlap between

online and offline cohorts that are reached during a WOMM campaign. Our research

reports only offline multiple exposures. Neither are we able to make any observations

about multiple exposures that might be achieved in a multimedia, multichannel campaign,

such as when new products are launched with TV, radio, online, and WOMM campaigns

aimed at the same target markets and striving to produce synergistic outcomes.

We do not mean to devalue multiple exposures to marketing communication

managers. Marks and Kamins (1988, 267) noted that

Consumers may encounter either a sample followed by exposure to advertising or exposure to advertising followed by a product sample usage experience. These possibilities suggest some interesting issues, including the effect of each exposure sequence on consumers’ product beliefs, the magnitude of the attitude change created by each exposure sequence, and the attitude formed after these two different exposure sequences.

We believe there is an opportunity to explore these issues in further depth in the product-

seeding context.

Multiple exposures may be a desirable outcome of a WOMM or multi-media

campaign, as we previously discussed. This will depend on the specific product-market

context and the cognitive, affective, or behavioral responses that are sought. For example,

for a technologically advanced innovation, it may seem reasonable to aim for a larger

proportion of multiple exposures to support a more complex learning process. However,

maximizing reach and minimizing multiple exposures could be appropriate for low

involvement products. We believe that agencies should explicitly consider the question of

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http://www.chatthreads.comhttp://www.chatthreads.com


multiple exposures when planning and evaluating WOMM campaigns on behalf of their

clients. This clearly is an important matter for those who are interested in advertising

effectiveness, especially in the context of integrated marketing communications, and

remains a significant research challenge. Clients would be better advised by agencies

reporting the number of unique persons reached by the campaign and the average exposure

frequency of those persons to the campaign message. We support Carl’s (2011) appeal for

the intelligent integration of WOMM into the Integrated Marketing Communications mix

as opposed to a silo approach.

The estimates from study 3 suggest that about 21% of all the offline campaign-related

messaging, in this particular case study, consists of multiple exposures. WOMM agencies

will overreport reach and underreport multiple exposures if they assume that every

conversation is with a unique person. We recommend that agencies no longer confound

reach with the total number of conversations. The erroneous assumption that every

conversation is with a unique person not only exaggerates WOMM campaign reach but

also campaign efficiency metrics. If we assume (hypothetically) that our case study

campaign cost $150,000 to mount, the agency’s research would suggest that the campaign

reached 1.2 million persons (see Table 1) at an average cost of 12.1 cents per person

reached ($150,000/1,240,000). The data we collected in study 3 suggest that the true reach

was only 980,000 persons, meaning that the cost per person reached was 15.3 cents.

One solution to account for multiple exposures might be to apply a correction factor to

agency estimates of campaign reach, which on the basis of this evidence would be 0.8.

However, we do not believe we are yet in a position to recommend such a factor. This case

study reports three pieces of research for a single WOMM campaign. We cannot assume

that the results from these participants in this campaign are typical of all campaigns. A

change to the campaign setup could impact the choice of attendees and subsequent

conversations. For example, providing participants with the product on premises and

encouraging them to invite acquaintances rather than close friends could lead to an

activation of fewer transitive ties and entail fewer multiple exposures. Product

characteristics also influence immediate and ongoing WOM. Products that are more

publicly visible (cars, fashion, etc.) receive more immediate and ongoing WOM (Berger

and Schwartz 2011). The duration of campaign-initiated WOM impacts not only the

number of conversations, but also the probability of multiple exposures. Further research

across various campaigns is necessary before any such correction factor could be

confidently asserted.

Limitations and opportunities for future research

Our results suggest that WOMM agencies overreport reach and underreport multiple

exposures if they (incorrectly) assume that every conversation is with a unique person.

However, we do not know whether this estimate also applies to online messaging, a

question that remains to be explored.

One limitation of the agency-conducted studies is the failure to test for non-response

bias. Both studies reported data from a subset of the populations of interest. Because there

was no follow-up of non-respondents, it is not known whether these subsets typify the

larger populations or non-respondents were materially different in their online and offline

WOM behaviors. The 630 who participated became the population for the second agency

study. If the 21% of non-respondents from study 1 are materially different, then the second

study starts from a position of bias. Further bias may have been introduced into the results

because only 48% of the 630 participated. Again, there was no test for non-response bias.

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The research for study 3 was performed on the same population as study 2, and was thus

also subject to potential non-response bias.

This research was conducted in the context of a new product launch. There could be no

pre-existing marketplace WOM about the brand since it was new-to-market. The brand

launch was supported only by print advertising, point-of-sale, and this WOMM campaign.

It is not possible to conclude with absolute certainty that the POS and advertising did not

evoke some of the WOM that were identified in the research.

There is a possibility to conduct further SNA of our data-set. A fuller SNA of our data-

set could consider the influence of, for example, degree, density, and clustering on

WOMM message dissemination (Webster and Morrison 2004). Density is a measure of the

number of ties in a network expressed as a proportion of the maximum possible number of

ties. Denser networks, other things being equal, may indicate a predisposition to multiple

exposures since there is higher number of connections between network members. It

would also be possible to conduct a cluster analysis of each participant’s network. Also

known as component or subgroup, a cluster is a relatively dense group of nodes that are

only weakly connected to other clusters. Examples of real-world clusters include

university friends, sporting associates, work colleagues, family members, and partners.

Campaign participants select which persons they communicate to. If all those persons are

members of one particular cluster of an actor’s network – say, family – but not other

clusters, such as college friends and workmates, and then other things being equal, the

message may have constrained reach. We intend to continue this line of partnered research

and test a number of these propositions.

Conclusion

In summary, we believe that our research makes a number of contributions. First, it makes

an original contribution to an emerging area of academic study – the measurement of

WOMM campaign effects. Second, it uses an innovative methodology to track how

WOMM messages spread in social networks, an approach that to our knowledge has never

been used before. Third, it is a case study that contrasts industry practices with academic

standards, showing the insights that SNA can deliver. Fourth, it is an exemplar of industry–

academy collaboration. Finally, it has significant practical implications for agencies’

methods for measuring the effectiveness and efficiency of their WOMM campaigns.

WOM marketing is still far from being a line item in every company’s marketing

communication budget. There are still obstacles to greater acceptance of WOM marketing,

in particular regarding widely accepted ways to measure WOM. Agencies and their clients

can use this research to enhance their understanding of the influence of social networks on

message dissemination and on reach and frequency. Whilst acknowledging that there is

still scope for improvement, we apply a more rigorous approach to campaign measurement

and, in so doing, provide ample evidence that a WOMM campaign can work.

Notes 1. Email: [email protected] 2. Soup works with a panel of more than 100,000 influencers who sample, advocate and help shape

its clients’ products. Soup works across many categories such as technology, FMCG, retail, finance, pharmaceuticals, automotive, and government in Australia, New Zealand, and the UK.

3. This measure of closeness as an indicator of tie strength is not to be confused with the SNA centrality metric of closeness that is defined as the sum of graph-theoretic distances from one actor to all other actors (Freeman 1979).

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Notes on contributors Lars Groeger, PhD, is Lecturer in Management at Macquarie Graduate School of Management where he teaches MBA students and Executives in Sydney and Hong Kong. His research aims to help managers gain an increased understanding of the various forms of social interaction between customers and the firm’s power to stimulate these interactions in a systematic and value-creating way. Lars has been educated at leading international business schools in Germany, France and the US. Prior to joining academia, Lars gained extensive management consulting experience in the private and public sectors.

Francis Buttle, PhD, FCIM has over 30 years‘ experience in marketing and customer relationship management. He is Visiting Professor at Macquarie Graduate School of Management, Sydney, and was previously Professor of Marketing at Manchester Business School. He was the world’s first professor of CRM. Francis founded and serves as Principal of Francis Buttle & Associates, a worldwide network of customer management experts established in 1979. He has published over 300 items including 8 books. His most recent books are Customer Relationship Management: Concepts and Technologies and the eBook Social CRM: what is it and what does it mean for your business?

Dr Frank Jacob is a professor of marketing at the European Marketing Department of ESCP Europe and located at their Berlin campus. His research interests center on word-of-mouth communication in marketing, on service marketing, and on international marketing. email: [email protected]

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AbstractIntroductionLiterature reviewProblem definitionThe case of Hahn WhiteStudy 1: first agency-conducted census survey of Gen0Study 2: second agency-conducted survey of Gen0Study 3: social network investigationMethodologyFacebookData collection through Facebook appResults from the Facebook app survey

DiscussionManagerial implicationsLimitations and opportunities for future researchConclusionNotesReferences

Word-of-mouth marketing influence on offline and online ... · word-of-mouth; word-of-mouth...

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