About viral marketing through email:
does email still matter?
Abstract
The marketing landscape has been revolutionized over the past years. Online marketing strategy is getting on the top of the list of marketers, because influencing consumers through traditional advertising is becoming less effective. In this new marketing landscape, there has been much debate about one specific tool: viral marketing. Is viral marketing an art or a science? This thesis provides valuable insight in how to design a viral marketing strategy. By combining research from different research areas, an unique synthesis is made to better understand the determinants of a viral marketing strategy. The rise of online social network platforms in the past decade has highlighted the potential benefits which a successful viral marketing campaign offers. While the concept of viral marketing ones started with an email campaign, email has been largely disregarded as an appropriate tool for viral marketing these days, despite several advantages that it offers. This study investigates if email is still an appropriate tool for viral marketing purposes. To test this, a real-world experiment was conducted at a high-potential start up, Veeds. Following a certain algorithm, Veeds collects the most shared news and entertainment videos. Via a series of email newsletters, the effect of email on the virality of Veeds’ product was tested. The results suggest that email has a positive effect on the peer-to-peer adoption of a product, but email doesn’t show a viral effect. The inclusion of rewardless incentives to forward an email does not show a positive effect on the virality of Veeds’ product either. Marketing managers should therefore direct their viral marketing efforts not to email but towards other channels, possibly online social networking platforms.
Patrick Nijssen 10387013
29-06-2015 2014/2015
Supervisor: dhr. drs. ing. A.C.J. Meulemans Bachelor of Economics and Business Bachelor Thesis
2 Statement of Originality
This document is written by Patrick Nijssen who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
3 Table of contents
1. Introduction………...4
2. Theoretical overview………...7
2.1.Introduction into viral marketing strategy………...7
2.2 Message content………...9
2.3 Social network structure and seeding strategy………...10
2.4 Sharing mechanisms………...11
2.5 Understanding virality: the viral coefficient and cycle time………....13
3. Conceptual model……….15
4. Research design and data collection method………17
4.1 Experimental design and limitations……….17
4.2 Variables………...19
5. Data analysis and results………...21
6. Discussion and conclusion………23
6.1 Managerial and theoretical implications………...23
6.2 Limitations and suggestions for future research………...23
6.3 Conclusion………...25
4 1. Introduction
Nowadays we live in a highly globalized society, wherein technological progress has changed the behaviour of individuals and organisations. People are more connected than ever. Technology as the driver of human innovation has resulted in the invention of the internet, smartphones, the development of online social networking and many new insights about communication and marketing methods. The world is changing rapidly. People, companies and organizations are coping and adapting to these new developments and trends.
Ever since Apple’s introduction of the world’s first successful smartphone in 2007 and the launch of pioneering online social network platform Facebook in 2004, peoples’ lives have been rearranged. People spend hours every day on social network platforms and are thus continuously connected to their peers. The emergence of these technological advances has revolutionized the standard ways of communication between companies and consumers.
In the offline context, word-of-mouth communication allows verbal opinions about products or brands to be expressed, developed and spread from person to person. Consumers are not intentionally acting as evangelists: they are not necessarily trying to spread the word about a product. Growth happens automatically as a side-effect of consumers using the product (Ries, 2011, p. 206). The message is perceived as non-commercial, although it refers to a product, and it influences consumers purchase decisions (Miquel-Romero & Adame-Sanchez, 2013). However, through online networks all this can be achieved considerably faster, resulting in what is called ‘electronic word-of-mouth’ or ‘viral marketing’ (Phelps, Lewis, Mobilio, Perry & Raman, 2004). This is exactly why the emergence of online social networks sites like Facebook and Twitter have boosted interest in electronic word-of-mouth and viral marketing.
The term ‘viral marketing’ describes the phenomenon by which consumers mutually share and spread marketing-relevant information, initially sent out deliberately by marketers to stimulate and capitalize on word-of-mouth behaviours (Van der Lans, Van Bruggen, Eliashberg & Wierenga, 2010, in Hinz, Skiera, Barrot & Becker, 2011, p. 55). One of the most famous examples of a successfully performed viral marketing strategy dates back almost twenty years ago. In 1996, Sabeer Bhatia and Jack Smith launched a new web-based email service, Hotmail, which offered customers free accounts. At first, growth was very modest; with only a small investment, the team could not afford an extensive marketing campaign. However, everything changed when they made one small tweak to the product. They added to the bottom of every single outgoing email the message ‘P.S. Get your free email at Hotmail’ along with a clickable link. Within weeks, that small product change produced massive
5 results. Within 6 months, Bhatia and Smith had registered more than 1 million new customers. Eighteen months after launching, the service, with 12 million subscribers, was sold to Microsoft for $400 million (Ries, 2011, p. 206). The concept of ‘viral marketing’ appears to have first been mentioned by venture capitalist Steve Jurvetson in 1996 to describe this marketing strategy of Hotmail (Bampo, Ewing, Mather, Stewart & Wallace, 2008). The example of Hotmail shows that viral marketing presents great potential in email, due to its capacity to transmit messages through consumers’ mailing lists. However, for the past years, email has not been the most attractive tool for viral marketing purposes. Online communication appears increasingly important as more websites offer user-generated content, such as blogs, video and photo sharing opportunities, and online social networking platforms (Hinz et al., 2011). Not surprisingly then, viral marketing has become a hot topic.
The recent trend towards viral marketing has been mostly fuelled by the growing popularity of online social network platforms such as Facebook, through which more than 1 billion connected consumers share marketing messages with a single click on their computer or mobile devices (Schulze, Schöler & Skiera, 2014). The rise of these social network platforms in combination with the growing use of mobile devices has accumulated viral marketing possibilities. As mobile devices have the potential to reach a large amount of customers, they appear to be well suited for viral marketing campaigns (Pescher, Reichhart & Spann, 2013). Mobile devices enhance consumers’ ability to quickly, easily and electronically exchange information about products and to receive mobile advertisements immediately at any time and in any location (Drossos, Giaglis & Lekakos, 2007, in Pescher et al., 2013, p. 43). In addition, mobile phones are a very personal media which are used in a more active way compared to a desktop or a laptop (Bacile, Ye & Swilley, 2014, in Pescher et al., 2013, p. 44).
In the era preceding online social network platforms, email was the marketing tool seen as best suitable for viral marketing. But times have changed. On social network platforms, automated broadcast notifications can be triggered by user activity. When a user engages with the social network site in a certain way (e.g. sends a message, updates his or her status), those actions are broadcasted as notifications to the user’s list of contacts. These unpersonalized, broadcasted messages have a higher reach, but a lower effectiveness as compared to personalized messages, such as send via email (Aral & Walker, 2011). Word-of-mouth is generally considered a more effective way of promoting peer-to-peer product adoption when it is personalized and active. When individuals choose to share information about products and services with their friends, they tend to activate their strong tie
6 relationships. We tend to trust information from close sources more and respond more often to them (Frenzen & Nakamoto, 1993, in Aral & Walker, 2011, p. 1624).
Using email for viral marketing purposes has great potential thanks to individuals’ personal mailing lists. According to a report produced by Radicati Group (2010), it is estimated that there were about 2.4 billion worldwide users and 3.8 billion worldwide active email accounts in 2014. Around 75% of all these email accounts belong to consumers, who transmit hundreds of billions of messages every day ((Miquel-Romero & Adame-Sanchez, 2013). Although personalized viral messages, like those via email, are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, broadcast messaging, like messages via social network platforms, is used more often, generating more total peer adoption. The effort required from the user to actively select and invite peers to adopt a product may curtail widespread use of the personalized referral and so limit its effectiveness in encouraging broad adoption (Aral & Walker, 2011).
Viral marketing efforts are increasingly directed to the broadcast messaging approach. Email, the marketing tool that gave birth to the concept of the viral marketing in the first place, has been largely disregarded as an effective tool for viral marketing in the past years. In order to determine if these trends towards sticking to social network platforms are justified, this thesis will focus on investigating if email can still be effective as a tool for viral marketing. Therefore, the following research question is formulated:
What is the effect of email on the virality of a product or service?
To properly answer this question, this thesis is organized as follows. At first, an extensive overview of existing literature in the field of viral marketing is provided. By connecting research from different research area’s to that of viral marketing, a unique synthesis is created. Second, the conceptual model is proposed, wherein the hypotheses that will be tested are formulated. Third, the research design and data collection methods are clarified. Fourth, there will be elaborated on the data analysis, as well as the results. The thesis finishes with a discussion and conclusion, where the managerial implications, limitations and suggestions for future research are covered.
7 2. Theoretical overview
This chapter covers an overview of the existing literature in the fields relevant to viral marketing. It starts with an introduction into viral marketing and shows its relevance for practitioners. After this, viral marketing strategy will be broken down into three consecutive elements: message content, social network structure and seeding strategy, and sharing mechanisms. After elaborating on these elements, the theoretical overview will conclude with a mathematical approach to viral marketing, which is essential in order to identify the effects and implications of a viral marketing strategy.
2.1 Introduction into viral marketing strategy
Sharing online content is an integral part of modern life. People forward newspaper articles to their friends, pass on YouTube videos to their relatives, and send restaurant reviews to their neighbours (Berger & Milkman, 2012). Some customer service experiences spread throughout the blogosphere, while others are never shared. Some newspapers articles earn a position on their website’s most emailed list, while others languish. Is virality just random, or might certain characteristics predict whether content will be highly shared? Is viral marketing an art or a science?
Understanding new product adoption behaviour is critical for firms, striving to understand and influence consumers’ decisions (Hinz, Schulze & Takac, 2014). Since influencing consumers through traditional advertising seems to become less effective, firms continuously seek new ways to promote products and influence consumers. Marketers must realize that 65% of the consumers consider themselves overwhelmed by too many advertising messages, and nearly 60% believe advertising is not relevant to them (Porter & Golan, 2006). Such information overload can cause consumers to defer their purchase altogether, and evidence indicates consumers actively avoid traditional marketing instruments (Hann, Hui, Lee & Ping, 2008, in Hinz et al., 2011, p. 55). Because viral marketing campaigns leave the dispersion of marketing messages up to consumers, they tend to be more cost efficient than traditional mass media advertising (Schulze et al., 2014). Traditional advertisements and pure word-of-mouth stand on opposite sides of a spectrum. Whereas traditional ads provide the marketer with control over the message content, they don’t result in much trust in the message by the consumer. With pure word-of-mouth, the opposite applies. Trust in the message by the consumer is high, but the marketer loses control over the message content. Viral marketing combines the benefits of both traditional ads and pure word-of-mouth, by providing trust over the message, without losing control of it. Besides the cost advantages which viral marketing
8 offers, firms benefit from the fact that those forwarding their messages will be more likely to know which of their peers have similar interests and thus more likely read the message (Dobele, Toleman & Beverland, 2005). This results in more effective targeting.
Nevertheless, viral marketing does not only have upsides. The inappropriate use of viral marketing can be counterproductive by creating unfavourable attitudes towards products (Leskovec, Adamic & Huberman, 2007). For example, a company named Plaxo launched its product in November 2002 with a viral marketing campaign that attracted 15 million users by August 2005. A tremendous viral success. However, a few years later, those numbers had shrunk to less than 600.000 unique monthly visitors, due to worldwide complaints and a damaged brand image because of their spam-perceived email notifications (Kalyanam, McIntyre & Masonis, 2007).
Viral marketing has benefits as well as drawbacks. However, when a practitioner decides to proceed with a viral marketing campaign, how does this look like then? In general, a viral marketing campaign is initiated by a firm that actively sends a stimulus to selected or unselected consumers. However, after this initial sending, the viral marketing campaign relies on peer-to-peer communications for its successful diffusion among potential customers. Marketers refer to this phenomenon as flipping the sales funnel; instead of targeting a wide range of potential customers and aiming for a few real customers, the process is turned upside down: letting a few (potential) customers spread the word via their communication channels to get more customers interested (Jaffe, 2010, p. 164). Therefore, viral marketing campaigns build on the idea that consumers attribute higher credibility to information received from other consumers via referrals than to information received via traditional advertising (Godes & Mayzlin, 2004, in Berger & Milkman, 2012, p. 193). Thus, a successful viral marketing campaign requires that consumers value the message that they receive, and actively forward it to other consumers within their social network (Pescher et al., 2013). However, to enjoy viral success, firms must consider four critical viral marketing success factors:
(1) Message content, that what is being said (Porter & Golan, 2006)? (2) Social network structure, who is being targeted (Bampo et al., 2008)? (3) Seeding strategy, when are consumers reached (Hinz et al., 2011)?
(4) Sharing mechanisms, how does the message spread between consumers (Schulze et al., 2014)?
9 2.2 Message content
What drives people to share content, and what type of content is more likely to be shared? One reason might be that people share stories and news because they contain useful information. People share this content for altruistic reasons, to help others. Or they might share information for self-enhancement purposes, in order to appear knowledgeable (Wojnicki & Godes, 2008). People may share emotionally charged content to make sense of their experiences or to deepen social connections. Berger and Milkman (2012) look at how content characteristics drive social transmission and affect virality. They observe that emotionally evocative content may be particularly viral. But which kind of content is more likely to be shared, positive or negative content?
Consumers often share content for self-presentation purposes or to communicate identity (Wojnicki & Godes, 2008). Consequently, positive content may be shared more because it reflects positively on the sender. People prefer to be known as someone who makes others feel good rather than someone who shares things that make others sad. In addition to being negative or positive, emotions differ on the level of physiological arousal they evoke (Berger & Milkman, 2012). Arousal is a state of activation and readiness. The heart beats faster and blood pressure rises. When aroused, we do things (Berger, 2013, p. 59).
Some positive emotions generate arousal. For example excitement. When we feel excited we want to do something rather than sit still. The same goes for negative emotions like anger and anxiety. When you are angry you yell at someone, and your heart starts beating faster. When we’re anxious we check and recheck things. Anger, anxiety and sadness are all negative emotions, but while anger and anxiety can be related to increased activation, sadness is characterised by low arousal (Feldman-Barret & Russell, 1998, in Berger & Milkman, 2012, p. 193). Sad people tend to curl up the couch, they want to be left alone. This is what low-arousal emotions do: they stiffen action. The same applies to a positive emotion like contentment. When people are content, they relax, their heartbeat slows down. They are happy, but they don’t particularly feel like doing anything. Besides the emotional arousal that people experience, Berger (2011) found that pure physical activity also increases arousal. Running doesn’t evoke emotion, but it is just as physiologically arousing. It gets your heart rate up and increases blood pressure and therefore shapes social transmission.
However, emotions and physical activity may not be the only factors that marketers have to take into account when trying to influence arousal. Design and color usage can have similar effects. The color red can be linked to excitement as it is considered an arousing, exciting and stimulating color (Labrecque & Milne, 2012). It is generally associated with the
10 characteristics of activity, strength and stimulation. Research has consistently shown that longer wavelength hues (e.g. red, orange, yellow) induce states of arousal and excitement (Walters, Apter & Svebak, 1982). Many studies have also linked both saturation and value to arousal. Saturation refers to the amount of pigment in a color: it is measured on a scale from low (appearing gray and washed out) to high (appearing vivid). Value is the amount of lightness or darkness relative to a scale that ranges from black (low) to white (high) (Labrecque & Milne, 2012). Previous research indicates that saturation has a positive effect on arousal and value has a negative relationship with arousal (Valdez & Mehrabian, 1994, in Labrecque & Milne, 2012, p. 717). This means that high saturation, a vivid color, increases arousal and excitement. And high value, a white-ish color, decreases arousal and excitement.
Given that sharing information requires action, activation should have similar effects on social transmission and boost the likelihood that content is highly shared.
2.3 Social network structure and seeding strategy
A second step in a viral marketing strategy would be to map the social network structure of potential individuals to which you want to initially spread the message. Diffusion research has examined how certain people and social network structures might influence social transmission (Berger & Milkman, 2012). Social network models are useful for identifying conditions under which widespread product adoption is possible, but little diffusion may actually occur if a particular item is unlikely to be transmitted. On the other hand, certain items may be highly viral, but they fail to spread widely if they start with people who are not well connected to the rest of the community (Stephen & Berger, 2009). Research conducted by Bampo et al. (2008) confirms that social network structures have a significant impact om campaign performance.
Conventional wisdom adopts the influentials hypothesis, which states that targeting opinion leaders and strongly connected members of social networks ensures rapid diffusion (Hinz et al., 2011). Seeding strategy distinguishes three types of consumers, and these strongly connected people are referred to as ‘hubs’ (Bampo et al., 2008). The opposite of hubs are ‘fringes’, who are poorly connected. The third group are labelled as ‘bridges’, people who connect two otherwise unconnected parts of the network. A viral marketing campaign aims to inform consumers about the message and to encourage them to participate in the campaign by sending the message to others. Research by Goldenberg, Han, Lehmann and Hong (2009) indicates that hubs tend to be better informed than others because they are exposed to innovations earlier through their multiple social links. However, in some cases hubs do not
11 adopt innovations first, such as when the innovation does not suit the hub’s opinion, which may mean that adoption occurs first at the fringes of the network. A person’s social position may also effect the degree of persuasiveness, which relates to the share of referrals that lead to successful referrals. Logical reasoning dictates that fringes might not refer a lot of messages, but when they do, this referral is of high quality. Hinz et al. (2011) find that the highest number of referrals can be achieved if a message is seeded to hubs or bridges. These two strategies yield comparable results, and are up to eight times more successful than seeding to fringes. Still, these higher numbers do not reflect a higher persuasiveness of hubs and bridges, but rather demonstrate increased activity of hubs and bridges.
Initiators must not only start the contagion, but also play a role in keeping it alive, continuing to pump enthusiasm throughout the social network. Enthusiasm is contagious and can be transferred between people through word-of-mouth, similar to high-arousal emotions (Stephen & Berger, 2009). People can be enthusiastic right after they hear about something, but enthusiasm mostly fades over time. However, some products or services are easier to stay excited about than others, meaning that enthusiasm for them declines at a slower rate. Still, in the absence of reinforcement, enthusiasm will decline. Therefore viral growth requires that consumer’s enthusiasm is continually reinforced.
2.4 Sharing mechanisms
Sharing mechanisms refers to the way in which the message is spread among consumers. Does the message spread naturally between consumers, or is the consumer specifically incentivized by the company to refer their product to others? Referrals which result from a viral marketing campaign attract new consumers who are likely to be more loyal and therefore more profitable than consumers acquired through regular marketing campaigns (Trusov, Bucklin & Pauwels, 2009, in Pescher, Reichhart & Spann, 2013, p. 44). Since referral is an effective marketing strategy of introducing new customers at a low acquisition cost, companies are increasingly aware of the need to manage customer referral programs (Guo, 2012). The case of Dropbox, a file hosting service, shows this perfectly. By implementing a referral program where users got more free storage space when inviting their friends, Dropbox’ signups went from 100.000 on September 2008 to over 4 million on January 2010.
Referrals can take many forms: emails, text messages or referrals via online social networking platforms. The latter has been widely recognized as an important vehicle to influence the adoption and use of products and services (Guo, 2012). Schulze et al. (2014)
12 investigated the differences of sharing mechanisms on Facebook for hedonic (e.g. fun) and utilitarian (e.g. useful) products. Their study reveals that a well-chosen sharing mechanism can increase success by a factor of 19. Conversely, a viral marketing campaign that uses an inappropriate sharing mechanism can severely limit a product’s chance on success. Schulze et al. (2014) note that primarily utilitarian products should not rely on the same sharing mechanisms as fun-oriented products.
For low-utilitarian products (e.g. games, music services) the ideal sharing mechanism uses incentives and suggests that customers recommend products to their friends rather than to strangers. Recommendations for viral marketing campaigns for high-utilitarian products on Facebook (e.g. job search, stock market applications) require another approach. Sharing mechanisms for such offerings should avoid using unsolicited messages or broadcast messages from friends. Instead, viral marketing campaigns for primarily utilitarian products should rely on solicited messages that consumers can either direct to individual friends or broadcast to strangers (Schulze et al. 2014). Research conducted by Aral and Walker (2011) confirms that personalized messages, like a solicited message directed to an individual friend, can be more effective in encouraging adoption per message than broadcast messaging to friends. Aral and Walker (2011) find that one additional personal invite increases the rate of peer adoption by 6%, whereas one broadcast notification increases the rate of peer adoption by only 2% on average, confirming that more personalized active messaging has a greater marginal impact on the rate of peer adoption per message than broadcast messaging. However, broadcast messaging is used more often, generating more total peer adoption.
Personalized messages are not only possible via Facebook or other online social network platforms. Email, for example, is a medium that qualifies to send or forward a message to individual friends, instead of sending a message to a large group. Before the rise of the online social network platforms, email was one of the main tools appropriate for viral marketing efforts. This kind of viral marketing relies on the forwarding of a message, initially send by a company. Forwarding of the message is determined by its opening, and by the individual’s perception about the value of the message it may provide to others, besides the need for communication with others held by the individual (Miquel-Romero & Adame-Sanchez, 2013). This relates to the characteristics of viral content being ‘useful’, as mentioned by Berger (2013, p. 83). As inboxes become more crowded, consumers often hit the delete key when they know the message is from a marketer. They are much more reluctant to delete a message from a person they know. This fact is a key component is understanding the potential power of viral marketing through a sharing mechanism like email.
13 2.5 Understanding virality: the viral coefficient and cycle time
Two variables have to be understood to fully comprehend the effect of a well-designed viral marketing campaign: the viral coefficient and cycle time. The viral coefficient measures how many new customers will use a product as a consequence of each new customer who signs up. In other words, how many friends will each customer bring with him or her? Since each friend is also a new customer, he or she has an opportunity to recruit yet more friends (Ries, 2011, p. 207). The viral coefficient is defined as the product of the number of invites each user sends and the conversion rate at which invites convert new users (Lawyer, 2011). For a product with a viral coefficient of 0.1, one in every ten customers will recruit one of his or her friends. This is not a suitable loop for viral growth. Imagine that one hundred customers sign up. They will cause ten friends to sign up. Those ten friends will cause one additional person to sign up, but there the loop will stop. By contrast, a viral loop with a coefficient that is greater than 1.0 will grow exponentially, because each person who signs up will bring, on average, more than one other person with him or her (Ries, 2011, p. 206).
Focussing on increasing the viral coefficient is necessary to accomplish viral growth. Nevertheless, one more variable is even more important: cycle time. Cycle time refers to the amount of time it takes for a new user to send his batch of invites (Lawyer, 2011). As mentioned by Stephen and Berger (2009), it is challenging to get consumers to continue using and talking about a product, because enthusiasm decays over time. This is exactly the reason why the minimization of cycle time has a dramatic impact on the viral growth of a product. Shorter cycles mean that enthusiasm spreads faster and less is lost to decay.
Lowering the amount of time it takes for a user to invite other users may be substantially more effective than increasing the number of invites users send or the rate at which invited non-users convert. This can be derived from the following formula, where the number of cycles the invite process has gone through, is raised to the power of the viral coefficient (Lawyer, 2011).
𝑈(𝑡) = 𝑈(0) × 𝐾
( 𝑝 + 1 )𝑡
− 1 𝐾 − 1
Making it easy to share information about a product or service is therefore vital for viral success. Minimizing the cycle time can have extreme results, as shown in Table 1.
14 Table 1. The impact of cycle time on viral growth
Initial set of customers ‘U (0)’ 10 10 10
Number of invites ‘I’ 10 10 10
Conversion rate ‘C’ 15% 15% 15%
Viral coefficient ‘K’ 1.5 1.5 1.5
Cycle time ‘p’ 5 days 2 days 1 day
15 3. Conceptual model
After elaborating on the different steps to take in a viral marketing strategy in the theoretical overview, the conceptual model will include a synthesis of the relevant parts of the literature to formulate testable hypotheses. These hypotheses will be tested in order to answer the research question: what is the effect of email on the virality of a product or service? Investigating this question is highly relevant since email used to be the most applied marketing tool for viral marketing efforts. However since the rise of online social networking platforms, and the incredible potential they provide, email has been largely disregarded as an effective tool for viral marketing in the past years. In order to determine if this trend of switching to online social network platforms and ‘forgetting’ about email when trying to create viral growth are justified, this research will focus on investigating whether email still matters as a viral marketing tool.
Multiple considerations have to be taken into account with regard to email marketing. Whereas in offline situations contact between the company and the consumer may be more natural and spontaneous, this may not be true in the case of email, since the user must develop an initial active behaviour by opening the email in an environment where many of messages received are considered spam and subsequently deleted (Miquel-Romero & Adame-Sanchez, 2013). Email being notified as spam, and unsubscribes form email lists are signs of disengaged customers. Such may even decrease the number of customers, quite the opposite of viral growth. Spam and email based viruses have negatively influenced electronic communications, making viral marketing campaigns challenging to deploy. The key driver in viral email marketing is the effectiveness of referrals to create awareness, trigger word-of-mouth and forwarding (De Bruyn & Lilien, 2008). However, it can be assumed that people are initially satisfied when signing up for a product or service. The number of non-users that will sign up for a product or service as a consequence of a received email will thus exclude the number of unsubscribes or spam complaints. Therefore, it is proposed that:
H1: Email as a sharing mechanism is positively related to the peer-to-peer adoption of a product or service.
The first hypothesis tries to determine if there is a positive relation between email as a sharing mechanism and the peer-to-peer adoption of a product or service, but this does not mean that email has a viral effect on product adoption. In order to have a viral effect, the viral efficient of email should be 1 or higher. If this viral coefficient is lower than 1, viral growth is
16 not possible. This means, that for every email that is initially send by the company, at least 1 email has to be forwarded, and the receiving customer of this email should adopt the product, or sign up for the service. This seems incredibly challenging for an email campaign because consumer behaviour in viral marketing is strongly influenced by individual’s risk (Palka, Pousttchi & Wiedemann, 2009). The email could possibly be a virus, what can be considered a risk. A viral coefficient of 1 or higher is thus unlikely for email as a sharing mechanism. If the viral coefficient is lower than 1, the relation between email as a sharing mechanism and the virality of a product or a service can be labelled as ‘negative’. Therefore, it is proposed that:
H2: Email as a sharing mechanism has a negative effect on the virality of a product or service.
In an email campaign the forwarding and thus the peer-to-peer adoption of a product or service is dependent on the utility the customer receives from forwarding the initial email to his or her peers. Some companies offer incentive to spark virality. Consumers are offered discounts, for example, if they convert new users. On the other hand, compensation could dilute the power of recommendations if recipients come aware of it (Phelps et al., 2004). Adding a rewardless incentive could have similar effects. If in the content of the email is indicated that it would be highly appreciated to forward the email to peers, without offering a compensation for this forwarding, then the recipients might even be more prone to not forward the message. Therefore, it is proposed that:
H3: The inclusion of a rewardless incentive to forward an email has a negative effect on the virality of a product or service.
Figure 1. Visual representation of the conceptual model Relationship + - - Email as sharing mechanism Rewardless incentive in email Peer-to-peer adoption of a product or service Virality of a product or service
17 4. Research design and data collection method
This chapter will elaborate on the research design of this study and data collection method. The study is quantitative and an experiment was conducted. Hinz et al. (2011) stress the importance of using experiments to compare seeding strategies in viral marketing campaigns. Moreover, participants might not really know the deeper reason why they share certain content with certain people. Therefore, an experimental approach is appropriate to measure the effect of using email as a sharing mechanism on the virality of a product or service, instead of a survey.
This experiment is conducted at a startup based in The Hague, called Veeds. Veeds’ product is an online video application. Following a certain algorithm, Veeds collects the most shared news and entertainment videos in the Netherlands. At Veeds the social crowd decides what is most valuable and entertaining at this very moment. Veeds is in the beginning stages of their development, with their product just released, but still in a beta-test fase. This means that the product is freely accessible, but people have to log in with their Facebook account to use Veeds. On the platform, the users are encouraged to provide feedback to Veeds, in order to learn from this feedback, and iterate on this. Veeds is an incredibly ambitious and is already working with a substantial investment from a venture capital firm, and is seen as a high-potential start up.
4.1 Experimental design and limitations
The experiment consists of a series of four email newsletters, which are send to the current users of Veeds. The intention of these newsletters is to excite the current users of Veeds, and encourage them forward these emails to their peers. When a peer of a current user receives the email, views a video of their interest in this email and signs up via the link on the landings page of that video, then this new sign up will be attributed to that particular newsletter email. This sign up can be seen as a result of the virality of the newsletter email. These signups are reported in Google Analytics, by using tracking mechanisms, via utm-parameters.
The newsletters are designed and send via Mailchimp, one of the most used email software products worldwide. The amount of emails send, opened and clicked are reported here. Open and click rates in Mailchimp are measured for the known receiver of the email, which is the current user of Veeds. When an email is forwarded by the current user of Veeds and then opened by the recipient, Mailchimp reports this as a second open of the current user of Veeds. This current user of Veeds can forward his email to an endless amount of people,
18 and he can open his email also multiple times. Therefore the amount of opens by the know receiver does contain some information about forwarding, but there are too many uncertain variables that it is not possible to draw relevant conclusions. There is an option in Mailchimp to add a special ‘forward to a friend’ merge tag, where the current user of Veeds can fill in the email of someone he wants to forward the email to. Then, it is possible to accurately measure forwards. However, the current user of Veeds can only forward to one other peer at a time, and he has to fill in additional information about his peer, which makes it much less likely he will forward the email. Using this ‘forward to a friend’ merge tag will thus significantly lower the changes of new signups through email, so the decision was made to not use this option. Because Mailchimp can’t accurately report on the forwards of an email, this research focusses solely on the amount of signups, and does not take the amount of forwards into account.
The viral coefficient is usually defined as the product of the number of invites each user sends and the conversion rate at which invites convert new users (Lawyer, 2011). Since it is not possible to correctly derive the number of forwards, which are the number of invites each users sends, the viral coefficient in this experiment is defined as the conversion rate at which new users are converted. As shown in the viral formula, just measuring the viral coefficient does not say everything about virality (Lawyer, 2011). To speak more accurately about virality, the cycle time should be included. Due to the limitations of Mailchimp, it is not possible to track the time it takes for someone to forward the email. Thus, it is not possible to take cycle time into account.
The newsletters were designed to be as viral as possible. The newsletters include content that is more likely to be shared, so that the emails are more likely to be forwarded, following the arousal evoking emotions mentioned by Berger and Milkman (2012). Per newsletter, six videos are included, which are listed in the top of their category for that day. Being at the top of their category means that these videos where most shared, and thus have the most reach that day in the Netherlands. At the bottom of the first two newsletters, the receiver of the email was kindly asked to forward the newsletter to a friend, because: ‘if more people sign up for Veeds, we can make Veeds even better’. This rewardless incentive was removed in newsletters three and four in order to test the effect of rewardless incentives on the virality of email.
When someone received the email, they would see as sending person ‘Patrick from Veeds’, because personalization of the email proves to increase engagement (Mailchimp, 2015). A separated landings page on the website of Veeds had to be created only for the videos in the newsletters, because it is not technically possible to play the videos within
19 someone’s email client. In this way, people who received the email and were not yet member, were able to click on this video and watch it, without having to log in with their Facebook account. These persons were offered to watch six more videos in the category of the previously watched video without having to log in. If they wanted to watch more videos than this and have full access to Veeds, they needed to log in via Facebook. This would then be counted as a new sign up. These new members of Veeds would be automatically signed up to receive the next newsletter email. With an unsubscribe link at the bottom of each newsletter, it was possible to unsubscribe from future newsletters.
Due to the circumstances of conducting the research within a dynamic environment, the seeding strategy and the social network structure were not taken into account. The initial group to which the first newsletter was send consisted of people that voluntary had signed up for Veeds previously, and a handful of friends and family of the people working at Veeds. Email address, first name and gender was the only information about the people in this group. Thus, thorough research into the social network structure of these people, and a forthcoming seeding strategy was therefore not conducted.
The four newsletters where send out between 29 May 2015 and 6 June 2015, so that every two days a newsletter was send. An advantage of the research design is that, except for the newsletters, there hasn’t been any previous marketing efforts for Veeds yet. No advertising, no sharing on online social network platforms. Therefore, all the newly gathered awareness and signups can be almost fully attributed to the newsletters. A limitation of the research design is the fact that this experiment will take place within a startup. This means that the business environment is extremely dynamic and therefore it was difficult to have similar circumstances during the experiment. Furthermore, this research reflects viral and email marketing efforts for a business that focusses on entertainment. Therefore, the results could be relevant for other entertainment-related business, but can be counterproductive for non-entertainment businesses.
4.2 Variables
Peer-to-peer adoption, measured as new signups resulting from the newsletter emails, are the leading metric in this research. Still, forwards that don’t lead to new signups do lead to awareness, which is positive if the receiver that received the forwarded email has a positive attitude towards the newsletter. As previously mentioned, it was not possible to track these forwards precisely, and that’s why the focus is only on the new signups generated by the newsletters. Just like the open and click rates that will be calculated, these are numerical,
20 continuous data. The amount of people to which the newsletters are initially send is the independent variable. The dependent variable is the amount of new signups as a consequence of the forwarding of a newsletter email. The conversion rate at which these new signups occur is defined as the viral coefficient.
21 5. Data analysis and results
In this chapter the testing of the hypotheses is discussed and the results of the experiment conducted for this research are provided. Table 2 shows the outcome of the experiment. The amount of signups are the signups that are related to the forwarded emails. Note that the amount of newsletters send doesn’t grow with the same amount as the amount of new signups indicated. This is because there were also some signups that had nothing to do with the newsletter emails. The amount of newsletters send is of course the same as the amount of people to which the newsletters are send. The opens refer to the people that opened the email. The clicks are the amount of people who actually clicked on an item in the newsletter. These opens and clicks are unique opens and clicks, which means that when one person opens the email or clicks on an item more than once, this is still recorded as one open or one click. The unsubscribed are the amount of people that chose to unsubscribe from future newsletters.
Table 2. Data from experiment
Delivered Opens Open rate Clicks Click rate New signups Unsubscribed Newsletter 1 38 27 71,05% 11 28,95% 2 1 Newsletter 2 41 28 68,29% 12 29,27% 0 2 Newsletter 3 41 21 51,22% 10 24,39% 3 0 Newsletter 4 48 17 35,42% 5 10,42% 0 0
H1: Email as a sharing mechanism is positively related to the peer-to-peer adoption of a product or service.
The total amount of signups from the series of four newsletters sums up to five, where the total amount of unsubscribes is three. This means that more people have signed up for the service, than that people have unsubscribed from the newsletter. This means that for every newsletter that is send, on the margin, the amount of registered users increases. This result suggests that email as a sharing mechanism is positively related to the peer-to-peer adoption of a product or service. Thus, hypothesis 1 is supported.
H2: Email as a sharing mechanism has a negative effect on the virality of a product or service.
22 To determine the viral coefficient, which is the conversation rate of the email campaign, the amount of signups are divided by the amount of newsletters send. In order to have viral success, you need viral growth. In order to have viral growth, a viral coefficient of 1 or higher is necessary. The viral coefficients of newsletters 1, 2, 3, 4 and the average viral coefficient are shown in table 3. The results show that none of the newsletters experienced a viral coefficient of at least 1. Because the viral coefficient of the newsletter is substantially low, there is no possibility for viral growth, because the loop will quickly vanish. All the newsletters thus have a negative effect on the virality of Veeds. Therefore, the second hypothesis is supported.
Table 3. Viral coëfficiënt
Necessary for viral growth 1
Newsletter 1 0.053
Newsletter 2 0
Newsletter 3 0.073
Newsletter 4 0
Average 0.029
H3: The inclusion of a rewardless incentive to forward an email has a negative effect on the virality of a product or service.
In the first two newsletters, a phrase was added that stated: ‘Please forward this email to your friends, because when more people use Veeds, we can make Veeds better’. This can be viewed as a rewardless incentive. The customer is kindly asked to forward the email to his or her peers, without receiving any direct compensation for it. This rewardless incentive was not added in newsletters three and four. The analyses show that that the viral coefficient of the first two newsletters combined is 0.025. For the third and fourth newsletter, this is 0.038. The emails including a rewardless incentive, as well as the emails excluding this incentive have a viral coefficient lower than 1, which means there is no viral effect. For the emails with the rewardless incentive, the viral coefficient is even 34,2% lower than for the emails without the rewardless incentive. These findings suggest that the peer-to-peer adoption of a product or service is higher when there is not an inclusion of a rewardless incentive to forward an email. The findings also suggest that the inclusion of a rewardless incentive to forward an email has a negative effect on the virality of a product or service. Thus, hypothesis three is supported.
23 6. Discussion and conclusion
The findings suggest that email may not be the most appropriate marketing tool for viral marketing purposes. Intuitively, this makes sense. We live in a world where it seems that time has become our most valuable asset. Everything has to go faster and faster, every extra click we have to perform on our devices is one too much. It is so much easier to press the button ‘share’ ones, and share content with you entire social network, which may consists of several hundreds of people. When you want to forward an email to the same amount of people, you will have to select or type in all these email addresses, which just takes too much time. Aral and Walker (2011) also mentioned that the effort required from the user to actively select and invite peers to adopt a product may curtail widespread use of personalized referral, like email, and so limit its effectiveness in encouraging broad adoption. Therefore, it seems that email is losing the race against the viral possibilities that online social network platforms offer.
6.1 Managerial and theoretical implications
The aim of this research was to investigate whether email still matters as an appropriate marketing tool for viral marketing purposes. The results show that the trend of largely disregarding email as an appropriate tool for viral marketing purposes is justified. This has multiple implications. Managerial insight is gained here, meaning that managers should continue to focus their (viral) marketing budget towards online social networking platforms and other means of massive reach. Directing a lot of money and time on email for viral purposes might be a waste.
More importantly, this research provides managers with a general framework of how to design a viral marketing strategy. This framework is the result of a synthesis of available literature in the field of, and related to, viral marketing. Why certain content goes viral is still a mystery in some cases, but a framework has been provided to breakdown the success of a viral marketing into consecutive pieces. These pieces can be thoroughly analysed individually, in order to gain more insight into the specific contributions of these elements to the success or failure of a viral strategy.
6.2 Limitations and suggestions for future research
As mentioned before, this research deals with some limitations that have to be taken into account when interpreting the findings. These limitations can be in most part related to the difficult and dynamic environment wherein the experiment was conducted. The small
24 sample size is a consequence of this. Also, the use of the mailing software program, Mailchimp, had severe limitations. Although Mailchimp is one of the most used mailing software programs worldwide, used by Fortune 500 firms, it was not possible to accurately measure the amount of forwarded emails. In determining the virality of a product or service it is preferred to have this kind of data. Because of this lack of reporting, it was not possible to investigate the cycle time and the conversion rate of the number of invites send by the initially invited customers. Therefore, the results are built upon weaker data and statistical significance as would be preferred. Since the experiment was conducted at a company that just started, it is possible that the recipients of the emails didn’t know Veeds well enough to open or forward the email. Viral email campaigns conducted at well-established firms might provide different results. Veeds is also a company that focusses on entertainment content mostly. Companies with a different kind of service or with different products may find totally different results. Moreover, the only way to sign up for Veeds is via a Facebook account. Although more than 1 billion people worldwide do have a Facebook account, there is still a substantial amount of people that are not present on Facebook. For these people it was therefore not possible to sign up for Veeds, and this may have caused the amount of signups to be lower.
Additional insight could be gained by conducting a research under circumstances where it is possible to accurately measure the amount of forwarded emails and the time it takes between receiving an email and subsequently forwarding it. Therefore, cycle time and other relevant metrics can be derived, which result in more accurate and relevant findings. It is definitely also worthwhile to take the social network structure of the recipients and the compliant seeding strategy into account. Adjusting the content of the emails to the social network structure and the seeding strategy can possibly give a much more reliable result, because all the steps of a viral marketing strategy are then taken into account. Furthermore, rewardless incentives can also be further researched. Future research might focus on the effects of (rewardless) incentives on the trustworthiness of the source, and therefore on the rate of recommendation. It could be interesting to find the optimal level of incentives, so that marketers can weigh the benefits of sparking customers to forward content against the dilution of source credibility. Moreover, it would be interesting to conduct more research in the field of viral product design. This relatively unexplored field was initially supposed to be the area of research for this thesis. Unfortunately, due to different circumstances, it was not possible to perform further research into this. Viral product design focusses on how to make products intrinsically viral. As mentioned in this study, there could be certain colors that makes sharing more likely, and thus make a product intrinsically more viral. Extensive research into the
25 increased virality of content related to the color, shape and the placement of the ‘share-button’ would be certainly worth investigating.
6.3 Conclusion
In today’s digital-focused marketing environment, the question came to mind if email would still be a relevant tool for viral marketing purposes. To formulate an answer to this question, this thesis started off with a synthesis of the available research in the field of viral marketing. By also combining research from the areas of psychology and sociology, this study contributes significantly to the existing literature. Viral marketing demonstrates obvious benefits, but is still not fully understood by practitioners and researchers. Viral marketing is therefore a special tool in the online marketing spectrum. Where online marketing is all about being able to test, analyse and iterate, viral marketing success is still a mystery in many cases. In this study, viral marketing strategy has been structurally broken down into different elements in order to analyse these element individually.
Why certain content goes viral shows to be strongly related to the level of arousal. Arousal seems to be the main determinant in the process of social transmission. People tend to share information when they are activated by arousal. This is stimulated on the emotional level, but also physically, and possibly by certain colors. However, to make a viral strategy successful, the right people need to be targeted to which the message is initially send. The chosen seeding strategy can have sever implications for the success of a viral strategy. When seeding the initial message to highly-connected people, the chances of the message going viral is much greater than initially seeding the message randomly to people. As a last element of the viral marketing strategy, the choice of sharing mechanism must also be considered. For different kinds of products or services, different sharing mechanisms should be used. Sharing mechanisms that are successful for ‘fun’ products or services, can be extremely harmful for ‘useful’ products or services.
In order to maximize the success of a viral campaign, marketing managers should understand the mathematics behind viral marketing. This starts with the comprehension of two figures: the viral coefficient and cycle time. Where marketing managers should invest time and effort to increase the viral coefficient, it is even more important to decrease cycle time. Making it effortless for consumers to share content is therefore invaluable.
By simply comparing the viral coefficient of the experiment, with the necessary viral coefficient for viral growth, the conclusion is made that email has a negative effect on the virality of a product or service. In other words, email may not be an appropriate tool to reach
26 viral growth. Adding a rewardless incentive to the email also does not result in an increase in peer-to-peer adoption of a product or service.
While there will may not be a definite answer to the question if viral marketing is an art or a science, this thesis has tried to theoretically demonstrate that their might be more science to it than previously assumed.
27 7. References
Aral, S., & Walker, D. (2011). Creating social contagion through viral product design: A randomized trial of peer influence in networks. Management Science,57(9), 1623-1639.
Bampo, M., Ewing, M. T., Mather, D. R., Stewart, D., & Wallace, M. (2008). The effects of the social structure of digital networks on viral marketing performance. Information Systems
Research, 19(3), 273-290.
Berger, J. (2011). Arousal increases social transmission of information. Psychological science, 22(7), 891-893.
Berger, J. (2013). Contagious, Why Things Catch On. Simon & Schuster
Berger, J., & Milkman, K. L. (2012). What makes online content viral?. Journal of Marketing
Research, 49(2), 192-205.
De Bruyn, A., & Lilien, G. L. (2008). A multi-stage model of word-of-mouth influence through viral marketing. International Journal of Research in Marketing, 25(3), 151-163.
Dobele, A., Toleman, D., & Beverland, M. (2005). Controlled infection! Spreading the brand message through viral marketing. Business Horizons,48(2), 143-149.
Goldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009). The role of hubs in the adoption process. Journal of Marketing, 73(2), 1-13.
Guo, Z. (2012). Optimal decision making for online referral marketing. Decision Support Systems, 52(2), 373-383.
Hinz, O., Schulze, C., & Takac, C. (2014). New product adoption in social networks: Why direction matters. Journal of Business Research, 67(1), 2836-2844.
Hinz, O., Skiera, B., Barrot, C., & Becker, J. U. (2011). Seeding strategies for viral marketing: An empirical comparison. Journal of Marketing, 75(6), 55-71.
28 Jaffe, J. (2010). Flip the Funnel: How to Use Existing Customers to Gain New Ones. John Wiley & Sons, Inc.
Kalyanam, K., McIntyre, S., & Masonis, J. T. (2007). Behind the scenes of a viral marketing campaign: How plaxo crossed the tipping point and avoided the fate of the ebola virus. Rapport technique, Santa Clara University, Santa Clara, California, 40.
Labrecque, L. I., & Milne, G. R. (2012). Exciting red and competent blue: the importance of color in marketing. Journal of the Academy of Marketing Science, 40(5), 711-727.
Lawyer, K. (2011). A Virality Formula. http://kevinlawler.com/viral. Visited on June 10, 2015.
Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007). The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1(1), 5.
Mailchimp (2015). ‘Resources’, available at: http://mailchimp.com/resources. Visited on May 5, 2015.
Miquel-Romero, M., & Adame-Sanchez, C. (2013). Viral marketing through e-mail: the link company-consumer. Management Decision, 51(10), 1970-1982.
Palka, W., Pousttchi K., & Wiedemann D. G. (2009). "Mobile word-of-mouth–A grounded theory of mobile viral marketing." Journal of Information Technology 24.2: 172-185.
Pescher, C., Reichhart, P., & Spann, M. (2013). Consumer decision-making processes in mobile viral marketing campaigns. Journal of interactive marketing, 28(1), 43-54.
Phelps, J. E., Lewis, R., Mobilio, L., Perry, D., & Raman, N. (2004). Viral marketing or electronic word-of-mouth advertising: Examining consumer responses and motivations to pass along email. Journal of advertising research, 44(04), 333-348.
29 Porter, L., & Golan, G. (2006). From Subservient Chickens to Brawny Men: A Comparison of Viral Advertising to Television Advertising. Journal of Interactive Advertising, 6(2), 30-38.
Radicati Group (2010), “Email statistics report, 2010-2014”, available at:
www.radicati.com/wp/wp-content/uploads/2010/04/Email-Statistics-Report-2010-2014-Brochure.pdf
Ries, E. (2011). The Lean Startup. Crown Business.
Schulze, C., Schöler, L., & Skiera, B. (2014). Not All Fun and Games: Viral Marketing for Utiliterian Products. Journal of Marketing, 78(January 2014), 1-19.
Stephen, A. T., & Berger, J. A. (2009). ‘Creating Contagious: How Social Networks and Item Characteristics Combine to Spur Ongoing Consumption and Reinforce Social Epidemics. working paper, Columbia University, New York, NY.
Walters, J., Apter, M. J., & Svebak, S. (1982). Color preference, arousal, and the theory of psychological reversals. Motivation and Emotion, 6(3), 193-215.
Wojnicki, A. C., & Godes, D. (2008). Word-of-mouth as self-enhancement. HBS marketing research paper, (06-01).
