Is hacking your growth a good idea?
The influence of growth hacking on the online reputation of a
company
Monika Kauliute 10700870 Master thesis
Graduate School of Communication: Corporate Communication Supervisor: Arie den Boon
Abstract
Maintaining a good reputation online is crucial for every business, because customers’ concerns about the products on social media are public. Start-ups, however, usually have limited budgets for online advertising. That is why the new type of marketing, called growth hacking, was developed. This paper analyzes the cases of two famous start-ups, Uber and Spotify, aiming to test if their sudden growth had an influence of the online reputation. Content analysis looked trough the three different time periods, taking into consideration the comments by the followers, word of mouth spread and type of communication by the company. Results do not show significant differences between the time periods, leading to the conclusion that growth hacking does not have an influence on the online reputation. Thus, it leaves an open door for the future research about the effects of growth hacking and its relation to the traditional corporate communication.
Keywords: growth hacking; online reputation; word of mouth, webcare, Uber,
Table of Contents Introduction ... 4 Theoretical background ... 6 Method ... 12 Material ... 13 Sampling ... 14 Design ... 15 Cases ... 17 Data analysis ... 18 Inter-coder reliability ... 19 Results ... 19
Conclusion and discussion ... 25
Limitations ... 27
Future research ... 29
Introduction
How to attract your first one million customers? This question is asked by every new and ambitious start-up business. It represents the core idea of a start-up - being scalable, growing quickly and without the geographical limitations (Graham, 2012). The recent years show the increase of popularity of online communication, which practically enables new businesses to reach multimillion audiences around the world. Most of the companies have their profiles on several social media platforms – Facebook, Twitter, Google+, they use search engine optimization (SEO), Google adwords1 etc (Hird, 2013). Everything is done in order to attract new potential customers and communicate with the current ones.
The corporate communication classic Grunig (2001) said that the perfect way of the business interaction with its stakeholders is two-way symmetrical communication, which nowadays could be implemented with a help of social media. Yet, very often the main goal of using previously listed mediums is to increase the growth of the users rather than create meaningful interaction with them. This tendency can be explained by the new concept called growth hacking, which was introduced in 2010 and describes a way of online marketing. It is mostly used by startups and focuses on gaining exposure with a small budget by clever use of social media and viral marketing to attract new customers and increasing sales (Deeb, 2014). The most classic example of a premature growth hacking is Hotmail’s viral marketing campaign in 1996. Founders of one of the first free email services decided to ad a tagline with a clickable URL “Get your free email at HoTMaiL.” to the bottom of every email, sent
by the present users. This technique created an online word of mouth and helped Hotmail to attract the average of 20 000 new users per day without spending a single dollar on advertising (Penenberg, 2009). As the years passed, social media started to gain more and more influence, the whole new generation of marketers 2.0 originated (Deeb, 2014). The growth hacking techniques, which they have implemented, helped such companies like AirBnB, Dropbox, Whatsapp, Spotify, Uber (Rowan, 2014; Desmond, 2014).
Growth increases faster when the initial target audience spreads the word to their friends. People are more likely to recommend products or services to their friends if the producer of it has a positive reputation (Cruz & Mendelsohn, 2010). This is the reason why, while initiating the growth, startups also need to work on developing good reputation for their stakeholders through a well-planned communication. However, growth hacking is a new term in the field of communication sciences. Therefore, this paper will contribute to the scientific field of the modern online communication. The research combines one of the core values in corporate communication – online reputation – with the sequel of modern days way of promotion – growth hacking, which makes this paper unique and definitely relevant. It questions an interesting contradiction – Do more followers lead to increased reputation of a company? Do the numerous followers create real value?
Therefore the main research question is: To what extent does growth hacking lead to a better reputation for a company?
The first part of the paper will present the theoretical background, it will introduce the main concepts of the study and how they are related to each other. The second part will explain the method, which was employed in this research, followed by the results part. The paper will be concluded with the discussion, limitations and
the future research implications.
Theoretical background
The following part of the paper will introduce the main concepts of a research, present the hypothesis and the academic reasoning for them.
To begin with, the Excellence theory by Grunig explains the importance of public relations for an organization. It indicates on how can strategically planned public relations improve organization’s effectiveness and increase its profit (Grunig, 1992). The Excellence theory advocates that two-way symmetrical communication creates good relationships between the company and its stakeholders, which help the company to achieve its goals, reduce the negative publicity and even increase the revenue (Grunig & Grunig, 2008). Two-way symmetrical communication model says that companies should be open for a dialog with their stakeholders, listen to their needs and opinions, which creates mutual understanding (Leeper, 1996). Taylor and Kent suggested that the Internet is a proper medium to implement two-way symmetrical communication, because it allows establishing dialogs with a public and create relationships with them (1998). The two-way symmetrical communication is supposed to be used as a way to implement open, ethical, give and take style of dialog (Kent & Taylor, 1998). In practice it means that companies should use social media channels in order to demonstrate their best qualities, answer to their followers concerns, react to criticism and solve problems quickly.
More than a decade passed after the initial creation of the Excellence theory ideas and many social media channels (Facebook, Twitter, blogs) came into the market, which help accomplishing dialogical communication (Briones et al., 2011). Even
2009), still many companies fail to apply it strategically well in their daily operations (Bortree & Seltzer, 2009), therefore it is still very relevant to investigate corporate online communication further. Research proves that dialogic way of communication via social media leads to the growth of the network, higher user responsiveness and the overall network activity (Bortree & Seltzer, 2009). Also, companies, which have defined social media strategies and interact with their customers online, have a positive impact on their sales (Dholakia & Durham, 2010). Therefore, companies should initiate dialogical opportunities, post frequently, include pictures, videos, information about their upcoming events, respond to their followers posts, provide notifications about mutual concerns (Bortree & Seltzer, 2009).
As mentioned above, dialogical two-way communication on social networks is expected to increase the growth of company’s followers and eventually contribute to the monetary revenue. A new marketing method called growth hacking helps to increase the number of followers significantly by using a range of online tools, which enable to measure customers’ behavior online and apply various creative methods to attract new customers accordingly (Geru et al., 2014). Moreover, growth hackers analyze the collected data about their customers’ feedback and use it in order to improve the products (Schawbel, 2013). This effort keeps the existing customers loyal, encourage them spreading word of mouth, which attracts new publics. Eventually successful growth hacking can remarkably increase the number of company’s customers with a minimal investment in advertising (Geru et al., 2014). Growth hacking methods are particularly popular amongst start-up businesses, because they all seek for scalability, using limited budget for the advertising.
The growth is stimulated by the positive word of mouth. The term itself is one of the oldest means of marketing, which was reborn in the age of Internet. Online
platforms allow individuals to share their experiences and opinions about various products and services. Therefore, it is also a mirror of company’s reputation (Resnick et al. 2000). The report made by comScore in 2011 claimed that the average Facebook user has 130 friends. When this “average user” posts a recommendation about a certain product or service on his or hers Facebook timeline, there is 15% higher possibility that his or hers Facebook friends will become customers of that company (Radwanick, 2011). The possibility that people will recommend a product increases after they start following a brand on social media (Cruz & Mendelsohn, 2010). If social media users experience positive interaction on the social media with a brand, they are more likely to buy more products again and recommend it to their friends (Cruz & Mendelsohn, 2010). Yet, people tend to recommend products only if they had positive experience themselves. This is why, together with the growth, companies also have to concentrate on building positive reputation.
Good corporate reputation is a core of a well-developed business. It is a construct, which is built from the opinions of companies’ stakeholders over the period of time and is socially transmitted(Brown et al. 2006;Smaiziene & Jucevicius, 2009). It means that the corporate reputation of a company is highly dependent on how the stakeholders evaluate company’s actions. The main predictors for a good reputation are the quality of the relationships with the stakeholders and organization’s behavior (Grunig et al., 2002; Ebert, 2009). With that being said, organizations, which manage to implement effective communication strategies, are more likely to build a positive reputation (Portmann, 2013). Also, considering the companies’ wish to grow and attract more customers, the good reputation is needed, because then it is more likely that the existing stakeholders will spread the word to their friends or other contacts. In
where consumers share their insights most frequently (Beal & Strauss, 2009). Social media creates a platform for an open conversation with a public, but leaves the doors for criticism, which affects the company’s reputation negatively (Bennett, 2003; Portmann et al., 2014). Therefore, messages of users on social media form a proper source for measuring the reputation of a company – if reflects companies’ stakeholders concerns and how the company is dealing with it.
Theories, discussed above prove that there is a strong connection between two-way symmetrical communication, word of mouth, online reputation and growth hacking. Theories, which were introduced before, claim that the online platforms are the proper mediums for gathering communities and implementing two-way symmetrical communication. However, social media also creates an environment for open criticism and damage to the reputation. Growth hacking is beneficial for the companies, but also sets a challenge to manage the rising number of followers on the social media. Positive reputation is crucial for the stimulation of word of mouth and growth. This study will test whether the communication strategies by Spotify and Uber on Facebook change in terms of two-way communication style and if the growth of followers has an influence on these companies’ reputations. The following paragraphs will elaborate on the connections between the variables, based on the theoretical background. It will also propose the hypothesis, which will lead to answering the research question How does growth hacking influence the online reputation of a company?
To begin with, the research question suggests that online reputation should vary throughout the years due to the growth hacking. Therefore, the research should be looking at the differences in reputation, word of mouth, types and frequencies of communication during the years. This research will test three time periods, which will
be described more in detail in the sampling part of the paper. Briefly, for this research, as the first period of time for testing the reputation will be taken first active year on the social media. Second time period will be taken when the companies started gaining a lot of media attention and receiving major investments. Third time will be taken the whole period of 2014, because it portrays the most current year, when both of the companies are at the peak of their business. A period of one year is chosen, because the analyzed start-ups are present in the business world for 6 years or less. Therefore, in order to analyze the change throughout the time periods, shorter or longer time cuts would not provide reasonable results. This research will be aiming to find significant differences in reputation, word of mouth, types and frequencies of communication during those time periods.
The reputation itself is a construct, which builds up over the time – it can become either positive, negative or remain neutral. When a company just starts its activity on the social media, it does not have any prejudice. Company needs time to establish the online reputation. Therefore, it is expected that during the first year of companies’ active participation on social media the reputation is going to be neutral, because there was not enough time to create negativity or positivity. Taking into account the fact that within the years companies are eager to stimulate their growth and the positive reputation stimulates it, it can be assumed that reputation will be the most positive during the second time cut of a research. Reputation is expected to be the most negative at the third time cut, because at this point both startups already have biggest amount followers, which also attracts more attention and criticism. Therefore, at the point when a company has the highest amount of followers, it is also likely to receive more negative commenting.
H1a: Reputation will be indicated as neutral during the first active year on social media compared to the other time points;
H1b: Reputation will be at the highest point during the second cut of time period compared to the other time points;
H1c: Reputation will be at the lowest point during the last time cut compared to the other time points
H2: Positive reputation stimulates more word of mouth actions from the followers Secondly, growth is stimulated by the positive word of mouth. Therefore, it is expected that the amount of word of mouth actions will be increasing respectively throughout the years, because the community is also growing as the years pass. H3a: Word of mouth spread will be the least active on the first time period. H3b: Word of mouth spread will be moderately active on the second time period. H3c: Word of mouth spread will be the most active on the third time period
Thirdly, as mentioned before, frequent, dialog contributes to creating a better brand awareness, enforces the growth and leads to a better reputation. Therefore, it can be expected that both startups will communicate most often in the beginning of the business.
H4a: The frequency of the communication is the highest in the first active year on social media compared to the other time points;
H4b: The frequency of communication is lower at the second time cut H4c: The frequency of communication is the lowest at the last time cut
Lastly, companies need to enter the market, get noticed and build up their reputation in the beginning of the business. Therefore, it can be assumed that the level of interactive communication (initiate dialogs, include pictures, videos, post upcoming events, respond to their followers concerns), will be increasing during the
years in the beginning of the business and this level will be decreasing accordingly. It is expected, because in the beginning of the business companies need to put a lot of effort in order to engage their publics and stimulate the growth. Also, after reaching the high amount of the followers, word of mouth effect starts to work and companies do not have to strive for attention anymore. What is more, interactive communication is expected to lead to a better reputation and more word of mouth actions.
H5a: Two-way communication is the most frequent during the first time period.
H5b: Two-way communication is less frequent during the second time period.
H5c: Two-way communication is the least frequent during the third time period.
H6: The use of more two-way communication lead to a more positive reputation H7: Implementing more two-way communication, stimulates more word of mouth actions from the followers.
These hypotheses will be tested using the collected data from two well-known startups Uber and Spotify profiles on Facebook. The next part of the paper will present the information about the method, material, sample and design.
Method
The following paragraphs will further introduce the procedures, which were used for testing the hypotheses and eventually answering the research question. It will also explain the specifics of the research sample and the measurements of independent and dependent variables.
Material
For this research four main variables were used: growth hacking, online reputation, word of mouth and type of communication. They were described and explained in the theoretical background part and here it will be indicated how these variables are going to be measured.
Growth hacking refers to the increasing amount of companies’ followers on Facebook during the period of time. However, the information about the increase of Uber and Spotify followers on Facebook is not available. In this case, the most eligible way to measure the growth is dividing their activity on Facebook into several time periods, taking into account that the number of followers is increasing as the years pass. It is needed to select several time periods, because it is expected to find the differences between them due to the influence of growth hacking. Therefore, growth is measured by the time periods: for Uber it is 2010 (first active year on Facebook), 2013 (significant public notice) and 2014 (the highest point in growth). For Spotify it is 2009 (first active year on Facebook), 2011 (significant public notice) and 2014 (the highest point in growth).
Reputation is measured by the Facebook users tone of voice in their comments - negative, neutral, positive. Appreciating the services or behavior of the company, advocating the company, indicates positive tone of voice. Neutral tone of voice is indicated by the comments, which do not express any specific emotions of the followers. Negative tone of voice is indicated by complains about the services or behavior of the company.
Word of mouth works as mediating variable between the growth hacking and the reputation, because it enhances the growth. It is indicated by the amount of message
shares on Facebook profiles of Uber and Spotify.
Type of communication is a moderating variable between growth hacking and company’s reputation. It is measured by coding company’s messages, taking into account the type of a messages - is it creating dialogical opportunities, involving visual information (pictures, videos), posting relevant information. This measurement is based on the previous studies done by Bonson and Ratkai (2013), Grunig & Hunt, (1984) and Kent and Taylor (1998). Posts will be evaluated according to the basics of Gruning’s model of public relations, but adapted to Bonson and Ratkai (2013) study. Therefore, posts by the company are coded as one-way way communication if it does not have any comments by the followers, coded as semi two-way communication if posts have comments only by the followers and coded as two-way communication if posts also have replies from the company. In addition to that, dialogic principles by Kent and Taylor (1998) will be included as well. Usefulness of information is measured with two variables. First one indicates if the post on Facebook contains link to company’s own website, different website or a Facebook Application, secondary social media site of the company, a news article, a Facebook Event or to a another company’s social media website. Second indicates if the post contains various images, videos, polls or any other visual information.
Sampling
The content analysis on Uber’s and Spotify’s Facebook posts will be limited to the sample. For coding Uber posts and users’ comments there will be picked three time periods: the first active year on Facebook – 2010; 2013 - the year when the company received a major investment from Google and 2014 as the indicator for the current situation (Ferenstein, 2014). The analysis of Spotify will also have three time
periods: the first active year on Facebook – 2009; 2011 - when the company had the major increase of followers and 2014, which will provide the information about the reputation in the most current year. The content analysis will concentrate on coding a sample by taking every third month of the year for both of the companies: January, April, July and October.
The total amount of 273 Facebook posts was coded, of which 53.1% (n= 145) were from Uber and 46.9% (n=128) were from Spotify. The reactions to those posts, comments by the followers were also coded. During the period of the research, Uber has received 2029 comments to their posts and Spotify received 7295 comments, which were indicated as positive, negative, neutral, other or made by the company itself. Table 1 presents the overview of the analyzed material.
Table 1: overview of the analyzed material
Company Posts (N)
First time period (n)
Second time period (n)
Third time period (n) Comments (N) Uber 145 46 66 33 2029 Spotify 128 71 44 13 7295 Design
The main question of this research is: How does growth hacking influence the online reputation of a company? This question requires measuring the online reputation, indicated by the followers’ opinions and type of the communication, also the growth, which is indicated by the increase of the followers over the time. Therefore, the content analysis will be implemented in order to test the hypotheses and answer the research question. With the use of the online codebook (Appendix 1),
Uber’s and Spotify’s Facebook posts together with the followers answers will be coded.
Firstly, Uber and Spotify, used social media in order to foster their growth. Therefore it requires reaching users, who have social media as stimuli to build their opinions about both of the companies. That being said, the research question can be answered the most properly by employing content analysis technique on the social media. This research will concentrate on Facebook as the social media channel, used to stimulate the growth. Facebook was selected, because it enables to reach all the messages, posted by Uber and Spotify together with the direct reaction to those messages – the comments by their followers. Moreover, both companies actively used Facebook for activating their growth and have more followers there compared to the other social networks.
Secondly, reputation, as mentioned before, is measured by the customers’ opinions about the company. The research question requires looking at the change of reputation throughout the years, in order to see if there are significant differences on perceived reputation due to the growth of the followers on the social media. Also, the paper suggests that the reputation depends on the specifics of communication – if it is one way or two-way interaction with the audience. Content analysis will allow testing both: reputation will be measured by coding the comments of the followers and the type of communication (one, semi two-way or two-way) will be measured by coding the messages, posted by the companies.
Thirdly, while coding comments and posts, it is also very important to take the time frames into consideration, because it represents the growth of the company. Therefore, the method of content analysis is the most proper way of examining
messages, posted by the companies from the beginning of their business until the most recent days, which is needed for indicating the growth over the years.
To sum up, measuring people’s opinions with content analysis is the most reliable way, because it gives an access to the companies’ followers’ opinions during the different periods of time. It ensures, that the research tests the opinions of people, who have social media as a stimuli to form their opinions about Uber and Spotify and excludes the possibility of respondents lying, which is common for surveys.
Cases
Two start-up companies, Uber and Spotify, were chosen for this research. It is possible that results can be biased due to the peculiar characteristic of one company, therefore testing two companies, instead of one, grants more reliability for the research. The main criteria for the selection were: well-known start-up business, the use of growth hacking and data availability on Facebook. Both of the companies, Uber and Spotify, fit to those criteria. Differently from other successful start-up companies, Uber and Spotify still have their posts available for public since they have started using Facebook accounts actively. For both of the profiles, Uber and Spotify, the global versions of their Facebook pages will be analyzed, because it will provide the most generalizable perspective of a company’s reputation, excluding specific cases per particular countries. The following paragraphs will introduce Uber and Spotify more explicitly.
Uber
Uber is a proper case for the study, because their specific business model relies on trust between the company, driver and a customer, in order to compete with traditional taxis. It was officially launched in 2010 and since that time grew into a
multibillion international business. Uber offers the unique transportation services, where the usual taxi drivers are changed into anyone, who owns a driver license, insurance, a 4-door vehicle, 2005 or newer (Andrew, 2014). Uber, as a company, provides an app, which connects drivers with their potential passengers. It is argued that the company employed growth hacking techniques, which helped to develop business and attract new customers. Uber engages with the local partners, spreads special offers (Desmond, 2014), uses customer acquisition and customer optimization (Gogineni, 2014). Despite its large user base, there are many controversies about Uber, which raises a doubt if this company managed to build a good reputation while boosting financially (Frizell, 2014).
Spotify
Spotify is another case for this study. This company provides music-streaming service and has partnerships with most of the major record labels. The subscription to Spotify can be either free or paid. The company was launched in the end of 2008 and has an active account on Facebook since 2009. It faced a very significant growth after announcing the partnership with Facebook in 2011(Olson, 2011). Spotify managed to increase the amount of users up to 50 million all around the world until the current year by using growth hacking. The most noticeable technique was integration with Facebook (Holiday, 2013), when every time a current Spotify user was listening to the music, all of his or her Facebook contacts could see a link to Spotify. The company also did not avoid criticism for its business (Dredge, 2013), which makes it a good case for testing the reputation.
SPSS program. Hypotheses were tested using one-way Anova, regression, and frequencies analysis. The level of significance was set at .05. Prior to the main coding, a pilot test was done with a random sample of 20 posts by Uber and Spotify, together with the comments by the followers. The pilot test checked if the codebook is covering all the significant aspects of the research material. After the test, some questions were added to the codebook, in order to make it more comprehensive.
Inter-coder reliability
The content analysis is going to be done by one researcher. Therefore, Inter-coder
reliability test had to be performed in order to ensure that the different people would interpret the material in the same way. For this test the coefficient of .60 is chosen as minimum Krippendorff’s alpha rate, which assures the most reliable research results1. Two coders analyzed 20 randomly selected posts, together with the comments and implemented Krippendorff’s alpha test. The tests showed that all the variables are reliable, all scoring a KALPHA score of .60 or higher. The positive results let to continue the analysis further.
Results
In order to indicate the overall reputation of both companies, negative comments were subtracted from the positive ones and divided by the total amount of the comments. This type of calculation shows the proportion of positive and negative comments amongst all the comments, made about the certain post by the company. Other comments were excluded from the analysis, because they do not form the reputation directly. In addition to that, the outcome numbers of this calculation were grouped into the categories. Uber had the following categories: negative (1.00 to -.11); neutral (.00); fairly positive (.31 to .93); moderately positive (1.00 to 5.00);
positive (5.89 to 8.93); strongly positive (11.93 to 26.90). Spotify was applied to those categories: negative (-.17 to -.04); neutral (.00); fairly positive (.83 to .98); moderately positive (1.00 to 5.00); positive (5.97 to 9.96); strongly positive (10 to 122.99). The frequencies of those categories are pictured in the Table 2 and Table 3.
Table 2: the frequencies of Uber reputation Table 3: frequencies of Spotify reputation
For the hypothesis H1a: Reputation will be indicated as neutral during the first active year on social media compared to the other time points; H1b: Reputation will be at the highest point during the second cut of time period compared to the other time points; H1c: Reputation will be at the lowest point during the last time cut compared to the other time points one-way ANOVA analysis was used.
For Uber, change of the reputation throughout the years 2010 (n=46), 2013 (n=66), and 2014 (n=33) was analyzed with one-way ANOVA. The outcome of this calculation shows the following results: F (2, 142) = 5.1, p < .05. The post hoc test shows that the reputation throughout the years increased respectively (2010<2013<2014).
For the case of Spotify, Levene’s test of Homogeneity (p < .05) says that the groups are not significantly different from each other. Also, the model itself is not significant: F (2, 125) = 2.13, p> .05. Bonferroni post hoc test shows that the
generalizable.
Therefore, these hypotheses cannot be accepted. In the case of Uber, there is a significant change in reputation throughout the years, but opposite from what was expected, the index of reputation has increased. The reputation of Spotify was also increasing, but not in a significant way. These results are illustrated in the Table 2 and Table 3.
The hypothesis H2: Positive reputation stimulates more word of mouth actions from the followers was intended to test if the level of reputation had an influence on word of mouth spreading. In order to check this relation, regression analysis was used. First, the analysis was used with all the reputation indexes included (negative, neutral, positive). For the case of Uber, the results are F (1, 143) = 30.7, p < .05. In addition to this, R= .42, which is a moderate positive association between the index of reputation and word of mouth spread, R2 = 18%, which means that 18% of variance in the word of mouth spread can be explained of the index of reputation. The second time, negative and neutral reputation indexes were excluded in order to check if it is going to change the results. Hence, from the second attempt F (1, 79) = 32.6, p < .05, R=.54, R2 = 29.2%. Therefore, the results show that positive reputation indeed stimulates more word of mouth actions, because the second model has strong positive linear relationship and the explanation of the variance in word of mouth spread increased to 29.2 %.
For the case of Spotify, results show that F (1, 126) = 10.67, p > .05. R= .28, which stands for a weak association and R2=7.8%, which means that only 7.8% of variance in Spotify’s word of mouth is explained by its reputation index. When negative and neutral reputation cases were excluded, results showed the following
numbers: F (1, 98) = 3.82, p < .05, R= .19, R2=3.7%. It means, that in Spotify case the index of reputation does not have a strong influence on spreading word of mouth.
To sum up, hypothesis can be partially accepted, because positive reputation have a significant effect on word of mouth for the case of Uber, but not for Spotify.
The group of hypotheses H3a: Word of mouth spread will be the least active on the first time period; H3b: Word of mouth spread will be moderately active on the second time period; H3c: Word of mouth spread will be the most active on the third time period was tested with one-way ANOVA. For the analysis, the amount of shares was divided into the categories: no shares, very low (1-10 shares), low (11-19), moderate (20-40), medium (41-60), high (61-100) and very high (101-500).
In the case of Uber, the one-way ANOVA analysis shows that the model cannot be accepted. Levene’s test indicated equal variances p >.05, therefore even though there is a slight variation between the groups, it cannot be generalizable. In the case of Spotify, Levene’s test also indicated equal variances p >.05. Therefore this hypothesis has to be rejected, which means that there is no significant change in word of mouth spread over the years. These results are illustrated in the Table 4 and Table 5.
Table 2: overview of Uber’s results
Levene's test F (2,142) p Online reputation .05 5.1 .05 Word of mouth .00 26.33 .00
Table 3: overview of Spotify’s results
Levene's
test F (2,125) p
Online
The group of hypotheses H4a: The frequency of the communication is the highest in the first active year on social media compared to the other time points; H4b: The frequency of communication is lower at the second time cut; H4c: The frequency of communication is the lowest at the last time cut tested the change of the amount of the posts by the companies throughout the years. The descriptive frequencies analysis was run for testing these hypotheses. Results show that Uber posted 46 messages in 2010 (46 %), 66 in 2013 (24.2 %) and 33 (12.1%) in 2014. Spotify made 71 (26 %) posts in 2009, 44 (16.1 %) in 2010 and 13 (4.8 %) in 2014. Therefore, these hypotheses can be partially accepted, because Uber was posting the most actively not during the first time period as it was expected, but during the second one. On the other hand, Spotify was indeed the most active in the beginning compared to the other time periods.
Hypotheses H5a: Two-way communication is the most frequent during the first time period; H5b: Two-way communication is less frequent during the second time period; H5c: Two-way communication is the least frequent during the third time period were tested with using cross-tabulation.
The results showed that Uber has used two-way communication throughout the three time periods 22 times amongst 145 posts. The analysis showed that two-way communication occurred 11 times during the first time period in the year 2010, 7 times in the year 2013 and 4 times in the year 2014. Spotify over the three time periods used two-way communication 16 times amongst all 128 posts. Cross-tabulation analysis shows that in the year 2009 two-way communication was not used (n=0), in the year 2011 it was used more (n=4) and in the year 2014 it was used the most (n=12). Therefore the hypotheses are only partially accepted, because it fits to the case of Uber and shows the opposite results for Spotify.
Hypothesis H6: Companies, using more two-way communication, have a more positive reputation checked if there is a relation between the style of communication and the reputation. In the beginning of this analysis, correlation was used in order to test if a model itself of the style of communication and reputation is significant. For the second time only the cases of two-way communication were used and their influence on reputation and the third time only the cases of two way communication and positive reputation.
Therefore, in the case of Uber, results are the following: F (1, 143) = 45.62, p < .05, R=.49, R2=24.2%. It means that the initial model with the style of communication as a predictor for the reputation has a moderate association. The calculation with two-way communication exclusively shows that F (1, 143) = 5.52, R = .19, R2=3.7%. Ultimately, conducting only two-way communication and positive reputation results are the following: F (1, 143) = 2.74, p > .05, R= .14, R2=1.9 %. Therefore, it means that the association between the type of communication and reputation is decreasing if the calculation is done exclusively with the two-way communication and positive reputation.
In the case of Spotify, results are the following: F (1, 126) = 34. 2, p < .05, R = .46, R2=21.3%, which also shows a moderate association between the style of communication and the reputation. Using only the cases of two-way communication, results have changed as follows: F (1, 126) = 2.35, p > .05, R = .14, R2=1.8%. These results show, that using two-way communication weaken the explanation about the variance in word of mouth. Finally, using the cases of two-way communication and positive reputation, the model becomes not significant: F (1, 126) = 2.4, p > .05, R= .14, R2=1.9%. These results also show that even though there is a moderate
be specified if the two-way communications leads to a positive reputation.
To conclude, the regression analysis shows that type of communication explains a fair amount of variance in the index of reputation. However, it cannot be specified which type of communication influence positive reputation more.
Next hypothesis H7: Companies, using two-way communication, stimulate more word of mouth actions from their followers was calculated using several steps. Firstly, the main variable, which indicates the type of communication, was calculated together with the word of mouth variable. Then, the cases of two-way communication were selected in order to test if there is going to be a difference between those two analyses. Uber, when tested its type of communication and word of mouth spread, showed that F (1, 143) = 9.24, p < .05, R =.25, R2=6.1%, which present only a weak association. When calculating only two-way communication actions with the word of mouth spread, results become not significant: F (1, 143) = .75, p > .05, R=.07, R2=0.5%. In the case of Spotify, the regression was calculated between the variables Style of communication and Word of mouth. The outcome showed, that F (1, 126) = 17.3, p < .05, R=.35, R2=12.1%. It means that there is a moderate association between the style of communication and spreading word of mouth. When taking into the consideration only two-way communication, results improved slightly: F (1, 126) = 19. 21, p < .05, R=.36, R2= 13.2 %. This hypothesis can be partially accepted, because the case of Uber did not show any significant results, but for Spotify, when they use two-way communication, they receive more word of mouth actions.
Conclusion and discussion
The main aim of this study was to analyze if a sudden increase of the companies’ followers using growth hacking has an influence on their online
reputation on Facebook. In addition to that, the research examined the impact of the type and frequency of communication on the online reputation. The cases of successful startups, Uber and Spotify, were chosen and tested in three different periods of time. The overall analysis is expected to answer the question How does growth hacking influence the reputation of a company?
Regarding the influence of growth hacking on the online reputation, word of mouth, not all the conclusions are solid between Uber and Spotify. The results showed that for the both cases word of mouth spread did not change significantly over the years as it was expected – word of mouth actions were mostly consistent. Therefore, it can be concluded that the word of mouth, indicated by Shares on Facebook do not have influence on the growth. However, word of mouth could have been spread on the offline basis.
Concerning the online reputation, frequency and style of communication results were diverse between Uber and Spotify. Uber’s reputation was increasing during the years, frequency of communication was the highest during the second time period and the rate of two-way communication style was the highest in the beginning and started decreasing accordingly, as it was expected. However, Spotify’s reputation did not have significant changes during the years, frequency of communication was the highest during the first year and decreased accordingly and the two-way communication actions were most frequent in the third time period, opposite from what was expected. In addition to that, results showed that Uber receives more word of mouth actions for the posts, which are evaluated positively. Also, the case of Spotify shows moderate association between two-way communication and word of mouth spread.
These diverse results suggest the conclusion that the effect of growth hacking is specific for every company and cannot be generalizable. It makes sense, because companies apply growth hacking, which suits their own demands – there is no universal growth hacking technique that would be equally effective for all the businesses (Geru et al., 2014). Moreover, both of the start-ups are relatively young, still adapting their business models to their customers needs and building up their reputation. Therefore, if this study would be replicated after 10 years, it would give more comprehensive results about Uber’s and Spotify’s online reputation. In addition to that, this research proved that two-way communication is still not common amongst the companies. After coding the total number of 9324 Facebook comments, the tendency showed that many people express their concerns towards the company online, but they do not receive any response, which provokes frustration ands leads to the negative commenting. Hence, it demonstrates the importance of a well-planned online communication (Portmann, 2013).
Limitations
Despite of all the meaningful information about the growth hacking and its influence on online reputation that this paper has provided, there are several limitations in this research: selection of the companies and social media platform, the possibility of data discrepancy and limited previous academic research about the growth hacking.
To begin with, the reasons of selecting two particular companies – Uber and Spotify were mentioned before. However, the unique business models and history of both of the companies might have had an influence on the overall results. For example, selected companies provide very different type of services, Spotify is in the
market for a longer period of time than Uber, they have different amount of followers on Facebook.
What is more, content analysis on Facebook posts reveals only one side on the online reputation. Other social media sites, which those companies use (Twitter, LinkedIn, Google+, Instagram), could cover the overall picture of online reputation. Consequently, including more social media websites into the analysis could provide more comprehensive results.
Another limitation is the possibility of the data discrepancy. The researcher could not be guaranteed that, for example, Uber and Spotify did not remove negative comments from their posts. Moreover, it was noticed that for both of the companies, some of the comments by the followers were repeating throughout different days. Also, in the case of Spotify, a big part of the comments were excluded from the analysis, because they were in a foreign language. Therefore, it is likely that all these discrepancies could have had an influence on the overall results. However, this limitation can not be avoided, because it does not depend on the researcher.
Lastly, as mentioned before, growth hacking is a rather new term in academic world, with no relevant peer-reviewed articles to be found on the available databases (Web of Science, PsycINFO, Google scholar). Therefore, the part in theoretical background about this item was based on the articles, retrieved from the websites, which cover topics about new technologies. Moreover, the idea to use the change during the years for indicating the growth was applied for the first time in an academic article. It is also known, that all the companies create growth hacking techniques, which suit them personally. Therefore, it is very difficult to have an access to the information about those specific techniques and to, moreover, investigate their direct influence on company’s online reputation.
Future research
Regarding the limitations, which were listed above, the future studies should concentrate on improving the following points: test more companies with similar profiles, include more social media websites into the analysis also add qualitative interviews with growth hacking professionals.
More specifically, researching companies, which use growth hacking and belong to the same category of business could provide more comparable results. For example, future studies could concentrate on the sharing economy start-up companies (Uber, AirBnB, Lyft) and analyze their online reputation. In this way, the findings would be more generalizable in that specific field of the business and, potentially, the contrast in results would be avoided.
Another important improvement for the future studies is including more social media channels. Analyzing Facebook together with Twitter, Instagram, LinkedIn and Google+ would give more information about the online communication and the change of reputation due to the growth hacking actions. It would also show on which platforms followers are more likely to be complaining or expressing the support. It would provide guidance for the companies, showing on which social media platform they should use more customer support. Furthermore, future studies can also analyze traditional media in order to test if there are differences between the online reputation and the rest of the media.
Ultimately, the quantitative research could be supported by the interviews with the growth hacking or online marketing experts from the companies. As mentioned before, usually the information about specific marketing decisions is confidential. Therefore, interviews, taken for the academic research reasons, would disclose the
information about the actions, which were taken in order to foster the growth. Also, it would explain which actions have stimulated the fastest growth.
To sum up, the research about the influence of growth hacking on company’s online reputation has some visible limitations, which could be improved in the future studies. However, it still provides interesting results, which could be taken into the consideration while creating webcare strategies.
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Appendix 1
Codebook
Q1: What is the name of the company?
Uber
Spotify
Q2: Which year is it?
(Fill in manually)
Q3: Which month is it?
(Fill in manually)
Q4: Which day is it?
(Fill in manually)
Q5: Does it have “Shares”?
Yes
No
Q6: If yes, how many “Shares” does it have? (I will complete this question once I will have collected all the data, so I will see what intervals should I use)
(Fill in manually)
Q7: Does this post have “Likes”?
Yes
No
Q8: If yes, how many “Likes” does it have?
(Fill in manually)
Q9: Does this posts have comments by the followers?
Yes
Q10: If yes, how many comments does it have?
(Fill in manually)
Q11: If yes, how many comments have a positive tone of voice?
Positive tone of voice is indicated my appreciating the services or behavior of the company, advocating the company.
(Fill in manually)
Q12: If yes, how many comments have a neutral tone of voice?
Neutral tone of voice is going to be indicated by the comments, which do not express any specific emotions of the followers.
(Fill in manually)
Q13: If yes, how many comments have a negative tone of voice?
Negative tone of voice is indicated by complains about the services or behavior of the company.
(Fill in manually)
Q14: If yes, how many comments are not related to the post/cannot be described?
(Fill in manually)
Q15: If yes, how many comments are made by the company?
(Fill in manually)
Q16: What is a topic?
Announcement of the business expansion
Announcement about the new features of a product
Announcement about the upcoming/past events
Other relevant information about the product
Other interesting facts about the company
Customer service concerns
Other
Promotional information/offers
Q17: What is the style of communication in the post by the company?
One way communication refers to the posts, which do not have any comments by the followers; Semi two-way communication refers to the posts, which have comments by the followers, but no answers from the company; Two-way communication refers to the posts, which have comments by the followers and the company
One-way communication
Q18: If it is two-way communication, did the answer contain the features of “human voice”?
Human voice, in this case, refers to posts or answers by the company, when they are using informal language, names of their followers, personal pictures.
Yes
No
Other
Not two-way communication
Q19: Does a post contain any extra features besides the text?
Yes
No
Other
Q20: If yes, what does it contain?
Image
Video
Link to the internal website
Link to the external website
Other
Does not contain
Q21: Does a post in general contain “human voice” features, humor?
Human voice, in this case, refers to posts or answers by the company, when they are using informal language, names of their followers, personal pictures.
Yes
No
Other
Q22: Other remarks
