Title Exploring the effect of internet memes in social media marketing through A/B testingSub TitleAuthor 杨, 雪(Yang, Xue)

林, 高樹( Hayashi, Takaki)Publisher 慶應義塾大学大学院経営管理研究科

Publication year 2020Jtitle

JaLC DOIAbstract

Notes 修士学位論文. 2020年度経営学 第3757号Genre Thesis or DissertationURL https://koara.lib.keio.ac.jp/xoonips/modules/xoonips/detail.php?koara_id=KO40003001-0000202

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慶應義塾大学大学院経営管理研究科修士課程

学位論文（ 2020 年度）

論文題名

Exploring the Effect of Internet Memes in Social Media Marketing through A/B Testing

主 査 林 高樹

副 査 大林 厚臣

副 査 井上 哲浩

氏 名 YANG XUE

論 文 要 旨

所属ゼミ 林高樹研究会 氏名 YANG XUE

（論文題名）

Exploring the Effect of Internet Memes in Social Media Marketing through A/B Testing

（内容の要旨）

With the development of social network services, the way to contact with other internet users

has been changing constantly. For marketing communication, there are also many new challenges

and chances. Brands and companies are making effort to develop various effective approaches

to communicate with consumers through social media nowadays. As one of the growing famous

strategies, meme marketing is evolving under the environment that young generation began to

be the majority of internet society and a new culture phenomenon “internet memes” is acting

vigorously through online communication.

This paper was born via questioning about the exact effect of internet memes in social media

marketing. As for the methods, an A/B Testing was conducted with dozens of post experiments

on the SNS platforms to test the effect of internet memes by measuring post engagement rates

and an analysis on engaged sample users by generalized linear mixed effects models was carried

out as well for exploring the characteristics of consumers who tends to consume internet memes.

Regarding the results, the effect that posts with internet memes gained higher post engagement

rates was tested significantly, and the outcome that originally created memes were engaged

more actively than the derived memes that made from famous meme templates is found. It’s

discovered that consumers around 20s have a stronger preference to communicate by using

internet memes.

Based on the research results, several insights on the implications of internet memes in SNS

marketing communication are proposed. This paper contributes to verify the effect of internet

memes in SNS marketing practically and academically by conducting a novel and applied

experiments and analysis models, hoping to call more actions to do researches on this new

form of “language” in the digital future.

1

CONTENTS

1 INTRODUCTION: BACKGROUND AND MOTIVATION 2

1.1 SNS MARKETING 2

1.1.1 DEVELOPMENT OF SNS BASING ON “SMALL WORLD” PHENOMENON 2

1.1.2 SOCIAL MEDIA MARKETING 4

1.2 INTERNET MEME 4

1.3 MEME MARKETING 5

1.4 PURPOSE OF THIS STUDY 8

2 REVIEW OF RELATED LITERATURE 8

2.1 DEFINITION OF MEMES 8

2.2 DIVERSITY OF INTERNET MEMES STUDIES 9

2.3 MEME MARKETING 10

3 METHODOLOGY 11

3.1 A/B TESTING 11

3.2 HYPOTHESES 13

3.3 DESIGN OF CONTROLLED EXPERIMENTS 13

4 RESULT 15

4.1 A/B TESTING 15

4.1.1 TEST 1 15

4.1.2 TEST 2 17

4.2 ANALYSIS ON SAMPLE USERS 19

5 CONCLUSION 26

5.1 INSIGHTS ON MEME MARKETING 26

5.2 DISCUSSION 28

5.3 FUTURE WORK 29

ACKNOWLEDGMENTS 29

REFERENCES 29

2

1 Introduction: Background and Motivation

Living in the society whose backbone is the virtual system called “Internet”, we are equipped with

various digital devices to communicate with others via sharing contents through different social

software applications. The social media, especially the “Social Network Service (SNS)” is used

widely nowadays by individuals and organizations for differed purposes, including personal and

commercial purposes. A growing number of new strategies to utilize these online services

functionally and creatively appear. The meme marketing is one of the outstanding examples and

the utilization of memes in SNS marketing communication is paid more and more interest these

days. This paper starts with sorting out the development history of SNS briefly and discussing

about new marketing communication methods in the internet to help catch the vital meaning of

studying on meme marketing today.

1.1 SNS Marketing

1.1.1 Development of SNS basing on “Small World” Phenomenon

The debut of “SNS” could date back to the 1990s. The "SixDegrees.com"(operated from 1997 to

2001) is considered widely to be the first SNS platform (Boyd & Ellison, 2007). The name of this

site is derived from the idea of “Six Degrees of Separation”, which holds the view that: “any two

individuals could be connected directly or indirectly in a maximum of six steps”. While the origin

of this notion has been discussed by a multitude of researchers without a common conclusion yet,

the related studies concerning the structure of social networks, like the "Small World" problem,

which was submitted by Stanley Milgram (1967) firstly examining the average path

length quantitively for social networks of people in the United States, are continued constantly

and proposed a number of networks models analytically. One of the most refined study among

these models was formulated by Watts and Strogatz (1998) that has been applied to the analysis

of the hyperlink graph of the World Wide Web, and their unique model was well generalized

by Jon Kleinberg (2000) with a simpler framework. Meanwhile, their studies helped demonstrate

Mark Granovetter’s observation that “the strength of weak ties” makes a social network as well.

These studies provided compelling scientific evidence to prove the phenomenon “Six Degrees of

Separation” is reasonable and pervasive.

With the development of internet and the increasing studies about social networks, a few SNS

platforms with different services provided born successively and more people tried to pour more

attention into building and maintaining the connections on the virtual online society through SNS.

The needs of SNS grew, as a result, SNS platforms developed with a rapid speed. Especially when

entering the 2000s, the term of “Web 2.0” invented by Darcy DiNucci (1999) being increasingly

popular, many huge online platforms started providing diverse social media services globally and

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1.1.2 Social Media Marketing

As the penetration rate of internet and the number of SNS platforms increased, marketing

activities on social media is weighed of great value by brands of their marketing mixture strategy,

pursuing the reachable merit of more direct and effective communication between brands and

consumers. Marketing on the SNS platforms is considered as an increasingly essential approach

by almost every industry for reaching various business purposes.

“Social media marketing is the utilization of social media technologies, channels, and software to

create, communicate, deliver, and exchange offerings that have value for an organization’s

stakeholders … it has expanded rapidly, as much for its efficiency given its low absolute costs as

for its potential business applications as a tool for garnering customer attention, managing

customer relationships, developing new product ideas, promoting brands, driving store (online

and offline) traffic, and converting consumers to customers.” Tuten (2020) states it in her book

Social media marketing. She emphasized the interaction between brands and customers on social

media is more like a bottom-up way, differing from the traditional marketing. Social media

marketing today concentrates on niche online media with an attraction orientation to gain more

inbound traffic, while traditional marketing used the mass media to message in an outbound way.

Through social media platforms, brands could practice creative promotional plans to gain more

attention from consumers and finally move them to purchase process with comparatively low

cost and high performance. Furthermore, one’s marketing strategies and promotional plans

themselves could be influenced and develop further when basing on the interactivities with

consumers.

The studies on social media marketing are discussed abroad recent years and the recognition of

the advantages of social media is coming to a consensus. Social media marketing activities are

certainly related to increasing brand awareness, enhancing brand liking and image, building

brand equity, inciting desire, and moving consumers to purchase actions. They can definitely

influence targeted consumers’ attitudes (Joshi et al., 2013). For the outstanding performance of

social media marketing and the great user base of SNS platforms, it seems that almost every brand

is trying to invest more to improve their performance of social media marketing by utilizing SNSs.

1.2 Internet Meme

The “Internet memes”, generally known simply as “Memes”, are spreading through the internet,

especially SNS platforms. The notion originally came from an academic concept of “meme,” which

means “the unit of cultural transmission.” Due to the development of internet, this word has gone

beyond its original meaning and be known as a relative thing that refer to any piece of quickly

consumed comedic or relatable content. Internet memes are the outcome of the development of

internet culture. The actual form of an internet meme varies from different communities and

5

countries, but under the macro-environment of globalization, the term “internet meme” is being

a seemingly weird but common language for worldwide internet instance communication today.

A traditional internet meme consists of a combination of two main parts: an image and a

catchphrase. It is the most typical form of internet memes today, which is called “image macro”

(Figure 3) customarily when memes like “LOLcats” (Figure 4) spread widely on the internet and

became popular (Rutkoff, 2007). Internet memes have changed over time as well. Influenced by

the latest fashionable content online, a modern internet meme has a broader and multi-faceted

formation, evolving to include more comprehensive structures such as GIFs and videos.

Figure 3: Image Macro. Typical format for internet meme images

Source: Own work (Author: Barronwebster)

Figure 4: Examples of LOLcats meme

Source: Know Your Meme

In conclusion, internet memes can be seen frequently in the internet world and seems to be a kind

of new “language” for internet users, especially the users of young generations, to communicate.

This language is so powerful that various online applications have even been launched to provide

convenient services for making the process of creating "memes" easy, which helps facilitate the

propagation of internet meme at a tremendous speed.

1.3 Meme Marketing

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There is no doubt that both SNSs and internet memes turn into an important part of our life for

the majority of individuals in this digital age. Internet memes now, not only used in casual

situations such as conversations for personnel expressions between intimate friends, but also

utilized as a more formal way to connect between brands and consumers closer through social

media. The practice of using memes to market products or services is well known as “meme

marketing” or “memetic marketing.” For example, the luxury fashion brand "Gucci" once

conducted a promotional campaign on the SNS platform “Instagram” in 2017, applying internet

memes’ way to make a series of posts (Figure 5). Gucci creatively integrated their own aesthetic

with the memes and achieved a success. According to the stats from Gucci’s all 30 published

memes, 120,089,317 total reach, 1,986,005 total likes, 21,780 total comments and 0.5% average

engagement rate1 were achieved2, which outperformed Gucci’s other posts on Instagram. From

the experience of Gucci, creative meme marketing seems to assist gain more attention and affinity

and contribute to boost higher traffic and engagement from SNS users.

Figure 5: Meme marketing by Gucci

More examples of using internet memes in marketing communication could be seen at Figure 6

where listing four posts from three well-known internet-based companies (Taobao.com, Ele.me,

Meituan-Dianping) and one newspaper group (People’s Daily) in China. Little differed from the

Gucci’s application, these posts use internet memes to make their specific topics be expressed

vividly and amusingly. The memes are developed and localized on Chinese SNS environment.

1 Post engagement rate = (Likes + Comments) / Total Followers x 100

2 Data resource from: https://blog.dashhudson.com/gucci-meme-luxury-brand-instagram-marketing-content-strategy/

7

Though they are keeping the same structures as western original memes do, the expression s of

memes are more varied with various purposes, not only humor. Brands in China also try to add

internet memes to their textual introduction of new products or events and make more users to

interact with them, such as liking, commenting, sharing, etc. These engaging actions will definitely

be linked with higher exposure for the recommendation mechanism designed by platforms. Even

just a click of “like” within one second by a user will help the posts to be seen by his or her friends

of the followers list.

Figure 6: Examples on Weibo

Benefiting from the powerful functions of SNS platforms, adding the outstanding characteristics

of internet memes, meme marketing has many advantages for brands obviously. To sum, first of

all, it could gain plenty attention from followers and potential fans for their funny unique humor,

driving great traffic to brands’ homepages and boosting followers of brand official SNS account.

This is the most direct object for brands to try their best to make effort. The number of

engagement and accounts’ followers are extremely significant indexes in social media marketing,

which are accounted as important assets that could have a high flexibility to convert it to profits.

Secondly, users that see the posts of meme marketing will feel closer to the brands. Using internet

memes by official accounts of brands to make consumers active can also help form a boundaryless

online consumer communities. In this way, it is readily to build a relationship between brands

and consumers, which means raising and maintaining loyal fans of brands. Then, as one study

8

reminds us, the most visible memes on the internet (such as ones from search engine findings and

online articles, etc.) can be recognized as a smart market research tool, using which can target a

consumer group of a certain preference or opinion on related products and services specifically

(Csordás, 2017). Lastly, it costs little to carry out a meme marketing on social media, which would

be one of the best benefits of it. The investment to plan, create contents, and put it into practice

is much lower than the return. Considering these favorable points, meme marketing on social

media is of great potentiality.

1.4 Purpose of this study

Various merits of studying internet memes and the effective utilization of it in the marketing

activities exist. As a rising style of communication and a novel culture phenomenon on the

internet, internet memes must be valued more basing on diverse academical fields, especially in

this changing digital era where individuals have a strong tendency to consume in a more

entertaining way and user/consumer generated contents are seen almost every SNS platforms in

a great amount. Meme marketing should be discussed further to acquire more insights.

Observing that the appearance of internet memes on SNS platforms increases these years, the

author also starts to wonder the true effect of them in the promotion content by brands and

communication through brand community would like to be. Specifically, for the prosperity of

online shopping and social media marketing in China and other countries, it can be valuable to

study more about measuring the effect of internet memes quantitively with Chinese specialty in

marketing communication by scientific methods to show its significance and make supplement

to the real business practices. There is a wish that this study could contribute to help brands and

companies, which operate business in an area where internet memes language works, to apply a

deeper understanding of meme marketing and provide suggestions on how to utilize internet

memes in marketing communication effective and enter the Chinese market particularly through

digital marketing nowadays.

2 Review of Related Literature

2.1 Definition of Memes

Studies on internet memes are increasing these years with the prevalent of this humor type of

communication among new digital generations. The researches mainly focus on the ontology and

history, the definition, and the spread of memes in various perspectives, as well as the functionary

effect of internet memes in marketing and other fields.

9

The word "meme" was older than the Internet. It could firstly be found in the book The Selfish

Gene (first published in 1976) written by Richard Dawkins, where "Meme" was academically

coined as "the unit of cultural transmission", attempting to explain how ideas replicate, mutate,

and evolve. According to Dawkins, “Examples of memes are tunes, ideas, catch-phrases, clothes

fashions, ways of making pots or of building arches, as genes propagate themselves in the gene

pool by leaping from body to body via sperms or eggs, so memes propagate themselves in the

meme pool by leaping from brain to brain by a process which, in the broad sense, can be called

imitation.” Afterwards, Dawkins developed his view in his book Viruses of the Mind (1993) defined

that memes behave more like computer viruses do: they spread horizontally, from vehicle to

vehicle, and from medium to medium at a very high speed (Dawkins, 1993). According to his view,

the internet memes today could be known as “an image-form DNA” with the perspective of gene,

and it is agreed that the internet meme has a similar propagation mechanism to the original

notion "meme" by Dawkins (Juza, 2013). Afterwards, internet memes are defined as “units of

popular culture that are circulated, imitated, and transformed by individual internet users,

creating a shared cultural experience” and as “groups of content items that were created with an

awareness of each other and share common characteristics” (Shifman, 2013).

Castaño Díaz (2013) compared the famous idea of Dawkins with other claims, and concluded that

memes could be comprehended by two main perspectives: Meme Genes one and Meme Virus one.

Besides, Castaño Díaz also emphasized the perspectives on the view of Daniel Dennet on

understanding meme by its content/meaning and the sight of Dan Sperber on knowing meme by

a structural approach. He insisted that the structure and meaning behind memes are independent

and both of them are connected to each other at some certain. From the discussion on memes, the

internet memes also can be talked under these systematic views. The internet meme has its own

definite structure and the common humor meaning to express. Both of the features are

independent and correlated to the other.

2.2 Diversity of Internet Memes Studies

Apart from these studies on memes’ definition above, many researchers also tried to observe

internet memes phenomenon in various research fields, for instance the study about finding the

factors that influence the diffusion of internet memes through social media platforms (Johann et

al., 2019). The discussion about the existing types of memes under media’s view, like one study

recently of Yanqi Ding (2019) categorized and summarized the most active types of internet

meme that be seen in Chinese SNS platforms. She provided several ideas under propagation

perspectives such as "Purpose of Use," "Visual Styles" and "Expressed Emotions” to classify

internet memes well for different objects. Besides, the study on self-expression utilized internet

memes in politics and participation in political life (Milner, 2013), and a quantitative analysis on

posts in an internet memes’ style by German Identitarian Movement (GIM) on Facebook

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(Guenther et al., 2020), which found that the strategic framing of internet memes did helped gain

various user engagement, are conducted recent years, making us think more about the

relationship between internet memes and user engagement. Accordingly, in this paper, the effect

of internet memes in marketing communication could be measured in a similar way by the

measurement of user engagement effectively.

Many other studies were hold out via different methodologies. A novel system based on latest

technic of neural network (LSTM model) in deep learning study is designed to generate internet

memes automatically by producing caption to a given meme template (Abel et al., 2018), which is

inspired by the widely recognized Show and Tell Model. Besides, there is also a study of classifying

internet memes and clustering them by deep learning and graph learning (Beskow et al., 2020).

In the future, more new technics can be expected to implication, contributing the diversity of

studies on internet memes.

2.3 Meme Marketing

The number of discussions on meme marketing is booming recently. Increasing scholars

questioned and researched the relationship between internet memes and marketing

communication of business. On the effect of internet memes to branding, a quantitative survey

was carried out for testing the effect of the brand related internet memes basing on CBBE

(Consumer Based Brand Equity) dimensions and successfully proved that the influence of brand

related memes on brand association exists (Hallgren et al., 2018). Additionally, a study of

measuring whether the internet memes can impact the perception and interpretation of

consumers on online messages by the method of online survey to show paired pictures of posts

with internet memes was significantly proved that it’s true for internet memes to support in

terms of expressing emotions (Hecker, 2020). Other studies also paid attention on the impact

from internet memes to brand awareness by experimenting and testing on the SNS platforms.

Referring and developing these related valuable literatures, this study has several contributions

and novelty to mention:

• The method of controlled experiments and the analysis of samples succeed the previous

researches, while there is something special: The target consumers of experiments are

mainly collected from China, and the experiment platform is China-launched. The finding

from the specialized circumstance can give supplementary evidence to related academical

studies and practical implications.

• The analysis using general linear mixed models to analyze the characteristics of users who

tends to consume internet mems is thought to be one of new attempt in among the studies

related to the measurement of internet memes’ effect from users to post engagement.

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• There are many works on measuring the effect of internet memes by a traditional

questionnaire way to gain the attitudes and preference from respondent users. Yet there is

not quite much that using the objective scale of post engagement that calculated by SNS

platforms. Since that the post engagement are valued more for its direct connection to

account equity/brand equity, it’s important to figure out whether the internet memes used

in posts could have a stable impact on the number of user engagement. It can be of

financially worthy to measure the post engagement.

• The number of replicated experiments is much more than the previous researches, which

help the results in this study reach higher reliability and validity to explain at some certain

extent.

• Great effort of providing the practical suggestions and useful implications about doing

creative marketing works with internet memes basing on the experiments’ results and

related experience is considered as one of the most central components in this study. It is a

goal that the pragmatic ideas offered in this paper could be accepted successfully in real

application cases.

3 Methodology

3.1 A/B Testing

A/B testing is a fundamental and empirical experiment that consists of a randomized experiment

with two versions (A and B) of a single variable to compare . It is known as bucket testing or split

testing as well. The simplest structure of A/B tests is made up by two versions (A and B) of one

single variable for comparing, evaluating and deciding which is of better effect. With more

variables adding to it, A/B tests could be organized in various complex forms. The theory of a

controlled experiment could date back to Fisher’s experiments at the Rothamsted Agricultural

Experimental Station in England in the 1920s, and the study of Rubin (1974) that conducted to

estimate causal effects of treatments in randomization are quite well-known for its two-trial

testing design. Besides, online controlled experiments instructed by A/B testing methodology

began to be adopted into practice increasingly in the late 1990s under the environment of the

rapid growth of the Internet (Kohavi et al., 2009).

At present digital days, numerous sites, like Amazon, Facebook, and Google, run thousands to tens

of thousands of experiments to test user interface improvements, enhancements to algorithms

(search, ads, personalization, recommendation, etc.), changes to apps, content management

system and so no. Online A/B Testing is highly recognized as a powerful mean to measure the

effect of product/services’ design change on end user behavior and determine whether it should

be made with the benefit of uncomplicated and high-speed process. It is believed that this

13

3.2 Hypotheses

Basing on the author’s experience that observing the posts with internet memes by brands’

official accounts on several famous SNS platforms and paying mind to the engagement from users,

several preliminary points were discovered:

• Towards the posts with memes, the reaction of users tended to be positive and active.

• Posts with memes earned more "likes" and "comments".

• A meme that created originally with fresh new image, comparing to one made by a well-

known image (which is used to being called as a meme template that have been applied to

make memes quite a lot) on SNS platforms, seemed to obtain better performance on user

engagement.

It seems that the posts with memes by brands have a call to make users react and contribute to

boost engagement. The positive effect of meme marketing tends to exist in practice of business,

which conforms in appearance with the argument talked above that base on various reference

materials. Here comes the problem that how to verify in a scientific way whether the main

conjecture is plausible.

According to the observations and discussion above, two main hypotheses were formulated:

H1: Posts utilizing internet memes have higher Post Engagement Rates than posts without

internet memes utilization.

H2: Posts with memes that created with original images have higher Post Engagement Rates

than posts with memes derived upon other well-known template images.

3.3 Design of Controlled Experiments

As claimed at the beginning of this chapter, an A/B testing is designed to hold out. In addition to

the design of A/B testing, several more terms are defined and controlled rigorously to form a

complete and effective testing:

• WeChat/Weixin, a popular SNS platforms owning the most users in China (as of Oct. 2020),

is chosen as the testing SNS platform. Its Moments service provides quite ideal

environment for testing, where the operation of posting towards specific groups is feasible.

• 400 sample users that chosen from the author’s social networks are randomly divided into

2 groups in paired per experiment for carrying A/B test, and the members of each groups

are shuffled regularly during 50 tests.

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• Concerning the content of each post, except the main textual content in the body of a post,

it is consisted of two photos of a well-known product or service, and a meme as a variable.

The memes express the same idea about the product as the main context says. Two photos

are fixed to post for the reason of avoiding the unattended influence of the image display.

The display of posted photos would be seen bigger in size when only one photo existed

than the occasion that two more photos were posted, and the former could be considered

to impact users more likely to engage into the content. Controlling the size of the posted

photos is vital for testing. The photos of products are from following brands: Nike,

Starbucks, SK-2, YSL, JINS, Disney, etc. The memes are created to express some

impressions basing on the views of consumers in a humor way. Readers can image the

posts as the examples shown in Figure 6.

• The posts are randomly conducted during all day.

• Visible period of each post is set to be 3 days (72 hours). Each post will be invisible after

72 hours automatically from its posting time, which help control the data of engagement

to be collected in the equally same time period.

• In order to test two different hypotheses, Test 1 and Test 2 are designed for two different

variables (Table 1). Test 1 is designed to test Hypotheses 1. The presence or absence of

internet memes in contrast is the only variable. Test 2 is designed to test Hypothesis 2.

Memes’ type that is classified into two types here, is set as the variable. Regarding the type

of internet memes, one is the originally created memes whose image macros have both

originality of images and catchphrases, and the other is the memes derived upon other

well-known template images. About distinguishing the original memes and memes made

from templates, the latter’s image templates resource limitedly from “popular memes”

(top 20 templates) of “Mematic” application and the “hottest posts” (top 20 templates) of

“Meme_bot” on Weibo, and the former are referred simply the memes that are created from

raw materials which are not known as famous templates.

• The notion of Post Engagement Rate used here is defined as “the ratio of the number of

Likes and Comments by the number of group members”.

• Posts are made to imitate the real situations in real social media marketing.

• To enhance the reliability and significance of the testing, plural paired experiments are

conducted.

Table1: Comparison of 2 varied tests

2 Tests Variables Group Contents

Test 1 Presence of memes A Two photos + An internet meme

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B Two photos + No internet meme

Test 2 Types of memes

A Two photos + An original meme

B Two photos + A derived meme

4 Result

There are 50 paired experiments of test 1 and 20 paired experiments of test 2 conducted totally

from Oct.04 to Nov.11 in 2020. After handling the collected data with statistical analysis tools, the

hypotheses were proved. In addition, basic features of sample users that tended to consume

memes were discussed as well and explored some views with analysis.

4.1 A/B Testing

4.1.1 Test 1

The post engagement data onto two groups in contrast basing on the variable of memes’ existence

from the experiments of test 1 was recorded for testing. At first, the distribution of post

engagement data collected in each group was tested via Shapiro-Wilk normality testing. The p

values showed underneath were far below an alpha level of .05, which was set as a standard

possibility of type I error occurring statically. Therefore, the null hypothesis that the population

was normally distributed was rejected and there was the evidence that the data tested were not

normally distributed.

Shapiro-Wilk normality test

data: Test1$Post_engagement_A

W = 0.81533, p-value = 2.014e-06

Shapiro-Wilk normality test

data: Test1$Post_engagement_B

W = 0.85545, p-value = 2.18e-05

For the sample data with non-normal distribution of paired groups, Wilcoxon signed rank testing

fitted for testing whether their population mean ranks differed. The result of it was showed below

with p value calculated less than .05, which demonstrated that the null hypothesis (the two

16

medians of paired sample groups were the same) was rejected and it was proved that there was

significant difference between the post engagement rates of Group A and Group B.

Wilcoxon signed rank test with continuity correction

data: Test1$Post_engagement_A and Test1$Post_engagement_B

V = 659, p-value = 0.02504

alternative hypothesis: true location shift is not equal to 0

Other statistical hypothesis tests were processed as well for reference purposes.

Two Sample t-test

data: Test1$Post_engagement_A and Test1$Post_engagement_B

t = 2.3683, df = 98, p-value = 0.01983

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

0.001186713 0.013457062

sample estimates:

mean of x mean of y

0.01834215 0.01102026

F test to compare two variances

data: Test1$Post_engagement_A and Test1$Post_engagement_B

F = 2.9152, num df = 49, denom df = 49, p-value = 0.0002699

alternative hypothesis: true ratio of variances is not equal to 1

95 percent confidence interval:

1.654319 5.137171

sample estimates:

ratio of variances

2.915222

The range and variability of the post engagement rate data set gathered apart are listed in Table

2 and illustrated visually in the boxplot below (Figure 8). The mean post engagement rate of

Group A where the posts with memes were exposed to users was about 1.8342%. On the opposite,

1.1020% post engagement rate was gained in average in Group B where the posts without memes

were seen by users. There was a difference of 0.7322% existing between them. It indicated that

the use of memes as strategy in marketing communication did have an obvious impact to promote

user engagement higher.

Table 2: Post engagement rate (Test 1)

18

W = 0.74773, p-value = 0.0001583

Shapiro-Wilk normality test

data: Test 2$Post_engagement_B

W = 0.8797, p-value = 0.01748

Alike test 1, for the sample data with non-normal distribution of paired groups, Wilcoxon signed

rank testing was carried for verifying whether their population mean ranks differed. The result

was showed below with a p value less than .05, showing that the null hypothesis (the two medians

of paired sample groups were the same) was rejected and it was proved that there was significant

difference in the post engagement rates between Group A and Group B.

Wilcoxon signed rank test with continuity correction

data: Test 2$Post_engagement_A and Test 2$Post_engagement_B

V = 40.5, p-value = 0.02621

alternative hypothesis: true location shift is less than 0

Other statistical hypothesis tests were processed as well for reference purposes.

Two Sample t-test

data: Test 2$Post_engagement_A and Test 2$Post_engagement_B

t = -1.6044, df = 38, p-value = 0.05846

alternative hypothesis: true difference in means is less than 0

95 percent confidence interval:

-Inf 0.0004227538

sample estimates:

mean of x mean of y

0.01151323 0.01982804

F test to compare two variances

data: Test 2$Post_engagement_A and Test 2$Post_engagement_B

F = 0.79422, num df = 19, denom df = 19, p-value = 0.6206

alternative hypothesis: true ratio of variances is not equal to 1

95 percent confidence interval:

0.3143618 2.0065572

sample estimates:

ratio of variances

0.7942197

20

Knowing that memes works effectively in a marketing communication situation by the hypothesis

testing above, this part will go further exploring some characteristics among sample users who

consumed internet memes. The collected data is composed by the 5 more key attributes about all

400 sample users: Age, Gender, Educational Level, Region and Overseas Experience, which were

considered to be related to the action of engaging posts with internet memes. Table 4 provides

the demographic profiles of the users. Regarding the main analysis methods, ANOVA (analysis of

variance) and analysis of Generalized Linear Mixed Effects Models were conducted.

Table 4: Demographic profile of users (n = 400)

Characteristics Descriptor Distribution (percent)

Age 10s 5 20s (<25) 10 20s (≥25) 73 30s 9 40s 2 50s 1 Gender Male 65 Female 35 Education Secondary Education 6 Bachelor 40 Master & Doctor 44 Others & NA 10 Region China 64 Japan 16 U.S. A 5 Others & NA 15 Oversea Experience Yes 57 No & NA 43

As the first step, a simple analysis of variance, which was applied to test whether there were

differences among groups means of cumulated engagements (from test 1 for hypothesis 1, aiming

to test the variable that the presence of internet meme in a post) to the posts with memes in total,

was carried out. Several of these attributes seems to show the outstanding impact on consuming

internet memes. The result of ANOVA is shown below, indicating that there were significantly

distinct differences of engagement among Age groups and Education groups for the statistically

significances. The groups divided by attributes of Gender, Oversea Experience and Location did

not show the powerful impact as the age groups and education groups on the engagement of posts

with internet memes. Thus, the effect of age groups and education levels of all 5 attributes will be

discussed only.

Df Sum Sq Mean Sq F value Pr (>F)

Age 5 19.50 3.901 5.140 0.000141 ***

Gender 2 0.87 0.433 0.570 0.565864

Education 2 26.4 6.611 6.155 8.51e-05 ***

Oversea experience 2 0.0 0.019 0.017 0.98284

21

Location 37 27.8 0.751 0.699 0.90797

Residuals 384 378.1 1.074

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

For references, the engagement per user that separated by A/B groups in test 1 of different age

groups and education levels is depicted in the form of interaction plots (Figure 10 and Figure

11). Users of 10s, 50s and 60s was of small numbers and showed the same performance with

their adjacent groups, so they were merged with 20s group and 40s group into “~20s” group and

“40s~” group respectively.

There was an interaction effect that between age groups and meme effect viewed clearly in these

plots. A non-linear characteristic was shown as well that the engagement data of different age

groups and education level groups were not able to be explained and predicted with a linear

regression. It could be read that the younger generations had a higher preference to engaging the

posts with memes. Regarding the education levels, the plots indicate that every level seemed to

have preference to engaging the posts with memes.

Figure 10: Comparison between A/B groups regarding engagement per user

by age groups

22

Figure 11: Comparison between A/B groups regarding engagement per user

by education level groups

To understand one’s preference on meme consuming, the mean of each user’s Meme

Engagement Rate (defined originally in this paper as the ratio of engagement in A group’s posts

with memes to total engagement from both groups) is underlined especially here, which could

show one’s attitude on the contents with internet memes. The mean meme engagement rate of

each age groups (Table 5) and education level groups (Table 6) was calculated separately.

Primarily to say, from Table 5, a trend that younger age groups (“~20s” and “30s”) had higher

meme engagement rates at average than the “40s~” group could be observed. While the 30s

group performed better in meme engagement rate here than “~20s” group that was constituted

by the “digital native” users (a new notion defined by Prensky in 2001) who would be more likely

to use internet memes for communicating, and it was little incompatible with the common guess

though. One of the possible reasons might be that the products and services introduced in the

posts was more easily favored by the consumers of 30s group. About the attribute of education

(Table 6), a little advantage of meme engagement rate seemed to be possessed by the group of

higher education level. For that the notion of meme engagement rate is not defined with a high

explanation to the action of engaging, the results revealed here are just for references.

Table 5: Means of meme engagement rate by age groups

Age Meme Engagement Rate (Mean)

~20s 0.6553204

30s 0.8214286

40s~ 0.2761905

23

Table 6: Means of meme engagement rate by education level groups

Education Meme Engagement Rate (Mean)

Secondary Education 0.5177019

Bachelor 0.6500000

Master & Doctor 0.7000000

To treat the sample analysis more accurately, the panel data that formed from repeated 400

sample users’ action towards 50 paired posts and their basic information of age and education

level, was taken into account, and more complex models (generalized linear mixed effects

models) were taken into analysis to assess the factors that influence the engagement. Model 1 ~

3 are modeled as below. The dependent variable 𝑌𝑖𝑗 is the action of engaging a post (1: engaged,

0: unengaged). Presence of meme in a post (1: Yes, 0: No), age (~20s, 30s, 40s~) and

education (Secondary Education, Bachelor, Master & Doctor) levels of users are the main

predictors in the models. The generalized linear mixed effects models used is of the following

forms,

𝑌𝑖𝑗 ~ Bernoulli ( P𝑖𝑗), where:

𝑴𝒐𝒅𝒆𝒍 𝟏: 𝐿𝑜𝑔𝑖𝑡(P𝑖𝑗) = 𝛽0 + 𝛽1𝑀𝑒𝑚𝑒𝑖𝑗 + 𝛽2𝐴𝑔𝑒𝑖 + 𝛽3𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖 + 𝑈𝑖 + 𝑃𝑗

𝑴𝒐𝒅𝒆𝒍 𝟐: 𝐿𝑜𝑔𝑖𝑡(P𝑖𝑗) = 𝛽0 + 𝛽1𝑀𝑒𝑚𝑒𝑖𝑗 + 𝛽2𝐴𝑔𝑒𝑖 + 𝑈𝑖 + 𝑃𝑗

𝑴𝒐𝒅𝒆𝒍 𝟐′: 𝐿𝑜𝑔𝑖𝑡(P𝑖𝑗) = 𝛽0 + 𝛽1𝑀𝑒𝑚𝑒𝑖𝑗 + 𝛽2𝐴𝑔𝑒𝑖 + 𝛽3𝑀𝑒𝑚𝑒𝑖𝑗 ∗ 𝐴𝑔𝑒𝑖 + 𝑈𝑖 + 𝑃𝑗

𝑴𝒐𝒅𝒆𝒍 𝟑: 𝐿𝑜𝑔𝑖𝑡(P𝑖𝑗) = 𝛽0 + 𝛽1𝑀𝑒𝑚𝑒𝑖𝑗 + 𝛽2𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖 + 𝑈𝑖 + 𝑃𝑗

𝑴𝒐𝒅𝒆𝒍 𝟑′: 𝐿𝑜𝑔𝑖𝑡(P𝑖𝑗) = 𝛽0 + 𝛽1𝑀𝑒𝑚𝑒𝑖𝑗 + 𝛽2𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖+ 𝛽3𝑀𝑒𝑚𝑒𝑖𝑗 ∗ 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖 + 𝑈𝑖 + 𝑃𝑗

• P𝑖𝑗 = ℰ(𝑌𝑖𝑗 = 1) measures the probability of the event that dependent variable 𝑌𝑖𝑗 (the

action of engaging a post) equals 1 (which means “engaged”).

• 𝛽′s are the parameter estimates.

• Meme, Age and Education are the independent variables (dummy variables).

Age*Meme and Education*Meme are the interaction terms.

• 𝑈𝑖 and 𝑃𝑗 are the random effects:

𝑈𝑖 is the 𝑖𝑡ℎ user’s individual effect, which can account for correlations within observations

taken from the same individual.

𝑃𝑗 is the 𝑗𝑡ℎ post’s specific effect, which can account for correlations within observations

taken from the same post.

• The logit is defined as follows:

𝐿𝑜𝑔𝑖𝑡(P) = 𝑙𝑜𝑔P

1 − P

24

The logarithm of the odds is the logit of the probability.

The results of five differed models are listed in Table 7 ~ 11 below. Concerning the significance

of random effects “User” and “Post”, both were tested successfully via LRT (Likelihood Ratio Test).

The mixed models that integrated with random effects have a better performance in controlling

unobserved heterogeneity that is uncorrelated with the independent variables, and the five

models are well modeled without the unexpected influence of heterogeneous users and posts.

Model 1 is formulated for checking the performance of all three main predictors to the post

engagement. The results of model 1 shown in Table 7 indicates that the variable “effect of internet

meme” is statistically significant predictors of consumers’ engagement to posts (p value < .01).

The effect of meme has the estimate of 0.55555, which means when keeping all other variables

constant and independent variable “Meme” equals 1, it is 1.74 (exp(0.5555)≈1.74) times more

likely to impact on odds rather than the occasion that independent variable “Meme” equals 0.

Thus, the effect of internet memes in posts is proved successfully for boosting the probability of

engagement. However, “Age” and “Education” seem not to be good predictors as “Meme”, but the

education level group of secondary education that compared to the baseline group “Master &

Doctor” is tested significantly (p value < .01), meaning that it did contribute to boost the post

engagement whatever internet memes were present or not in posts.

Model 2 & 2’ are made in pair for comparing and checking the interaction of two variables: “Meme”

and “Age”. The result of model 2 displayed in Table 8 shows almost the same outcome with model

1. It could be known that the age groups “~20s” and “30s” did not engage as much as the 40s~

did, which means that older generation were engaging the posts more on the individual

engagement amount in mean. Considering the sample users of “~20s” and “30s” age groups are

further much more than “40s~” in number, it is not surprised to see this result basing on a sample

selection bias. In comparison, the result of model 2’ shown in Table 9 gives the evidence that the

interaction effect of “Meme” and “Age” exists with a high statistical significance (p values < .01).

The combination of “Meme 1” and “Age ~20s” has a significant effect to engagement, so does

“Meme 1” and “Age 30s”. It indicates that when keeping all other variables constant and

independent variable “Meme” equals 1, age groups of “~20s” and “30s” have a greater impact on

engagement than other occasions that “Meme” combines with other age group. This result is

corresponding with the analysis above, demonstrating that younger users have a strong

preference to engage the posts with memes.

Model 3 & 3’ are made in pair for comparing and checking the interaction of the variables: “Meme”

and “Education”. The result of model 3 displayed in Table 10 shows almost the same outcome

with model 1, showing the effect of the meme and the contribution of secondary education group

to post engagement. It could be thought that the people with lower education level might show a

higher probability to give engaging reactions. At the same time, considering the selection bias that

25

sample users of “Secondary Education” group are further less in number than “Bachelor” group

and “Master & Doctor” group, it is acceptable to get the result like this. To compare, the result of

model 3’ shown in Table 11 demonstrates that the interaction effect of “Meme” and “Education”

does not perform well as the “Meme” and “Age” do. However, “Meme1” and “Secondary Education”

has a significantly negative effect to engagement (p values < .01), which indicates that when

compared with education level of “Master & Doctor”, ones with “Secondary Education”

background have a higher possibility to engage the posts without memes. It is corresponding with

the analysis above, knowing that people with higher education background have a strong

preference to engage the posts with memes. This result does not conform with our common

knowledge at some certain, and for the lack of study on relation between the attitude on memes

and individuals’ education backgrounds, and the considered selection bias as well, it could not

confirm the finding here though, wishing further researches to be hold on figuring out this

question in the future works.

Table 7: Model 1

Independent Variables 𝛽 SE(𝛽) z value Pr(>|z|) (Intercept) -9.29867 2.35704 -3.945 7.98e-05 *** Meme 1 0.55555 0.31462 1.766 0.0774 . Age 20s 0.03348 2.25102 0.015 0.9881 Age 30s 0.65872 2.34831 0.281 0.7791 Education Bachelor -0.84172 0.66523 -1.265 0.2058 Education Secondary Education 2.45808 1.36634 1.799 0.0720 . Signif. codes : 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 8: Model 2

Independent Variables 𝛽 SE(𝛽) z value Pr(>|z|) (Intercept) -8.3090 1.8880 -4.401 1.08e-05 *** Meme 1 0.5938 0.3278 1.811 0.0701 . Age 20s -1.7483 1.7699 -0.988 0.3233 Age 30s -0.8490 1.9755 -0.430 0.6674 Signif. codes : 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 9: Model 2’

Independent Variables 𝛽 SE(𝛽) z value Pr(>|z|) (Intercept) -7.5866 1.9000 -3.993 6.53e-05 *** Meme 1 -0.7037 0.7668 -0.918 0.3588 Age 20s -2.5520 1.8068 -1.412 0.1578

Age 30s -1.8346 2.0439 -0.898 0.3694 Meme1: Age 20s 1.3921 0.7694 1.809 0.0704 . Meme1: Age 30s 1.6581 0.9400 1.764 0.0777 . Signif. codes : 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 10: Model 3

Independent Variables 𝛽 SE(𝛽) z value Pr(>|z|) (Intercept) -9.3062 0.7806 -11.923 <2e-16 ***

26

Meme 1 0.5915 0.3248 1.821 0.0685 . Education Bachelor -0.8040 0.6623 -1.214 0.2248 Education Secondary Education 2.5564 1.1703 2.184 0.0289 * Signif. codes : 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 11: Model 3’

Independent Variables 𝛽 SE(𝛽) z value Pr(>|z|) (Intercept) -9.5428 0.8152 -11.706 <2e-16 *** Meme 1 0.8370 0.4169 2.008 0.0447 * Education Bachelor -0.9469 0.7711 -1.228 0.2194 Education Secondary Education 3.1123 1.2226 2.546 0.0109 * Meme1: Bachelor 0.1869 0.5280 0.354 0.7234 Meme1: Secondary Education -0.9896 0.5466 -1.810 0.0702 . Signif. codes : 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

For contrasting and selecting the better models, AIC (Akaike’s Information Criteria) and BIC

(Bayesian Information Criteria) are calculated and listed below. As a whole, model 3 could be

considered to be the best one to predict the post engagement in this study.

* AIC & BIC to compare models and measure goodness of fit:

Model Selection Criteria Model 1 Model 2 Model 2’ Model 3 Model 3’ AIC (Akaike’s Information Criteria) 1032.3 1034.9 1035.2 1028.7 1028.2 BIC (Bayesian Information Criteria) 1089 1077.4 1091.8 1071.2 1084.8

As a consequence, after analyzing the characteristics of sample users that related to engagement

of posts with internet memes, the finding that younger users are more likely to engage the posts

with memes is obvious under the above primary exploration. Regarding the effect of education

levels, there is a weak finding that high education level groups prefer the posts with memes. This

outcome may give out an idea and direct a new way for further researches.

5 Conclusion

5.1 Insights on Meme Marketing

In this paper, two tests designed for testing two different hypotheses on the effect of memes used

in SNS marketing communications were run mainly and significant results were obtained. It is

verified that both Hypothesis 1 (Posts utilizing internet memes have higher Post Engagement Rates

than posts without internet memes utilization) and Hypothesis 2 (Posts with memes that created

with original images have higher Post Engagement Rates than posts with memes derived upon other

well-known template images) are supported. Besides, this study succeeds in proving that younger

generations and users with higher education levels have higher preference to consume internet

27

memes. Consulting form the tested outcome, some meaningful insights and implications on SNS

marketing with memes can be come up with.

First of all, using memes definitely gives a positive impact on post engagement directly and bring

brand exposure and corporate awareness up rapidly, especially when brands produce smart and

real contents and have clear calls to action positively. In this way, more brand fans and the raw

voices of users are gathered readily. For that the engagement from users on SNS is becoming an

important equity, the better performance on marketing communication with internet memes is

definitely able to improve brands equity in a more beneficial way with less costs.

Secondly, the idea of originality and creativity is considered the most essential in marketing, and

the unique and valuable creation of internet memes is also acquired as the same in SNS marketing

communication. Rather than the meme templates that are often seen, originally new memes

created with novelty and imagination are more likely to cause favorable user engagement. It's a

good signal for marketers that using own memes could prevent authorized issues and other

legitimate problems. In this view, brands have no necessary to stick to prevalent templates for

gaining attention from users. The effect of original and highly brand-related memes does perform

better. In addition, the wide transmission of compound networks and fast propagation as the

most symbolic characteristics of memes should be stressed. To maximum this characteristic, for

brands, being a fashion creator is the central point.

Thirdly, it is vital to target the right consumers with internet memes. Using internet memes not

only helps one brand contact more smoothly and closely with consumers of young generations,

but also appeal to targeted consumers that are interested in specific topics or issues if the posted

memes are created basing on them. At the same time, it should not be ignored that there are

growing number of aging users consuming the internet memes with the prevalent of SNS among

older generations. Especially in China, one well-known type of internet memes called “middle-

aged memes”, which is created basing on the aesthetic of old school style, is becoming fashionable

among the increasing middle-aged internet users. It’s important to treat internet memes in more

creative ways for brands when build connection with consumers of diverse characteristics.

At last, there are several more points that marketers should be awareness of, or else meme

marketing could be risky and insecure as well:

• Inappropriate and unacceptable expressions of memes are harmful. Terrible memes, which

may be insulting and vulgar are hurtful to consumers. Try best to avoid the unsuitable

expressions after thoughtful consideration.

• The fitness of meme style to one’s brand should be thought over to attain a good brand

integration smartly without harming the brand image.

28

• The copyright issues and other legal issues should be considered particularly if you use

some materials that possessed by others in law to make an internet meme.

In spite of these sensitive terms, there is no denial that meme marketing is a revolutionary novel

way to help online promotion work even more creatively and effectively in this digital social

media era.

5.2 Discussion

With the originality of the experiment design, the effect of internet memes in SNS marketing

communication is tested significantly. Yet there are a few limitations to discuss:

Primarily, this testing is aiming to test the effect of memes limitedly in the specific situation of

direct communication between brands and consumers. The posted contents were created by the

best effort to imitate the scene that brands post contents to get contact with their consumers,

which couldn’t be treated completely the same as the real marketing communication cases. To

avoid the bias from the content affair, the post experiments were hold as more as possible.

Next, there is no manipulation check to be done through the experiments, which means the lack

of a process for checking if the measured variables or other possible affect exist to influence on

the dependent variable. With this secondary evaluation in addition, the result of experiments and

testing can obtain higher reliability to explain the effect of memes.

Then, only the post engagement rate is taken into account as the scale to measure the effect of

meme marketing in this study, lacking the diversified measurement to estimate the effect of

meme marketing. There are multiple metrics and analytics by brands and businesses to measure

how their online business is performing, including measurement of actions such as reacting to,

commenting on, the number of viewing posts, etc. For different cases of posts on various SNS

platforms, assessing post engagement could differ from each other. It can get more

comprehensive understanding on the effect of internet memes for measuring users’ behavior

diversely basing on the digital environment and conducting the same experiments on other

platforms to compare and supplement.

Furthermore, the selection of sample users may be kind of arbitrary. Considering the main

customers of internet memes consumption are the ‘digital natives’ tending to age around or

underneath early adulthood, the majority of sample users taken into tests are in their 20s and 30s,

contributing the ability to explain the testing result. If sample users had owned a better-balanced

distribution of diverse characteristics and backgrounds, it could have yielded a more reliable and

interpretable result regarding the effect of meme marketing and its targeted users. Besides, the

29

process of grouping sample users randomly into A/B groups is completely manual works without

automatic tools, so the result may show more significance in case that preventing this error.

5.3 Future Work

This paper reaches an initial success to provide a tested evidence that the memes work in a

practical and functional way in SNS marketing communication. In the time of SNS, internet memes

can be thought as an active “language” for effective communication to closer the distance and

strengthen the connection between brands and consumers. “Catch Phrases + Images”, the basic

structure of an internet meme actually could bring out infinity creativity and it does assist

marketing communication further energetic. Taking advantage of internet memes worthy further

awareness and investigation. In the future, new methods of dealing with “big data” of consumers

and new visions from authorized theories in diverse fields are expected to perform in studies on

internet memes. There will be more fascinating matters for us to study, like: How the actual

actions of consumers towards memes except measuring the numeric data collected by SNS

platforms, and how the memes effect consumers visually and literally in a marketing

communication situation. It would be inspired that these concerning questions are investigated

further, and more attention would be paid on this new contact style in constantly changing digital

society.

Acknowledgments

Foremostly, I would like to thank sincerely to the professors who have given valuable advice

throughout my researching and writing works at Keio Business School, Keio University. Prof.

Takaki Hayashi, as my supervisor, offered great support and inspired me to pursue further

learning about statistics and big data analysis. Prof. Atsuomi Obayashi and Prof. Akihiro Inoue

supported precious comments and suggestions to improve the quality of this paper. I also wish to

thank all the participants for the active and kind reaction in the experiments. They did great

contributions to the birth of this paper.

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