Research based on SPSS
- Background
The marketing theory that I am using for this research is revenue-driven and seeing how it compares to how popular a channel is; which is marketing because we are trying to find what kind of factors influence revenue the most. I also talk a bit about promotion because YouTube Influencers promote the site when using it and vice versa. As well as product because I discuss YouTube and the platform as a whole, how they make profit and give people jobs. Since its launch in 2005, YouTube has become one of the most viewed sites on the internet where people can watch a wide variety of video content that can be viewed using a smartphone, laptop or tablet (Dickey 2013). YouTube has become a video platform for the people by the people, which makes it easier for creators to share their distinctive content with a large viewing audience. Users prefer YouTube because this social media provides them with visual messages which are more modern for today’s people than print (Warr 290). As people perceive the information about the surrounding world, they choose YouTube when they want to receive visual information that facilitates their perception of messages conveyed via YouTube.
In 2006, Google saw the rising growth that came with YouTube and acquired it for 1.65 billion dollars (Dickey 2013). Since then, its growth has been supported by the decline of traditional TV while 96% of 18 to 24-year-old American internet users use YouTube. This means that it has become a high growing network for that age group (Smith 2019). On an average month, 8 out of 10 18- to 49-year-olds watch YouTube (O’Neil-Hart 2016). The engagement between subscribers and the YouTube content creators is one reason that people have been switching to YouTube instead of traditional television.
The YouTube world has become a major platform for rapid growing multimedia information. YouTube has reached more people from the United States who are between the ages of 18-34 than any TV network (O’Neil-Hart 2016). There are billions of channels on YouTube and many more that are developing and being added, there is a lot of content that people can discover through many genres. It has also become more mainstream in recent years with creating congressional YouTube channels, even the Vatican has launched a YouTube channel. Actors and Actresses, such as Shay Mitchell from Pretty Little Liars, have started to make their own channels. This gives celebrities a way to promote their content and in return more users go on YouTube to watch their favorite people make videos. Likewise, singers have their own channels for music videos, behind the scenes or other content they deem fit (YouTube.com). Brands have YouTube channels as a marketing tool for them to easily upload videos and the videos can easily be shared from there.
From where it originated to now, it has transformed from a video-sharing site into a job opportunity for content creators in both new and mainstream media. In 2007, YouTube made it possible for everyday people to be able to make money (Dickey 2013). Content creators get paid by either in-video advertisements or from sponsored content and product placements. Sponsored content and product placements are when YouTubers use the product in their videos, but it has to be stated that the products are sponsored. There are various implications when it comes to what videos can become monetized and who is qualified to have a sponsorship. A sponsorship is when a brand and a YouTuber collaborate in order for the brand to get exposure and the person advertising it to get compensated. The requirements for a sponsorship are very broad but the reason it relates is because brands usually look for YouTubers with higher user engagement. User engagement refers to viewer interaction such as views a video has, comments, and subscribers gained. Some of the most successful YouTube creators have earned six-figure incomes by making content.
According to Business Insider, YouTubers can earn a profit through advertisements by monetizing a high volume of viewer traffic that their videos receive from their subscriber count and engagement level. Users watching videos on YouTube will see some type of advertisement. Advertisers can choose from six different advertisement forms to play either during the video or have one on the screen (such as a pop up). According to Forbes, YouTuber PewDiePie could generate $12 million, while Smosh and Fine Brothers could generate $8.5 million, and several other people managed to pass $1 million annual advertising revenue mark in 2015 (Dogteiv 2019). All of these YouTube celebrities usually had a high subscriber rate and would frequently post videos along with being active on other social media platforms. While YouTube grows its own species of celebrity, many other types of influencers start to grow by looking at their suggestions generated by an algorithm.
YouTube’s algorithm sometimes plays a huge part in the whole scheme of how many views a video can get. Sometimes, the popularity of videos determines how frequently it is seen by users and the perception of information by the audience is influenced. For instance, the video that has the highest number of views becomes more and more popular and becomes mainstream, even though in its essence the video may have little cultural value or poor messages (Warr 291). In fact, the point is to make videos entertaining. A large majority of producers who publish their videos on YouTube are concerned with the popularity of their videos than their content. Such a competition encourages users to focus on performativity of their videos above all.
YouTube is one of the top internet sites with 1.9 billion users that visit YouTube every month and the billions of hours spent on videos. The Social Blade is a website that is one of the foundations for my research. “Social Blade tracks user statistics for YouTube… Get a deeper understanding of user growth and trends” (SocialBlade.com). Many people use the website to check their live subscriber count or view engagement within the recent weeks and it is verified by YouTube itself. It can also be used to check the estimated revenue that is made from a video. The site provides statistics for each youtuber including total views, total videos, total subscribers, and comments. Along with Tube Buddy, which is a plug-in that also provides user analytics and channel tags, Social Blade will provide valuable insight of my variables for the research. I am using this research to find more information relating to what factors play into making a YouTube channel more successful, financially, in order to better my knowledge on how I can make more money as a producer.
2.0 Research Question and Justification
The question of this research project is to determine whether there is correlation between revenue and the factors that make-up a YouTube channel. For my research, I want to see the correlation between revenue and the factors that identify a YouTube channel as popular. YouTube channels that are popular usually consist of a high subscriber count, high total view count, and typically a good amount of total video count. I have decided to include tags and genre as other variables to see the influence of those. My goal is to demonstrate the importance of these factors on revenue because this is a topic that doesn’t have many resources available to find out. These factors also play a part in who gets sponsored. Those with more subscribers and views typically receive more sponsorship opportunities (McAlone 2016). My fascination with YouTube came about because I wanted to make a channel for my passion of editing and taking videos of my everyday life. For this reason, I want to look at the topic of YouTube and its different attributions. I want to further my YouTube channel to determine what factors will make my channel most successful and turn it into a career.
I saw the most important features of defining the popularity of a channel include subscribers, views, videos, and the type of video produced. In addition, I saw that the more videos and views a channel has, the more revenue they earn. Through these observations, I have decided to research the relationship between the make-up of popular YouTubers and their estimated earnings. This research helps to determine what type of factors correlate with estimated revenue earned by a YouTubers. This information is important to me as a youtuber because it helps me understand the relationship between genre, videos, views, tags and their connection to revenue if I want to see it as a possible career choice.
This data is significant to youtubers worldwide because there aren’t many studies on this since YouTube is a fairly new industry and career option. This also helps people identify why some channels make more money even if they might not have as many subscribers or views. It would also show if different channel tags or genres give different revenue results. Overall, the information is useful for people who make channels in pursuit of making it into a career and want to know the factors that determine a higher revenue.
3.0 Hypotheses
H1: On average, the entertainment genre will generate more revenue than the people genre
There are different categories that YouTube videos can fit into. Content is a key concept of becoming successful on the platform. According to YouTube, the different categories are just contrasting demographics that are each looking for videos that are similar. Media Kix has identified the top 20 platforms and why each one stands out compared to the others. They also provided some popular creators to look at for each category. Some videos get more views because they are trending at the moment. Taking a look at ASMR trigger videos, those are taking up more views than others because ASMR is trending at the moment (Lopez 2018).
Generally speaking, the entertainment genre is broader and many shows on TV have their own channels on YouTube to make it easier to share their content with the world. Shows, like The Ellen Show and The Late-Night Show starring Jimmy Kimmel, already have a wide audience and user engagement on other social media platforms therefore it would make sense for them to have a higher revenue than gaming channels. People channels, on the other hand, provide a smaller audience than entertainment and generally have less of a viewership.
H2: Music and Entertainment genre would influence revenue more than the other genres.
Some Music and Entertainment channels come from other platforms onto YouTube. Meaning that usually regarding music, people watch their favorite artist’s YouTube channel after they listen to their song on a music streaming service. Likewise, some entertainment channels have started off on tv and have made channels to encourage their viewers to watch anytime. Both would have a higher revenue income since they have higher following on social media then in other genres (Instagram 2019). Some music channels make entertainment videos and some entertainment channels make music videos that is where the boundaries cross. According to some of the videos found on YouTube, some entertainment channels like to include singers and have them sing on their show which causes an overlap since the singer is singing in the other person’s video.
H3: The number of videos a channel has a positive impact on the amount of revenue it produces.
There are many YouTube videos that are uploaded every day. YouTube suggests in their Creator Academy program that in order to keep or develop viewer engagement, continuous video uploading is helpful. It helps subscribers to stay engaged. In fact, some of the best-known YouTube channels have schedules of when they upload so their fans know when to view them. YouTuber Jake Paul is known for uploading every day and uses the catchphrase “It’s everyday bro” Referring to the fact that he produces a new video each day. Other creators have a schedule of uploading 1-3 times a week and usually on the same days of the week. Consistency is key when it comes to user engagement. Looking at other channels, “Despacito” by Luis Fonsi ft. Daddy Yankee has received over 6 billion views and has become the highest viewed video on YouTube (YouTube.com). When looking at the amount of videos Luis Fonsi has, he has significantly a smaller number of videos than Jake Paul. Comparing the two, each both make a lot of money but because of Despacito, Luis Fonsi makes more on YouTube advertisement on that one specific video that is replayed over and over again.
H4: Youtubers with more than 10 million subscribers will generate a greater revenue than those with a lower subscriber rate.
According to YouTube guidelines, a person must have 4,000 watch hours in the past 12 months, as well as, at least 1,000 subscribers total to start making revenue. The number of videos does not affect where one person makes revenue because some YouTubers don’t have many videos, instead they have a few videos and more viewer counts. As a YouTube channel starts to receive recognition and income traffic to their site, their subscriber, and view count are likely to rise. That means that at least 10% of a channel’s subscribers will want to view any new videos it uploads. With that analysis, that means that the more subscribers there are, the more of a chance that a video will be viewed. According to Bärtl’s research (2016), the top 1% of creators earn views of 2.2-42.1 million per month, in comparison to other YouTubers who don’t have as many subscribers. Again, this is relaying the fact that other research has been founded that supports how views and subscriber count are influential factors in creating revenue.
YouTubers with more than 10 million subscribers will generate a greater revenue than those with a lower subscriber rate. This is because the more followers or fan base the youtuber gains the more money they get. Their revenue doesn’t exclusively come from companies that are willing to sponsor the videos it also comes from fan donations. Someone with 10 million subscribers is more likely to have a higher number of views for the videos that get posted versus someone who has less subscribers. However, as mentioned before, companies are less likely to give out sponsorships to someone with less than 10,000 subscribers. Also, the more active users/fans are the more they will watch of the YouTube Channel. All in All, more subscribers mean more views which means more people are watching advertisements from the videos.
4.0 Description of Dataset
Throughout YouTube, there are many categories that videos fit into. I have picked four categories that seemed to provide general famous creators that viewers of my age range enjoyed in 2018. This variable is nominal with the categories being: How To (1), Games (2), Music (3), Entertainment (4), Comedy (5) and People (6). The genre of video measures the type of video and video attraction of a particular genre. It is important to look at genre because it shows a general idea of the type of videos that people are more inclined to see.
The second variable that I included is the total subscriber count. This information shows viewers who want to interact with the content creator more and want to see more that there is to come. It is continuous because the number of subscribers for a YouTube channel is always changing. Total subscriber count measures the engagement that a YouTuber has. People subscribe to channels because they like the content that is provided from the channel.
Total number of videos measures how frequent the content is produced. This variable is continuous because content creators are always making new videos to engage more viewers. For the purpose of this project, I will not be including exclusive videos. These videos include membership only or YouTube Red since they are not available to all users and they earn different revenues then other types of videos.
Total views are the number of videos combined with the views from each video. It does not show long a viewer watched the video for but just if they clicked onto the video. It is continuous and measures the popularity of the combined videos of an account. Likewise, Total estimated revenue and number of channel tags are both also continuous. Total estimated revenue tracks the earnings that might be generated as a whole while considering viewership and engagement. It measures the demand for a channel because of the engagement needed to get paid. Meanwhile, number of channel tags are used to identify a YouTube channel using keywords. Last but not least, my last variable is nominal and split into two categories. 0 was used for YouTube channels with under 10 million subscribers and 1 was used for channels with over million subscribers. The reason for this being that channels with higher than 10 million subscribers are considered more popular than those with under 10 million.
5.0 Tests and Results
H1: On average, the entertainment genre will generate more revenue than the people genre
| Group Statistics | |||||
| Genre | N | Mean | Std. Deviation | Std. Error Mean | |
| Total Estimated Revenue | Entertainment | 29 | 443241.38 | 559462.909 | 103889.654 |
| People | 18 | 127011.11 | 126875.121 | 29904.753 | |
| Independent Samples Test | ||||
| Levene’s Test for Equality of Variances | t-test for Equality of Means | |||
| F | Sig. | t | ||
| Total Estimated Revenue | Equal variances assumed | 10.563 | .002 | 2.352 |
| Equal variances not assumed | 2.925 | |||
| Independent Samples Test | ||||
| t-test for Equality of Means | ||||
| df | Sig. (2-tailed) | Mean Difference | ||
| Total Estimated Revenue | Equal variances assumed | 45 | .023 | 316230.268 |
| Equal variances not assumed | 32.465 | .006 | 316230.268 | |
| Independent Samples Test | ||||
| t-test for Equality of Means | ||||
| Std. Error Difference | 95% Confidence Interval of the Difference | |||
| Lower | Upper | |||
| Total Estimated Revenue | Equal variances assumed | 134472.796 | 45388.155 | 587072.382 |
| Equal variances not assumed | 108108.069 | 96145.020 | 536315.516 | |
For the first hypothesis I have decided to use an independent samples t-test. The reason for this is because I am trying compare these two separate genres to determine whether there is statistical evidence that the two populations are significantly different. The assumptions that we make are that they are independent (which they are since they are completely different groups), each population is normally distributed and there is a homogeneity of variance. Revenue is the continuous dependent variable and Genre is the categorical independent variable.
Taking a look at the Levene’s Test for Equality of Variances, the significance is less than 0.002 which means that there is a significant different and we have to reject the null hypothesis that both genres generate the same revenue and assume that the variances are not equal. Then moving on to the Significance (2-tailed), with a 0.05 sig. level, I accept my alternative hypothesis because the sig. level is less than 0.05. By taking a look at my mean and standard deviation, it can be determined that the entertainment genre produces a higher revenue than the people genre does.
H2: Music and Entertainment genre would influence revenue more than the other genres.
| Descriptives | ||||||
| Total Estimated Revenue | ||||||
| N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | ||
| Lower Bound | Upper Bound | |||||
| HowTo | 15 | 222713.33 | 357852.789 | 92397.193 | 24541.06 | 420885.60 |
| Games | 8 | 273200.00 | 142834.240 | 50499.530 | 153787.59 | 392612.41 |
| Music | 20 | 228086.65 | 344480.948 | 77028.282 | 66864.60 | 389308.70 |
| Entertainment | 29 | 443241.38 | 559462.909 | 103889.654 | 230433.07 | 656049.69 |
| Comedy | 10 | 145400.00 | 120503.269 | 38106.479 | 59197.15 | 231602.85 |
| People | 18 | 127011.11 | 126875.121 | 29904.753 | 63917.60 | 190104.62 |
| Total | 100 | 266822.33 | 386906.977 | 38690.698 | 190051.59 | 343593.07 |
| Descriptives | ||
| Total Estimated Revenue | ||
| Minimum | Maximum | |
| HowTo | 1800 | 1400000 |
| Games | 115600 | 522900 |
| Music | 535 | 1100000 |
| Entertainment | 14800 | 2300000 |
| Comedy | 3800 | 363800 |
| People | 7300 | 460500 |
| Total | 535 | 2300000 |
| ANOVA | |||||
| Total Estimated Revenue | |||||
| Sum of Squares | df | Mean Square | F | Sig. | |
| Between Groups | 1461388264045.621 | 5 | 292277652809.124 | 2.057 | .078 |
| Within Groups | 13358615629520.488 | 94 | 142112932228.941 | ||
| Total | 14820003893566.110 | 99 | |||
Post Hoc Tests
| Multiple Comparisons | ||||||
| Dependent Variable: Total Estimated Revenue | ||||||
| Scheffe | ||||||
| (I) Genre | (J) Genre | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
| Lower Bound | Upper Bound | |||||
| HowTo | Games | -50486.667 | 165040.334 | 1.000 | -611535.23 | 510561.90 |
| Music | -5373.317 | 128762.736 | 1.000 | -443097.52 | 432350.88 | |
| Entertainment | -220528.046 | 119894.293 | .642 | -628104.30 | 187048.21 | |
| Comedy | 77313.333 | 153900.905 | .998 | -445867.15 | 600493.82 | |
| People | 95702.222 | 131792.862 | .991 | -352322.78 | 543727.22 | |
| Games | HowTo | 50486.667 | 165040.334 | 1.000 | -510561.90 | 611535.23 |
| Music | 45113.350 | 157701.500 | 1.000 | -490987.12 | 581213.82 | |
| Entertainment | -170041.379 | 150547.542 | .936 | -681822.23 | 341739.47 | |
| Comedy | 127800.000 | 178816.693 | .991 | -480080.79 | 735680.79 | |
| People | 146188.889 | 160185.141 | .974 | -398354.62 | 690732.40 | |
| Music | HowTo | 5373.317 | 128762.736 | 1.000 | -432350.88 | 443097.52 |
| Games | -45113.350 | 157701.500 | 1.000 | -581213.82 | 490987.12 | |
| Entertainment | -215154.729 | 109572.317 | .573 | -587641.80 | 157332.34 | |
| Comedy | 82686.650 | 146003.219 | .997 | -413645.94 | 579019.24 | |
| People | 101075.539 | 122477.792 | .984 | -315283.22 | 517434.30 | |
| Entertainment | HowTo | 220528.046 | 119894.293 | .642 | -187048.21 | 628104.30 |
| Games | 170041.379 | 150547.542 | .936 | -341739.47 | 681822.23 | |
| Music | 215154.729 | 109572.317 | .573 | -157332.34 | 587641.80 | |
| Comedy | 297841.379 | 138245.214 | .466 | -172118.16 | 767800.91 | |
| People | 316230.268 | 113117.677 | .178 | -68309.13 | 700769.66 | |
| Comedy | HowTo | -77313.333 | 153900.905 | .998 | -600493.82 | 445867.15 |
| Games | -127800.000 | 178816.693 | .991 | -735680.79 | 480080.79 | |
| Music | -82686.650 | 146003.219 | .997 | -579019.24 | 413645.94 | |
| Entertainment | -297841.379 | 138245.214 | .466 | -767800.91 | 172118.16 | |
| People | 18388.889 | 148682.400 | 1.000 | -487051.48 | 523829.26 | |
| People | HowTo | -95702.222 | 131792.862 | .991 | -543727.22 | 352322.78 |
| Games | -146188.889 | 160185.141 | .974 | -690732.40 | 398354.62 | |
| Music | -101075.539 | 122477.792 | .984 | -517434.30 | 315283.22 | |
| Entertainment | -316230.268 | 113117.677 | .178 | -700769.66 | 68309.13 | |
| Comedy | -18388.889 | 148682.400 | 1.000 | -523829.26 | 487051.48 | |
Homogeneous Subsets
| Total Estimated Revenue | ||
| Scheffea,b | ||
| Genre | N | Subset for alpha = 0.05 |
| 1 | ||
| People | 18 | 127011.11 |
| Comedy | 10 | 145400.00 |
| HowTo | 15 | 222713.33 |
| Music | 20 | 228086.65 |
| Games | 8 | 273200.00 |
| Entertainment | 29 | 443241.38 |
| Sig. | .435 | |
For my second hypothesis, I did an ANOVA-Scheffe. The reason for this being that I want to compare all possible simple and complex pairs of means. My null hypothesis is that all variables would influence revenue the same. Music and Entertainment are not matched pairs, but they are also not independent of each other because they both have outer influences such as TV and streaming services that are unmatched to popularity of any other genre. Because of their outer influences they are not independent, there can be a cross-over. Taking a look at all the significance levels, none of the values are significant therefore we will reject the alternative and say that all genres have the same influence over revenue.
H3: The number of videos a channel has a positive correlation to the amount of revenue it produces.
| Model Summary | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .200a | .040 | .030 | 18881.303 |
| a. Predictors: (Constant), Total Estimated Revenue |
| ANOVAa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 1458594577.292 | 1 | 1458594577.292 | 4.091 | .046b |
| Residual | 34937353729.708 | 98 | 356503609.487 | |||
| Total | 36395948307.000 | 99 | ||||
| a. Dependent Variable: Number of Videos |
| b. Predictors: (Constant), Total Estimated Revenue |
| Coefficientsa | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 2690.431 | 2297.314 | 1.171 | .244 | |
| Total Estimated Revenue | .010 | .005 | .200 | 2.023 | .046 | |
For this hypothesis, I used a regression because I wanted to predict the value of my dependent variable (revenue) using an independent variable (number of videos). In this case, I wanted to see if the number of videos correlates to the amount of revenue a channel produces. First, I take a look at my R2 value to determine the amount of variance in my dependent variable based on my independent variable or regression line.
The R2 value is 0.04 or 4% in this case. The ANOVA test has a significance level that is lower than 0.05 at 0.046 which means that there is a very small significant difference so therefore there is only a slight linear relationship. I then take a look at my constant and total estimated revenue to check the significance level so I can make my equation. My constant has a significance level of 0.244 which means we cannot add it to the equation, but total estimated revenue has a sig. level of 0.046 which means we can add it to the equation. The VIF is not greater than 10 which means that there is no multicollinearity issue.
Yi=b0+b1(x1)+ei TotalRev= 0.01(NoV1)
H4: YouTubers with more than 10 million subscribers will generate a greater revenue than those with a lower subscriber rate.
| Descriptives | ||||||
| Total Estimated Revenue | ||||||
| N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | ||
| Lower Bound | Upper Bound | |||||
| 0 | 36 | 119519.44 | 144278.807 | 24046.468 | 70702.52 | 168336.37 |
| 1 | 64 | 349680.20 | 451995.758 | 56499.470 | 236775.02 | 462585.38 |
| Total | 100 | 266822.33 | 386906.977 | 38690.698 | 190051.59 | 343593.07 |
| Descriptives | ||
| Total Estimated Revenue | ||
| Minimum | Maximum | |
| 0 | 3800 | 691200 |
| 1 | 535 | 2300000 |
| Total | 535 | 2300000 |
| ANOVA | |||||
| Total Estimated Revenue | |||||
| Sum of Squares | df | Mean Square | F | Sig. | |
| Between Groups | 1220520380230.862 | 1 | 1220520380230.862 | 8.795 | .004 |
| Within Groups | 13599483513335.250 | 98 | 138770239931.992 | ||
| Total | 14820003893566.111 | 99 | |||
I am using a one-way ANOVA for this hypothesis because I want to compare YouTubers with more than 10 million subscribers to those with less than that. These are two unique groups that I want to compare based on variance around their means. My dependent variable is continuous and my independent consists of 2 categories. The null hypothesis is that there is no relationship between revenue and how many subscribers a channel has.
The significance value is 0.004 which is way lower than 0.05, therefore, we can establish that there is statistically significant difference. Looking at the mean and standard deviation in the descriptives, I can determine that I can accept my alternative hypothesis that YouTubers with more than 10 million subscribers will generate a greater revenue than those with a lower subscriber rate.
6.0 Managerial Implications
As I have mentioned, there has been minimal research done on this subject. It’s hard to find other research on the subject because there are many articles that make assumptions that if a channel engages their audience and post videos that relate to the audience, they will get that viewership and make money. My research project breaks it down even more that that. My project shows the breakdown of how different factors have influenced revenue when it comes to U.S. YouTube channels for my age-group. It is in-depth about what variables actually effect revenue and what doesn’t.
This is useful information for a firm who wants to know about how user engagement effects revenue or user engagement on YouTube as a whole. My paper is insightful to a firm that wants to see who they should sponsor, if they should sponsor a smaller channel that has an up and coming user engagement rate or a bigger channel. For some YouTubers, once they choose that certain genre that they want to do, they stick to it. The more they get the hang of the genre, the better their videos are going to be. The better the videos, the more revenue they can generate because more people are going to view the video if it is a good video.
It’s a reason why when people start off their YouTube video, they start it off with saying that a certain company is sponsoring the video and they shout out the company because the company is paying the YouTuber for shouting them out. Some companies do this because they know that the YouTuber’s channel is very big, has many subscribers, and they gain many views on their videos. Another thing, many YouTubers put ads throughout their videos. When people view these ads throughout the video, the company that the ads are on are giving a percentage of the money that they make from people who viewing these ads to the YouTuber who made the video. My research helps find what YouTube personnel are more of a good fit for companies depending on the size of the subscribers, the genre, and it is especially helpful if the company is U.S. based since all the YouTubers included live in America. They would pay to see my data if they wanted to see what YouTube Channels are big and at the same time need to make more money. If they have a high subscriber base and low revenue, then those influencers would be more available to appeal to smaller companies.
It is also a very useful tool for people who run their own YouTube channels, such as me. It helps to determine what genres are better for making revenue and is a useful tool in general considering my excel shows comparisons about top YouTube channel’s user engagement. People can get ideas on how to improve on their channels using that sheet. Therefore, other users would want to use this in order to learn how to grow their channel and use it as a tool to improve their user engagement.
7.0 Limitations and Future Research
First of all, there has not been much research this particular topic. If I were to do it again, I would try to find something that had more research to back up my hypotheses. It was hard to find anything that was really close to what my project was focused on. The other research I have found are breakdowns of how YouTubers receive money, not how interaction with viewers causes a change. There were also limitations regarding where to receive some of my data. I wish that TheSocialBlade.com, where I got my information for revenue from, had a breakdown of where the revenue exactly was coming from.
The data collected regarding money that they earned was only found on one site and it has been verified by YouTube but how they got their information is vague. It would be more helpful to be able to compare the information from that site to that of another site in order to make sure that it is the exact information that I am looking for, even though the one website I did use was verified. Point being, if there were more information available to use and more than one source it would have been less limiting, and I would’ve been able to entertain more variables to compare.
Regarding further research, I think it would’ve been interesting to see what advertisement was on different channels. I know that YouTube has a few different ways of advertising a video in order for a channel to make money off of it, I would like to see what factors play into that. Meaning that I would focus on advertisement. Along with that, it would have been interesting to find out if more popular videos had different advertisement than less popular videos. Along with this, I think it would have been interesting to see where other sources of revenue YouTubers receive and how that relates to the rest of my research. Lastly, I decided to take a look at only YouTubers from the U.S. who are popular to my age-group which was difficult but after I ran my tests, I would’ve like to see the stats compared to that of a U.S. YouTuber. For example, does the number of videos a channel have, have a positive impact on the amount of revenue it produces for that of a Bulgarian YouTuber? In conclusion, I would love to one day expand my research to be able to include more information about other components of YouTube.
8.0 Conclusion
The world of YouTube has always fascinated me because YouTube channels that feature regular people go on to be able to become celebrities by making videos in their room. It is crazy to realize that mediocre filming, editing, and constantly uploading can equivalate to them making over millions of dollars a year like YouTube personnel PewDiePie, who currently has the most subscribers on YouTube at the moment. Through this project, I wanted to research what variables where influential to the profits that YouTube channels made. The reason is that I have always seen articles on how to boost user engagement/become more popular or how YouTubers make money off of YouTube, but I have rarely found research on how the two relate.
In my research I have found that genre effects revenue because the entertainment genre earns a higher profit than people genre did by a lot. Therefore, I have learned that broader genres generate a lot more viewing traffic than other, less broad genres. As a result of that, there is more revenue generated by the broader genres. I have also determined that the number of videos a channel has a positive correlation to the amount of revenue it produces. That means that the more videos that a person posts on their channel, the more likely the person is to receive money from the more videos they upload. Last but not least, my research concluded that YouTubers with more than 10 million subscribers will generate a greater revenue than those with a lower subscriber rate. Therefore, the more subscribers that a channel has correlates (but is not necessarily the underlying cause) to more revenue.
I am surprised about my first two hypotheses, but the other two hypotheses made a lot of sense as to why the results came up as they did. Overall, the project was challenging but rewarding because I discover new things that I wouldn’t have even thought to look at if it weren’t for this project. My favorite finding was the one about entertainment vs people genre because when I am on YouTube those are the two genres I lean more towards. But again, interesting project with great findings.
9.0 References
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