The Most Comprehensive Collection of Data-Supported Theories about How To Succeed on Twitch

Four years of deep research studies, data collection, and data analysis all culminating in one “how-to” guide to doing everything within a person’s control to succeed as a Twitch streamer.

by S4B0T4G3FIRE | March 19, 2022, 8:00 AM EDT

Introduction

There is no universal recipe for success, but there is data, and that is just as valuable. With data, you can visualize infinite cause-and-effect relationships between variables and predict how informed decisions will affect your future. Doing just a little bit of research now can help you plan out your childhood, so you arrive at a particular goal during adulthood. Having complete control over your future is arguably the most important thing in life.

For a young girl who wants to grow up to be a doctor, what must she know? What must she do? How many years of school must she attend? What is the dropout rate of those schools? Which hospital will she work at? How long before the paychecks cover all of her expenses? How plausible is it to start a family alongside work and loans? When can she comfortably retire? The answers to these questions form an algorithm for that one girl to follow if she wishes to navigate life in a way that will check off every box on her bucket list. Then, it is up to her work ethic to get her there.

Now that times have changed, “Doctor” might not be the best example for today’s generations. Many young people would rather become content creators nowadays. Although content creation as we know it has been around for at least a few centuries in some form or another, and many established marketing principles still apply when trying to sell content, it is practically brand new in terms of the internet’s newly-evolved video-streaming platforms. Online information changes at an exponential rate, so virtual content creation has not yet been trailblazed or proven to the same degree of certainty as it has for doctors, astronauts, or even teachers, which makes it a whole new dream to pin down.

One thing seems certain, however, and that is the dominance of video content over every other virtual medium of content like podcasts, books, and articles. And while Netflix, Disney+, Prime Video, Hulu, Peacock, and a few other services run the show as far as corporate content is concerned, YouTube and Twitch dominate the user-generated content market. Both of these platforms allow anybody in the world to create content at little to no cost.

To take this comparison one step further, live content takes the cake over pre-recorded content because it allows creators to pump out content as quickly as possible to keep up with the ever-evolving trends and ongoing journey to maintain relevancy. Combined with real-time connections and relationships, the ability to adapt content on the fly, and not wasting any time on editing or editors, live streaming is the king of content creation, and Twitch is the king of the live streaming industry.

Twitch is still fairly new, however, getting its name only 10 years ago. An aspiring Twitch streamer can find advice and suggestions online, but they will not find a pre-determined path. Streamers are largely on their own during this journey, just hoping that their name will stick and that their content will someday reside at the top of the list of their millions of peers. A dream that, without a proven path, could never be reached. Or could it? Remember, in the absence of a path, data is just as valuable. Data can analyze a million different content-creation options and create a reliable path. At the very least, the highly-educated guess it provides is way more reliable than the hesitant, complete shot in the dark that the majority of aspiring streamers take every time they press “Go Live.”

For a teenage boy who wants to stream video games to thousands of viewers daily, what must he know? What must he do? What videogame(s) should he stream? Which days of the week should he stream on? How many hours per day should he stream for? Which hours of the day should he stream at? Is it worth the extra effort to stream on weekends? How should he go about mitigating harassment in chat to sustain a healthy, inclusive environment? With help from the data presented in this article, the teenage boy can answer all of these questions to give himself the best possible chance of achieving his dream. Your dream, perhaps. So, what could it hurt to place a bit of faith in some real data?

Getting Views

Part 1: Finding the Right Game

Threads and forums all over the internet will say that “You should stream whatever you find fun.” This may be highly encouraging to read, but it is of very little use to someone who dreams of climbing the ranks. While passion is crucial, it is silly to dive head-first into streaming on the notion that passion alone is a path to streaming success. There is much more to it. The popularities of certain categories have ceilings and lifespans. If you limit yourself to a niche category because you enjoy it, you are already stunting your growth as a content creator, even if you somehow make it to the 90th percentile of that category. And if you decide to switch categories in the future, you will very quickly realize that the majority of your viewers are “non-transferable” and will not come with you. Even the platform’s most successful creators know and experience this to some degree when they want to try something new.

What should I stream?

The “Top 20 Categories” are at the top for a reason. That is where the viewers are and where the money is. That is what gamers want to play and what viewers want to watch. That is where corporations want to dump all of their money for advertising, esports, and sponsorships. These categories are set up for success, and it would be a mistake for you not to take your share of that goldmine. So, before deciding to go all-in on your favorite game, consider things like:

• Games with esports
• Viewer-to-streamer ratio in certain categories
• Games that experience large, frequent, and/or exciting new updates
• Social media trends that boost the popularity of certain games

Some of the answers are a mere Google search away, but the math required to put all of this data and more into perspective has already been done for you. It follows a very specific procedure and uses terminology that is specific to the data interpreter (the author of this article), however. An example of this math from October 2021 can be found in Figure 1 below. Different scores are awarded to each category based on their performance during the month, so the resulting weighted average can help aspiring streamers decide which categories perform the best during a particular month.

Figure 1: Raw Data from October 2021’s Most Streamed Categories

1. The first criterion weighed into the average is the “Best Viewer Distribution.” Every category has a different distribution of broadcasts. “Just Chatting” might have 20 channels above 5,000 viewers and 2,000 channels below 5 viewers, with a bunch of other channels in between at any given time of day. A category like “Hearthstone” might have 2 channels above 5,000 viewers and 30 channels below 5 viewers. For a new broadcaster with 0–1 viewers, Hearthstone would be a better category to stream because there is a lot less competition in that vicinity, even though Hearthstone is overall less popular than Just Chatting.
2. The second criterion is the “Viewer-to-Streamer Ratio.” The higher this ratio, the more viewers each streamer gets. 293,000 “Just Chatting” viewers split among 4,000 channels is about 73 viewers per channel (in an ideal scenario). 26,500 “Hearthstone” viewers split among 203 channels is about 131 viewers per channel. Hearthstone would be the better category based on its ratio.
3. The third criterion, “Above Regression Line,” is another way to reward channels with good ratios of viewers to streamers. It works similarly to the second criterion, but there are a few nuances.
4. The fourth criterion is the “Greatest Viewership.” Even a category like “Just Chatting,” which has a bad viewer-to-streamer ratio, still deserves credit for the number of viewers it pulls in. The more, the merrier.
5. Similar to the fourth criterion, the fifth criterion credits the categories with the “Fewest Streamers.” Having a low number of channels is not great for the growth of a category, but it does still mean there is less competition for aspiring streamers, which is a good thing.
6. Finally, the sixth criterion is “Esports.” Esports are great for categories. They bring competition which brings in money which brings in viewership. They also fuel the fire when it comes to “Watch Parties.” Everybody wants to watch a $1,000,000 tournament, but many viewers would rather watch it from their favorite streamers’ perspectives, and watch parties are a great way to reel in those viewers. This is similar to how “reaction videos” work on YouTube, and we all know how popular those became.

For October 2021, the criteria suggested that League of Legends, New World, Dota 2, Counter-Strike: Global Offensive, Valorant, and Call of Duty: Warzone were among the best categories for an aspiring Twitch broadcaster to stream. This changes from month to month, however. Besides, this is just a small factor in the algorithm. Many more questions need to be answered, and much more data needs to be factored in before a streamer can confidently embark on their journey.

Part 2: Finding the Right Stream Schedule

Once again, leave it to threads and forums to advise you to “stream whenever you have time,” but that simply will not cut it for the goal you have in mind. As it turns out, there are quite a few factors that influence this answer. For instance, the ratios of viewers to streamers change every day, and different categories experience fluctuations every day. This could boost some channels and hurt others. To help identify these fluctuations, an enormous sample of 11,000 English Twitch broadcasts from October 2021 has been collected (from TwitchTracker.com) and thoroughly analyzed, the parameters of which are as follows:

• 16 of the most popular Twitch categories from September 2021 — Just Chatting, League of Legends, Grand Theft Auto V, Counter-Strike: Global Offensive, Apex Legends, Fortnite, Valorant, Minecraft, Call of Duty: Warzone, Dota 2, Dead by Daylight, New World, Music, Hearthstone, Rocket League, and Escape from Tarkov
• 10 Viewership Ranges — 0–5, 6–10, 11–25, 26–50, 51–100, 101–250, 251–500, 501–1,000, 1,001–5,000, >5000
• ≈5 randomly-selected channels from each viewership range for each Twitch category

Note: More channels were selected for categories with an abundance of streamers, just for the sake of strengthening the sample when possible (this helped to accommodate a lack of female representation in “weaker” categories). Also, every selected channel featured a streamer who used a face-cam so their demographics could be easily differentiated. More data about these samples can be seen below in Figure 2 through Figure 4. Keep in mind that the numbers represent broadcasts as opposed to people. If you are not concerned about the sample statistics, then you can skip right to the part titled “Which days of the week should I stream on?”

Disclaimers:

• TwitchTracker was only referenced for a sample collection so trends could be identified.
• None of the sample data below was collected using “data scraping.” It was collected manually (ie. a human typing publicly-available data into a spreadsheet because it explicitly states on TwitchTracker’s website (as of the publication date of this article) that “Scraping is prohibited.”
• No data found on TwitchTracker is being redistributed anywhere in this article.
• This article is for non-commercial, educational purposes only.

Figure 2: Sample Statistics Breakdown by Category, Gender, and Ethnicity

Figure 3: Sample Statistics Breakdown by Viewership Range and Gender

Figure 4: Sample Statistics Breakdown by Viewership Range and Ethnicity

Briefly, a couple of clarifications need to be made about this sample. When it was being selected, some bias was used to keep the number of female streamers and male streamers approximately equal. This was possible for some categories but not for others. Male representation was much higher at high viewership for many of the categories. Overall, this method yielded 38.6% of broadcasts belonging to female streamers, which is just above the 35% (according to sources) of the entire platform that is female. There was also a slight bias to ensure that under-represented ethnicities were sufficiently accommodated, which yielded 29.2% of broadcasts belonging to streamers who were not white. This percentage is almost identical to the percentage of Twitch users who are not white, which is 29.5% according to sources.

Then, the “Yes”s and “No”s in Figure 3 and Figure 4 are just there to inform whether or not the representation in certain viewership ranges met the known percentages of the Twitch platform as a whole.

Disclaimer: Ideally, the sample would extend to all genders and/or lack of genders, but binary is the reach for now. So, “100% of Twitch” just refers to the binary users. The lack of representation of other genders in this study is intentional, but it is for simplicity purposes, not to discriminate, omit, or fail to recognize an important part of the platform’s users.

Which days of the week should I stream on?

Sources say that the viewer-to-streamer ratio of the entire platform is worse on weekends than it is on weekdays due to a superfluous number of streamers, so the hypothesis for this segment is that “streamers’ views per hour will be lower on weekends.”

Figure 5: Average Views Per Hour according to Day of the Week (top 2 values per viewership category are highlighted in green; lowest value per viewership category is highlighted in red)

The 11,000 broadcasts yielded quite an inconclusive batch of day-of-week data, but there are still some important takeaways. Day of the week does appear to influence views. The data even goes on to disprove the hypothesis. At no point during the study did the lowest views per hour fall on a weekend. In fact, many of the highest views per hour fell on or around the weekends (Friday, Saturday, and Sunday). Monday broadcasts performed poorly, but Tuesday looked okay. A streamer can very easily do a month of trial runs on different days if they wish to confirm the best and worst days for them specifically, but it seems worthwhile for them to put in a bit more effort on the weekends.

To reiterate, this is just a small factor in the algorithm. There are still a few more factors that require consideration.

How many hours per day should I stream for?

Many sources say that “3–4 hours make for a good broadcast,” so that is the hypothesis for this next section. The data regarding stream duration looks pretty concrete in proving this hypothesis.

Figure 6: Average Views Per Hour according to Stream Duration (top 2 values per viewership category are highlighted in green; lowest value per viewership category is highlighted in red)

Initially, Figure 6 went all the way to 24 hours, but those values are not even worth mentioning because the trends were obvious after 10 hours. The only viewership category that benefited from longer broadcasts was the “0–5” category. This is most likely because streamers at these numbers are so difficult to find, so it takes a while for viewers to accumulate there. Also, the longer these channels stream for, the higher their chances are of receiving a host/raid to boost numbers (which is technically true for all channels, but especially so for the smallest ones).

As for the rest of the streamers, it looks like the best durations to stream for (in terms of maximizing views with as little time and effort as possible) are between 0 and 4 hours. Does that mean you would not get new viewers after 4 hours? It does not. You would still get some new viewers (and subscribers and donations as well) after 4 hours, but the rate may slow down after the broadcast drags on for a while. This is similar to how a YouTuber will see a gradual decline in retention as their viewers progress through a video.

For instance, if a 10-minute video gets 10,000 views, all 10,000 of those viewers will have seen the first second of that video. After that, maybe 80% of them see the first minute, 50% see the first five minutes, and only 15% watch the entire video. For a Twitch broadcast, maybe a channel’s followers receive a “live” notification at noon, tune into the channel for the first hour, and then the regular viewers slowly begin filtering out. If new viewers are not replacing the ones lost, then it would not be worth it to continue the broadcast.

The important takeaway is that it is sufficient for a streamer to only spend a couple of hours per day growing their audience. There is no need to do more work than necessary, except in cases of raids, hosts, or when the energy of the broadcast is going fun and strong. If you receive a raid right before ending a 4-hour broadcast, then of course you should go at least another 30 minutes. Use your judgment too in special cases!

Which hours of the day should I stream at?

Now that it has been established how many hours are best, it is time to uncover which hours of the day are best. Is it better to stream mornings, nights, or during the day when streamers and viewers are at peak volume? The data below can answer this with confidence. Be sure to look at the row of graphs/trends at the bottom of Figure 7.

Figure 7: Average Views Per Hour according to time of day (top 3 values per viewership category are highlighted in green; lowest 2 values per viewership category are highlighted in red)

For the most part, the trends show some peaks early in the day, followed by some pretty lengthy plateaus for the rest of the day. Technically, this can be looked at in two different ways. On one hand, these trends could reveal that 2:00 AM ET through 11:00 AM ET is just a great period for streaming. On the other hand, they could reveal that the most popular streamers (worldwide) stream during this period. Either way, it means these hours of the day correlate to the highest views per hour. After about 1:00 PM ET, Figure 7 shows only 1 maximum, 2 other highs, and quite a few minimums. Thus, streaming in the morning hours (according to the Eastern Time Zone) is a pretty safe bet to maximize views per hour.

Convert these times to your time zone, and you might just discover the perfect streaming schedule. Also, be sure to use your judgment here as well! If your regular viewers are conditioned to morning broadcasts, you may lose some of them if you switch to nighttime broadcasts. Is this a sacrifice you are willing to make?

Part 3: Finding Gender Bias

Streaming should never be a competition between genders, but gender data is still very useful. Do women earn more views per hour than men do? The results of the 11,000-broadcast study are confident. See for yourself in Figure 8 below.

Figure 8: Average Views Per Hour Sorted by Viewership Range, Outliers Removed (Logarithmic Scale)

Quick Tip: There is a difference between “views” and “viewership.” Read the graph’s axes carefully.

Although trends cannot make conclusions, the data recorded is definitive that women do earn more views per hour at every level of viewership from 0 viewers to more than 5,000 viewers. Due to a couple of shortcomings in the sample, the two sets of points furthest to the right should be taken far more lightly than the rest of the points, but all of the “Female” points were still higher than their respective “Male” points. Why is this? To keep this article as data-forward as possible, only light assumptions will be made based on facts about how Twitch’s demographics actually work. Here is one possibility:

Since there are fewer women on Twitch (35%, compared to 65% men), they would generally get a higher number of views because views would be split between fewer streamers. In other words, it is a statistical truth that “fewer women” means “higher average views” because users seeking female streamers would be divided between fewer female channels.

If you need an analogy, think of it like this. If a city has 50 McDonald’s (men on Twitch) and 10 Wendy’s (women on Twitch), any given Wendy’s restaurant will have a greater number of customers at any given time of day because there are fewer Wendy’s restaurants in the area. McDonald’s will see more customers overall, but they will be split between a greater number of restaurants (hence fewer views per hour for male streamers). If you add 1 gourmet restaurant to the mix (an eSports channel, for instance), it will have a line out the door and around the block.

50 McDonald’s x 20 customers = 1,000 McDonald’s customers.
10 Wendy’s x 25 customers = 250 Wendy’s customers.
1 Gourmet Restaurant x 100 customers = 100 Gourmet customers.

This is not necessarily something that anyone can control like they would control the game they stream or the time of day they stream at, but an aspiring streamer should still be mildly aware of these statistically-proven gender biases.

Turning Views into Viewers

Part 1: Finding the Right Channel Identity

Realistically, being yourself is the key to success. Unfortunately, this advice will only take streamers so far. There are certain habits and personality traits that the most popular streamers manifest in themselves and their channels. To identify the strongest characteristics of a successful (in terms of growing viewer count) Twitch streamer, a survey was conducted by the author of this article between July 2020 and January 2021. Respondents were asked to rate their channels based on nine (9) unique criterium labeled as:

1. Brand — channel page setup completion (including a profile picture, cover photo, any essential social media links, and an up-to-date biography)
2. Originality — originality of content (titles and the games you play, in comparison to those of other broadcasters in the same category and viewer range)
3. Positivity — attitude during the stream (despite unfavorable outcomes in games or current real-life events/issues)
4. Consistency — streaming regularly (at least consistently following a set schedule)
5. Unpredictability — periodically changing the content (whether or not every one of your broadcasts features the same game/category and order of operations)
6. Energy — the energy being applied to entertain (the volume and animation of your broadcasts, such as music volume, whether you speak loudly or mumble quietly/inaudibly, and if you move around a lot or are mostly still)
7. Appreciation — the appreciation showed to supporters (whether or not you say “thank you” or even acknowledge your audience at all)
8. Commitment — willingness to put in time and effort required to grow (whether or not you get frustrated right away when your channel isn’t growing as you hoped it would)
9. Inclusivity — including all types of viewers of different genders, beliefs, etc. (whether or not you are inadvertently turning people away with the games you play, the words you say, the jokes/insults you make, and/or the way you dress)

The major findings of this survey can be seen in Figure 9 below.

Figure 9: Results of a Twitch Streamer Self-Rating Survey

Quick Tip: In statistics, R^2 is known as the coefficient of determination. For “Energy,” a coefficient of determination equal to 0.8027 means “80% of the variation in Rating can be explained or predicted by the change in Average Viewer Count.” In simpler terms, this means that the Average Viewer Count is directly related to the Rating, with 80% certainty. It is an assessment of how strongly related the x-axis and the y-axis are, so researchers know whether or not to trust an apparent trend in data. Correlation by itself may not equal causation, but R^2 sometimes does. At the very least, it validates a trend.

The major findings of this survey were that all nine (9) criteria trended positively, but three (3) of them stood out above the others. These were “Brand,” “Consistency,” and “Energy,” as shown in Figure 9. The streamers who a) had the most complete, up-to-date channel pages and biographies, b) streamed on schedule most consistently, and c) applied the highest amount of energy through liveliness, animation, and/or volume tended to average the highest concurrent viewership.

So, for all of the aspiring Twitch streamers out there, get a profile picture and a cover photo, have your broadcasts recorded as Videos on Demand (VoDs) so users can watch them during off-hours, share your interests, link your social media accounts, stream consistently, and give your all every time you hit “Go Live.” The numbers do not lie. This is how proven streamers are converting views into viewers. You can be yourself all you want to be, but that will not entitle you to anything. Fine-tuning, whether a little or a lot, will be necessary to increase your chances of finding success.

Part 2: Finding the Right Moderators

Another major part of converting views into viewers is providing a sense of community for visitors. This means providing a place for people who share similar values/interests and want to socialize. The only difficult part is maintaining safety for everybody within the community. Luckily, all you need to do is make the rules and lead by example. The policing can be left to your most trustworthy viewers. You would promote these viewers to moderators because they understand your vision for a community, are familiar with the tolerance levels of certain jokes in your community, and know where “the line” is drawn for your channel specifically.

Unlike what many sources would say, moderators cannot just be random viewers whom you trust. The overall behavior tolerance in chat changes from channel to channel, game to game, gender to gender, and ethnicity to ethnicity. Moderators need to be aware of the types of harassment they will face while serving a particular streamer. “How will they know what forms of harassment to expect?” Well, to identify how harassment varies from gender to gender and ethnicity to ethnicity, the author of this article conducted an observational study in 2018 where (a stratified random sample of) 160 channels’ chats were observed for an hour at a time so instances of harassment could be recorded and eventually summed for later analysis.

Note: Not all of the harassment observed was directed at a streamer but rather simply took place in a streamer’s chat. Regardless of the target or intention, harassment still affects the overall sense of community and should not be overlooked or dismissed.

Which types of harassment should I look out for?

The most common forms of harassment that were noticed in various Twitch chats of different-sized channels and games were Homophobia, Racism, Sexism, Gender-Exclusivity, Mental Illness, Religious, Sexual, and General Bullying. Not every form was experienced equally, however. You can see more about this below in Figure 10.

Figure 10: Results Sorted by Type of Harassment Noticed

As you can see, sexual harassment is out of control on Twitch. The blatant sexual harassment should not be an issue for moderators, but the rest of it could be too subtle for an amateur moderator to identify. A lot of the time, sexual harassment is disguised in the form of a joke and is done “with good intentions” between friends (that is just how Twitch users act nowadays). These cases are when a moderator needs to focus on “respect” and “safety.” Sexual harassment is never appropriate, but an experienced moderator who knows the viewers well can usually get a feel of the situation just by observing for a moment. If the intentions are unknown, a moderator should ask the recipient if they are joking around or not, just to be certain whether or not actions need to be taken to restore respect. The messages can always be deleted afterward anyway (without further action), just to enforce a more friendly setting for all types of viewers. Regardless of intentions, though, sexual harassment should be discouraged in all channels.

The rest of the harassment types are never a joking matter (hence why they occurred less frequently during this study), even in the form of “good-intentioned” jokes, so there is less gray area for moderators to worry about. Unfortunately, Twitch is not an ideal world, and deviants will still target individuals with homophobia, racism, sexism, misogyny, bullying, etc. because they know they can escape with nothing more than a “slap on their wrist” (i.e., a ban or timeout).

Do different genders experience harassment differently?

While moderators should know what to be on the lookout for, they should also know who on the platform is most vulnerable to harassment. Figure 11 and Figure 12 below can offer some assistance with that.

Figure 11: Results Sorted by Streamer’s Gender

Figure 12: Results Sorted by Streamer’s Ethnicity

To summarize, female streamers faced almost twice the amount of sexual harassment and more than three times the amount of gender exclusivity that male streamers faced. Male streamers, on the other hand, faced almost six times the amount of racism and almost twice the amount of homophobia that female streamers faced.

Looking at the data from the perspective of ethnicity, a different story is told. Within the scope of this observational study, white streamers saw almost double the amount of sexual harassment than streamers of color. On the other hand, streamers of color experienced almost double the amount of general bullying and almost three times the amount of homophobia.

Ultimately, all that can be said is every streamer faces different forms of harassment. Some will be predictable, which is just one of the many sad truths of (virtual) society, and others will not be. For the more predictable forms, streamers should work with their moderators to identify certain hateful words and phrases that will inevitably be said in their chats, so those words and phrases can be blacklisted (automatically removed from the chat) before they ever become a problem. Streamers have the tools available to them, and they really should use them. Safety is crucial not only to turn views into viewers but also to keep the entire Twitch ecosystem healthy.

What if none of my viewers have moderating experience?

This is the only question in this article that will be answered without data. If you and your moderators have no idea how to deal with deviants in chat, then the best tips you should consider are available below:

1. Respect absolutely everyone, even when they do not show respect themselves.
2. Greet every name that enters the chat, every stream (This only applies to aspiring streamers whose chats scroll slowly due to fewer messages being sent).
3. Always keep the discussion going. Do not let the flow of messages in chat slow down too much, or else potential viewers will become disengaged.
4. Remember everyone who participates in the chat. The more information that is remembered about a potential viewer, the more valuable to every broadcast a potential viewer feels.

Without knowing what to expect, these are the most basic things you can do to build a community.

Turning Viewers into Followers

Part 1: Finding Demographic Bias

There is a multitude of ways to get viewers to press the “Follow” button, but measuring their success rate is tricky and requires a lot of testing. Ideally, an experiment would be done where some channels would be given specific titles, vibes, and “Follower Goals,” and other channels would be set up with different ones. Unfortunately, no such experiment has been set up to manipulate certain channels and record their follower rates. What has been set up instead is an 18-month observational study to see if demographics (that are not necessarily “manipulable”) like relationship status, ethnicity, and gender, influence the follower rates of Twitch channels. The parameters of this study are as follows:

Thirty (30) “small” channels from each of Twitch’s Top 10 categories were strategically collected to be organized by:

• Male
• Female
• Single
• In A Relationship
• White
• Of-Color

The day before the commencement of this study, follower counts were recorded for each of the 300 channels. On random days throughout the 18 months, each of the 300 channels’ follower counts was recorded and averaged within each demographic subsection. In other words, the updated follower counts of female streamers were averaged, as were the follower counts of male streamers, single streamers, etc., during each data-collection session. The average follower growth for each demographic can be seen in Figure 13, Figure 14, and Figure 15.

Note: To be certain that the demographics were accurate, biographies were read, and streamers were respectfully asked questions under zero pressure of answering. Also, to ensure that follower rates were not influenced by bots, video recordings and social media were checked whenever uncharacteristic spikes in follower rates were observed. Once any of the 300 channels became “illegitimate” (by quitting, changing their name, getting banned, or using bots to gain followers), their follower count was halted, making it as though they never gained another follower for the remainder of the 18 months. This was done to nullify their legitimate growth, without totally nullifying their previous legitimate growth.

Figure 13: Follower Growth Trends per Relationship Status

Figure 14: Follower Growth Trends per Ethnicity

Figure 15: Follower Growth Trends per Gender

Note: To keep these observations as statistical as possible, only minor speculations will be made about each of the graphs above. Keep in mind that these are mere educated assumptions to explain trends. They are not facts. They are possible explanations based on how Twitch actually, certainly, provenly works.

First, relationship status is not often a topic of discussion between streamers and viewers. Many streamers, in fact, (rightfully) prefer to keep their personal life separate from their streaming career. This means a single streamer would be indistinguishable from a streamer who is in a relationship, which explains why the growth rates are so similar.

Second, since the majority (71.5%) of Twitch users are white, it may be easier to find and follow white streamers due to their abundance on the platform. This could explain why white streamers saw a small advantage in follower growth.

Third, since the majority (65%) of Twitch users are male, perhaps they are seeking out female streamers to watch and follow as though Twitch is a dating website (which does happen often enough to be taken into consideration). This could also come down to the fact that there are fewer female streamers on the platform, so follows would be more abundant among them. Whatever the case, the follower growth for women was much more rapid than that of men.

Conclusions

The easiest way to provide a conclusion for all of the incredible information provided by this article is by bulleting every major point. If you did not care to read through all of the analysis and explanations, this is your “Too Long, Didn’t Read (TL;DR)” best friend. Print it out. Tape it to the wall beside your streaming setup. If you are confused about a bullet point, scroll up to where it is explained in the article. Anyway, in chronological order of how the data was presented in this article, here are the major trends that the data suggests:

Getting Views:

• Based on numerous criteria, some Twitch categories are better to stream than others. For October 2021, the criteria suggested that League of Legends, New World, Dota 2, Counter-Strike: Global Offensive, Valorant, and Call of Duty: Warzone were among the best categories for an aspiring Twitch broadcaster to stream. This changes from month to month, however.
• Despite the larger influx of streamers on the weekend, the data suggests that it is worth an aspiring streamer’s time to stream on the weekend.
• For streamers with 0 to 5 concurrent viewers, it is beneficial to stream for long periods. For streamers with 6 to >5,000 concurrent viewers, 0 hours to 4 hours seems to be the sweet spot to maximize views per hour (the most views for the amount of time/effort applied).
• In general, the higher numbers of views per hour occur between 2:00 AM Eastern Time and 11:00 AM Eastern Time.
• The data is confident that female streamers earn higher views per hour than male streamers do.

Turning Views into Viewers:

• Survey data suggests that the three most important criteria for aspiring Twitch streamers to meet are Energy, Consistency, and Brand.
• The results of an observational study suggest that sexual harassment is, by far, the most abundant form of harassment on the Twitch platform.
• The data suggests that the chats belonging to “female” streamers experience more sexual harassment and gender exclusivity than the chats belonging to “male streamers,” so a streamer should acquire moderators accordingly.
• The data suggests that the chats belonging to streamers “of color” experience more general bullying and homophobia than the chats belonging to “white” streamers, so a streamer should acquire moderators accordingly.
• As a moderator, it is important to respect everybody, greet every username, keep the discussion going, and remember as many details as possible about viewers.

Turning Viewers into Followers:

• According to an observational study, there is no distinguishable difference between the follower growth of aspiring Twitch streamers who are “single” and those who are “in a relationship.”
• The data suggests that there may be a slight advantage for the follower growth of aspiring Twitch streamers who are “white” as opposed to those who are “of color,” but it is negligible.
• The data suggests that there is a pretty heavy advantage for the follower growth of aspiring Twitch streamers who are “female” as opposed to those who are “male.”

In the end, numerous factors go into deciding who succeeds and who fails in terms of content creation on Twitch. Some of them are within a broadcaster’s control, but many of them are not. If it would not hinder your happiness on the platform to stream popular categories at specific times of day on specific days of the week for a specific number of hours each day, then you might want to at least consider doing what the data suggests. It may not reveal to you a 100%-guaranteed, streamlined path to success, but it might give you that added nudge you need to climb to the next rank. That boost alone is invaluable.

The intended audience for this elaborate series of research studies consists of aspiring Twitch streamers who are aiming to take their content to the next level. If you read up to this point, I assume that is your mission, and I wish you the best of luck! This is no guarantee of success, but data can be very useful for predicting the future of your channel. The rest is on you, however. You know your channel better than anyone else, so use your best judgment alongside the data presented.

Also, all of the data presented is currently being shaped into a “calculator” that a streamer can use to determine their best streaming options for a particular month. All that will be required are a few simple inputs as shown in the following screenshot of an earlier version of the tool. Ideally, it will be ready by this summer, so stay tuned! It is extremely useful.

If you enjoyed this article and would like to read some of my others, you can find everything here. A lot of it pertains to the likes of Twitch and other social media, but you will find some miscellaneous content in there as well (life, sports, television, etc.).