Welcome to the Steem Hall of Fame!

In this article, we're going to call out the top earning posts and authors in July of 2018. Then we'll see if we can find a couple tips and tricks used by the top-earning authors that you can start using yourself!

What data are we looking at?

The best way to get data for this analysis would be to download the steem blockchain and use an open source querying library to get the raw, complete data. However, this would take forever and right now steem's data is in the 130+ GB range. Ain't nobody got time for that. I decided to start with a shallower version of the data:

As of 8/31/2018, there is a site called https://steemdb.com/ . From this site I copied the data for the top 100 earning posts created every day during the month of July 2018. Data collected includes: post title, author handle, date created, amount earned, number of votes. You can view the data here. Please feel free to comment if you notice any issues. If people enjoy this post, I'll try and find time to get the full steem data.

Limitations of this Data:

• I assume that SteemDB data is correct
  • I have not validated the data with the steem blockchain directly, so there may be some inconsistencies.
• I assume steemDB measures rewards in USD
  • that is what the symbols on its site imply, but I could be wrong
• This is not the complete set of posts made in July 2018. The steemDB data stores the 100 highest-earning posts created on each day in July 2018. The dataset we're dealing with covers 31000+ posts, so I feel like it's pretty good coverage of the month. That said, the lowest amount earned by any post in this dataset is ~$52, so we definitely aren't covering all posts made on Steem.
• There's no way to tell who paid for upvotes.

Hall of Fame

What were the top earning posts this month?

The top earning post by @chbartist is an absolute anomaly. It earned more than any other article posted in the month of July by a solid $200. The average amount earned by posts in the top 25% is $131. Which is to say: start with the 31000 top posts made in July. Order them by the amount earned. Now look solely at the 32 highest earning posts. On average, those posts earned about $131 each. The top-earning post made 9x that amount. The top 5 posts made roughly 6-7x that amount. That's impressive.

Who were our top earning authors this month?

Our top earning author this month is @haejin! There are a few interesting things to note here about @haejin:

1. Their strategy is to post a lot. They make $99 per post. However, they had 224 posts make the steemDB list in the month of July. They posted almost 4 times as much as the next highest earning author while making less per post.
2. They consistently make $99 per post. The standard deviation of @haejin's earnings per post is ~$13. This means that 66% of @haejin's posts made between $88 and $115. That's a tight band.

This raises the question: Is @haejin's approach of posting a lot the winning strategy? What tips can we use from these authors in our own posting practices?

Tips from the Winners

A Quick Note on the Data

Note:It's worth reminding ourselves here of who is represented in this data set. These are authors of the top ~3100 created during the month of July. It's not representative of everything posted, but it's a reasonable chunk.

We're going to look at the 3 groups of authors:

1. the 0th to 50th percentile of authors: if you order the authors in this dataset by how much they earned, this is the bottom 50% (Group 1)
2. the 50th to 75th percentile: if you order the authors by how much they earned, this is the middle 25% (Group 2)
3. the 75th+ percentile: this it the top earning 25% of authors (Group 3)

Tip #1: Post More

It's important to note here that the ranges are pretty wide. There were posts made by the top earning authors that made less than some posts by the less-earning authors. I think this is explained by posting frequency. Notice that on average, a Group 1 author posted just once over the course of the month, whereas Group 3 authors posted 33 times as much.

Notice something very interesting in the difference between groups 2 and 3. They average the same earnings per post. If you want to make more money, all you have to do is post more.

"Now, duha," you say, "If I post 5 times as much, my posts will drop in quality!" This is technically true. And creating good content around something simple can take a lot of effort. But if your audience is big enough that you average $100 per post when you upload something as every-day as "Vlog #150 - OMG. 😳First Haircut in Mexico", and you have the bandwidth for it: experiment with posting a couple extra times next month and see what happens. (Shout out to @tangerinetravels! You're one of the authors in Group 2!)

Tip #2: Leverage Engaged Audiences

Let's take a look at the categories most used by Groups 1, 2, and 3:

Authors posted about #money a LOT! About 7% of all posts looked at were categorized as "money." And 18% of posts created by the top-earning authors were categorized as "money" as well. Money and contest were the two most-used categories by top earning authors this month, whereas utopian-io and dlive were more popular categories by other authors. Money is a pretty general topic, but a lot of these other categories are either platforms (utopian-io is a library, dlive and steemhunt are both platforms.) or contest-based (photography has a daily contest and contest, of course, is all about contests). This suggests that authors are tapping into platforms with large audiences. Except for money....the concept with the biggest audience of all?

How does this compare with how categories are used overall? Let's look at the most used categories this month:

Categories that earned the most money overall:

Categories that earn the most per post on average AND have at least 11 posts in that category:

This raises an interesting question: is Steem only a good place to earn money if you're writing about Steem platforms? Are Steemians an audience with limited interests? Or are these purely trends for July 2018 and don't hold up if we look at the year as a whole or different months? That's a question to be answered in a future post.

Thank you for reading! I hope you've enjoyed this deep dive into Steem data from last month. If people are interested in further analyses, I'll continue looking into things each month. While the bulk of this post is over, continue reading to hear more about the limitations of this data set, next steps of analysis for me, and other users in the hall of fame!

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Hall of Fame Honorable Mentions

We talked about the top-earning posts and the top-earning authors, but I also wanted to give a shout-out to some of the amazing folks in groups 1 and 2!

The 5 highest earning authors in group 2 were: @gallerani, @tangerinetravels, @mahsumakbas, @reseller, @stackin. And the 5 highest earning authors in group 1 were: @suniltikar, @yanzel4lyf, @newsflash, @leotrap, @nkraft.

Congrats! You're the authors of some of our top-earning posts in the month of July! That's a ridiculously difficult feat. I would love to hear from y'all about your posting strategy: would you be open to trying to post a little more frequently? Or is the content you create too time-consuming to experiment in that regard? Or maybe you are posting more, but those extra posts aren't doing well enough to make this dataset. Would love to hear about your strategy and what's worked for you!

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Limitations and Next Steps

There are a lot of limitations to this data. The biggest issue, in my opinion, is that there's no way to measure who paid for upvotes and resteems. Did #utopian-io earn the most money as a category because of organic audience engagement or because a bunch of people paid for engagement? It's impossible to tell, but the difference between "This is a category that drives organic engagement" and "A lot of people in this category paid for engagement" is enormous to those of us interested in reaching real people with our work.

Another limitation is lack of tag data. In Steem, you can tag your content with multiple tags, but the first tag you use is the "category." Without knowing what other tags a piece of content has, it's impossible to tell if...#utopian-io was really the category that earned the most money overall or if everything in #utopian-io was also tagged with #blockchain and that led to the huge surge in engagement. This is less impossible to resolve than the first limitation, so next steps for me definitely involve trying to get tag data for these posts.

Other data I don't have but would love to use in the future:

• length of post
• words in post
• whether or not a post used images
• vote data (I have some vote data, but this data set is the top-earning posts for each day, not the top-voted. So to make any conclusions based on voting amount ignores a potentially huge swath of highly-voted, low-earning posts)
• total post data: is there an author who earned more than @haejin this month by posting 700 times with each post earning ~$30? This data will not tell us
• taking data from steemdb does not give me post ids, so it's impossible to easily look up these pieces of content and grab the other data like tags, etc.