Why I use Python and Onomics to Create Data-Driven Content

What I’m most known for is creating data-driven content. This is when you analyze data and create content from it (usually in the form of charts and a blog post).

I’ve tested a lot of different tools in my experiments with data posts. While there are exceptions, I’ve found that I’ve only needed two technologies to create data posts for myself and clients: Python and Onomics.

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Benefits I’ve Received from Creating Research Content

I was recently asked to be on Omar Elbaga’s podcast Now the Future (website here). We talked about my article analyzing 100,000 viral posts as well as how I pitch journalists to get coverage.

The crazy thing is that Omar reached out to me on his own to be on his podcast. I submitted the article to Zest, and when he saw it he was so impressed that he reached out to me to be on his podcast.

This got me thinking: what are all the benefits I’ve received from creating research content?

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When to spend $1,000 or more on a Blog Article

Something I hear frequently when talking to leads is that my article price is much more expensive than a regular article. It’s true. Finding original data, cleaning it, using programming to analyze it, and then writing the article will take a lot more time than other articles. Why would companies decide to create expensive content?

There are many reasons why it’s worth it for your business to spend a lot of money on one article. I listed some of them here:
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I Analyzed 100,000 Viral Posts: Here’s What I Learned (New Research)

How have viral posts changed from 2016 to 2017? Since viral posts today could be different from viral posts in the past, I used BuzzSumo to analyze viral posts on social media for 2016 and 2017. I made sure to pick articles from eight different categories (marketing, dating, fitness, pets, beauty, health, technology, travel), and I filtered out articles with less than 500 shares.

After consulting with influencers and journalists, I came up with several measurements that I thought would be interesting. I analyzed web pages in several different ways, such as number of shares, Domain Authority (DA), Page Authority (PA), and number of words.

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