Imagine you could skim over 4,000 product management articles in less than 10 minutes. Imagine you could easily discover great authors to follow and the content that makes them great without effort.
We've got it for you here.
ABOUT THIS RESEARCH
In the last 2 years we have seen an explosion of content on product management, from blog posts and newsletters to podcasts, and it is easy to feel overwhelmed by the abundance of information available on a large number of publications. We especially noticed a staggering number of posts being published on Medium.com so we decided to take a step back and look into the publishing patterns in the product management content landscape on Medium and see if we could find any metrics that could help us navigate this avalanche of information.
NomNom and DataStories joined forces to analyse over 4,000 posts in the search for trends, hot topics, and influencers.
The research you are about to read is purely based on what the data showed us.
We analysed multiple metrics such as content shares, likes, publishing frequency, as well as lists of all the influencers we could find on the web [Footnote 1]. We hope this research helps you navigate the currents of information available on product management and steers you to new waves worth riding or reefs worth exploring.
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IN THIS REPORT:
WHY MEDIUM.COM CATCH THE WAVE USER EXPERIENCE IS LEADING THE CHARTS TOP 17 INFLUENCERS ON MEDIUM WHAT MEDIUM READERS LIKE A GROWING TREND LIKEABILITY ISN'T EVERYTHING PUBLISHING PATTERNS DISCOVERING CONTENT ON MEDIUM
In preparation for this research, we looked at ways to retrieve information about product management on the web. We used ahrefs and buzzsumo to identify content with a given keyword (we got 44K posts from ahrefs and 6.7K posts from BuzzSumo Pro). Upon close inspection we discovered that the data was incomplete. It did not capture important blogs and publishers like uxmag.com, Mindtheproduct.com, and svpg.com.
We decided to focus on Medium.com, scrape the data ourselves, and not use any third party tools. We searched and pulled out all available posts with the tag “Product Management” (from here).
Yup, we scraped.
The scraping process consisted of five careful steps:
- We retrieved JSON files containing descriptions of all posts tagged as PM.
- We pulled out a total of 4759 individual url links from these JSON files.
- We cleaned up the links (removed 41 links referring to authors instead of posts, and 2 links to external resources).
- We pulled out all contents of each link and created 26 metrics per post (text, title, author, url, number of words, images, videos, sentiments metrics, post date, etc).
- We removed all posts with fewer than 100 words in English characters (963 posts were removed).
This got us 3,582 posts with a guaranteed tag of “Product Management”.
Then we dove deeper into the data.
We thoroughly analysed all posts tagged “PM” on Medium. To create this analysis we used DataStories tools and Python.
We assessed the sentiment
We looked at post titles and narratives using the Natural Language Toolkit (NLTK) API from Mashape.
We checked social media response.
After using LinkedIn and Facebook APIs to pull the social shares of the Medium.com posts, we discovered that many posts with a high number of Medium likes had 0 LinkedIn and 0 Facebook shares. Playing it safe, we did not base any conclusions on the social shares stats. We focused on Medium likes.