product management landscape on medium.com

A DEEP DATA DIVE ON PRODUCT MANAGEMENT CONTENT ON MEDIUM.COM

N O M N O M & DATA S T O R I E S P R E S E N T S :

A B O U T T H I SR E S E A R C H

M E T H O D O L O G Y F I N D I N G S F I N A L T H O U G H T S

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CONTENTS

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NOMNOM & DATASTORIES

A DEEP DATA DIVE ON PRODUCT MANAGEMENT ON MEDIUM.COM

WHY MEDIUM?

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

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2 1

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3 0

3 1

3 2

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PA G E 3 4PA G E 1 7PA G E 0 7PA G E 0 3

01ABOUTTHIS RESEARCH

03 / 35A DEEP DATA DIVE ON PRODUCT MANAGEMENT ON MEDIUM.COM


04 / 3501 A DEEP DATA DIVE ON PRODUCT MANAGEMENT ON MEDIUM.COM


INTRODUCTIONA B O U T T H I S R E S E A R C H

In the last 2 years we have seen an explosion of content on product management, from blog posts, newsletters to podcasts, it is easy to feel overwhelmed by the abundance of information available on a large number of publications. We specially 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 of trends, hot topics and influencers.

¹ We know that some amazing thought leaders may not publish content regularly. We may have missed some iconic industry figures in this report.

The research you are about to read is purely based on what the data showed us. We analysed multiple metrics like content shares, likes, publishing frequency as well as lists of influencers we could find on the web.¹ We hope this research helps you navigate the currents of information available on product management and points you to new waves worth riding or reefs worth exploring.



01

A B O U T T H I S R E S E A R C H

ABOUT NOMNOM

NomNom is all your customer feedback and user research in one place. NomNom helps product, UX and CX teams aggregate, organise and make sense of customer feedback easily. Try NomNom for free today.

A DEEP DATA DIVE ON PRODUCT MANAGEMENT ON MEDIUM.COM01

A B O U T T H I S R E S E A R C H

ABOUT DATASTORIES

DataStories is all about letting the data speak for itself. DataStories is a done-for-you analytics platform that helps data owners understand what matters in their data and what to do next through interactive narratives and instant what-if scenarios. Try DataStories with a personal walk-through at beta.datastories.com or call for custom research like this one.


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02METHODOLOGY





M E T H O D O L O GY

‘We decided to focus on Medium.com, scrape the data ourselves and not use any third party tools.’

PREPARATION

In preparations 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 a tag “Product Management” (from here https://medium.com/tag/product-management).




YUP, WE SCRAPED

The scraping process consisted of five careful steps:

1. We retrieved JSON files containing descriptions of all posts tagged as PM.

2. We pulled out a total of 4759 individual url links from these JSON files.

3. We cleaned up the links (removed 41 links referring to authors instead of posts, and 2 links to external resources)

4. 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).

5. We removed all posts with fewer than 100 words in English characters (963 posts were removed)


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This got us 3,582 posts with a guaranteed tag “Product Management”.




THE DATA

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 to 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.

TOOLSWE USED


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The data we used contained 3,582 posts on Medium.com published between November 19, 2015, and May 17, 2016, in the English language and tagged as “Product Management”. These posts came from 2,071

2,071

unique authors.




WE USED MULTIPLE METRICS.

We liked the likes on Medium. But identifying quality content is hard. A single metric approach to filtering content does not always work.

Because Medium.com offers curated content, sorting the posts by the number of Medium likes was reasonable. This approach would not work for data aggregator tools. If you try to identify the top posts, authors, or media by the total number of shares, or the total number of referring domains, or the total number of followers of the author, you will arrive at different and often irrelevant results. Several metrics need to be looked at together to identify genuine producers of consistent and highly-liked content. 1000+




WE WATCHED THE TRAFFIC

If we had the data on how Medium audience has been evolving over the last 1.5 years we could establish with certainty whether or not the growing number of posts on PM is related to either a growing interest for Product Management, or is merely a reflection of the growing number of Medium readers and authors. In absence of this data we can only check some implicit metrics about the growth of Medium - the traffic.

The ahrefs tools provide the estimation of the traffic to Medium.com since January 2015. See the graph opposite:

The estimated traffic does not display any growth in the first months of 2015 compared with January 2016. This gives us a reason to hypothesize that the increase in the number of posts on PM is an indication that it’s the interest for PM is growing, and not just the number of writers.

SEP NOV JAN 2016 MAR MAY0

2M

4M

6M

JUL




WE CONSIDERED POST LENGTH

We wanted to see whether the post length has been growing over time as these days super-long, 4000 + word educational posts are popular marketing tools (see, e.g. datastories.com/gallery/case-blogs) But on Medium there is no need for tall tales. Short stories get a good buzz.

Only 15 out of 3500 posts got more than 1,000 likes, and all these posts contain a relatively small number of words (the average number of words in these posts is 1490 words, while a median is 989 words). For example, the post ‘Let’s talk about Product Management’ by Josh Elman has 3300 likes on Medium (this is the highest number across all PM posts), and only contains 240 words. However it also presents 35 images and one video (see https://news.greylock.com/let-s-talk-about-product-management-d7bc5606e0c4#.trhoi9xwq)

‘Only 15 out of 3500 posts got more than 1,000 likes, and all these posts contain a relatively small number of words...’




WE CONSIDERED POST LENGTH

Bottom line - In 2016 some posts were super-long. But exceptionally well-liked posts are not that long.

In the graph opposite we plot all posts by the day they were published. The size of the circle depicts the number of words in the posts (the smallest is 100 words, the biggest has 10,108 words). The Y axis also shows the number of likes per post (as of May 17, 2016).

# LI

KES

DATE

JAN 16 MAR 16 MAY 16MAY 15 JUL 15 SEP 15 NOV 15JAN 15 MAR 15

3000

2000

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0

Note: You can see an interactive version of this graph with further information here: http://datastories.com/product-management-landscape-in-medium

03FINDINGS





2 The Marketer’s Guide to Medium https://blog.kissmetrics.com/marketers-guide-to-medium/: “The ethos of Medium is inherently democratic; it seeks to give a voice to people who have something interesting to say, even if they don’t have thousands of Twitter followers, an active blog or friends in the right places. Medium is built to reward content for its quality, not for the pedigree or popularity of the author….[and] On Medium, the content that is made most visible is not necessarily the most recent, but it is almost certainly the best.”

‘Medium’s Editors Picks are followed by 625,000 people’

Medium = Curation.

Medium’s Editors Picks are followed by 625,000 people.² Its democratic ethos and its focus on quality content offers a rich playground for analysis. We’re following the traffic, As Medium’s popularity has increased, companies are migrating their blog posts to this organized content publisher. Our research using different aggregators indicated that Medium offers the most representative sampling of the internet’s credible product management content. Because Medium is curated by both an editorial team and the community, the chances for completely irrelevant posts to be tagged as “Product Management” are small. And additionally, in both our datasets from content data aggregators (ahrefs and BuzzSumo Pro data), Medium.com appeared as the top domain with respect to the number of posts.

1. WHY MEDIUM?F I N D I N G S



3 A Click-Bait Experiment and the Navel-Gazing Problem that Threatens to Ruin Medium

Even though Medium has often been accused of flooding readers with self-indulgent content from startups and the tech industry, we decided we could sift out that stuff.3 We searched and pulled out all available posts with a tag “Product Management” (from here https://medium.com/tag/product-management).

1. WHY MEDIUM?F I N D I N G S

#PRODUCT MANAGEMENT



Product Management content is on the rise.

Look at the number of posts with a tag Product Management on Medium.com since the beginning of time up to May 17, 2016:

Only 6 months into 2016, product management content has already matched the total number of posts published in 2015.

2. CATCH THE WAVEF I N D I N G S

# PO

STS

2014 2015 20162010 2011 2012 20132007 2008

1500

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0



F I N D I N G S

Among 3,582 Medium.com posts on product management, the largest portion is about user experience. We analyzed 38 keywords of interest to see the presence they had in all posts, in the top 436 posts, and in the posts of the identified influencers.

To our pleasant surprise we discovered that User Experience (UX) is leading the charts among all posts considered. See the fractions of posts using a particular keyword opposite:

3. USER EXPERIENCE IS LEADING THE CHARTS

UNDERSTAND CUSTOMERS

0% 5% 10% 15%

DATA-DRIVEN DESIGNUSER CHURN

REDUCE CHURN

UNDERSTAND USER BEHAVIOUR

UNDERSTAND USER BEHAVIORCUSTOMER FEEDBACK MANAGEMENT

CUSTOMER CONVERSION

CUSTOMER RETENTION

FEATURE PRIORITIZATION

USER RETENTION

USER FEEDBACKCUSTOMER FEEDBACK

USER STORIES

USER CONVERSION

FEATURE PRIORITIZATIONUNDERSTANDING CUSTOMERS

UNDERSTANDING USERSUNDERSTAND USERS

DATA-DRIVEN PRODUCT MANAGEMENT

DATA-DRIVEN PRODUCT

CUSTOMER DEVELOPMENT

CUSTOMER EXPERIENCEUSER RESEARCH

USER EXPERIENCEUX


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Customer-Conversion Product-Management

Customer-Development Product-Roadmap

Customer-Experience Reduce-Churn

Customer-Feedback Understand-Customers

Customer-Feedback-Management Understand-User-Behavior

Customer-Retention Understand-User-Behaviour

Data Stories Understand-Users

Data-Driven-Design Understanding-Customers

Data-Driven-Management Understanding-User-Behavior

Data-Driven-Product Understanding-User-Behaviour

Data-Driven-Product-Management Understanding-Users

Feature-Prioritisation User-Churn

Feature-Prioritization User-Conversion

Measure User Behavior User-Experience

Measure User Behaviour User-Feedback

Measuring-User-Behavior User-Research

Measuring-User-Behaviour User-Retention

Predict-User-Behaviour User-Stories

Predicting-User-Behaviour UX

WE ANALYSED THE FOLLOWING 38 KEYWORDS We looked for both American and British spelling.



F I N D I N G S

Out of curiosity we also looked into Google trends and compared customer experience vs user experience and found an upwards trend for both terms.

This is just another indication of the increasing interest in user experience and customer experience as disciplines. We may be finally entering the customer obsessed era. After all, according to Walker’s research, by 2020, customer experience will overtake price and product as the key brand differentiator.

3. USER EXPERIENCE IS LEADING THE CHARTS

Source: Google Trends



F I N D I N G S

Out of 2,071 unique authors writing on PM on Medium.com only 17 authors emerge as influencers according to our definition- people who consistently write unique, highly liked content. This is only 0.8% of authors. Eight of the 17 authors happen to have more than 4,500 followers on Medium.

Most authors either have a lot of posts or comments with few to no likes or only one or two posts with lots of likes.4

4. TOP 17 INFLUENCERS ON MEDIUM

207117

4 Comments to other posts are also included in our data set as posts by Medium.



F I N D I N G S

THE TOP MEDIUM.COM INFLUENCERS ON PRODUCT MANAGEMENT ARE:

8.2 7.6 7.4 7.3 7.2

7.2 6.9 6.9 6.8 6.8

6.7 5.9

5.6

5.9 5.9 5.9

5.8

Julie Zhuo

3 posts with a median 2500 likes

Kimber Lockhart


Giff Constable


Nathan Creswell


Steven Sinofsky


Noah Weiss

2 posts with a median 510.5 likes

Ken Norton


Brandon Chu


David Cancel


Intercom


Rian Van Der Merwe


Jason Fried


Paul Adams


Josh Elman


Chandra Kalle


Simon Cross


Matt LeMay




4. TOP 17 INFLUENCERS ON MEDIUMF I N D I N G S

Opposite we show authors as bubbles where the vertical axes defines the number of their posts tagged as PM, and the horizontal axis depicts the median number of likes. (Average number of likes depends heavily on the outlier posts).

# PO

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MEDIAN # LIKES

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0 500 1000 1500 2000 2500

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The most active users without any worthy content

Real influencers

People who created good content in PM one or two times



F I N D I N G S

We discovered that a high number of followers does not correlate to the number of likes authors get for their posts.

Look at the graph opposite. We would expect the top right corner to be filled with authors (more followers - more likes), but this is not the case at all, except for a single person. Julie Zhuo, Facebook’s vice president for product design has 64K followers, and a median of 2,200 likes for the three posts she wrote for Medium.com!

5. MORE FOLLOWERS WILL NOT GIVE YOU MORE LIKES

The top posts with 23 or more likes are so rare that they are statistically considered as anomalies. There are 436 posts, and they constitute 12% of all data. As we examined these posts, we noted that people share their “hearts” sparingly on Medium.

MED

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# LI

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# FOLLOWERS

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Most of the authors are here

More followers doesn’t mean more likes



IND

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Author# Post tagged as

product management Median # Likes # Followers Influencer Score Total Posts on Medium

Julie Zhuo 3 2500.0 73181 8.2 100

Brandon Chu 8 71.5 1845 7.6 17

Steven Sinofsky 7 273.0 17751 7.4 33

Jason Fried 7 156.0 89582 7.3 126

Chandra Kalle 7 35.0 958 7.2 29

Giff Constable 7 26.0 2018 7.2 26

Intercom 6 114.5 16072 6.9 40

Ken Norton 6 81.5 8204 6.9 20

Matt LeMay 6 57.0 2054 6.8 12

Josh Elman 5 346.0 27686 6.8 64

Kimber Lockhart 2 1359.0 1543 6.7 4

David Cancel 4 210.0 9692 6.3 45

Paul Adams 3 232.0 11210 5.9 16

Simon Cross 3 221.0 2164 5.9 14

Noah Weiss 2 510.5 4343 5.9 9

Nathan Creswell 2 422.0 338 5.8 4

Rian Van Der Merwe 2 255.5 3293 5.6 62



IND

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AuthorMedian #

Likes

Most popular words used (based on the term frequency -

inverse doc frequency method)

Julie Zhuo 2500 [design, designer,pm, id, try, problem, like, good, work, think]

Kimber Lockhart 1359 [sense, team, decision, fast, course, make, time, action, day, individual]

Noah Weiss 510.5 [google, search, time, idea, pm, week, ceo, structure, core, engineer]

Nathan Creswell 422 [pm, engineer, youre, product, see, something, sound, get, go, thats]

Josh Elman 346 [product, twitter, user, facebook, manager, would, turn, talk, hear, company]

Steven Sinofsky 273 [product, work, new, change, time, company, best, pm, year, many]

Rian Van Der Merwe 255.5 [research, end, live, product, experience, revenue, need, design, people, didnt]

Paul Adams 232 [meet, month, someone, people, hire, pm, need, owner, facebook, build]

Simon Cross 221 [pm, book, facebook, trust, read, ive, list, people, post, youre]

David Cancel 210 [product, matter, customer, company, team, pm, wrong, get, ceo, co]

Jason Fried 156 [work, weve, project, thing, make, time, version, http, client, get]

Intercom 114.5 [customer, product, feedback, first, user, cost, market, business, youre, make]

Ken Norton 81.5 [google, meet, product, pm, hire, engineer, interview, manager, one, ceo]

Brandon Chu 71.5 [product, pm, team, company, launch, user, work, make, get ,youre]

Matt LeMay 57 [pm, product, skill, work, team, ive, manager, process, customer, often]

Chandra Kalle 35 [product, theyre, user, http, push, com, data, mobile, dont, engineer]

Giff Constable 26 [team, pm, com, people, talk, usually, thing, part, new, work]

5. WHAT MEDIUM READERS LIKE

We analyzed all posts by the influencers for content and the popular words used (based on the td-idf method) and depict them opposite:



F I N D I N G S

Expect 4,125 Product Management Posts in 2016

We observe a consistent growing trend when we display the posts on PM. In the graph opposite the blue bars represent the actual number of posts in our data, and the green bars are predicted numbers based on the simplest prediction model for linear growth (which has 97% correlation accuracy on actual monthly volumes):

6. A GROWING TREND

MED

IAN

# LI

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600

400

200

JAN

15

FEB

15

MAR

15

APR

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JUN

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Actual Value

Trend

Prediction

So, based on the linear growth and wild predictions over a seven-month period from May to December 2016, we can hypothesize that this year a whopping 4,125 posts will be published in the year of 2016.



F I N D I N G S

Thought leadership on Medium emerged with few likes.

The median number of likes for all PM posts is only 2. The largest fraction of posts has none to few likes. In fact, 88% of PM posts on Medium get 22 likes or less. If a post got more likes than that, it falls among the top 357 outlier posts.

We also looked at the distribution of keywords in the highly liked posts and found it is very similar to the entire universe of PM posts. However, it appears readers liked the posts with more emphasis on the top keywords. Note the values are higher than in the earlier keywords graphic:

7. LIKEABILITY ISN’T EVERYTHING

UNDERSTAND CUSTOMERS

0% 5% 10% 15% 20% 25%

DATA-DRIVEN DESIGN

USER CHURN

UNDERSTAND USER BEHAVIOUR

CUSTOMER RETENTION


USER RETENTION

CUSTOMER FEEDBACK

USER FEEDBACK

USER STORIES

USER CONVERSION


UNDERSTANDING CUSTOMERS

UNDERSTAND USERS

DATA-DRIVEN PRODUCT MANAGEMENT

DATA-DRIVEN PRODUCT

CUSTOMER DEVELOPMENT

CUSTOMER EXPERIENCE

USER RESEARCH

USER EXPERIENCEUX



F I N D I N G S

Some Product Management authors find that one post may be enough. Others plunge into posting.

Another interesting fact about top posts is that they have 319 unique authors (only 15% of 2,071 - all authors we considered). The most surprising for us was to see that the majority of these authors (259 people) have only one single post on Medium - this is 81% of authors of “top posts”.

At last we looked at yet a smaller, highly curated sample of Product Management posts only written by people whom we identified as “influencers” based on the number of posts and the median number of likes. In this sample only five keywords were present, with customer experience being the top keyword. (19.5% of posts by “influencers” in our definition use the keyword “customer experience”).

8. PUBLISHING PATTERNS:

0% 5% 10% 15% 20%

USER RESEARCH

USER EXPERIENCE

CUSTOMER FEEDBACK

UX

CUSTOMER EXPERIENCE



F I N D I N G S

Out of the 883 different tags present along with the tag “Product Management”, the top are ’Startup’, ‘UX’, ‘Design’, ’Tech’, ‘Agile’, ‘Scrum’ and ‘Entrepreneurship’.

Bonus: Try our tool to quickly retrieve posts that interest you. We built this quick tool to enhance search by keywords, since we found combining tags the traditional way does not always produce sufficient results.

Find posts by keywords here http://datastories.com/product-management-landscape-in-medium

9. DISCOVERING CONTENT ON MEDIUM

UX

TECH

SCRUM

AGILE

STARTUPS ENTREPRENEURSHIP

DESIGN

PRODUCT MANAGEMENT

04FINAL THOUGHTS





F I N A L T H O U G H T S

FIND OUT MORE

NomNom Insights. All your customer feedback in one place.

Website: www.nomnom.it

Twitter: @heynomnom

DataStories

Website: www.datastories.com

Twitter: @datastoriespro

We hope this research helped you discover new content, interesting people to follow and gave you a panoramic view on what’s going on Medium.com when it comes to product management content.

If you want to find out more about the companies behind this research you can find us here:

product management landscape on medium.com

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