Is Twitter’s R&D a case of flogging a dead (race) horse?

Twitter has invested $2.2bn in research and development over the last three years. But with growth in net revenue slowing considerably in 2016, it appears this sizeable investment is no longer paying dividends. Data for this post can be found here.

As Twitter’s revenue growth slows and its share price continues to slide, it’s not surprising to find the company’s costs being scrutinised. Earlier this week Techcrunch looked at executive pay and the remuneration of CFO/COO Anthony Noto in particular.

Research and development expenditure is a key area for a technology company like Twitter. You would expect it to be a significant cost, and sure it enough it is. You would also expect to see a significant return from such investment in user growth and ultimately in revenue. Twitter’s lack of user growth has been widely discussed so I’ve focussed on the $s.

Twitter’s 2016 R & D Performance

Twitter spent $713m on research and development in 2016.

Twitter financials last 5 years

At the same time its net revenue (revenue minus the direct costs of earning that revenue) grew by $109m, a 7.3 per cent increase on 2015.

That represents $0.15 of additional net revenue for every $1 spent on R&D.

Facebook’s performance 10x better

To put that in context Facebook’s equivalent numbers were:

  • R & D spend – $5,919m
  • Net revenue growth $8,788m.
  • $1.48 of additional net revenue for every $1 spent on R&D.

If Twitter had achieved the same level of net revenue growth for every $ spent on R&D its net revenue would have risen by over $1bn in 2016 ($1.48 x $713m = $1,055m).

This would have represented a 70.8 per cent increase in net revenue versus the actual growth achieved of 7.3 per cent.

Decline in Twitter R & D returns

As the graph below shows, 2015 and 2014 were considerably better by this metric.

Twitter R and D impact on net revenue

Twitter achieved $0.66 of net revenue increase per $ in R&D spend in 2015 and $0.81 in 2014.

These figures were similar to those achieved by Alphabet (Google), though still only half (2014) and two thirds (2015) of Facebook’s.

These numbers show that matching Facebook’s 2016 performance of $1.48 revenue/$ R&D was probably too much of a tall order.

However even if Twitter’s 2016 performance had only been equal to its own 2015 figure the company would still have seen a rise in net revenue of $470m ($0.66 x $713m), $361m more than was actually achieved. This would have meant a growth rate of 31.6 per cent.

You can imagine the significant impact on the company’s share price that a stronger growth story like this would have.

So R&D is still being heavily funded, but revenue growth is fast disappearing. This begs the questions, where, in who and in what are these funds being invested?

Share based compensation

Another factor here is Twitter’s R & D staff are the single biggest recipients of share based compensation (SBC). Anyone unfamiliar with share based compensation please skip to the end of the post for a brief explanation of how it works before reading further.

In the last three years Twitter’s total share based compensation expense has totalled $1,929m and has exceeded the total adjusted EBITDA generated by the company ($1,610m).

Effectively all the underlying profit generated (and more) since its IPO has been invested in remunerating employees and officers.

Research and development SBC has totalled $1,099m over the last three years. This represents half of the $2.2bn invested in R&D and shows how crucial share based remuneration is to Twitter’s strategy for attracting and retaining product and engineering talent.

The problem is that this represents a transfer of value from shareholders to R&D staff of over $1bn since the start of 2014. Meanwhile any innovation and improvements made are having a declining impact on revenue growth, resulting in a reduction in Twitter’s value of $25bn over the period.

Not what you’d call a great deal for shareholders.

What does this mean?

I can see two key factors at work here:

  1. Competition for talent – Twitter needs to attract the same quality of people who could work at Facebook, Google et al. It therefore needs to offer competitive remuneration packages. Share based compensation has apparently formed a key part of this to date.
  1. Creative block – lack of successful new ideas, innovation and improvements to drive revenue growth.

Implying the following question:

Should Twitter be getting more out of its R&D resources or are even these talented individuals unable to add significant value to the platform?

If the former, then this is a management issue which needs addressing and fast.

If the latter then the company should be looking outside of its own teams for ideas and innovation and allocating funds accordingly.

Either way, if it continues to invest huge sums in R&D without significant improvement in net revenues, then the company’s declining value appears unlikely to turnaround.

Share based compensation expense – an explanation

In a company’s accounts share based compensation (SBC) expense represents value given to employees of the company in the form of shares or options.

It’s calculated in various ways, but in simple terms it’s equal to:

SBC expense = Market value (of shares/options provided) – Amount paid by employee

Share based compensation generally forms part of an employee’s remuneration package to attract new joiners, motivate them to create value and/or improve retention.

SBC is a non cash expense, however because it relates to the creation of new shares – either immediately or potentially in the future when options become exercisable – its effect is to dilute existing shareholders. Again in simple terms this dilution is equal to the value of the expense.

Shareholders therefore expect to see a return on this investment greater than the value that they have been diluted by.


Note this is a highly simplified example, but it should help to get the gist.

Today ABC Company has a market capitalisation (the value of its equity to its shareholders) of $1bn.

It gifts shares to a group of highly sort after new employees who it believes will be instrumental in creating a new product.

The SBC expense of these shares is $10m at the point they are gifted i.e. 1% of its equity value.

The new employees are prohibited from selling the shares for a period of one year.

All things being equal the existing shareholders now only own 99% or $990m of the company.

A year later the company is valued at $1.2bn, with the $200m increase in value being wholly attributed to the success of the new product created by the team.

The shareholders 99% is therefore worth $1.188bn representing a gain of $188m.

The new joiners can now sell their shares worth $12m ($10m value when they were granted plus 1% of the increase of $200m in the overall value of the company).

Everyone’s a winner. The company didn’t have to find any cash. The new employees have banked $12m (less associated taxes) and shareholders have seen the value of their shares rise by nearly 19%.

Note: I do not own any shares in Twitter, Facebook or Alphabet. All analysis is based on publicly available information from Annual Reports, SEC filings and Proxy Statements.

Twitter needs to find its “pulse”


Lissted’s Tweetsdistilled experiment is coming to an end. The Twitter accounts created have been popular with all types of users.

They provide a blueprint for how Twitter could use such feeds to: increase ad impressions, encourage engagement, improve sign up conversion, potentially pop some filter bubbles and enhance its return from investments like Moments.

In its early days Twitter used to talk about being “the pulse of the planet”. Years later and Twitter’s still struggling to help users easily find its best content. And time is running out.

These tweets from M G Siegler of Google Ventures a couple of weeks ago, and his related post, had me both cheering and beating my head against the wall in equal measure.

I could not agree more. I’ve been a broken record on the subject for a while now. And it’s not been based on just words.

Tweetsdistilled – finding Twitter’s pulse

Lissted’s Tweetsdistilled application was created in 2014. The objective? To design a system to identify the best of Twitter:

breaking news

influential commentary


and the downright bizarre.

In very simple terms Tweetsdistilled is sort of what you’d get if you crossed Twitter lists with the Highlights and While You Were Away features.


– you don’t have to find or create the Twitter lists (Lissted’s algorithms do that),

– we aren’t limited by list sizes (some feeds are distilling 10,000s of accounts in real time),

– and we reckon our tweet selection recipe is way better!

And unlike something like Nuzzel (which I think is great by the way), Tweetsdistilled is 100% focussed on identifying individual tweets, not external content that’s being shared.

Tweetsdistilled: a short history

In order to test the effectiveness of the app’s recipe, in September 2014 we created a Twitter account – @Tweetsdistilled – and a bot that auto retweeted the items identified by the system. We also tried creating a feed relating to a specific community – @PoliticsUKTD.

The initial results told us we were on the right track.

Later on we split out US community tweets into their own feed – @USTweetsDistill – and made the @Tweetsdistilled feed based solely on UK Twitter community reaction.

Over the subsequent 2+ years we’ve experimented with a number of other feeds relating to different scenarios such as specific communities, geographies and topics.

These have included: @PoliticsUSTD, @HealthUKTD, @EducationUKTD, @AppleTwD, @WalesUKTD, @NUFCTD, @LogisticsTD, @EuropeTD and @PRUKDistilled. Last week we even added a Donald Trump specific feed – @TrumpDistilled!

The main feeds are broad in their focus and generally feature tweets from journalists, media outlets, celebrities, organisations, bloggers and other influencers. With a sample of good quality viral tweets from your average user thrown in!

Feeds focussed on a specific community will be a mixture of tweets that are about that area of interest and ones that could be about any topic, but which appear to be of greater interest to the group.

The end of the line for Tweetsdistilled

But all good things must come to end and as of 14th February the Tweetsdistilled accounts will be mothballed. Basically the cost/benefit equation of running the feeds isn’t justified anymore.

There are a number of reasons for this:

Objective achieved

The experiment achieved its objective a while ago and we now have a great system for identifying what matters on Twitter. (Any social listening/MarTech companies out there interested in such a system drop me a line!

Data cost

We have to purchase a significant amount of data from Twitter to power the feeds. Ironically having refined it and published the insights publicly via the feeds the biggest group of followers is Twitter staff!

Limited organic follower growth

This is due to a combination of the nature of the accounts themselves i.e. they only retweet others and don’t have tweets of their own, and a lack of word of mouth – many followers are journalists who appear to want to keep the accounts as “secret” weapons!

But the biggest reason is:

Why isn’t something like this not part of Twitter anyway?

As M G Siegler points out

“The first tab on Twitter, the one currently labelled ‘Home’ should be replaced by a tab made up of the ‘While You Were Away’ / ‘Highlights’ content. But on steroids. Thousands of tweets. The “best of” Twitter.”

I’d go further and say you don’t even need two tabs. Using accounts like these you can have everything all in one feed. Simples.

Tweetsdistilled has been a proof of concept of the value of Twitter having a feature like this.

The accounts have been popular with every type of user: influential, power, occasional and new. Here are some examples of reactions to our announcement re closing the feeds down:

Some of the most disappointed are Twitter staff!

Business case for “pulse” accounts like these

They inject the best of Twitter directly into your feed.

The approach would have a number of powerful benefits:


Most important of all in a commercial context followers are still looking at a Twitter feed and so can be served ads in the usual way. This is not the case with the Trending Now and Moments elements of Explore. Also ads could be targeted effectively when a user was following a niche community or topic feed e.g. @EducationUKTD, @AppleTwD or @NUFCTD.

More out of Moments

There’s real skill in crafting a good Moment and they can be a great way of discovering new content. But they rarely appear in your feed organically. Mixing relevant Moments into particular TweetsDistilled type feeds would potentially increase engagement and improve ROI on their creation.

Efficient listening

A follower of one of these Tweetsdistilled feeds is effectively getting served some of the best tweets from a group without having to follow the thousands of accounts that are being distilled.

They also don’t have to switch into a different part of the app or use Tweetdeck like they would do to use a Twitter list. And finally they can still follow specific accounts as well if they want to see all of an individual’s tweets.

Ideal for a new user

No requirement for a new user to follow lots of accounts, they can follow one stream based on an interest area and then start to follow individual accounts over time if they want.

Optional for existing users

Instead of forcing an algorithmic approach onto someone’s feed, where they potentially miss content they wanted to see, these accounts simply augment the user’s native feed.

Filter bubble popper

It’s been well established that most users of social media only follow accounts that are similar in views to themselves. A system such as Tweetsdistilled allows you to see some of the most important and interesting tweets from a particular community without having to follow the specific members. This lack of direct relationship could encourage users to broaden their horizons.

So overall the accounts should:

– increase advertising impressions;

– get more out of existing investments like Moments;

– encourage engagement;

– improve sign up conversions; and

– potentially broaden the content users see.

Thanks and goodbye

Surely this is all a no brainer. If we can build Tweetsdistilled with our constrained resources then surely Twitter could? We’ve probably got the technological equivalent of duct tape and cardboard toilet rolls at our disposal compared to them!

No one will be more sad to see the feeds switched off than me. Leaving aside any vested interest, I’ll miss the fantastic content they gave me and their ability to keep me up to date with everything that was happening.

I’ll be following up this post with a few based on the data we’ve analysed.

In the meantime thanks to everyone who followed the feeds. We’ve really enjoyed helping you keep your finger on “the pulse”.

How Twitter could help solve Facebook’s fake news problem

Twitter shares by influential individuals and organisations could be harnessed in an automated news content rating system.

This system could assist Facebook in identifying articles that have a high risk of being fake. The methodology is based on a journalistic verification model.

Examples: the model would have rated as high risk:

-FINAL ELECTION 2016 NUMBERS: TRUMP WON BOTH POPULAR ( 62.9 M -62.2 M ) – about the election results. It was ranking top of Google for a search for “final election results” earlier this week and has had over 400,000 interactions on Facebook. It was identified as fake (obviously) by Buzzfeed.

–  Pope Francis Shocks World, Endorses Donald Trump for President, Releases Statement‘. Shared  nearly 1 million times on Facebook. Now taken down, having been reported as fake by The New York Times

The rating system described below is subject to patent pending UK 1619460.7.

At the weekend Mark Zuckerberg described as “pretty crazy” the idea that sharing fake news on Facebook contributed to Donald Trump being elected President.

He went on to say in a Facebook post:

“Of all the content on Facebook, more than 99% of what people see is authentic. Only a very small amount is fake news and hoaxes. The hoaxes that do exist are not limited to one partisan view, or even to politics. Overall, this makes it extremely unlikely hoaxes changed the outcome of this election in one direction or the other.”

“That said, we don’t want any hoaxes on Facebook. Our goal is to show people the content they will find most meaningful, and people want accurate news. We have already launched work enabling our community to flag hoaxes and fake news, and there is more we can do here. We have made progress, and we will continue to work on this to improve further.”

Yesterday, Business Insider reported a group of students had hacked together a tool that might help.

I think part of the answer lies in another social network, Twitter.

An important aside

It’s important to note the topic of “fake” news is not black and white. For example, parody accounts and sites like The Onion are “fake news” that many people enjoy for the entertainment they provide.

There’s also the question of news that is biased, or only partially based in fact.

The idea proposed below is simply a model to identify content that is:

1. more likely to be fake; and

2. is generating a level of interaction on Facebook that increases the likelihood of it being influential.

Verification and subsequent action would be for a human editorial approach to decide.

Using Twitter data to identify potentially fake news

In its piece on Zuckerberg’s comments, The New York Times highlighted this article Pope Francis Shocks World, Endorses Donald Trump for President, Releases Statement (now removed) that had been shared nearly a million times on Facebook. It’s fake. This never happened.

If it had been true it would obviously have been a big story.

As such you’d expect influential Trump supporters, Republicans and other key right wing media, organisations and individuals to have been falling over themselves to highlight it.

They weren’t.

Lissted tracks the Twitter accounts of over 150,000 of the most influential people and organisations. This includes over 8,000 key influencers in relevant communities such as Republicans and US Politics, as well as potentially sympathetic ones such as UKIP and Vote Leave.

Of these 150,000+ accounts only 6 shared the article.

Extending the analysis

Lissted has indexed another 106 links from the same domain during the last 100 days.

The graph below shows analysis of these links based on how many unique influencer shares they received.


You can see that 74 of the 107 links (including the Pope story) were only shared by a single member of the 150,000 influencers we track. Only 5 have been shared by 6 or more and that includes the Pope story.

That’s just 196 influencer shares in total across the 107 links.

Yet, between them these URLs have been interacted with 12.1 million times on Facebook.

And of course these are the stories that have been shared by an influencer. There could be more that haven’t been shared at all by influential Twitter users.

Lissted’s data also tells us:

– 133 of the 150,000 influencers (less than 0.1%) have shared at least one of its articles; and

– the article published by the site that has proved most popular with influencers has received 10 shares.

How could this help identify high risk news?

You can’t identify fake news based simply on levels of reaction, nor based on analysing what they say. You need a journalistic filter. Twitter provides a potential basis for this because its data will tell you WHO shared something.

For example, Storyful, the Irish social media and content licensing agency, has used Twitter validation by specific sources as a way of identifying content that is more likely to be genuine.

I don’t know why very few of the influencers Lissted has been tracking shared the piece. But my suspicion would be that as influential members of their communities they’re:

– capable of spotting most fake news for what it is, and/or

– generally less likely to share it as even when it serves their purpose they know that they could be called out for it (they’re more visible and they’ve got more to lose); and /or

– less likely to be exposed to it in the first place.

Obviously, not all content will be shared on Twitter by these 150,000 accounts. But you can bet your bottom dollar that any vaguely significant news story will be. The temptation to want to highlight a genuine story is just too great.

Comparison to example of genuine content

To give the Pope story numbers some context, the table below shows a comparison to this piece on the Donald Trump website – Volunteer to be a Trump Election Observer (NB: post victory the URL now redirects to the home page).


Both URLs have similar Facebook engagement, but there’s a huge difference in the influencer metrics for the article and the domain.

This is just one example though. If we build a model based on this validation methodology does it provide a sound basis for rating content in general?

NB: the model that follows focuses on content from websites. A similar, approach could be applied to other content e.g. Facebook posts, YouTube videos etc.

Proof of concept

To test the methodology I built a rating model and applied it to three sets of data:

1. The 107 links identified from – data here.

2. Links that Newswhip reported as having 250,000+ Facebook interactions in the period 15/9/16 – 14/11/16 – data here.

3. A random sample of over 3,000 links that were shared by influencers from the specific communities above in the period 15/10/16 -14/11/16 – data here.

The rating model gives links a score from 0 – 100. With 100 representing a links that has a very high risk of being fake and zero being a very low risk.

To rate as 100 a link would need to have:

– received 1,000,000 Facebook interactions; and
– be on a site that has never been shared by one of the 150,000 influencers, including the link itself.

The distribution of rating for the random sample is as follows:


Mark Zuckerberg’s commented that less than 1 per cent of content on Facebook is fake. If we look at the distribution we find that 1 per cent corresponds to a score of 30+.

The distribution also shows that no link in the sample scored more than 70.

Finally over 90 per cent of URLs rated at less than 10.

On this basis I’ve grouped links in the three data sets above into 4 risk bands:

Exceptional – 70+
High – 30 -70
Medium – 10 – 30
Low – 0-10

Applying these bands to the three sets gives:

distribution-of-articles-by-risk-rating across three sets

Unsurprisingly a high proportion of the 250,000+ group are rated as Medium to Exceptional risk. This reflects the fact that there are so few of them – 182 – and the implicit risk of being influential due to their high engagement.

Verifying these would not be a huge drain on resources as that translates to just 2 or 3 links per day!

The graph also shows how high risk the endingthefed site is with over 95 per cent of its content rated as High or Medium.


1. Being ranked as medium – exceptional risk does NOT mean the content is fake. It is simply an indicator. Just because one article on a site is fake does not mean that all the risky content is.

Also an article could be genuine viral content that’s come out of the blue from a new source.

The value in the model is its ability to identify the content that needs verifying the most. Such verification should then be done by professional journalists.

2. The rankings only reflect the 150,000 individuals and organisations that Lissted currently tracks. There could be communities that aren’t sufficiently represented within this population.

This isn’t a flaw in the methodology however, just the implementation. It could be addressed by expanding the tracking data set.

Example findings

The top 10 ranked articles in the 250,000+ group are as follows:

1. Mike Pence: ‘If We Humble Ourselves And Pray, God Will Heal Our Land’ (506k Facebook interactions, 0 influencer shares)

2. Just Before the Election Thousands Take Over Times Square With the Power of God (416k Facebook interactions, 0 influencer shares)

3. TRUMP BREAKS RECORD in Pennsylvania “MASSIVE CROWD FOR TRUMP! (VIDEO) – National Insider Politics (207k Facebook interactions, 0 influencer shares)

4. SUSAN SARANDON: CLINTON IS THE DANGER, NOT TRUMP – National Insider Politics (273k Facebook interactions, 0 influencer shares)

5. FINGERS CROSSED: These 11 Celebrities Promised To Leave America If Trump Wins (455k Facebook interactions, 1 influencer share)

6. Trump: No Salary For Me As President USA Newsflash (539k Facebook interactions, 0 influencer shares)

7. I am. (454k Facebook interactions, 1 influencer share)

8. A Secret Has Been Uncovered: Cancer Is Not A Disease But Business! – (336k Facebook interactions, 0 influencer shares)

9. The BIGGEST Star Comes Out for TRUMP!! Matthew McConaughey VOTES Trump! (294k Facebook interactions, 1 influencer share)

10. Chicago Cubs Ben Zobrist Shares Christian Faith: We All Need Christ (548k Facebook interactions, 1 influencer share)

My own basic verification suggests some of these stories are true. For instance Donald Trump did indeed say that he would not draw his Presidential salary.

However the Matthew McConaughey story is false and by the article’s own admission the Pennslyvania rally image is from April not October, plus there are no details on what “records” have been broken.

From outside the top 10 this post, rated as high risk, FINAL ELECTION 2016 NUMBERS: TRUMP WON BOTH POPULAR ( 62.9 M -62.2 M ) about the election results was ranking top of Google for a search for “final election results” earlier this week. It was identified as fake by Buzzfeed.

It would be great if any journalists reading this would go through the full list of articles rated as high risk and see if they can identify any more.

Equally if anyone spots URLs rated as low risk that are fake please let me know.

Further development

This exercise, and the mathematical model behind it, were just a rudimentary proof of concept for the methodology. An actual system could:

– utilise machine learning to improve its hit rate;

– flag sites over time which had the highest inherent risk of fake content;

– include other metrics such as domain/page authority from a source such as Moz.

Challenge to Facebook

A system like this wouldn’t be difficult to setup. If someone (Newswhip, BuzzSumo etc) is willing to provide us with a feed of articles getting high shares on Facebook, we could do this analysis right now and flag the high risk articles publicly.

Snopes already does good work identifying fake stories. I wonder if they’re using algorithms such as this to help? If not then perhaps they could.

Either way, this is something Zuckerberg and Dorsey could probably setup in days, hours perhaps!

@London2012: golden social media assets going to waste

Social media accounts with huge associated audiences are lying dormant. Examples like @London2012’s Twitter account could be repurposed to make the most of these assets.

So, the Olympics are over for another four years. Having gorged myself on the heroics of TeamGB, I’m personally suffering from withdrawl.

TeamGB’s social media team also did a sterling job over the two weeks, sharing content about our athletes’ magnificent performances.

Their two primary platforms based on fans and followers were Facebook and Twitter. Their Twitter account has an impressive 822,000 followers.

But there’s another relevant Twitter account with an even larger audience, and it’s dormant.


London 2012   London2012    Twitter

This account has 1.32 million followers. It’s tweeted seven times since the end of the Paralympics in 2012, the last in July 2013.

Since then, nothing.

Will this account and its audience of 1.3 million potentially sport mad followers just sit and fester forever?

And it isn’t just the number of followers that’s impressive, it’s the quality too.

Here are some examples of significant followers of @London2012 who don’t follow @TeamGB:

@coldplay, @WayneRooney, @idriselba, @astonmartin, @thetimes, @Harrods, @EvanHD, @cabinetofficeuk, @WomensRunning, @KathViner and @andyburnhammp.

The identity shouldn’t get in the way of using it. Behind every account is a unique TwitterId (it’s 19900778 in @London2012’s case if you’re interested). This means you can change your @username and still maintain your follower and following relationships. Here are the Twitter instructions to do this.

I don’t know who “owns” this asset, but surely whoever it is could think of a change of identity that would still be relevant to the majority of its followers. Perhaps it could have been used to support the Games’ legacy? @UK_Sport’s 91,400 followers rather pales in comparison.

And @London2012 isn’t the only account like this.

What are the BBC going to do with accounts relating to shows that are no more, like @BBCTheVoiceUK and its 521,000 followers, or the @ChrisMoylesShow with 518,000?

Nothing by the looks of it.

On a sombre note, there are accounts that become dormant because someone dies. Examples like @ebertchicago and @davidbowiereal demonstrate that even then there can be circumstances where it’s appropriate for the accounts to live on.

As of writing Lissted‘s data shows 28,401 accounts with 10,000+ followers who haven’t tweeted in the last 90 days.

Not all of these accounts will be dormant. Some like Ed Sheeran may be just “buggering off for a bit“. But many will.

Between them they have a combined untapped group of 1.5 trillion followers.

Now there’s a number worthy of a gold medal!

Unicorns, content and engagement flights of fancy

When you’re seeking influential content, engagement metrics such as a Facebook likes and LinkedIn shares are too simplistic. You need to know more about who engaged with it and why.

On the Road to Recap Venture Capital Community ReactionLast week venture capitalist Bill Gurley published a post called On the Road to Recap.

For anyone who doesn’t know, a Unicorn in this context is a startup company with a valuation in excess of $1bn.

The post analysed in depth the current investment situation in relation to Unicorns and concluded:

“The reason we are all in this mess is because of the excessive amounts of capital that have poured into the VC-backed startup market. This glut of capital has led to (1) record high burn rates, likely 5-10x those of the 1999 timeframe, (2) most companies operating far, far away from profitability, (3) excessively intense competition driven by access to said capital, (4) delayed or non-existent liquidity for employees and investors, and (5) the aforementioned solicitous fundraising practices. More money will not solve any of these problems — it will only contribute to them. The healthiest thing that could possibly happen is a dramatic increase in the real cost of capital and a return to an appreciation for sound business execution.”

The post lit a fire in the VC and startup communities.

In fact Lissted ranks the post as the most significant piece of content on any investment related topic in the VC community in the last two months. 

So I thought I’d see how it compares to other recent posts about Unicorns.

Comparison with other “Unicorn” content

I searched across the last month for posts with the most shares on LinkedIn (URLs listed at the end). If you search across all platforms you end up with very different types of unicorn!

Having found the Top 10 articles on this basis, I then looked at the number of distinct members of Lissted‘s VC community on Twitter who shared each of the articles. The community tracks the tweets of over 1,500 of the most influential people and organisations in relation to venture capital and angel investment.

Finally for completeness I also looked at the number of distinct Lissted influencers from any community who tweeted a link to the piece.

In the graph the engagement numbers have been rebased for comparison, with the top ranking article for each measure being set to 100.

On the Road to Recap Venture Capital Community ReactionThe difference in reaction by the VC community and influential individuals in general is considerable.

15x more influential members of the VC community (169) shared ‘On the Road to Recap’ than the next highest article (11 -Topless dancers, champagne, and David Bowie: Inside the crash of London’s $2.7 billion unicorn Powa).

9x more influencers across all Lissted communities (419) shared the post (46 for the Powa piece).

VC Community reaction examples

Influential retweeters of Bill’s initial tweet above included Chris Sacca, Om Malik & Jessica Verrill.

Examples of key community influencers who tweeted their own views were:  

And people are still sharing it days later:  

Mythical measurement

So, the next time you set out to find influential content, don’t get too carried away with big engagement numbers. Focus on understanding where and who that engagement came from.

That way your conclusions will be legendarynot mythical.

If you’d like to get a daily digest of the influential content in the Venture Capital community, sign up for a free Lissted account here, then visit the Venture Capital page.

Lissted Venture capital page


1. Forget unicorns — Investors are looking for ‘cockroach’ startups now

2. What investors are really thinking when a unicorn startup implodes

3. On the Road to Recap: | Above the Crowd

4. Next Chapter: Cvent Acquired for $1.65 Billion

5. The fall of the unicorns brings a new dawn for water bears

6. Why Unicorns are struggling

7. Oracle just bought a 20-person company for $50 million

8. Silicon Valley startups are terrified by a new idea: profits

9. Topless dancers, champagne, and David Bowie: Inside the crash of London’s $2.7 billion unicorn Powa

10. 10 Startups That Could Beat a Possible Bubble Burst