Where LinkedIn’s Strategy Leaves Big Gaps

LinkedIn certainly has many, many strengths, including a world-class team, rich profiles of most of the top professionals in the world, and a wide set of features around connecting, learning, and job hunting. But, just like there are multiple social networks that fulfill our need to connect, in the future there will be multiple professional networks that fulfill our need to grow professionally.

So, as a thought experiment, I’m thinking through what could be considered LinkedIn’s Achilles heel that another network could use to gain share.

*** Since my goal is only to identify gaps here, I would in no way interpret what follows as a “LinkedIn is doomed” message. Also, I’m not trying to explain LinkedIn’s recent share price drop. If you are looking for that, here’s a good article: Five Reasons LinkedIn Had Its Worst Day Ever ***

Gap #1: LinkedIn’s product (features, onboarding, flow) targets 600M professional users instead of 3B who work worldwide.

Linkedin’s mission applies to everyone: connect the world’s professionals to make them more productive and successful. When you join LinkedIn, you get access to people, jobs, news, updates, and insights that help you be great at what you do. But the way they go about this mission ends up targeting 600M people instead of the potential 3B working people globally:

  • First, the jobs on LinkedIn tend to be focused on higher wage jobs, in part because LinkedIn’s fee structure is expensive but also because LinkedIn is viewed as a way to target passive candidates who are already employed. There is less of a need to target passive candidates in the non-professional segment.
  • Furthermore, LinkedIn doesn’t capture important information for workers that work hourly jobs – e.g., which hours they are available, what specific locations they’ve worked at, and what their current address is and how they plan to get to work, and what hours they are available.
  • Each feature needs to be adapted to the non-professional audience. For example, the skills feature misses attributes that are important differentiators for a workforce whose core skills are personality attributes: e.g., friendly or resourceful.

Because of these reasons, even though their vision is broad, LinkedIn’s numbers show they are not on track to reach everyone in the global workforce. LinkedIn’s MAUs seems to be plateauing in its latest earnings release at about 100M (See below). Someone could make a big bet on penetrating the  remaining 2.4B people in the market.

 

Screen Shot 2016-02-22 at 2.25.34 PM
Member growth doesn’t look like it is on track to hit the 3B working people globally. 

 

Opportunity #2: Low engagement. From the above numbers, only 100M users use LinkedIn monthly out of 400M signups (see the first graph again). 25% of users sign in monthly or more frequently –the other 75% use it less than once a month.

Compare that with Facebook where you have 1B DAU and 1.5B total users (that’s 66% using it daily). Of course, a company does not have to incite daily usage to be successful (e.g., AirBnB), but, in this case, profession and work are a part of my daily life (unlike taking a trip). If  you don’t believe that a company could have daily engagement with a professional network, just look at Slack.

So why does LinkedIn fall short here? In a survey done a few years ago, users listed which features of LinkedIn they used the most:

 

Screen Shot 2016-02-22 at 2.26.37 PM.png
Features LinkedIn users found most useful (source at end of post)

 

 

These are really impressive and innovative features that the team has built but most of these features for me do not fulfill a set of daily unmet needs. Yes, if I’m looking for a job or hiring someone I will be on LinkedIn often, but this doesn’t happen on most days.

The ones that come close are messaging (Linkedin InMail), daily news (Pulse/News feed), and looking up people/research before a meeting (Linkedin search). However, for these features, the current value proposition for LinkedIn is not compelling enough for daily use:

  • For messaging, there are other platforms that I prefer (email, texting, and Facebook).
  • For reading the news/staying up to date, Linkedin has a feature set through its Pulse acquisition. But, there is so much competition for the time I have to read “news” – between pocket, Google Now article feed (which leverages my Chrome history) and publications themselves, it’s not clear I need an aggregator.
  • When I look at my Pulse feed, it’s not clear to me that it’s much better than any of the other aggregators I already use:

Screen Shot 2016-02-22 at 2.27.34 PMScreen Shot 2016-02-22 at 2.27.34 PM

(Two different Pulse feeds. I looked for a friend who’s an engineer who’s expressed sufficiently varied interests in his profile, but it turns out his Pulse feed was exactly the same!)

  • The only frequent need that I see is looking up a person before an important meeting or researching a Company. LinkedIn nails this use case.  

I may be being harsh here but I’d contrast this with Slack – a network that does fulfill a daily need. Linkedin, to its credit, did try to release an app called Lookup which allows you to connect with colleagues at your company, but it was focused on finding contact information for your colleagues. I haven’t seen LinkedIn building other features that can dramatically drive engagement.

Opportunity #3: Relatively low virality versus other networks  – When was the last time you invited someone to join LinkedIn? With Facebook (you’ve probably asked someone to please post their photos on facebook?) or with Slack you’ve invited the rest of your team to join. But with LinkedIn, when was the last time you asked someone to share content on the platform? Why is Linkedin a low virality product?

  • On LinkedIn, I’m not offered a clear benefit of inviting others to join. It’s not really communicated to me and I haven’t quite been able to figure it out
  • In Facebook’s case if I get my grandma on the platform and I post a picture I benefit from sharing something more important with one more person.
  • LinkedIn’s endorsements or skills feature, if done well, could create virality, but it hasn’t achieved that yet.

 

Opportunity #4: Quality/new content generation is a key part of good networks — e.g., Quora, Facebook, Instagram, Next Door. LinkedIn makes it hard to generate content. A company that exploits content generation in the professional space – i.e., makes it even easier than answering a question on Quora – could thrive.

  • When was the last time you generated original content on LinkedIn? On LinkedIn, I’m generally in the mode of being very cautious about what I say because everything I say is being broadcasted to my entire professional network. 
  • What about the people that blog via LinkedIn? The friction for writing a well-thought-out coherent article that you want the rest of your network to see is too high. As one Quora user says,  “ I often struggle to sit down and write content for my blog, but answering a question does not seem so hard. “

Various social networks have been successful in growing users/engagement by bringing the friction in posting down significantly. For example, Instagram will make your ugly pictures better with filters, YikYak (below) allows you to post anonymously, and Twitter only allows you to type short messages and is often used professionally. There’s an opportunity to do the same for a competitor in the professional networks space.

2014-03-25-yikyak1
Original, interesting content drives growth in networks. YikYak brings down the barrier to posting by making posts short and anonymous.

 

Bottom line: A company looking to build another professional network on the scale of LinkedIn could do one (or more) of the four:

(1) Target non-professionals

(2) Engage professionals/non-professionals on high engagement daily use cases

(3) Design a product with stronger incentives for users to invite more users

(4) Lower the friction to generating new content

 

Sources:

  1. “About Us | LinkedIn.” 2013. 18 Feb. 2016 <https://www.linkedin.com/about-us>
  2. “LinkedIn Q4 2015 Earnings Call – SlideShare.” 2016. 17 Feb. 2016 <http://www.slideshare.net/linkedin/linkedin-q4-2015-earnings-call>
  3. “Most helpful LinkedIn features according 2015 | Statistic.” 2013. 17 Feb. 2016 <http://www.statista.com/statistics/264135/most-helpful-linkedin-features-according-to-users/>
  4. “LinkedIn Lookup: Your Company At Your Fingertips … – iTunes.” 2015. 18 Feb. 2016 <https://itunes.apple.com/us/app/linkedin-lookup-your-company/id1000842861?mt=8>
  5. “10 Facts Small Business Owners Should Know About Social …” 2015. 17 Feb. 2016 <http://thrivenetmarketing.com/uncategorized/10-facts-small-business-owners-should-know-about-social-media-engagement-for-linkedin/>
  6. “LinkedIn now has 400M users, but only 25% of them use it …” 2015. 17 Feb. 2016 <http://venturebeat.com/2015/10/29/linkedin-now-has-400m-users-but-only-25-of-them-use-it-monthly/>

 

 

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A “common application” for jobs?

The Common Application (Common App) is an undergraduate college admission application that students can use to apply to over 500 member colleges. It’s an extraordinary feat if you think about it. These colleges compete with each other in sports, for professors and for students. Yet they have managed to merge all their application processes into one common form.

commonapplogo

If I think back to when I was applying for jobs, the process of filling out applications seems similar to college applications in the pre-Common App era. When the forms are long, there are endless usernames, passwords, and employment histories to fill out. This is especially true for non-professional jobs. On the other hand, when the forms are short, it is difficult to provide enough nuance to make yourself stand out.

If we step back, the incentives between recruiters and college admissions officers are similar. Both want to increase applications, increase acceptance rate, and improve diversity. Both are in continuous competition with each other, but could benefit from this type of collaboration.Yet, educational institutions have ended up in a better equilibrium than the job market.

It seems reasonable that in some end-state of the world the job market would move towards a common application as well. To make the job application process more efficient and less painful, what can we learn from the Common App?

(1) Having both a common form and a supplemental forms can help strike the balance between efficiency and getting the information you need. Most institutions that accept the Common App require at least one supplemental form. Special common applications exist for graduate schools. Studies show schools get higher quality and more diverse applications with this balance. Employment applications could have similar supplements by role and company.

(2) Creating an incentive to not over-apply is important to not flooding the market with applications. The common app reduces friction to apply to a job. But, the application fee creates a disincentive to apply to too many school. There’s no equivalent of that in the job market. A good system should create such disincentives — e.g., limit the number of applications per day or expose how many jobs an applicant has applied to. This puts the onus on both ends to make sure the candidate is a good fit. It also makes keeps recruiters from being flooded with too many applications to respond to.

(3) Facts need to be verified by recommendations up front to keep the process credible. Students can lie on the CA (e.g., about extracurriculars) but the recommendations provide a check against that. Similarly, the a common job application could ask for data from references up front. This would create more transparency and trust earlier on.

(4) Local scale is important for achieving widespread adoption. Research shows that higher Common App membership rates within state increase the chance that an institution that is not a member will join the Common App. A common application for jobs could try to build local scale first.

A coalition of companies or an independent company could spearhead such a system.  It would save frustration and valuable time on the applicant end and result in better outcomes for the companies themselves.

(1)   Ehrenberg, Ronald G., and Albert Yung-Hsu Liu. “The Common Application: When Competitors Collaborate.” Change 41.1 (2009): 48. MasterFILE Premier. Web. 18 Jan. 2016.

(2)  LLiu, Albert Yung-Hsu, Ronald G Ehrenberg, and Jesenka Mrdjenovic. “Diffusion of Common Application membership and admissions outcomes at American colleges and universities.” (2007)

Is it a good time to buy a house in San Francisco?

Real estate in SF is up 33% since its low in March 2009.  To someone living in the Bay Area thinking about buying a house that seems daunting. And it doesn’t help that the likes of Twitter, Dropbox, Snapchat and Facebook have crazy valuations.

This post is my attempt at getting confidence in these questions:

1)   How much appreciation can I expect if I have to buy now and decide to sell in 5 years?

2)   If we are in a mini tech bubble and the bubble pops, what will be the impact on housing prices?

Model basics

My regression model consisted of the following:

1)   Supply of new homes – Privately Owned Housing Starts Authorized by Building Permits: 1-Unit Structures for San Francisco-Oakland-Fremont, CA (MSA) SANF806BP1FHSA

2)   Nasdaq as an indicator of how technology was doing (my assumption here is that a lot of the growth and decline in employment/salaries in the valley in the bay area is driven by tech); appreciation in the Nasdaq also incorporates asset inflation

3)   Long term interest rates – 10 year US treasury note to reflect what’s happening in mortgage rates; one flaw is that I haven’t taken into account the risk premium associated with mortgages which is also changes.

These are used to priced the home price index for SF (~25 years of data). Here is a plot of all the variables indexed to 100.

Housing prices, interest rates, nasdaq, and supply growth over last 25 years
Housing prices, interest rates, nasdaq, and supply growth over last 25 years

One thing to note here is that when the tech bubble burst housing prices slid only 10%. So that answers the question about if there is a bubble in social media the impact on housing prices may not be severe necessarily.

Does the model work?

Kind of. The model outputs show that these variables explain about 50% of the variance in the housing market and its significant:

Regression model outputs: Housing prices regressed against supply, interest rates, nasdaq;
Regression model outputs: Housing prices regressed against supply, interest rates, nasdaq;

Where does it work and where does it not?

Times when predicted  actuals buy - fundamentals are on your side
Times when predicted < actual mean don’t buy. When predicted > actuals buy – fundamentals are on your side

Model correctly predicts that home prices should not have risen in 2004-08 era. This means that given interest rates, Nasdaq, and supply the market was valued higher than these fundamental indicators would predict. If you bought then you would be taking a risk that values would adjust back to what was suggested by these indicators and you could lose money.

So now we can develop a measure of how good of a time it is to buy a house: when the prediction is much lower than the Home Price Index, don’t buy prices could fall (the red areas in the graph above). When the prediction is much higher than the Home Prices Index, homes are undervalued. Time to buy (the green areas).

Testing this new iteration

Let’s test how accurate this would have been. Take a new variable which I will call Delta =  Predicted prices / Actual prices. So in times when Delta > 1, you are getting a good deal (green areas in the graph above).

Now we plot Delta versus the 5 year forward return on home prices (unlevered) if you had invested in a home at that time (see Figure below). For example if you bought in 1988-01-01 (the first point), Delta would be 1.15 so it would be good time to buy (Delta >1). So if you bought then the red line represents that if you bought in 1988  your home would have appreciated about 1.3x or 30%.

When Delta >1 historically home prices appreciated over 5 years (and vice versa)
When Delta >1 historically home prices appreciated over 5 years (and vice versa)

The correlation between the measure of Delta and the 5 year appreciation is 84% and further more whenever the 5 year home price appreciation has been negative ‘Delta’’ has been less than one. Thus I can feel good if I buy a home I can predict the 5 year appreciation based on the delta and as long as Delta is above 1 I won’t lose money.

Bottom line

At the end of the graph you will see delta (the blue line) is hovering around 1. Based on where the Nasdaq, 10 year rates, and supply is right now, buying now will not yield to you a big upside in 5 years. You could wait to buy when Delta is >1 but it could take 5-7 years. If you can’t wait that long,  find something you feel is a relatively good deal but be picky.

 

Update: Excel model incase you want to change / update.

Are Amazon Book Reviews Completely Biased?

Is Amazon missing some helicopters?
Is Amazon missing some helicopters?

My ratings obsession

Anyone who knows me knows that I look at a lot of ratings before I decide to do anything. A good friend of mine once joked I wouldn’t turn left if Yelp didn’t tell me to. Have I taken it too far? – probably – I mean I’m writing a blog post on ratings.

Recently, I was looking for a new book to read when I came across Goodreads. I signed up because it has a recommendation engine that can tell  you what you might like based on books you have read and what your friends are reading.

As I browsed the ratings on Goodreads I wondered if they were any different than Amazon’s. What I saw was surprising – books had a very different ratings on Amazon vs Good Reads, even after having hundreds and thousands of ratings. So which one is better?

vs    


The Data: Goodreads versus Amazon – Ratings distribution

Based on an index of 10 books equally weighted (weighted towards business books):
These books received 15K reviews for Amazon and 315K reviews for Good Reads

Goodreads versus Amazon - Ratings distribution

How can thousands of reviews have such a different mean on one site versus the other? And why do Amazon’s ratings have such a  “U” shape compared Goodreads?

Alexa Goodreads.com
Alexa Goodreads.com
Alexa stats Amazon.com
Alexa stats Amazon.com

At first thought you might think – different audiences?

Thanks to Alexa we see it’s not age, education, or family status. The only major difference is that Goodreads skews female more heavily than Amazon. But that doesn’t explain it – why would women on average rate books lower and have a more uniform distribution in ratings. It must have something to do with the ratings system itself. It turns out it probably does (thanks to Sid for helping remind me of the bias in #1):

1) The more work one must do to submit a review, the more extremes you will see in the rating (“U” shape) On Goodreads you simply have to fill out a star rating while on Amazon you have to write a full review. Hence you’ll see many more 1s and 5s on Amazon than what might be true in the real world. So when you look at reviews on Amazon, realize in reality there are a lot of people in the middle who think the books are really 3s and 4s who didn’t review it.

2) There are penalties for dishonesty on Goodreads. Goodreads is a recommendation site. To start getting recommendations you have to rate 20 books you have read. If you aren’t harshly honest about the books you stand the risk of getting bad recommendations.
Amazon, on the other hand, is much more outward facing. In a public review of the book, you may say it was good but it wasn’t for you and settle at 4 stars, but you definitely don’t want to read something like that again so it might get 2 stars on Goodreads.
(Amazon has recommendations too – but these are much more skewed towards browsing history)

All this is to say I think Goodreads reviewers have more skin in the game and are a more representative sample. For now Goodreads = better reads.

5 Things we can learn from Amazon’s Acquisitions

Online retail, though it has grown leaps and bounds, still has significant secular growth left – only 10% of US sales are online today. Ok, so that’s a big market but Amazon owns approximately 20% of U.S. Online Retail sales (ex Travel / Auto / Auction) and the larger it gets the faster it could grow.

Top eCommerce Sites Gaining Market Share in US, 3Q’09 vs. 3Q’10 Excludes Auctions, Autos and Large Corporate Purchases
So how do you compete with Amazon given the significant economies of scale its been able to capture namely purchasing power, shipping infrastructure and mountains of consumer data?
The answer can be found by looking at Amazon’s acquisitions. You can bucket Amazon’s acquisition in US in the last 5 years in 5 major categories:
  1. Vertically Focused sites (e.g., Diapers.com, Zappos.com, Drugstore.com, Fabric.com, Abebooks) SPENT $1.5B
  2. Infrastructure solutions (Kiva Systems) SPENT $0.8B
  3. Digital sales (e.g., Audible.com, Brilliance Audio, Lovefilm International, LexCycle, Amie Street) SPENT $0.3B
  4. Flash sales / Local Sales  (e.g., invesment in Livingsocial.com, buyout of Buy VIP and Woot) – went on to build MyHabit SPENT $0.1B on disclosed acquisitions – not counting its partial stake in Livingsocial
  5. Content (e.g., Digital Photography Review, Foodista) UNDISCLOSED so likely not material to financial results

Given the number of e-commerce operations that exist out there – Amazon has hardly been acquisitive choosing to bet big on a few names. But there are a number of things you can learn from the data points or the lack thereof:0

0. Its tough to get acquired by Amazon. This may be obvious to some of you but I’m amazed at how few acquisitions they’ve made.

  1. Avoid being a content creator Content creators, even those that link very clearly to purchases, don’t get a big payoff. They reside in a different part of the value chain and create leads for the retailers and that hasn’t led to large $ acquisition by Amazon.
  2. Develop a vertical focus and build efficiencies around that vertical. This intense focus on a category enables you to have effiecieny and understanding of a customer that would be difficult for Amazon to easily replicate. In the case of Diapers.com the intense focus on Moms allowed Diapers to solve a major problem: How do you make money selling / shipping bulky low value items? The answer was to build super-efficient supply chain and warehouses, kill diapers as a category, and make money on other household items.
  3. Avoid deep discounts. Margins online are already thin. And there will be constraints to growth as you outgrow your deal inventory.
  4. Obsess over customers – that’s what Zappos did. It built a culture around customer obsession and that was one of the primary reasons Amazon bought Zappos. Categorizing shoes, paying for returns, etc. – Amazon could have done this itself and thrown a lot of money at, but the culture is difficult for any company to replicate
  5. Keep betting on digital delivery – its the way the world is moving and Amazon is investing.

Okay so this begs the question – What verticals are left to focus on? Electronics, toys, and baby products categories, for example, already have near 20 percent online market share.

So the things to compete on are apparel, home furnishings, cosmetics etc. Or tackle consumers segments like Diapers did e.g., child going to college /moving, buying a house, career change, mid-life crisis (just brainstorming), etc. Picking a category of items or a customer and really studying the microcosm intensely is the way to build a retail business that Amazon would look at acquiring.

Prices of iPhone Apps will go to Zero

Over time the prices of personal software (ex-Games) for desktops has gone to zero and I see no reason why the same won’t happen for software for iPhones. Furthermore profitability of these apps will decrease as the market is getting extremely competitive.

Here’s how I think of the stages that PC personal software went through (excluding Games):

Remember this?

Image

Personal PC Software Market Evolution

  1. Expensive Software Phase / Software engineers are scarce: fragmented base of developers producing software (remember when download.com was the place to go to get new software; you could find software like HotKeys or Greeting Card Creators or WinZip / Compression software for sale)
  2. Freemium Software: software begins to give 2 week free trials or trials with limited features which often get the job done for most people. E.g., during the first half of 2011 game revenue in App Store shifted dramatically from premium to freemium, with 65% of all revenue generated among top 100 games now coming from freemium games. Source
  3. Supply of Software Engineers Grows / More open source development: As this happens, people begin to develop as a hobby vs profit driven motives. Since there has been a growth in supply of engineers, engineers have more free time and more and more people begin to contribute to open source for fun / recognition / creative outlet / stepping stone for commercial opportunities.
  4. Acquisition Large companies who’ve made it big on software acquire smaller ones. They have reach to a larger user base and can drop the prices sometimes to zero (e.g., when Google acquired Picasa)
  5. End state today: People still pay for certain categories of software: business productivity software like Office, antii-virus security, Operating Systems and games (most of these have very good free versions too – Hotkeys today is free).  Everything else is also available for free:Image

What’s different this time? 

I don’t think much. There are only things that will cause it to move faster and slower. My sense is that we are at the third stage (increasing supply of software engineers) of this and we are about to see a huge increase in the number of software developers for iOS and other mobile platforms. But its not going to happen overnight – it may take 2-4 years, because it will take a while to build new development capacity. But, look at what’s happening at students entering Stanford:

Stanford has seen its computer science enrollments recover at about 20% per year since 2007-08, after turning the corner the year before that … In 2009-10, Stanford fell just short of its all-time record enrollment in our CS1 course, which we call CS106A.That record of 762 for the three regular-term quarters was set back in 1999-2000 at the height of the dot-com bubble. The numbers are now in, and the enrollment in CS106A in those three quarters is 1087, which represents a year-on-year growth of 51%. More frightening still, the enrollment this quarter in CS106A is running ahead of last spring by 120%, suggesting that the trend is accelerating rapidly. That conclusion is supported by enrollment data from other courses. Compared to last spring, enrollments this quarter are up by 74% in CS107 (Computer Organization and Systems), by 78% in CS109 (Introduction to Probability for Computer Scientists), and by 111% in the course I’m teaching, which is CS181 (Computers, Ethics, and Public Policy).  From Eric Roberts Source

Its a good time to be a developer (the greatest was probably a little time back when you could charge for  simple apps) but if you are banking your future on an app that’s providing you steady 99-cents-a-download-income, chances are its going to get tough.

Image

Start up idea: Fix event search


As some of you know, I’ve recently moved to SF. Wanting to meet people, I’ve made it a point to get out to as start-up type events as I can to talk to people and learn more about the ecosystem. I have been highly disappointed with Eventbrite’s and Meetup’s discovery tools. Here’s why the space is ready for disruption:

  • Large market There are 11M searches per month for events in Google in the US alone (25M worldwide)
  • No way to search all the event websites out there at once  Someone needs to do what google did for products, recipes, news, etc. The good news is the event market is fragmented but not as fragmented as some of the things Google has already indexed. The 80/20 of events will be within college calendars, in Facebook, Eventbrite, Citysearch Ticketmaster, and Meet ups. Ticketmaster is the big elephant in the space. Crawling through this data across sites (except for college calendars) cannot be very difficult.
Based on data from compete.com unique visitors per month; Others includes: Eventbee, Skillshare, Acteva, and Regonline
  • Major improvements needed in search functionality. Do what kayak did and allow people to search by a lot of criteria. Here are somethings I usually don’t find:
  1. Hours and dates timing – obvious but still not done well (please see Eventbrite’s search for how not to do this … Why can’t I specify dates? What does $, versus $$ mean?)
  2. Are there tickets available?
  3. Number of attendees / size of event
  4. Is it a repeat or a first time event?
  • Integrate with my other accounts. Some (not all) are already integrated with Facebook and Google Maps but it would be great if they were integrated with:
  1. Google Calendar – as in find out when I’m busy vs Free
  2. Linked In – as in know in which field I work and tell me which professional events are best suited for me based on my professional contact