In the musical “How to Succeed In Business Without Really Trying”, JP Finch finds himself in a predicament.  In a span of just a few days he ascends from the mail room to the corner office, but in the process he accidentally becomes the Vice President of Advertising (against the advice of his trusty manual).  The advertising role forces him to come up with “good ideas” and his easy career advancement comes to a screeching halt.

The original book for “How to Succeed” was written in 1952, and senior marketing jobs have only gotten harder in the past 60 years.  Public companies change their CMOs every 18-24 months on average, making it the shortest job tenure of any C-level position.  Over the next decade the CMO will become responsible for more technology decisions than almost any other group in the enterprise, from marketing software platforms, to housing customer data, consumer privacy, and the endless breadth of digital point solutions.

When faced with uncertainty, the most common human reaction is to slow down and take fewer risks.  There is a saying that “no one ever got fired for buying IBM”.  In the online marketing world the equivalent would be buying Google search.  Most companies spend 50%-60% of their total digital budget on Google search.  This share continues to grow in spite of the fact that traffic to Google.com is flat.  To Google’s credit they have successfully morphed the design of their search landing page to deliver more advertising over time without losing customers.  What surprises me most is that companies are now spending half of their search budget (or more) buying their own brand as a keyword.

You would assume that someone who goes to Google.com and looks for “Nike Shoes” or “Northface jacket” is already a Nike or Northface consumer.  Yet Google will make billions in revenue this year by putting brand keywords like these up for auction.  And brands are afraid that if they don’t spend against their own trademark that the traffic will be re-routed to competitors.  This is magnified by Google’s web browser, Chrome, which takes shortened addresses (ie. try typing Nike instead of Nike.com) and sends them to search.

This is one of many clever ways that google has out-marketed marketers.  You can imagine that the search experience on Google.com would be awful if you looked for Nike Shoes and had to scroll through a page of Reebok links, so it is clearly in Google’s best interest to show relevant advertising.  In fact Google’s growth strategy for the past five years has been predicated on convincing marketers to spend money against traffic that would convert anyway – effectively turning “natural” search into paid search.

The way that Google has done this is with something called “last click attribution”.  The general premise is that marketers are weighing the activity on google.com higher than the brand building and awareness activity that got users to search for their products in the first place.  One of the tricks is that Google provides marketers with free tools to reinforce this world view.  Have you ever heard the expression “lies, damn lies, and statistics”?  It is more like “lies, damn lies, and Google Analytics”.

For the past two years I have been an investor in a company called Tagman that has solved this attribution problem.  We had a board meeting yesterday, and the conversation revolved around how to define the core of our value proposition for the hundreds of marketers who have chosen us as their partner.  We went around the table – are we selling software?  Multi-party attribution?  My response is that we are selling courage.  At the end of the day marketers need to be armed with better tools so they can take the leap of faith that buying their own brand is not a good use of money.

In the past few months we have seen five or six start-ups going after the virtual assistant category.  The idea is kind of like Apple’s Siri, but super charged with people who do stuff for you on the other end of the line.  This is a future where we all use on-demand assistants to help us achieve Tim Ferris’ illusory “Four Hour Workweek”.

After digging into the market I have to say I am left scratching my head.  There are some exceptions, but the typical customer of this service, by definition, does not already have an assistant.  Most early adopters fall into one of two camps:  A.) they need a full-time assistant but can’t yet afford one, or B.) they have a couple tasks here and there that require assistance but not enough for a full time assistant.  It turns out that both of these users are hard to scale.  The price sensitive consumer jumps from service to service, and likely churns after a few months.  The less needy consumer struggles to find continued use of the service, and he or she also churns regularly.  There are some fixes to help bridge the gap for both users, but it is a tough problem to solve.

It dawned on me that there is another idea out there that nobody is focused on (at least not anyone I have met).  Rather than try to convert people who don’t currently have assistants, how about developing software for people who do have assistants, but want to empower them to do more?

There are millions of professionals in the US with full time assistants – virtually every lawyer, banker, doctor, and business executive has one.  That is an easily quantified market.  And the ROI seems pretty clear – just by avoiding last minute airfare hikes a business traveler would save thousands of dollars a year.  And how much could you save on hotel rooms, rental cars, or airline change fees?  For professionals who charge by the hour, how much more money could be made by predicting when a customer or a patient is going to cancel their appointment?  All it takes it a little software.

On the other side, assistants need help answering email, logging contacts in multiple CRM systems, and even remembering hundreds of personal preferences.  The tool could learn from experience, automatically completing tasks and suggesting the right answers.  If you are having lunch with Jim, who is vegan and lives in the West Village, the system knows where to go.  If you are flying across country with multiple stops, the system knows how to book airfare based on weather delays, destination changes, and even the potential for cancelled meetings depending who you are visiting.

Maybe this is a pipe dream, but I know that Microsoft Outlook is not going to get there anytime soon.  This is the last mile of productivity, and it seems like a big opportunity to me.

When was the last time you added a new friend on Facebook?

I still go to Facebook every day to browse links and baby pictures, but like many people I know, I am not adding many new friends.  The problem is that my personal information on the site has built up over the past few years, and I have second thoughts now about sharing things like wedding photos with anyone outside my inner circle.

This is the paradox of Facebook.  As time goes on I have become more vested in the platform, which is what Facebook wants to happen.  I add photos of friends and family, and I comment on my friend’s stories.  But the flip side is that I find myself second guessing who I want to share this information with.  As of this morning, I am sharing wedding photos with 1,059 friends.  I think that is probably enough.  And yes, I know there are Facebook privacy settings to solve this problem, but it is a pain to go back and edit six years of content.

When was the last time you added a new connection on LinkedIn?

As of this morning I have 2,434 connections on LinkedIn.  I add a few new connections every week.  Even my friend Michael Lazerow, who is the most active Facebook user I know (and built a business on Facebook), has more connections on LinkedIn than Facebook.

This sample size of two may not be statistically significant, but the growth across the network is undeniable.

Does this mean that LinkedIn will topple Facebook for social media supremacy?  Maybe not.  The type of personal media on Facebook creates more page views and interaction than the professional sort of media on LinkedIn.  In a world where attention is traded for advertising dollars, that edge goes to Facebook.  But the two sites continue to evolve in different ways, and I can’t help but think that the number of individual connections will be a key component of value creation down the road.  At the very least, I believe it will help LinkedIn grow faster than Facebook for the foreseeable future.

For the past decade we have all benefited from cloud computing.  You may not realize it, but most Internet companies use some component of the cloud behind the scenes.  Cloud computing cuts down on the cost of launching applications, and as a result we get free services like Instagram, Facebook, Pinterest, and Dropbox that come with infinite storage and more frequent updates than they otherwise would.

At the moment we are entering a new innovation cycle, where the labor market is taking on characteristics of the technology market, with scalable, on-demand labor resources.  This trend is early and doesn’t have its own vocabulary yet, but at Greycroft we call it crowd computing.  There is a big efficiency element to crowd computing, but more importantly it has the potential to create new services.

The NY Times touched on the concept of crowd computing earlier this week, with coverage on how companies are turning to human intervention to improve applications and make sense of mountains of data.  Most companies have teams (ranging from one person to 100,000) who spend all day in front of a terminal manipulating data.  This covers every industry, from healthcare to financial services to consumer packaged goods.  And it is equally common for both large and small businesses.  Even at Greycroft we have two teams of people who spend all day entering data, one focused on portfolio company financial information and the other on pipeline information for our new investments.

If you want to build one of these teams, you either open a satellite office and hire workers, managers, and quality assurance professionals or you go to a business process outsourcing (BPO) provider that has already done some of the legwork.  Either way it is time consuming, inflexible, expensive, and hard.

In 2011 we seed funded a company to fix this problem, called Crowd Computing Systems (CCS).  They offer two different solutions – one is a “public” crowd and another is a “private” crowd.  For people who are familiar with cloud computing this distinction is self-explanatory, but the general idea is that a public crowd is a shared resource that many companies use at the same time, while a private crowd is a dedicated team that only performs work for one company.

CCS’s technology has three components. First, they have a reputation system that scores workers on error rates, availability, throughput, and a number of other factors.  This determines a crowd’s ability to process tasks.  Second, they have tools to break complex processes into simple tasks that can be easily performed in a few seconds.  This is akin to a production line in manufacturing, but for data and complex business processes.  The third and final component is an artificial intelligence system that learns over time from observation.  This allows companies to focus human effort on high skill tasks while pushing redundant work to machines.

While this is still early days for CCS, we recently closed a larger investment to scale the business and they are off to a good start with a handful of Fortune 500 firms including Thomson Reuters, Walmart, Amazon, and Coca-Cola.  I am still waiting on case studies to share but the early data is exciting.  Overall I expect this space will be one of the best areas to watch over the next decade, as well as create major economic change for many companies

In the last few months we have seen a number of companies attempting to transform the Human Resources space.  The big opportunity exists with large companies that are straining under the weight of hiring and on-boarding so many new employees.  For instance, a company like Accenture hires seventy thousand people per year.  In the process they screen a million candidates across a global network of recruiters, agents, job boards, software, and services.  The budgets are large and the systems are antiquated. There is also a nagging problem with mobile – job seekers research positions on their phone today, and the current applicant tracking systems (Taleo, Kenexa, etc) were built before the advance of the mobile web.

Once a candidate arrives for work he or she represents the company, which opens a whole new series of questions.  Does the employee describe their company in a flattering way on LinkedIn?  Is the company’s name even spelled correctly?  Is the employee connected to the right people?  The Internet has already democratized “what you know”, and it is only a matter of time before it democratizes “who you know”.

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I am very proud to announce Greycroft’s recent funding of Longtail Video.  Longtail owns the JW Player, which is the world’s most popular video player, and the company is a key part of our overall thesis in the online video space.

According to Comscore, there were approximately 40bn online video views in the US last month.  13bn of those views were on YouTube.  Hulu, Facebook, VEVO, Yahoo, and the rest of the major media sites accounted for another 4bn or so.  And the remaining 23bn views – 58% of the entire online video audience – were scattered across millions of websites, forming the proverbial long tail.

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The hardest part of building a start-up is hiring good people.  I realize this may not be intuitive with high unemployment nationally, but there is a shortage of talent when it comes to software development, sales leadership, marketing, web design, and finance positions that are the building blocks of Internet companies.

Companies typically come through my office looking to finance growth, which inevitably means hiring a lot of people, so I deal with this on a daily basis.  A common story is that a company has 10 or 15 employees at the time we invest, with a budget calling for 20 or 30 new employees in the first year.  Looking back I have found that companies almost always fall short of this hiring plan, and it gets worse as time goes on.  As a result I have developed a few rules and tips when it comes to hiring:

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Today is November 4, 2012.  It has been five days since Sandy swept through New York City. Electricity is back in  Manhattan, although it will be a few more days before ConEd fixes every building.  The subway system has made a remarkable recovery.  The parks are open.  The streets are cleared of trash.  From my apartment on 63rd street I can see First Avenue where bright orange “Marathon Route” flags are flying.  Normally the sidewalks would be packed ten deep, with cowbells clanging as runners pour over the 59th Street Bridge and into Manhattan.   However it is quiet this year.  The marathon was cancelled.

Opposition to the marathon began in earnest on Friday morning.  The New York Post ran a story showing two generators in Central Park, in stark contrast to people who still had no power in the city.  Petitions to cancel the marathon were quickly circulated on Facebook.  Entreaties were made to Mayor Bloomberg over Twitter to cancel the event.  Mary Wittenberg, the President of the NY Road Runners, received death threats.  The marathon had become a lightening rod of criticism.  By early afternoon, news broke that the marathon had been cancelled for the first time in over 40 years.  Social media erupted as people congratulated themselves on the victory.  It was another moment where voices in unison changed the will of politicians and planners.

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I have a confession.  I still use a Blackberry.  And not only do I use a Blackberry, but I recently traded in a Blackberry and… wait for it… got another Blackberry!  The horror!

I went to the Verizon store for a Blackberry replacement, and here is an exact transcript of my conversation with the sales associate:

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I learned in business school that many companies can be analyzed with three variables:

A = Monthly revenue per customer, B = Monthly customer churn, and C = Customer acquisition cost

Here is a quick example to show what I mean:

Verizon charges $50/month for cell service (A). 1.5% of Verizon’s users cancel their cell contract every month (B). Verizon’s sales and marketing costs are a few hundred dollars for every new user (C).

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