VC Investment Amounts

When I can't sleep, I play with data. It's like meditating.

The National Venture Capital Association recently released their latest venture investment numbers for the US. On their web site, they use the annoying iChart gadget. Annoying because as you scroll backwards and forwards in time, it adjusts the x axis, so I can't really compare amounts across time. Why would they do that? Are they mad? I'm going to send Edward Tufte to beat them over the head with Javanese railroad timetables.

Anyhoo, so I took their US regional data and charted it more congenially (click on the charts for larger versions. I graphed the three largest regions, Silicon Valley, New England and NY Metro. Here is total investment: I've heard a lot of talk recently about New York having a startup renaissance. My Boston VC friends have meanwhile been bemoaning how quiet Boston is, and living on the Acela to NYC. The data does not support this. New England may be a bit ghost-towny, but NY isn't taking off.

Let's look at number of deals:

Same. I mean, I don't have access to the NVCA database, just their publicly released data. If I did have access, I would do this analysis with early-stage internet companies; that may show a different story.

Average deal size:

This is interesting mainly because it shows average investment size to be about the same from region to region (although NY is noticeably more volatile, while Silicon Valley less so--probably because there are more deals.) I find this surprising, given the different character of industry in the three areas. I wonder if this is a product of the VC model, as opposed to actual business capital needs: the tail wagging the dog. This is anecdotally so, and certainly not a good or efficient thing if true.

The other interesting thought that came up was the big drop in investment Q1 and Q2 of this year and the slight pick-up in Q3. I thought this was happening, as I noted in Is the Time for Angels Passed? Here's the proof. There are many potential reasons for this, but the most obvious is that VCs invest when they see the economy getting better. Here's a graph of Total US VC Investment (left scale) and the S&P 500 (right scale):
Well, now. Aside from a bit of the euphoria in 2000, VC investment tracks pretty well. In fact, it looks like it may lag a quarter or two. Why is this? Leaving aside the canard that venture is uncorrelated, venture investment lifecycles are not the same as public company lifecycles. You might invest in IBM this year, expecting them to raise their dividend next year because of the improved economy. But you shouldn't invest in non-public companies that way. You should expect your venture investments to mature in three to five years (depending on the stage at which you invest.) Unless you really don't believe in the business cycle, the best time to invest is when the stock market is low. Venture investment should be counter-cyclical. Is this a case of a buy high, sell low mentality, or another structural failing of the VC model?

I can see the Empire State Building out my window. When they turn the lights out at 2am I know I'm going to be tired tomorrow.

Data consistency sidebar

I mentioned here that I would compare my post on online spending trends to Rob Leathern's and see why we came to different conclusions. The answer, unsurprisingly to anyone who's done any work with this type of industry data, is that we use different numbers.

I used TNS for ad spend data. Rob used both TNS and IAB numbers. TNS and the IAB have wildly different estimates for online ad spending. For example, US online spend in billions:

	2007	2008
TNS	$11.3	$11.8
IAB	$21.2	$23.5

TNS has consistently had smaller numbers for online spend than the IAB. I'm not going to pass judgement on whose numbers are better: I do not know. I merely noted the difference and decided that if I was going to compare online to other media, I would use spend numbers from a single source. This, I think, gives a better comparison, if not necessarily a better absolute number.

How to rake it in by screwing your customers

A long, long time ago I was at Prodigy when we decided to change from hourly pricing to a flat rate of $19.95 per month. Flat rate pricing was clearly preferred by our customers, and our competitors who were offering it were taking them away from us.

Problem was, when people are accessing the internet by dialing into 28.8k modems, more hours online meant more peak demand meant more modems needed, meant more expense.

As we evaluated the price change, I noticed that some of our formerly best customers would now become our worst customers. For instance, there was a bunch of die-hard group text-based RPG fans who spent 100+ hours online per month*. Paying by the hour they were great customers, but with a flat rate they were our worst. We had to ditch the RPG.

In fact, we had to ditch any content that people spent a lot of time with. It turned out that the people who liked the service the most, who spent the most time on it, were our worst customers. Our best customers were the people who never logged on but never got around to turning off the monthly bill.

I lost interest in this business model and moved on; businesses that do better as their customers do worse should not survive.

I was thinking about this today because of my friend Josh's excellent blog entry on the Google Mortgage thing and on retail banking in general.

Unlike most people's mental model of retail banking operations, banks do not make most of their money on the difference between the rates at which they lend versus the rate they offer for savings. American banks, quite distinctly from banks elsewhere in the world, make the bulk of their money from fees and charges. Invisible and often unavoidable consequences of little clauses in contracts that no one ever reads.

Banks' best customers are the ones who are getting screwed by the banks. Banks' worst customers are the ones who are probably pretty happy with their bank. This perverse incentive shouldn't persist, but it has. What's it going to take to change it?

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* This was a lot back then.

More on Ad Dollars per Hour

My friend Rob Leathern, over at CPM Advisors* was blogging about the same thing I was blogging about in Details, Details (unbeknownst to me, since he keeps changing where he's blogging from.)

We used the same approach, but seem to have come up with different conclusions. I don't have time today to figure out why, I'll do it tomorrow. In the meantime, read these:

Online Time Not Worth What It Should Be

More Television vs. Online “revenue per user hour”


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* Blogger disclosure: I'm an investor in CPM Advisors.

The 300%-500% Lift Meme

I talk to a lot of people trying to make internet advertising more effective, most of them startups or people socializing a new idea before starting a company. Over the past two years, I think about two-thirds of them have told me that their new technology/process is going to provide a "300%-500% lift."

I even heard a story of a VC, after being pitched on a more reasonable lift, say "your approach is interesting, but we need to see you deliver a 300%-500% lift to be competitive in the market." ( I looked at this VC's website and found no ad targeting companies in his portfolio.)

Sometimes upon hearing this, I drift into a daydream about combining behavioral targeting, social targeting, retargeting, creative optimization, rich media, distribution optimization, contextual targeting and offer optimization technologies into one super-arbitrage strategy. The resulting 328,050% - 19,531,250% lift would allow me to buy $0.50 CPMs and pretty much overnight control the US economy*.

Assuming, of course, that these lifts are real, are the average lifts, are replicable at scale, are actually the result of the data/process/technology itself and not some artifact of attention (i.e. the Hawthorne Effect), and are orthogonal (which I'd expect if they are real and not artifacts, maybe not to the extent of the last paragraph, but between, at least: targeting, distribution, creative/offer/media.)

Not sure where this meme originated, but it very clearly says one thing: noone knows nothing. How well any of this technology works is an open question.

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* I suppose, in a way, this is kind of what Google did, so I'm not saying it's not possible, just that I doubt we can all sit down at our laptops and replicate it.

Those projects that come to nothing

After I pulled data from umpteen sources and crunched it for Details, Details, I looked at the resulting graph and saw that I had found... nothing. If I was a journalist, I'm sure my hard-bitten, cigar-chomping editor would have chewed me out, "There's no story here, Neumann!"

But that's the way it is. I was hoping for some trend or abrupt change that would tell me something I didn't know, some result I could attribute to a cause.

Let's look again at the nothing, the ad dollars per hour of online use. I'm going to correct for changes in overall spending per hour of media use, because the period in question was a bit up and down for the ad world. Below is a graph of Online ad dollars per hour as a percent of total ad dollars per hour, from 2001 to 2007.

Data sources the same as for Details, Details.

Why is this not much? Because it is a graph of efficacy. This is what we online marketing people do. The online media folk and the online app folk and even the Verizon DSL technicians get people online and get people to spend more time there. This graph factors that out.

Macroeconomic conditions determine how much is spent on advertising. This graph factors that out.

All this graph shows is how much an advertiser is willing to pay to get their message to a consumer over the course of an hour online as a percent of what they are willing to pay over all media. If it is 50%, then that probably means the advertiser feels they are about 50% as likely to sell something in an hour as they would be otherwise. The ads are half as effective.

So, an increase of 5% over four years is not much of anything. Nothing, really.

What happened in this period? Here's a few things that apparently changed nothing:

Starting in 2000, Google revolutionized the purchase of intent.

Starting in 2002, Tacoda and the scores of companies following its lead, revolutionized what advertisers know about the people seeing their ads, allowing precision targeting.

Starting in 2005 (or thereabouts), Right Media and its followers revolutionized the purchasing of ads across the huge internet media landscape.

What has all the work done over the past ten years to build the infrastructure for a true one-to-one ad marketplace gotten us? Are advertisers, with all the data and mathematics and optimizing they have available, really getting no more for their money than they were ten years ago? I find it hard to believe, personally. What am I missing? Or, what are we all missing?

No starting gun

Chris Dixon's blog post, The Ideal Startup Career Path is spot on: if you want to start a company, go work for a startup. I'd add: find a company started by someone who comes out of the entrepreneurial community and has a really big idea, join as early in the company's lifecycle as possible, and make it clear through your actions that you will wear any hat needing a head on any given day.

At my last startup, we actively looked for and hired entrepreneurial people and several of them went on to found their own companies. The ones I've given support to include Greg Yardley with Pinch Media, Viva Chu with Handipoints, and Rob Leathern with CPM Advisors. There are others, and then others who will or are starting something. For a company that only had some 30-odd employees, that's a pretty good conversion rate.

I'm tempted to say that we made it look easy, but I'm pretty sure what went through their heads was "if these idiots can do it, then I certainly can." This realization, in its many forms, is certainly the spur to more startup formation than any other factor ever will be. I meet so many would-be entrepreneurs with good ideas who just can't figure out how to start. Who worry about knowing what to do next. Who are waiting for some intangible starting gun to go off, some sign from heaven. There is no starting gun, there will be no sign from heaven. You just need to know that it isn't really that hard.

Watching someone else manage the building of a company provides the crucial lesson: there's no special secret magic. Go work for a startup, and you'll see.