A picture worth significantly less than 1000 words

As an aside to yesterday's post, here's the mental model I am using for the industry this week.

Who Invests in Quantitative Marketing Companies?

About a month ago Jay Yarow wrote about the financing of The Trade Desk. One of the commenters insisted that only Redpoint, Union Square, Highland, Sequoia or First Round Capital were expert enough to invest in online marketing firms. I think this is wrong-headed (and not just because I was one of the investors listed in Jay's article.) But, my opinion aside, what's the truth?

To find out, I made a list of some 150 display-ad companies by flipping through AdExchanger.com*, adding a few companies that exited early and then discarding agencies, ad networks, ad ops and rich media companies**. Of these, only those that had VC investors listed in the Crunchbase database are in the final results***. There were 90 of these.

I also split the list into five general categories: buy-side (19 companies), sell-side (20), marketplace (8), targeting (30), and measurement/verification (13). Each company was put into only one sector, unrealistic as that is.

So, what jumps out?

1. Number of Investors

There are a lot of different investors. I counted 200 institutional investors in the 90 companies.

2. Number of Investments

Most investors have one investment in the sector.

There are 154 investors with one investment, 27 with two, 12 with three and seven with more than three.

The top seven:

1. First Round Capital, with 11
2. Accel Partners, 9
3. Union Square Ventures, 6
4. IA Ventures, 5
5. DFJ, 5
6. SVB Financial/Silicon Valley Bank, 5
   
7. Redpoint, 5
   

The firms with three investments: IVP, Rose Tech, Mohr Davidow, Menlo Ventures, DAG Ventures, Founder Collective, Venrock, Shasta Ventures, Mayfield Fund, The Founder's Fund, Battery Ventures, Coriolis and Maples Investments.

3. Most Diversified

Many of these firms have invested in more than one of the five broad sectors.

Here's a table of the most diversified.

	Buy	Sell	Target	Market	Measure
Accel Partners	✔	✔	✔	✔	✔
First Round Capital	✔	✔	✔		✔
Union Square Ventures	✔	✔	✔		✔
Draper Fisher Jurvetson		✔		✔	✔
Redpoint Ventures	✔		✔	✔
DAG Ventures		✔	✔		✔
Institutional Venture Partners		✔	✔		✔
Mayfield Fund	✔	✔	✔
Menlo Ventures	✔	✔	✔
Mohr Davidow Ventures	✔		✔		✔
SVB Financial Group	✔	✔		✔
Coriolis	✔		✔		✔

4. The Truth?

What makes an investor a desirable investor in a sector? Time spent on the board of similar companies certainly counts, as does number of non-competing investments in the sector and adjacent sectors.

Under these criteria, USV, First Round, IA Ventures, DFJ, Redpoint, and Accel all stand out.

But another criterion is deal flow, the number of deals an investor looks at even if they don't invest. This sort of activity gives them a good view of the market, and a great network of potential partners. The deal flow criterion is harder to measure. My sense, from talking to entrepreneurs in New York, is that the investors who have talked to the most entrepreneurs in the sector are First Round, IA Ventures, Founder's Collective, NYC Seed, Genacast, WGI/Point, Coriolis, True Ventures, USV, Greycroft, and Spark.

I've worked with or co-invested with most of these investors. I respect their opinions and, if an entrepreneur tells me that one of them is interested, it influences me. But I don't always think they're right. Sometimes one of them likes a company I don't and sometimes I like a company they don't. None of them are always right and none of them make any investment a sure thing.

Given all the uncertainty about which companies will be successful and which won't, the predictive quality of who the investors are is too small to notice. Picking out five or six and dismissing all the others is silly. If you're an entrepreneur, talk to as many as you can and find the one that is best for you, no matter where they work.

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* I had a conversation with Greg Hills a few months ago where I asked the metaphysical question of who is quantitative marketing and who is not and he averred that the community is essentially self-defined by who are mentioned in, contribute to or comment on AdExchanger. I think this is as good a screen as any.
** Some of these should logically be included in 'quantitative marketing', but figuring which would take more time than I'd be willing to spend. If anyone wants to volunteer a list, feel free. Or, if anyone wants my hacked-together python scripts so they can do it themselves, let me know.
*** Crunchbase data is a bit funky. I amended it where I knew off the bat it was wrong, but didn't dig into every investment or investor. I am sure the data in this post is incomplete.

How to completely disappear in 45 minutes or less (at least from marketers)

Just been reading the FTC complaint against... well, against everyone I know, it seems: the exchanges, the DSPs, the data targeters, the ad agencies, the analytics companies, the whole quantitative marketing shebang. It was pretty straightforward, quoting everyone's marketing material, making the industry seem much, much larger than it actually is*.

Ed Zimmerman has a much better response than I could write, so I'm not going to critique the thing, but one paragraph really jumps out at me.

The value of user data harnessed by these platforms and services is generating higher returns for marketers. Publishers, ad agencies, and marketers are all trying to capitalize on the data of each consumer--thereby causing that consumer a financial loss. None of the so-called consumer benefits of real-time targeting--the "faster loading times" for an ad and whether they provide a "better user experience make up for this financial loss. The availability of so-called free content is an insufficient return to a consumer for their loss of privacy, including their autonomy.

The first sentence is interesting because I doubt it is, in general, true, or will ever be. If RTB and its ilk make the whole system more efficient, it is not the marketer that will garner the efficiency. Economics tell us that the main beneficiary will ultimately be the consumer.

The rest of the paragraph is a bit murky and illogical until the last sentence: targeting is corrosive of privacy and autonomy. Long-time readers will know that I don't disagree with this sentiment.

But I've become a bit cynical about consumer privacy. Having been involved in a failed bid to create a product that would safeguard consumer data while allowing marketers to compensate the consumer to use the data, I don't believe that consumers really care enough about their privacy to actually do anything about it. Even the most ardent complainers about the intrusiveness of advertising won't spend more than a few minutes of their time protecting their privacy. This says to me that their privacy is worth nothing to them (but complaining about it is worth something, I think.)

If you really care enough to spend a few minutes, you can do something about it:

• Opt-out of behavioral targeting (NAI)
• Opt-out of telemarketing (Do not call)
• Opt-out of direct mail and other direct marketing (DMA)
• Opt out of the credit bureaus mailings (Experian, Equifax, Innovis, Transunion)
• Opt out at the big data collectors (Axciom, Intelius and Choicepoint)

This will protect your privacy from pretty much everyone except your government. If you want your department of motor vehicles to stop selling your information (yes, they do) you'll have to write them a letter and put it in the mail.

* It also highlighted the importance of AdExchanger.com, who was cited as a source of industry info in at least half the footnotes.

The End to End Principle in Ad Exchange Design (The Thin Exchange, 2)

Where should decisions about which ad runs where be made? Ajay Sravanapudi, in his recent article on AdExchanger says

a DSP is really just a feature on an exchange... A DSP simply uses [RTB API] of an exchange to buy media and run campaigns more effectively. The exchange has an ad server that can deliver campaign pacing, frequency capping, targeting, etc. All that is missing is some intelligence to “auto-magically” buy media on behalf of the campaign... Dozens of ad networks have done this for years on things like YieldManager on the RightMedia Exchange (RMX). If we can simply layer this “auto-magical” intelligence on the exchange then there is no need to pay for a DSP.

I disagree. There's a sort of businessman's view here, where an intuition about power leads to an answer at odds with systemic efficiency.

Exchange 1.0 did not sell real-time. So the exchanges had to have rules in-system. Like the NASDAQ (and other limit order book markets), the exchange hosted the rules about who wanted to buy and who wanted to sell at any given price on any of the thousands of things traded there.

But RTB ad exchanges don't have thousands of things being traded, they have an almost unlimited number of things. Each ad impression--the placement, the context, the viewer--is different than every other. There is no commodity, so there can be no order book.

With an order book, the commodities had to be limited: i.e. "if the ad presented is on the front page of CNN.com, and the person viewing the ad is a male 18-34 years old, then bid $5.00 per thousand." That doesn't work when the ad presented is a photo of a red flower on Photobucket presented to a non-logged in 33 year old male in Northern New Jersey at 12:13am on a Sunday and who searched for gardening tools at an online retailer yesterday but didn't buy anything and whose circle of acquaintances includes several people who bought sunglasses this week." What ad you put in front of this person and at what price is a difficult problem, not one that can be reduced to simple rules.

The only way to move away from rule-driven trading is to use algorithms. On the buy side, developing the right algorithm requires a ton of experience, a ton of data from live campaigns (and the insight into how well each individual impression worked), a huge amount of experimentation, mathematical savvy, and a dose of genius. On the sell-side the algorithms are even more complicated to develop. The algorithms are where the intelligence is.

Where should these algorithms be run? Here's an analogy. Let's say you wanted your computer to run the algorithms. Where do you think they should be coded? In the operating system? Of course not. In the application layer? Almost certainly not. Obviously, you'd code them as routines to be run by a more general application, like Excel. The lower down the stack, the more generic the functionality should be. This is called the End to End Principle:

Using performance to justify placing functions in a low-level subsystem must be done carefully. Sometimes, by examining the problem thoroughly, the same or better performance enhancement can be achieved at the high level. Performing a function at a low level may be more efficient, if the function can be performed with a minimum perturbation of the machinery already included in the low-level subsystem, but just the opposite situation can occur – that is, performing the function at the lower level may cost more – for two reasons. First, since the lower level subsystem is common to many applications, those applications that do not need the function will pay for it anyway. Second, the low-level subsystem may not have as much information as the higher levels, so it cannot do the job as efficiently.

Saltzer, Reed and Clark in "End to End Arguments in System Design". This paper described an idea that has been central to internet architecture since early days: don't put in the center what can be done at the edges. Similarly, David Isenberg's "Rise of the Stupid Networks" (predicting that the internet would beat out the "smart" telecom nets.)

Okay, I can hear all you adtech gearheads: is the exchange really "low" level? It's probably the most complicated piece of software in the whole ecosystem.

But low-level in this argument really means that the functionality is used by the most end-user applications. It has nothing to do with how close to the hardware the function is (the two are correlated, but that's outside my scope.)

Clearly the exchange has the functionality that is shared by the most end-users. Each agent is different: different approaches, different algorithms, different in ways none of us has yet imagined. Why should we attempt to encode this as-yet-undetermined difference into the exchange? Putting DSP functionality into the exchange simply means that everyone has to pay for it, even if they don't use it. This means that other, better ways to be a DSP do not get developed, because then the customer has to pay twice: once for the DSP's DSP and once for the exchange's DSP.

And this brings me to my real beef with the idea that DSP functionality should be built into the exchange, or that any functionality outside of what is absolutely necessary should be built into the exchange. The internet has been so phenomenally successful because the low levels are bare boned and flexible. HTTP, FTP, POP, SMTP, DNS, IMAP, etc. have all been built on top of TCP because TCP does nothing but transfer data from one place to another. It doesn't have much expectation about what that data is or what it should do. If the internet's designers had made TCP more "intelligent", we probably would have never had the Web or Skype. Building low level functionality that is simple and allows layers to be built on top enables innovation. And heaven knows what we need right now in interactive advertising is some innovation.

I don't think the ad exchanges should layer in "auto-magical intelligence." I don't think they should layer in anything. I think they should start dumping functionality like Carl Fredricksen tossing furniture out of his house. The ad exchange should do three things. It should do them fast, it should do them cheaply and it should do them six-sigma. What the ad exchange should do, and all it should do, is cookie-match, cross and clear. The ad exchange should be thin, and as dumb as possible.

Everybody's an ad exchange (The Thin Exchange, 1)

Everybody's a DSP? Everybody's a marketplace.

There's this confusing moving about in the marketplace. AdECN was a pure exchange, and is now part of a publisher. Right Media, same thing. OpenX was a publisher tool, and is now running an exchange. AdMeld similarly. AppNexus was an exchange and is now a DSP (I think.) Same with Turn (who was first an ad net before raising money to become an exchange.) Glam and FIM are publishers and now have some features of an exchange. Several of the ad agency holding companies are making unlikely noises about building their own tech. And AdEx, well... they're doing pretty much everything.

It was so confusing I made a picture of companies moving about.

A good entrepreneur will change strategies as they learn the lay of the land. But some companies who weren't exchanges are becoming exchanges and some companies who were exchanges are becoming something else so, um, how does the land lay?

Here's what I think:

1. The ad exchanges know that running a marketplace should not command what they are charging, and lie awake at night fearing that their customers might someday come to the same awful conclusion.
   
2. The customers already have.
   

Why did the exchanges add other functionality? Because they know that they won't make much money as an exchange. I don't have much to add to my analysis in the linked post, other than to say that it seems all the exchanges agree with me, if you look at what they do rather than what they say.

Why is everyone becoming an exchange? Because it's just not that hard to add exchange-like functionality and escape the 20% transaction fees being levied by Google et al. An exchange is a low marginal cost, high fixed cost system. Once you've built the system, you just need to amortize the cost over a sufficient volume. That means that anyone with good volume is better off building their own than paying someone else.

If you extend this economic logic into the future, you arrive at an inevitable conclusion. Someday, someone will garner a huge amount of volume by offering exchange services at the lowest possible price, somewhere just north of marginal cost, probably in the 1% to 5% range of transaction fees. Everyone else will find that it is cheaper to use this single exchange than it is to run one themselves. Non-exchanges will stop reinventing the RTB wheel. And exchanges will be glad they moved into other lines of business.

I know it's a good deal for you.

In the past few months I did not invest in a couple of companies whose businesses I really liked because they were raising seed financing through convertible debt.

Convertible debt is essentially a loan with an option to invest in the next equity round at a discount. So, for instance, a typical seed stage convertible note might give the lender the right to roll the principal and accrued interest into the Series A at a 20%-25% discount.

Chris Dixon posted about the reasons why convertible seed rounds are a bad idea. I agree with his reasons. But I have another, more investor-centric, one: seed stage convertible debt is usually a bad deal for the investor.

There's an old saying in the venture industry: lemons ripen before pearls are cultured. Startups that are going to fail usually fail quickly. Look at Scott Shane's numbers on startup failure rates, in the graph below. While these numbers are for all small businesses (not just venture-backed ones) and for companies started in 1992, they seem to jibe with what I have observed over my thirteen years of venture investing.

If you invest in companies at founding, 40% will survive to year six. If, on the other hand, you invest in the companies that survived their first year, 53% will survive to year six.

Let's say that, like Fred Wilson, 33% of your early stage investments fail, 33% go sideways (1.5x return) and 33% return 5x-10x*. (I assume that "early stage" means investing one year into the company's life.) Then, if 25% of businesses fail between year zero and year one, seed investors should expect 50% failure, 25% sideways and 25% success.

For a Series A investor (assuming the average successful exit is 7.5x and exits are five years after investment) the IRR of the fund under these assumptions before management fees and carry is 25%. Until the last decade, this was close to the average return for VC as a whole.

As a seed investor, I should expect higher returns (I am taking more risk): I want a 30% return, rather than 25%. I should also expect it takes six years to exit the investment rather than five. For sideways and successful investments, the return is the same dollar amount as the Series A investor (so, if I invest at a 50% discount to the Series A, I get 3x for sideways and 15x for successful exits.)

Running these numbers, I need to invest at a 50%-60% discount to the Series A to get my target return.

Even if my simplifying assumptions are too simplifying, the discount has to be at least 40% for a convertible note to be economically rational**. I doubt whether any Series A investor would let that stand because, in hindsight, the seed investors took very little risk***.

I've had a couple of entrepreneurs tell me that they wanted the round to be a convertible note because they wanted to limit dilution. What they are really saying is that they want me to invest at a higher price than I would if we actually agreed on a price. I understand that, and I sympathize, and I even recognize that many angels turn a blind eye to this because they want to pretend they are not getting a bad price. But let's be honest, they are getting a bad price.

As a matter of discipline, I have a handful of rules I won't break, no matter how much I like your company. One of them is that I won't invest in seed-stage convertible debt.

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* There is some evidence that venture backed firms are less likely to fail than startups as a whole, and there's a lot of evidence that Fred Wilson is a better than average investor, so his failure rates may not match Shane's. There is also some evidence that VCs keep companies afloat until about five years after their investment, then give up, so the failure rate curve for VC-funded firms may be distorted.

** Disregarding that some angel investors will take a worse return in the hopes that the Series A will be lead by a great VC, allowing them to take a carry-free ride on that investor's coattails.

*** To be fair, I think the VCs really just take the entirely sensible viewpoint of caring more about the ongoing management of the company than the people who put money in a year ago and haven't been involved since.

The long siege of Corbenic

Darren Herman's article on AdExchanger this week is more important than it might seem at first read: it's the first time a major figure in our little adtech industry has pointed out that what we've built in terms of exchanges and platforms and data is just a small first step to what we will need to build over the next decade. Bear with me.

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1. The state of the state

Those of you who have been reading this blog for the past year know I've been thinking about several online display advertising paradoxes:

• "There is an oversupply of inventory" vs. time spent with media (i.e. supply of eyeball time) is decreasing (also here)
  
• Dozens of targeting startups pitching radical lift vs. being told by practically everyone in the industry that targeting does not appreciably increase lift
• The common industry lament that CPMs are falling rapidly vs. the data that seems to contradict that

One fact about what our industry is doing resolves all of these.

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2. What we are trying to do for marketers

The first internet marketing rant I heard was "the internet will create true one-to-one, accountable, ROI driven marketing!" That was in 1995.

Still waiting.

But it's true, or will be some day. The ability to dynamically maximize the ROI of your campaigns across all media in real-time is what our industry is building. To do this we need to be able to formulate hypotheses, test them, figure out the ROI, and reallocate into the highest ROI buys. And then, because humans are changeable beasts, we need to do it all over again, forever.

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3. What is Quality?

ROI is the value of the customer divided by the cost of acquiring them.

ROI = LTV / CPA

Computing this isn't as simple as it sounds.

Back in the day, at Prodigy, we kept track of each customer by when they started and by what marketing channel they arrived. For each marketing channel we would track the average lifecycle. That is, for a customer arriving in month x, 80% would stay one month (first month was free!), 40% would stay two months, 30% three months, 25% four months, etc.* The churn was different by marketing campaign because some campaigns got people for whom the product was wrong to sign up: they did not stay long.

We then multiplied the probability that any given person would remain in a given month by their contribution margin for that month (say $10, except in the free trial month, when it was -$10) and discounted all that back to month 0 using the company's cost of capital**. This was the customer lifetime value (LTV).

This is a complicated example. If you are promoting an offer where you get paid $2 every time you bring someone a customer, your LTV for that customer is, essentially, $2. But in most cases, the right customers are more likely to remain customers (unless you subsequently scare them away), and the wrong customers are less likely to remain customers, so the LTV is usually dependent on the marketing campaign.

Now, to the cost of acquiring a customer using display advertising. This part most of you know. I'm going to do it the simplest way, though.

CPA = Cost of media x Conversion = CPM/1000 x Conversion

Conversion here is number of people who see the ad divided by the number of people who become customers of the advertiser. That is, they convert from ad viewer to customer. Conversion is key, but it is an incredibly complicated thing to predict because it depends on: who the viewer is, what they are thinking at that moment, what they are looking to buy, where and when they are seeing the ad, what surrounds the ad, how many times they have seen the ad before, what the ad itself is, whether they are interested in what is being advertised, etc.

ROI = LTV /(CPM/1000 x Conversion)

None of LTV, CPM and Conversion are independent of the others, each depends on the particulars of the campaign (i.e. targeting high-net worth individuals changes CPMs , changes conversion, and changes LTV, the latter two in ways depending on the product.)

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4. What are we doing now?

What we measure now in the adtech industry is CPM. Some DSPs, with some clients, will claim that they are doing more. But if there is true integration of conversion into their buys--or any integrations that go much beyond "the CMO likes the results"--then it is extremely rare.

Because the adtech people can't measure conversion*** or LTV, they optimize CPM while trying to hold LTV and conversion constant. Using the same creative and target viewer, they find lower CPMs. This explains the puzzles I mention above.

• There isn't more eyeball time (i.e. supply), but we are putting ads in places that previously had no ads. On social networking sites, on UGC sites, on small sites, etc. We do this by using targeting data to find the few "right" viewers in the sea of "wrong" viewers. There isn't more attention to sell, we are just selling more ads per hour of attention. This is the increase in supply. (I mentioned this previously.)
• People do not see "lift" (i.e. increase in the conversion rate) by using targeting data because that's not what we are using it for. We are using targeting data to increase reach and find the same conversion rates at a lower CPM. Targeting data increases ROI by lowering CPMs, not increasing conversion.
• If CPMs are falling at the major media purveyors (as reported) then that is being made up for by increasing CPMs on social networks, etc. On average CPMs are not falling, but there is an equalization of CPMs happening across internet media. We hear the lament of the New York Times' of the world, but not the jubilation of everyone else. Facebook's ad revenue came not entirely by taking time spent with the NYTimes away, it also came by lowering their CPMs. You can find the same person you were targeting at the NYTimes on Facebook, for lower cost.
  

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5. What's next?

This is a great service to marketers. Adtech increases ROI. But it's transitory. Once enough ad dollars use a specific way of finding the right viewer on overlooked sites, the price of the inventory on those sites rises. We are right-pricing all the inventory on the internet after years of it being overpriced on the high-profile sites and underpriced everywhere else.

But there are declining returns to this strategy, as the inventory becomes more rationally priced. I've heard some rumblings to this effect already, although I think we have another couple of years before it becomes widespread.

Step two is to focus on conversion. This is what Darren was implying in his article. But to know conversion, there has to be integration back into the marketer: did this particular instance of an ad result in a product being sold? When this feedback loop is in place****, marketer's agents can start to use the algorithms they used to find low CPMs to find higher conversion rates. This will be a sea-change. But implementing it will be difficult because it requires substantial tech investment at the marketer, as well as procedural and probably cultural change. It's also one of the primary reasons the DSPs will have to choose between being a marketer's agent--working directly with marketers--or a technology provider to marketer's agents.

Step two is now. If you are starting a company to do this, contact me.

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6. Step 3

Step 3 is the end of advertising. It is using marketing communication to increase LTV.

Kotler spends less than 5% of his seminal textbook on advertising, the rest is about customer value, loyalty, product strategy, pricing, etc. But in the adtech world, it's all about advertising/direct marketing/promotion. When we can measure the effect of marketing communications on LTV, that will change.

Increasing LTV means keeping customers, not getting them. It means figuring out what customers want and building or changing your product to keep them. It means finding the right pricing, providing the right customer service and out-innovating your competitors.

And that is the holy grail of marketing.

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* These numbers are illustrative, but directionally correct. I don't remember the real numbers. It's been a long time.
** I don't really want to explain contribution margin, discounting and cost of capital. This post is going to be long enough as it is. Ask Fred Wilson on Monday.
*** Click-through is not conversion. If you've ever been in lead-gen you know you can easily and cheaply generate poor quality click-through. Free i-pod, anyone?
**** Outside of the arbitrage world, where it is being done now. The arbitragers are and always have been the smartest people in marketing.