Much is written about both whether or not the VC model is broken and how to evolve startup business plans in the face of market changes. Today, it hit me just how inextricably linked the two issues are and how my own tactical process needs to catch up to market realities.

I spent from 1992 to 2006 presuming VC-backed startups were the business I was in, and I worked towards understanding that system. After all that time, two things happened to radically change my outlook.

1. I finally figured out that one should only raise VC if one is already rich,
2. I also figured out that being boring and late has better risk|reward characteristics than being sexy and early, and
3. Cloud computing arrived, making VC deal terms economic only as growth capital for Internet startups, leaving the early-stage field clear to angels (and bootstrapping).

Taking any number of lessons from Mashery’s good work, Lookery is a cloud-hosted SaaS vendor that uses an API to provide deep benefits to its customers and suppliers. Like Mashery in early 2007, we’re actively sorting our which customers and which decision makers love us and which look at us crosseyed (or don’t look our way at all). We have numerous data points in each category and the right kinds of patterns are emerging.

The problem is 15 years of old work vs. 3 years of new work. I haven’t finished retraining myself not to presume VC. I find myself mentally parcelling out multimillion dollar budgets that don’t exist. More importantly, calculating low-capital SWOT is not truly intuitive, particularly when analyzing VC-backed companies in Lookery’s market sector.

The VC-backed companies in our sector (principally Blue Kai and  Exelate) are doing a great job getting and giving data distribution via cookie exchange without the benefit or overhead of a centralized profile hosting system.  Cookie exchange works well for many user-targeting applications, but there are a few key tasks that aren’t covered including:

• Efficient combination of data from multiple sources;
• Forcing and enforcing the anonymization of targeting data without depending on good behavior by publishers and/or ad networks; and
• …

Lookery exactly runs that exact scaled profile hosting system, and it changes the equation — but how in a SWOT context? We’re angel-funded and intend to remain that way until we’ve completely nailed the revenue model (see #3 above). Relative to the other sector participants, our near-term enterprise value calculations and related tactics are different. My erroneous, knee-jerk reaction is to compete directly with them but that makes no financial sense. They have an order of magnitude more resources (from their VCs) and a lot more pressure to scale revenues quickly without much regard for expense (also from their VCs). We certainly grow revenues every month but breakeven in Q4 is a much higher priority than absolute scale right now.

The punchline on SWOT for Lookery in 2009 is to build on the unique strengths of our system putting priority on relationship depth and interconnectedness. We want to be our customers’ profile hosting and delivery system — and the one they want their partners to use. That means of our customers require a little more care and feeding, plus we have to be careful to disclaim all rights to their profile data. It’s business that the heavily funded startups can’t quite slow down enough to satisfy, gives them a good reason to do business with us, but is healthy enough to drive us to scale next year.

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We’ve been out selling Demographic Targeting to ad networks for five months, and the first stage of our Post-Facebook era is going fine. We have happy customers, stable infrastructure, etc., so now we know what our operations really cost. Thanks to @sawickipedia, we priced ourselves correctly for ad network sales, but that’s only a few hundred customers. Now that Lookery’s per-function IT costs and margins are clear, we can work on additional pricing plans with different value tradeoffs to greatly expand our available market.

That magic is that we were able to optimize our serving infrastructure after deployment. Over the course of the last 6 months, we’ve gone much further into Hadoop and Project Voldemort. That means we started with the wrong server count, big-small box ratio, et al. So we just shut them off and turned on the exact number and kind of server instances that we need when we need them. There’s another million bucks we never wasted.

The good news is that Lookery contributors of anonymous Age-Gender data have a very cool dashboard that shows them how much we owe them. It updates every 12 hours. The bad news is that there’s currently a bug that exaggerates what we owe everyone. It will be fixed (and the calculations made accurate) within a day or so. It applies only to the May calculations, and no data is actually being lost.

Update: < 3 hours later and @ckelly already fixed it.

“I checked the BlueKai and eXelate pages to see what they had on me. BlueKai’s said I was interested in online privacy, which is true, though probably not very useful for advertisers. At eXelate, though, I wasn’t identified as being interested in anything at all. And while it had my age range correct — 25 to 34 — it also had decided that I was male. Certainly it wasn’t scary — but it also showed how far these exchanges have to go.”

Therein lies Lookery’s differentiator regarding our data:  we aggregate only user-reported data to make available for ad targeting and make no assumptions about users.  If we don’t have data on a consumer, we don’t pretend that we do (or assume what we think it may be).

We get this real data by partnering directly with social and dating sites to collect anonymous information that a user contributes to our partners.  With no black box or special sauce, our data buyers are assured that they only get and pay for what they want.

Personally, as a female consumer, I would rather see ads for cute shoes than basketball jerseys.  Let’s hope the ad networks and agencies get my gender right.

Control Freaks Welcome: Public or Private Reports
Since launching our public directory of demographic and search site reports, Publishers have been asking for the ability to make their reports private. Now all site audience reports will be private with the option for publishers to make their reports public. For the first time in the audience analytics space, publishers can get reliable private demographic and search audience reports. We’re going to let the control freaks decide what is best for them – public or private.

Pricing
Subscriptions start at $10 a month and go higher based on traffic:

• $10 per month for up to 250,000 page views
• $20 per month for 250,001 to 500,000 page views
• $40 per month for 500,001 to 1 million page views
• $30 for every additional 1 million page views

Publishers can sign up at now http://www.lookery.com/analytics.

And here’s a summary of what our Audience reporting service offers:

• Get Demographic and Keyword Profiles
  Lookery’s reports provide a complete demographic and keyword profile of your site’s users. Lookery’s reports show the range of age, gender, location, and keywords for your users by month. Reports are updated daily so publishers can track how their audience profiles change over time and make month-to-month comparisons.

• How does Lookery Work?
  Lookery pools anonymous, empirical user data from a network of data contributors — no panels, no algorithms — but actual self-reported user registration data. When you add Lookery tracking code to your site, Lookery starts aggregating statistics into Audience Analytics reports about your site’s users for you automatically.

• Lightweight and Speedy
  Lookery already supports millions of daily users worldwide across thousands of publishers every day. To minimize latency, our JavaScript code is lightweight, while being served by a leading global CDN.

As we operate today, Lookery aggregates, cleans, sells, and distributes anonymous user profiles. Our entry product with almost every customer is Demographic Retargeting services, where we sell Age and Gender to ad networks, publishers, advertisers, and landing page optimizers. We’re having particular success with video ad networks. From there, the conversation quickly turns to Open Retargeting in which our customers add their own registration and targeting data to our system, which we distribute for their private use. Like any other marketing organization, we sought out the emotional common ground between our best prospects — the thing that hooks them and makes pay attention to our benefits.

That hook turns out to be their obsession with assembling and managing great audiences. Our customers obsess over creating coherent and profitable streams of interactivity, go to extremes to figure out how those streams work, and apply any control technique they can to refine the process — on their sites, in their campaigns, and wherever else they can think to do so. Lookery adds a lot of new control and a lot of controlled distribution, all without invading user privacy.

Background and Details
David, Rex, and I put together an ad network for Facebook apps in July 2007. We attracted a great crew to operate it and figure out a sustainable business to build behind it. We started building the data services in October 2007 and finished about a year later. We made three significant incorrect assumptions in building the overall business:

We expected an advertising downturn, but we thought that our ad network could remain breakeven or better with our remnant-ad focus. Nope; the ad downturn is more extreme than anyone guessed.

Along with several other startups, we bet that Facebook would allow third parties to use some anonymous data for ad targeting. They have not allowed it — nor have enforced their TOS which prohibits it, but we can’t build a business on the hope that FB never enforces their TOS.

Along with many other companies large and small, we spent a lot of time and effort collecting search keyword history on anonymous users. We expect that Google’s AJAX search page test will be rolled out across all of Google’s organic search traffic within a year. The purpose is seemingly to decimate the Facebook|Microsoft search partnership, but we do not want to become collateral damage in that fight. Our participation in that business is not over, but it will be far more cautious as described below.

Poor estimates and outright mistakes are unavoidable. It’s how you address them that matters. In October 2008, we quickly sold off our ad network to Cubics/AdKnowledge, the only company larger than us in FB app remnant — one well equipped to keep going as an ad network throughout the trough. Changes to the demographic data business started last summer when we started signing up Age and Gender data providers. Our demographic data collection system is completely API-based and super white-hat, so we never possess a single name, email address, friend list, or other bit of untoward personal data. We are up to 40 million age and gender profiles, the large majority of which are US residents.

Selling ad services is very seasonal, so the Lookery Demographic Retargeting efforts really kicked off in January. With six weeks of Age|Gender sales success our belts, we now know what it will take to grow the business. We are excited that the revenue path is clear, but our free services are not adding to that momentum. Most upsettingly, narrowing our services means that we’re separating from people who were delivering great results — Rex and Dan.

On the product side, we’re discontinuing our free reporting services. With Google closing off the search retargeting opportunity, we do not gain enough benefit from our free public reporting to continue it, though all current free services will remain available for a subscription. Today, all our publishers will be notified of the impending conversion of Lookery’s Audience Analytics, Ad Controller, and Network Directory from free to paid services. The actual service changes will take place in one week. Our data providers and customers will retain access to all services from within existing their password-protected accounts. We are also planning ways for very small sites to contribute age and gender data into the system, which will re-start their access to our previously free services. We’ll add them to the system as soon as we can.

If there’s a situation we’ve overlooked that make these service changes a problem, please let us know.

Data on every user on every web page. That’s the promise we are trying to deliver on at Lookery.

The natural audience for Lookery’s services are Ad Networks, mostly those specializing in performance-based advertising.

We’re seeing the next wave of interest coming from Search Marketers, Survey & Market Research Companies and Landing Page Optimizers. The latter being the most important in the performance advertising chain IMO. That’s why this slide from Jonathan Mendez’s Advanced Landing Pages presentation at SMX West made me smile this morning.

If you’re a small business that is interested in easily creating and optimizing landing pages then I recommend you check out my friends at HubSpot for help. But if you’re already creating landing pages for your business then Jonathan’s blog is required reading if you hope to optimize your efforts.

Their age range is quite diverse, spread evenly through out our age groupings.

It does seem that more females are present on Sampa sites.

Growth by countries appears to be making a move now from just the English speaking ones.

The state split in the USA is very even; meaning you can probably find a Sampa site near you.

Wouldn’t you like to know your true audience and maybe even target or re-target them? Perhaps you only want to make money, or be in total control.

Whatever your needs may be, we can meet them with our services. It all starts right here by filling out our simple sign up for your own Lookery account where you can become a proud site owner, just like Sampa did.

Efficiency Definition 1: developers spend as little time as possible not coding (meaning, as few meetings as possible, as little interaction with ticket systems as possible, etc).

Efficiency Definition 2: developers spend as little time as possible coding features which no one needs

Those two aren’t exactly in direct conflict, but they do imply different tradeoffs.  If you go for 2, you’re willing to hit a few meetings, jump through some ticket system hoops, because you live in abject fear of writing the wrong code.  That’s where I find myself, at this point in my career.  That’s part of why I don’t trust dev teams that romanticize overwork — it’s almost impossible to avoid writing the wrong code when you’re pulling 60 hour weeks, and working in constant low-grade fear.

-Dan M

But weak demand is simply highlighting the more fundamental oversupply problem — and pressuring prices. The cost per thousand views of display ads on big Web sites sold through ad networks — rather than sales forces of individual sites, which usually handle premium inventory — fell 54% in the fourth quarter compared with the year earlier, estimates PubMatic, which offers online services to publishers.

I’ve spent the last six weeks out selling Demographic Retargeting and Open Retargeting, much of it in NYC (It’s going ok, thanks). Selling our Facebook ad network last fall was a complete about-face for us, in a way that I didn’t appreciate at the time. We’ve gone from a service that brokered ad networks on behalf of FB app publishers to a data sales team that focuses on the strongest, most-controlling media sales forces we can find.

It’s a far trendier change than I’d normally sign up for, but the task of those sales forces is also changing rapidly this year. For the first time, even the super swanks of Madison Ave are getting forced by their customers to use real data. That’s a challenge we can meet as long as we’re quick, cheap, and careful.

It appears that the USA is where most of this site’s audience comes from, even with the site owner being from Spain who are from the USA but happen to live in Spain (see comment below!).

California being the #1 state is no big surprise, but Georgia being the #2 does seem a bit different than one would expect from a site that is trying to be the central blogger location for wine blogs.

While there are slightly more male visitors to this site, it appears that there is no dominate majority audience for wine bloggers.

While some audience data gives us a big swerve, you only can learn more about your audience if you sign up and give Lookery a try.

Do you really know who your audience is? The answer may surprise you!

At the start of each month, we have decided to start sharing a quick snapshot look into the number of profiles we currently have on hand. This of course is growing daily, and doesn’t reflect our further breakdown of male, female, age, or where these users are from. Nor does it take into account any custom data our data contributors have wanted to share with us.

Everyday more then a 150,000 new profiles are added. With Lookery’s Demographic Retargeting service commercially available, our network of data publishers are now making money in the easy way. This is what we have always meant by our little catchphrase we have been saying in conversations with our early data contributors – “Money For Nothing”

We will always be publisher focused, and there is no reason why people shouldn’t make money with their data including custom user data with our Open Retargeting service. Let us show you how easily it can be done. Why should the benefit of your data be the benefit of the growth of other companies only? It’s time you the publisher made some money with your data!

The audience data we are seeing on age appear to be well balanced across the various age groupings.

Audience data for gender, while a slight edge goes towards the male audience, there isn’t a very big gap between female and male readers of this site.

Unlike last week’s featured site of the week, there does seem to be a very big following in the USA.

While the site owner lives in Massachusetts, his core audience appears to be in California, with Massachusetts coming in second.

Isn’t it about time you learned more about your audience by signing up your site with Lookery?

In myapp/src/foo.erl I put a small header file inclusion:

-ifdef(TEST).
-include("foo_test.hrl").
-endif.

And myapp/test/foo_tests.hrl looks like this:

-include_lib("eunit/include/eunit.hrl").

foo_test_() ->
   [?_assertEqual(ok, public_function())].

bar_test_() ->
   [?_assertEqual(ok, private_function())].

With the right compile options, you get tests only built into the code during dev, and you get to keep your tests in a separate file (but still have full access to the internal functions, which I find absolutely necessary).

-Dan M