Everyone’s sick of hearing Cancel, Sawicki, and me whine about being too early with Lookery. So here I finally am with a constructive update — six months to the day after the Couldery Shouldery post in which we surrendered Lookery to the Deadpool. In those six months, the rest of Lookery has scattered to hot shops like Performable, ICanHazCheezburger, Sweepery, etc.

Lookery was so 2012. Here in 2010, there are a maybe a dozen scaled, data-orthodox opportunities for horizontal Audience Targeting winners (e.g. Google, Facebook, … Collective, Blue Kai, and Exelate), but there are a few thousand opportunities to build Audience Networks, from Glam and Federated Media to Halogen Network to Dogster to a couple of orthodontist audiences that I’ve never heard of.  The Audience Networks and their vendors will use the big guys’ data, exchanges, and clouds as raw material suppliers, layer highly vertical and client-specific information on top, figure out the nasty data-sensitive creative issues, add a good ole dollop of human customer service, and serve.

Deadpool Lookery was very picky about what sort of Audience Network infrastructure we were willing to offer. Lookery Prequel isn’t. If you are a publisher network, category-leading publisher, brand, or agency, we’ll run through walls to help you become an self-reliant Audience Network. If you’re a vendor, we’ll help you conform your existing marketing product/service to what Audience Networks need and will pay for. We’ll start the conversation around building you a proprietary targeting system of your own, that lives on your AWS account and your content delivery network account, but if you need us to do business development, vendor analysis/selection, data acquisition, market positioning, investor positioning, or whatever, just speak up.

We already have Halogen Network and Sprout signed up. The tech side is handled by Cloudspace, which includes MyBlogLog co-founders Todd Sampson, John Sampson, and Steve Ho.

While Lookery Prequel is a consulting business and not a product company, we do need focus in order to build value. To get there, I’m constructing a handful of customer tenets that will form the core of our work. We’ll do all sorts of gigs, but they need to move the market forward in at least one of these five ways…

Thou Shalt Have No Other Follow Before Me
By popularizing Follow, Twitter fixed friending both for humans and businesses. One way Audience Networks need to grow is by adding followers. We’ll be helping.

The Best Privacy Policy is Authentication
Similarly, the output of Audience Network marketing will often be an authenticated relationship between marketer and ‘Net user, most frequently using TwitterAuth or Facebook Connect. Online marketing and explicit identity are going to become increasingly linked.

UX beats Dotcom
Creative matters. Data-driven and personalized creative matter more. At the same time, destination sites are no longer the end-all be all, hence the rise of APIs and Mashery. Site representation isn’t dead but it had better evolve. To wit, check out Disney’s Alice in Wonderland promotions (authored by Sprout). They sing, they dance, they might even screech — but they don’t give you a link to click back to disney.com. The campaign is completely self-contained.

Cookie Proliferation — You Ain’t Seen Nothing Yet
Diversity of marketing services vendors is good for everyone that we care about. That means dropping a heckuva lot more cookies than are dropped today. Get over it. For that to work well performance-wise, the infrastructure for dropping cookies has to be a lot better configured. The infrastructure doesn’t need to be replaced, but big publishers need to start imposing far stricter content delivery network (e.g. Akamai, CDNetworks, Internap) guidelines on their marketing partners. Once content delivery network approaches are correlated and guidelines enforced, we’ll find that the current infrastructure can support a lot more cookies and regular old publishers can start glomming a few strategies from  the big advertising exchanges. (Coincidentally, we’re offering content delivery network audits and related services to publishers.  ;)  )

Goosing Everyone’s Numbers with Owned&Operateds
What works for the goose works for the gander. Our clients on all sides of the table really should own some of their distribution. We should too. For our clients, that generally means owning content sites or having exclusive deals with publishers. In Lookery Prequel’s case, we’ll be creating a series of single-purpose publisher and advertiser tools delivered as Javascript. Each will stand on its own as a small freemium/e-commerce business when they aren’t directly boosting our clients’ results.

Please Get in Touch
In addition to Halogen and Sprout, this quarter we’ll sign another customer or two of the half dozen qualified leads in our pipeline. I’m out looking for another client or two beyond that as well as our first commercial hire so we can expand further. No matter what your agenda, the form below is for you.

*Prequels are what science fiction authors write when they either can’t see any further into the further or when their last story lacked context — or both. If it can work for Battlestar Galactica, it can work for me.

FormSpring - HTML Form Maker

The new MySpace CEO is talking up Disney’s Alice in Wonderland promotion, which is great to see. It was put together by Sprout, a company I’ve been helping out for the past couple of months.

What’s most fascinating to me about Disney’s campaign is that there is no link back to Disney.com anywhere in the campaign. They and Sprout have worked so hard to engage you in the ad that the measure of success has nothing to do with their homepage.

It’s as Mashery’s BizDev 2.0 white paper says:

[The web today] is not about being a destination site; it is about providing the right information at the right time to your customers.

Image by MrTopf via Flickr

If you are going to accept the live-or-die risk of being completely dependent on FB, please make some effort to understand their priorities. The effect of this week’s FB platform changes was completely predictable in October 2008.

Several people have called me in the last 48 hours asking, “Why won’t FB let us pay them for app notifications and are instead killing them off? Won’t FB’s traffic drop too?” Of course Facebook’s traffic will drop a little. Facebook knows and couldn’t care less. They don’t care in exactly the same way they didn’t care about the traffic drop that resulted from their July 2008 redesign. The traffic they will lose is traffic that does not increase the value of their enterprise. These changes are very smart and appropriate self-cannibalization for a company that is trying to take over the Internet. They no longer need to build more traffic at facebook.com. For now, all that matters is building a huge network of Facebook Connect sites.

If you have a good app attached to a well thought out external web site that authenticates using FB Connect, then this week’s changes probably HELPED your business. I know of two specific startups where that’s the case. My description of which apps are supported by the changes is intentionally very specific. There are very few categories of developer that FB cares about right now — and the FB Connect sites is the only category they care about for new/raw startup developers. If you help FB spread their JS and Authentication out into the world, they will support you until their network is too big to stop. They are at roughly 20,000 publishers now. At ~200,000 publishers, watch out! Their priorities will change again and they will start taking cash out of the FB Connect network instead of providing it with free traffic.

Facebook obviously cares about the big game developers as well. Without Notification access, Zynga and the rest of the game companies will need to buy more and more media on Facebook. Probably a third of Zynga’s $200M runrate is paid back to Facebook in advertising now. Expect Zynga to start paying Facebook 50% or more of their runrate (which may drop) as a result of these changes unless Zynga makes some very fancy moves in the next month.

Facebook makes no empty moralistic claims about avoiding Evil. They do a great job of acting in their own best interest, seem to act ethically, and don’t try very hard to hide their motivations. If you want to do business in that ecosystem, please study the host on which you are dependent or find someone to do it for you.

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A number of companies reached out to us requesting Lookery’s benefits packaged as for-hire bizdev and engineering work. We’re figuring out how to satisfy those requests and will keep posting updates here as they happen.

We’re commencing an “orderly shutdown” of Lookery. We made the decision as late as possible without forgoing our responsibilities to the various stakeholders, including and especially orderly disengagement from our customers. We’re honored by their attention and support and regret that we couldn’t have served them even better and for much, much longer.

I left Wouldery out of the post title as it’s not relevant. The only information of value is what other choices were readily available to us and which ones I look back on with uncertainty. This is probably not my last public postmortem on the lessons learned, but I owe everyone near-immediate disclosure on the things I can put my finger on now. I’m trying to keep a stiff upper lip during this process. Please avert your eyes if I falter.

As co-founder and CEO of Lookery, the buck stops with me and no one else. I hope to have the opportunity to work again with each and every one of the dozen people who worked at Lookery since it was founded in July 2007. I hope I did as well by them as they did by Lookery and by me. Please take this post as a recommendation of each and all of them and never hesitate to get in touch with me for details. The huge majority of them went well beyond the call of duty to try and make the company succeed.

So did Allen Morgan, the outside Director that represented the Preferred investors. There’s no way we could have gotten this far or known how to behave well in the face of adversity without his guidance. He represented a great batch of angel investors on our Board and was one of the main reasons that they were so supportive of the company whenever I asked for anything. In May and June of this year, most of them agreed to participate in an inside round to keep us running through the next series of milestones. I cancelled the transaction a month ago, as I could not be as confident of reaching those milestones as ‘good faith’ requires.

Before delving into our market and product choices, I need to address our fundraising overall. On forming the company, David and I decided to pursue a low-cap model, postponing institutional investor involvement for the foreseeable future. It really sucks to be here right now, but I think it was the right call. Resigning politely at someone else’s behest, as I’ve done twice before, did not improve any of the stakeholders prospects in either case. Unless Lookery reached an expansion stage, where the sales model was known and repeatable, I’m ever-more convinced that raising institutional capital would not have increased our chances of creating value for the existing shareholders. The company would have survived longer, but that’s not the goal.

Going low-cap, David and I were careful to create a parsimonious and headcount-spare operation. Operating virtually was not a problem, nor was cutting corners on travel, schwag, and office rent. And, I’ll still only work on cloud-hosted businesses. Those parts of Lookery were appropriate, repeatable, and did not bring us to this point. We did invest too much in building market share in the original ad-network business in the first half of 2008, but that error was a symptom rather than the disease.

Moving on to our specific coulda-shoulda product and market choices, there are three key moments at which a different and defensible decision might have made all the difference. In chronological order, the sins Lookery committed under my leadership were continuing our dependency on a large partner (March 2008), not knowing when to cut bait on a failing asset (September 2008), and building ahead of the market (December 2008). I and we made any number of other mistakes, but all the rest were correctable. Avoiding even one of the three big errors might have been enough to get us over the hump.

I believe David and I started Lookery in July 2007 in the right way and for the right reasons. Based partially on my F8-launch work for LendingClub (a company I’m thrilled to know) David and I decided to quickly offer a no-frills banner network for Facebook app publishers. We went from commitment to live service in well under two weeks using AWS and OpenAds, pulled in Rex Dixon almost immediately to manage the publishers and Todd Sawicki soon thereafter as we needed a real ad pro. Both David and I had been keen observers of, and vendors to, the online ad business from the outside, and Todd was the online advertising insider that completed the early team. By March 1, 2008, we were getting pretty close to a billion impressions a week, had moved the ad ops to Atlas, started spec’ing Lookery’s targeting technology, and closed out a $1M convertible note financing that had been rolling in since October.

So far so good on using an ephemeral opportunity to create a company, but this is where I place Coulda-Shoulda #1. We exposed ourselves to a huge single point of failure called Facebook. I’ve ranted for years about how bad an idea it is for startups to be mobile-carrier dependent. In retrospect, there is no difference between Verizon Wireless and Facebook in this context. To succeed in that kind of environment requires any number of resources. One of them is clearly significant outside financing, which we’d explicitly chosen to do without. We could have and should have used the proceeds of the convertible note to get out from under Facebook’s thumb rather to invest further in the Facebook Platform.

Coulda-Shoulda #2. Predictably and reasonably, Facebook acted in their own interest rather than ours. Their Summer 2008 redesign supported Facebook’s goals elegantly but hurt our publishers and us in ways that became clear just weeks after we’d raised another ~$2M. At this point, we made a mistake endemic to startup people. We followed our natural inclination as problem solvers rather than getting out while the getting was good. If we’d sold the ad network the minute we understood that we could no longer make it successful, we would have saved a couple hundred thousand dollars in working capital. Plus, the ad network would have fetched three to five times its low-six figure sale price less than 60 days later. That’s a million dollar mistake I made in a very short period of time. I should understand sunk costs better than this.

To give credit where it’s due, Todd closed the sale of the Lookery ad network to AdKnowledge in less than two weeks from the moment we decided it had to go. I was off promoting Lookery’s targeting system to European demographic data sources and social networks and was not even in the country during those two weeks.

Coulda-Shoulda #3. Once we sold the ad network, I fell into a bad old habit — persuading my team to build something before the market was ready for it. Oren usually saves me from myself in this regard, but I didn’t pull him away from Mashery for the day or two necessary to diagnose the problem. Mashery is doing so well that I clearly could have. Lookery’s Profile SaaS/universal cookie mechanism is far more economic and effective than cookie exchange systems in a world where ad media and targeting data are separate commodities. That world is a year or more in the future.

This is the fourth blog post that I can find from a Lookery exec in which the primary theme is early = wrong. I felt pretty good when I wrote this one in December 2007 when Lookery was still doing all the right aka solely tactical things; the issue was weighing on me last month as I started to wonder if Lookery’s inside round was a bad idea; but it hurts the worst to read this post of Todd Sawicki’s from the day we met four months before Lookery was even founded.

Within the bounds of confidentiality agreements and showing respect for the many people who treated Lookery and me well over the past two years, I’m happy to respond to any questions that arise. My replies may sometimes take a day or two.

Random [to me] Manga
image via Wikipedia

Local advertising networks have begun to reach out to Lookery for our demo targeting as well as private retargeting via Lookery MyProfiles. We’re easily able to accommodate them, but I wasn’t expecting local ad interest so early in our history. So, I started looking for data.

The most revealing data I found is from the MAGNA, the forecasting side of Interpublic. That press release is the summary of their more detailed forecast. The whole deck is worth looking through, but two points come through that don’t seem intentional:

1. From the first two slides above, the Internet is not only the first true engagement medium as implied, but it’s also the first medium to meet all MAGNA’s “Illustrative Goals.” The latter point is made clear in the second slide as the Mom & Pop’s convenience store isn’t likely spending much “building brand.”
2. Wow, is offline local advertising crashing. The report promotes TV spend heavily:

   Democracy Television is the worst form of government advertising, except all the others that have been tried.

   Yet, it includes the nasty local TV spend chart above. The deck has similar graphs showing the devastation occuring to local radio and directories. Local newspapers even make the nationals look healthy.

My interpretation of the data is that local advertising is coming online in a different way than I’ve seen documented. Local direct response is coming online reasonably well, but local engagement is not. Local businesses can advertise around products and services but have not found a way to communicate around interest and likemindedness.

It’s not very surprising when you look at it — online communities of interest have no specific reason to be primarily local. The Internet helps us find people with our very same passions, no matter where they are. Statistically speaking, they are unlikely to be nearby. There are certainly exceptions which can be fun to play with, but I’m not surprised to see that our engagement with local communities is losing relative mindshare.

For local advertisers and their ad network vendors, one good solution is to plug people’s anonymous local buying behavior into a large profiling system and retarget them on other non-local web sites. It’s yet another example of ad networks using demographic targeting and Lookery MyProfiles to compete with Facebook Ads out on the Open Web.

[NB: Yes, I originally mistyped MAGNA as MANGA.]

After Lookery sold its ad network to Adknowledge in the Fall, we finished building our user-targeting service and started licensing data. We obviously needed pricing. I normally start by analyzing the competition but the existing demographic offerings did not offer much guidance. They’re panel data which is super cheap as it doesn’t perform for individual targeting, credit bureau data that is bundled with credit card transations and has all sorts of PII problems, one-off deals with portals that are individually and expensively negotiated, and spammer data which has been collected in ways that create civil or even criminal liabilty.

We also sell Lookery MyProfiles, which is SaaS profile hosting and distribution, though MyProfiles’ marketing is low-key for now. We’re not aware of a comparable service, so the competitive info is thin again.

In that kind of data vacuum, the steps we took aren’t very surprising, but they do vary a bit from what I usually read:

• Without useful competitive info, start with the obvious, and start quickly. Obviousness, which includes simplicity, is never the whole answer, but it’s the best way to the learn the most in the least time.
• Iterate slowly, customers and prospects don’t like prices to change. I try and keep the frequency down to twice a year.
• Price from value, not cost, but know what your costs are. Of increasing importance in a cloud-computing world is understanding the fixed versus variable costs. Because cloud-hosted enterprises are no longer buying or even committing to server hardware, our variable costs (i.e. our Amazon, Media Temple or Google App Engine bills) are a greater fraction of our total expense than previously. Watch those changes.
• Don’t worry much about cannibalism. My most influential professional mentor, who could sell you your own eyebrows, looks at distribution from the angle of, “If you don’t have channel conflict, then you don’t have a channel.” The same lesson applies here. If your pricing options don’t overlap a little, then your pricing is excluding in-between customers entirely. That’s more expensive than the alternatives.
• Decide what you are optimizing for — this month’s revenue, commitment, or building distribution for your next offering. Lookery cares most about building long-term financial relationships with its data licensees and licensors, so we offer lower prices for time and volume commitments.
• Keep the number of pricing options low. We had two types of Network Service (see below) at first. When we added Campaign Service, we were planning to offer two versions of that as well. Even with just four options, our sales prospects quickly started entering analysis paralysis, because we were asking them to make too many subtle financial decisions. Now in its final form, we offer just one way to buy Network-wide data and one way to buy Campaign data. The simplification is already a clear sales cycle improvement.
• Think about how future services will fit — very briefly. We all naturally overestimate our ability to foresee even the near future. Make sure there’s no live-or-die pricing conflict being created but attempt no optimization.

Lookery Network and Campaign Pricing
Using the above, the first pricing scheme Lookery offered was Network Service. We charge a flat monthly fee for every unique demo profile that we license each month. The data is delivered in the form of Birthyear, Gender, and Location, i.e. 1975, male, and San Jose, CA. That profile, completely anonymous before Lookery even gets it, can be used by the client for any targeting they like as long as they do not sublicense it. The profiles are stored in an  unusual and advantageous system on Amazon Web Services, and delivered via pixel dropping like the rest of the online ad world.

Midyear, it became time to update our pricing in order to address a much larger portion of our market. As is typical in advertising, more customers run their accounting on a per-campaign basis, so we added Campaign Service pricing. In campaign pricing, we deliver a different version of the same demographic data but at a lower price. Instead of querying Lookery for “What’s this person’s birthyear and gender?,” customers ask our system “Is this person a female between 35 and 44?” We respond with Yes or No, and the license to that data lasts for the campaign or the life of the cookie.

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When will referrer processing become significant for content sites as it already is for e-commerce sites?

By Referrer Processing, I mean changing the content of the landing page based on what site the visitor is coming from. The information in the referrer is primarily a domain name (Digg, StumbleUpon, MyBlogLog), but also includes search keywords and other parameters in specific cases. Dogster’s welcome panel for StumbleUponers visiting Snuzzy is pictured below as an example. This mechanism raises site visits by 25x for certain sorts of referrers.

Dogster has learned out to get more visits using referrer processing, by increasing the degree to which Snuzzy is promoted inside StumbleUpon. We had lots of anecdotal evidence at MyBlogLog that we created longer visits than Digg, which is another lever to pull. I also suspect that visit value (i.e. conversion to revenue) could be increased as the system was refined.

Lookery hoped to accelerate referrer processing for content sites by making search keyword retargeting easily and inexpensively available. Beyond what Hittail and other companies in the sector offer, we were planning to create a keyword network across everyone running our Javascript. Today’s Quancast article in the WSJ touches on both halves of the issue, but no one vendor does.

David Zinman, Yahoo’s vice president and general manager for display advertising, says his company recently launched a new ad-targeting service that allows advertisers to show display ads to visitors who searched for certain search terms. “We have data that is only available to Yahoo, and are using it in a very specific and customized way” for advertisers, he says.

Still, some ad professionals are enthusiastic about Quantcast’s new service. “Yahoo can do many of the things that Quantcast can do, but they only see behavior on their own networks,” says Jacki Kelley, president of North America for Universal McCann, a media agency owned by Interpublic Group. She says one of her clients is testing the Quantcast media-buying program.

Quantcast is building the network but does not help marketers or publishers directly target individuals. Yahoo! targets individuals, but does not include its networks. Now that social referrers are seen as an real sector, the problem will get solved. I’d bet on Facebook Connect in partnership with Microsoft Search to walk away with it, though bit.ly** has a shot if they want it.

UPDATE: I should have noted that Lookery also stepped out the search keyword business as we believe that Google will stop including keywords in its referrer URLs. This will crush the ecology initially but eventually provide a boost to Yahoo!, Microsoft, and Ask.

*Lookery’s free demographic reporting was Artificially Free because the value creation was not simultaneous with the expense of the free service and because the value creation was speculative (though either condition would have sufficed).
** I’m on bit.ly’s advisory board.

Running a low-capital web startup forces a different perspective on the Anderson|Gladwell “Free” dustup. In general, I’m glad Anderson brought attention to the topic, but web startups must take a more parochial and nuanced view of the issue — though not Gladwell’s. Masnick pithily sums up how most of the world views Free business:

The answer to Gladwell’s question is simply one of economic efficiency. You can pay people to write — just as Encyclopaedia Britannica does. Or you can get other people to write for non-monetary rewards — as Wikipedia does. The latter is a lot more efficient a solution, and the difference in productivity and output is quite evident. It’s not saying that there is no business in paying people to write, but it’s a very different business than the indirect business model, and it’s the economic efficiencies that come into play.

I haven’t read Free, and I’m unlikely to get to it. Free, freemium, and similar issues are subsets of Gift Economics, and Anderson’s book isn’t be actionable enough for what I need. His audience doesn’t care about capital consumption in building Free businesses, so he doesn’t address it deeply.

Startups need to see Free economics as two mutually exclusive options — the Really Free and the Artificially Free. In this context, Really Free offerings pair some kind of loss-leader service with simultaneous, concrete value creation, and Artificially Free offerings do not. Artificially Free goods are competitive giveaways with little or no current benefit to the vendor other than lowering the competition’s market share or soaking up distribution in a new market where the revenue model isn’t clear, per all the YouTube discussion. Companies with lots of cash have always done this sort of thing to push competitors out of the market. Other than anti-coMpetitive scenario$, it’s a completely valid approach.

The social web brings a new wrinkle to the Artificially Free competitive giveway — sometimes it masquerades as Really Free. There are four versions of the masquerade:

1. Big companies who have jumped on the social bandwagon and either don’t know or don’t care whether their giveaways are Free or Artificial;
2. VC-backed startups where both management and investors know that the giveaway is Artificially Free, though not scarce;
3. VC-backed startups where the investors (and maybe also management) don’t know that the giveaway is Artificially Free (but not scarce again); and
4. Startups of whatever size that represent that an inexpensive but scarce good as Free, trying to finesse the scarcity issue later.

To act, startup managers first need to figure out whether their own services are Really Free or Artificially Free. You must not fool yourself about whether your good is Really or Artificially Free. If your good is Really Free, then do the same analysis for your competition. If your good is Artificially Free, still analyze the competition next but with any eye to how much capital it will take to fight them. Endurance won’t be enough. If you are Artificially Free, and they are Really Free — bail. That’s not a situation in which founders make money.**

Once you know everyone’s Free status, set your course. By the numbers above, numbers 1 and 2 are a pure endurance or fundraising game. You will need to simply outlast their ability to lose money and make sure they don’t move from Artificial to Real without you in the interim. It’s important to note that VCs often choose to ignore the difference between Really Free and Artificially Free. I would too in their shoes. VCs achieve better investment returns when startups spend their way to Really Free than when it’s the starting point.

Number 3 eventually devolves into Number 2, a point at which point I’ve been fired at least once. Hit the competition hard at this moment, no matter how much money they’ve got. Number 4, competition that is giving away a scarce good as Free, almost always means you are dealing in a scarce good too. That’s beyond the scope of this analysis and is only listed for completeness. The most obvious examples are Pandora, imeem, et al, who gained distribution by giving away what the music companies have the [increasingly bizarre] legal right to charge for.

*Thank you, Zemanta, for the “Lordie, are you high!?!?” photo.
**My normal VC fundraising proviso also applies — don’t do it unless you are already rich.

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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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At Lookery, we have been working very hard to transform most of our data processing tasks into batch-oriented workflows in order to deal with growth. For example, we were already using Hadoop to compute our index and data files for our largest database, but the process of serving that information took place over too many network hops (load balancers, reverse proxies and Amazon S3). Therefore, as soon as I learned that Project Voldemort supported offline building of distributed stores, I decided to try it and we’re now running it in production. Just visit the guest blog post over that Project Voldemort blog for full details.

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.