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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.]

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Refining Lookery's Pricing -- Campaign Service

Taxi MeterMeter Image by JL08 via Flickr

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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Really Free vs. Artificially Free

Malcom Gladwell's blink on Showtime's WeedsBlink on Weeds by DanMelinger*

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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Deprogramming VC & Reprogramming SWOT

SWOT analysis diagram in English language.Image via Wikipedia

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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"It’s important to remember the board’s primary purpose: to hire (or fire) the CEO."

Cloud-Costing Rules

King CloudImage by akakumo via Flickr

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.

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"… age, gender and location, the three key elements for targeting."
Social networks take a different approach. On their profile pages, users declare many key aspects of their demographics, including age, gender and location, the three key elements for targeting. Targeting based on these self declared demographic elements can be very effective for performance advertisers within social media.

Performance advertising success stories in social media « Lightspeed Venture Partners Blog

Self-reported demographics can be used far and wide, distributing their effectiveness to any ad network and any publisher. So far, we’ve got 60M+ profiles that we’re licensing just for this purpose. All of them are gathered under explicit, upfront agreement of the providers and with the users’ privacy held paramount.

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"Database analyst Curt Monash told Computerworld that the study just reinforced his belief that MapReduce is better for limited tasks like text searching or data mining."

A Honeywell-Bull DPS 7 mainframe, circa 1990.Image via Wikipedia

MapReduce vs. SQL: It’s Not One or the Other

Rafer sez:
Everyone’s stuck in the speeds, feeds, and optimizations, but the point is money. First, money in the sense of building great businesses and self-cannibalizing them to keep them great. Second, professional analysts like Monash always stick up for their clients, in this case the database incumbents. Stonebraker’s motives [update: spellchecking by @joshu] are likely purer. He’s one of the biggest brains the Bay Area has ever produced, but I’m going to speculate he’s emotionally over-invested in structured DBs.

Most of the tasks being benchmarked were optimized for SQL DBs because they were the most cost-effective systems when those business processes were designed. We will be soon assigning them to the long, slow profitable declining category of “legacy systems.” As with any other transition, the change will not be universal. Mainframes are still the best for a number of tasks but no longer for the bulk of them.

Lookery and hundreds of other companies, many cloud-hosted, are building new business processes that are optimized for MapReduce and similar architectures. In many — even most — cases, these business processes will be far more cost-effective than the ones they will replace. New incumbents will arise, new benchmarks written, and new statistics reported by analysts with new biases.

Companies invested in SQL apps that can be replaced, most often indirectly, by MapReduce-esque apps need to start self-cannibalizing.

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"More than 90 percent of respondents called online privacy a “really” or “somewhat” important issue, according to the survey of more than 1,000 Americans conducted by TRUSTe, an organization that monitors the privacy practices of Web sites of companies like I.B.M., Yahoo and WebMD for a fee."

Concern Rises Over Behavioral Targeting and Ads - NYTimes.com

Rafer sez:
At Lookery, we’re obsessed with keeping Personally Identifiable Information out of our system, but this is inane. A very high fraction of Americans are concerned with their weight — not many are willing to do much about it. The useful question isn’t whether people are worried, it’s whether their actions will change based on that concern. Look to voting turnouts for the answer.

The level of privacy-reinforcing action described in the article is reflected in no population I’ve ever heard of. Where were these questions asked?


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