Octavianworld

#Facebook at 100 (Almost)

So Facebook's finally filed to do an IPO.  Should you like?  A year ago, I posted about how a $50 billion valuation might make sense.  Today, the target value floated by folks is ~$85 billion.  One way to look at it then, and now, is to ask whether each Facebook user (500 million of them last January, 845 million of them today) has a net present value to Facebook's shareholders of $100. This ignores future users, but then also excludes hoped-for appreciation in the firm's value.  

One way to get your arms around a $100/ user NPV is to simply discount a perpetuity:  divide an annual $10 per user cash flow (assumed = to profit here, for simplicity) by a 10% discount rate.  Granted, this is more of a bond-than-growth-stock approach to valuation, but Facebook's already pretty big, and Google's making up ground, plus under these economic conditions it's probably OK to be a bit conservative.

Facebook's filing indicated they earned $1 billion in profit on just under $4 billion in revenue in 2011.  This means they're running at about $1.20 per user in profit.  To bridge this gap between $1.20 and $10, you have to believe there's lots more per-user profit still to come.  

Today, 85% of Facebook's revenues come from advertising.  So Facebook needs to make each of us users more valuable to its advertisers, perhaps 4x so to bridge half the gap.  That would mean getting 4x better at targeting us and/or influencing our behavior on advertisers' behalf.  What would that look like?

The other half of the gap gets bridged by a large increase in the share of Facebook's revenues that comes from its cut of what app builders running on the FB platform, like Zynga, get from you.  At Facebook's current margin of 25%, $5 in incremental profit would require $20 in incremental net revenue.  Assume Facebook's cut from its third party app providers is 50%, and that means an incremental $40/year each user would have to kick in at retail.  Are each of us good for another $40/year to Facebook?  If so, where would it come from?  

My guess is that Facebook will further cultivate, through third-party developers most likely, some combination of paid content and productivity app subscription businesses.  It's possible that doing so would not only raise revenues directly but also have a synergistic positive effect on ad rates the firm can command, with more of our time and activity under the firm's gaze.

 

 

 

 

#Watson Wins #Jeopardy! The Singularity Is Near(er): Are You Ready for Computational Engine Optimization ("CEO")?

Saw the news (though missed the show) that IBM's Watson won on Jeopardy.  Interesting to see this and other articles call out Watson's "stumble" -- as though they expected perfection, which is a milestone in itself.  Here's a great explanation of "what went wrong". 

There are two notable things to me about this development / achievement.

The first is to ask whether this puts us ahead or behind Ray Kurzweil's schedule for 2019 (as predicted in 1999).  (Really worth reading his predictions, since we're within shouting distance!  What would you "keep / change / drop / add"?)  

The second is a little closer in.   Given the pace of this development, what does it mean for us as humans / users / consumers / citizens on the one hand, and as marketers / investors, etc. on the other -- from "now" to, say, "two years out"?

Imagine for example that in two years, IBM provides access to a more generalized form of Watson as a cloud-based API.   What might you, as a person or as a business or other organization, do with a service that can understand speech, parse meanings, and optimize spending and investment recommendations based on how sure it is of the answer?

Cesar: "Watson, our lease is up soon, can you suggest some available space options nearby that would make sense for a business like Force Five Partners?"

Watson: "Cesar, here are five choices, with suggestions for what you should be paying for each, based on what I can find out right now..."

A stretch?  Apple's integrated Wolfram Alpha - based support into the Siri app for the iPhone now.  Try asking Siri, out loud: "What is the market capitalization of Goldman Sachs, divided by the US population?"   Answer back to me, in three seconds (iPhone 3GS / AT&T):

(Cross-check: Wolfram Alpha direct. Or,  GS on Google Finance / US Population on Google Search.)

 (FWIW, this hits 3 of 4 criteria in a prediction framework I suggested nearly six years ago.)

Wow.  We had barely figured out SEO, when we got slammed with SNO -- Social Network Optimization (as well as the frozen kind)!  Now we have to figure out Computational Engine Optimization?  (Confusingly, natch, "CEO" -- you read it here first!)  How do I optimize for "What inexpensive steakhouses are nearby?"   How do we even think about that?  

(Possible direction: Semantic Web Optimization -- "SWO", of course.  Make sure you are well tagged-for, and indexed-by, the data stores and services where the terms "inexpensive", "steakhouse", and "nearby" would be judged.  Or, in plain English: if Wolfram Alpha looks to Yelp to help answer this question, make sure your restaurant's entry there is labeled as a steakhouse, has an accurate address, and is accurately price-rated as "$".  Whatever gaming ensues,  just don't blame IBM / Apple / Wolfram /(Google too) for going for the mega-cheddar.)

It's trite to say that change is accelerating as technology develops.  ("We're only in the second inning!")  Some dismiss this (as Arthur C. Clarke said, we always overestimate the impact of technology in the short term, but underestimate it in the long term).  But, if you doubt, this chart is worth a look.  And then think about the degree to which "social" and "mobile" are now reinforcing, amplifying, and accelerating each other...

(Insert shameless commercial:) What are you doing to help your organization keep up? 

 

#Google Search and The Limits of #Location

I broke my own rule earlier today and twitched (that's tweeted+*itched -- you read it here first) an impulsive complaint about how Google does not allow you to opt out of having it consider your location as a relevance factor in the search results it offers you:

I don't take it back.  But, I do think I owe a constructive suggestion for how this could be done, in a way that doesn't compromise the business logic I infer behind this regrettable choice.  Plus, I'll lay out what I infer this logic to be, and the drivers for it, in the hope that someone can improve my understanding.  Finally, I'll lay out some possible options for SEO in an ever-more-local digital business context.

OK, first, here's the problem.  In one client situation I'm involved with, we're designing an online strategy with SEO as a central objective.  There are a number of themes we're trying to optimize for.  One way you improve SEO is to identify the folks who rank / index highly on terms you care about, and cultivate a mutually valuable relationship in which they eventually may link to relevant content you have on a target theme.  To get a clean look at who indexes well on a particular theme and related terms, you can de-personalize your search.  You do this with a little url surgery:

Start with the search query:

http://www.google.com/search?q=[theme]

Then graft on a little string to depersonalize the query:

http://www.google.com/search?q=[theme]&pws=0

Now, when I did this, I noticed that Google was still showing me local results.  These usually seem less intrusive.  But now, like some invasive weed, they'd choked off my results, ranging all the way to the third position and clogging up most of the rest of the first page, for a relatively innocuous term ("law"; lots of local law firms, I guess).  

Then I realized that "&pws=0" tells Google to stop rummaging around in the cookies it's set on my browser, plus other information in my http requests, and won't help me prevent Google guessing / using my location, since that's based on the location of the ISP's router between my computer and the Google cloud.

 Annoyed, I poked around to see what else I could do about it.  Midway down the left-hand margin of the search results page, I noticed this:

 

So naturally, my first thought was to specify "none", or "null", to see if I could turn this off.  No joy. 

Next, some homework to see if there's some way to configure my way out of this.  That led me to Rishi's post (see the third answer, dated 12/2/2010, to the question).  

Unbelieving that an organization with as fantastic a UI aesthetic -- that is to say, functional / usable in the extreme -- as Google would do this, I probed further. 

First stop: Web Search Help.  The critical part:

Q. Can I turn off location-based customization?

A. The customization of search results based on location is an important component of a consistent, high-quality search experience. Therefore, we haven't provided a way to turn off location customization, although we've made it easy for you to set your own location or to customize using a general location as broad as the country that matches your local domain...

Ah, so, "It's a feature, not a bug." :-)

...If you find that your results for a particular search are more local than what you're looking for, you can set your location to a broader geographical area (such as a country instead of a city, zip code, or street address). Please note that this will greatly reduce the amount of locally relevant results that you’ll see. [emphasis mine]

 Exactly!  So I tried to game the system:

Drat!  Foiled again.  Ironic, this "Location not recognized" -- from the people who bring us Google Earth!

Surely, I thought, some careful consideration must have gone into turning the Greatest Tool The World Has Ever Known into the local Yellow Pages.  So, I checked the Google blog.  A quick search there for "location", and presto, this. Note that at this point, February 26, 2010, it was still something you could add.  

Later, on October 18, 2010 -- where I have I been? -- this, which effectively makes "search nearby" non-optional:

We’ve always focused on offering people the most relevant results. Location is one important factor we’ve used for many years to customize the information that you find. For example, if you’re searching for great restaurants, you probably want to find ones near you, so we use location information to show you places nearby.

Today we’re moving your location setting to the left-hand panel of the results page to make it easier for you to see and control your preferences. With this new display you’re still getting the same locally relevant results as before, but now it’s much easier for you to see your location setting and make changes to it.

(BTW, is it just me, or is every Google product manager a farmer's-market-shopping, restaurant-hopping foodie?  Just sayin'... but I seriously wonder how much designers' own demographic biases end up influencing assumptions about users' needs and product execution.)

Now, why would Google care so much about "local" all of a sudden?  Is it because Marissa Mayer now carries a torch for location (and Foursquare especially)?  Maybe.  But it's also a pretty good bet that it's at least partly about the Benjamins.  From the February Google post, a link to a helpful post on SocialBeat, with some interesting snippets: 

"Location may get a central place in Google’s web search redesign"

Google has factored location into search results for awhile without explicitly telling the user that the company knows their whereabouts. It recently launched ‘Nearby’ search in February, returning results from local venues overlaid on top of a map.

Other companies also use your IP address to send you location-specific content. Facebook has long served location-sensitive advertising on its website while Twitter recently launched a feature letting users geotag where they are directly from the site. [emphasis mine]

Facebook's stolen a march on Google in the social realm (everywhere but Orkut-crazed Brazil; go figure).  Twitter's done the same to Google on the real-time front.  Now, Groupon's pay-only-for-real-sales-and-then-only-if-the-volumes-justify-the-discount threatens the down-market end of Google's pay-per-click business with a better mousetrap, from the small biz perspective.  (BTW, that's why Groupon's worth $6 billion all of a sudden.)  All of these have increasingly (and in Groupon's case, dominantly) local angles  where the value to both advertiser and publisher (Facebook / Twitter / Groupon) are presumably highest.

Ergo, Google gets more local.  But that's just playing defense, and Eric, Sergey, Larry, and Marissa are too smart (and, with $33 billion in cash on hand, too rich) to do just that.

Enter Android.  Hmm.  Just passed Apple's iOS and now is running the table in the mobile operating system market share game.  Why wouldn't I tune my search engine to emphasize local search results, if more and more of the searches are coming from mobile devices, and especially ones running my OS?  Yes, it's an open system, but surely dominating it at multiple layers means I can squeeze out more "rent", as the economists say?

The transcript of Google's Q3 earnings call is worth a read.

Now, back to my little problem.  What could Google do that would still serve its objective of global domination through local search optimization, while satisfying my nerdy need for "de-localized" results?  The answer's already outlined above -- just let me type in "world", and recognize it for the pathetic niche plea that it is.  Most folks will never do this, and this blog's not a bully-enough pulpit to change that. Yet.

The bigger question, though, is how to do SEO in a world where it's all location, location, location, or as SEOmoz writes

"Is Every Query Local Now?" 

Location-based results raise political debates, such as "this candidate is great" showing up as the result in one location while "this candidate is evil" in another.  Location-based queries may increase this debate.  I need only type in a candidate's name and Instant will tell me what is the prevailing opinion in my area.  I may not know if that area is the size of a city block or the entire world, but if I am easily influenced then the effect of the popular opinion has taken one step closer (from search result to search query) to the root of thought.   The philosphers among you can debate whether or not the words change the very nature of ideas.

Heavy.

OK, never leave without a recommendation.  Here are two:

First, consider that for any given theme, some keywords might be more "local" than others.  Under the theme "Law", the keyword "law" will dredge up a bunch of local law firms.  But another keyword, say "legal theory", is less likely to have that effect (until discussing that topic in local indie coffee shops becomes popular, anyway).  So you might explore re-optimizing for these less-local alternatives.  (Here's an idea: some enterprising young SEO expert might build a web service that would, for any "richly local" keyword, suggest less-local alternatives from a crowd-sourced database compiled by angry folks like me.  Sort of a "de-localization thesaurus".  Then, eventually, sell it to a big ad agency holding company.)

Second, as location kudzu crawls its way up Google's search results, there's another phenomenon happening in parallel.  These days, for virtually any major topic, the Wikipedia entry for it sits at or near the top of Google's results.  So, if as with politics, now too search and SEO are local, and much harder therefore to play, why not shift your optimization efforts to the place that the odds-on top Google result will take you, if theme leadership is a strategic objective?

 

PS Google I still love you.  Especially because you know where I am. 

 

Facebook at Fifty (Billion)

Is Facebook worth $50 billion?  Some caveman thoughts on this valuation:

1. It's worth $50 billion because Goldman Sachs says so, and they make the rules.

2. It's worth $50 billion because for an evanescent moment, some people are willing to trade a few shares at that price. (Always a dangerous way to value a firm.)

3.  Google's valuation provides an interesting benchmark:

a. Google's market cap is close to $200 billion.  Google makes (annualizing Q32010) $30 billion a year in revenue and $8 billion a year in profit (wow), for a price to earnings ratio of approximately 25x.

b. Facebook claims $2 billion a year in revenue for 2010, a number that's likely higher if we annualize latest quarters (I'm guessing, I haven't seen the books).   Google's clearing close to 30% of its revenue to the bottom line.  Let's assume Facebook's getting similar results, and let's say that annualized, they're at $3 billion in revenues, yielding a $1 billion annual profit (which they're re-investing in the business, but ignore that for the moment).  That means a "P/E" of about 50x, roughly twice Google's.  Facebook has half Google's uniques, but has passed Google in visits.  So, maybe this growth, and potential for more, justifies double the multiple.  Judge for yourself; here's a little data on historical P/E ratios (and interest rates, which are very low today, BTW), to give you some context.  Granted, these are for the market as a whole, and Facebook is a unique high-growth tech firm, but not every tree grows to the sky.

c. One factor to consider in favor of this valuation for Facebook is that its revenues are better diversified than Google's.  Google of course gets 99% of its revenue from search marketing. Facebook gets a piece of the action on all those Zynga et. al. games, in addition to its core display ad business.  You might argue that these game revenues are stable and recurring, and point the way to monetizing the Facebook API to very attractive utility-like economic levels (high fixed costs, but super-high marginal profits once revenues pass those, with equally high barriers to entry).

d. Further, since viral / referral marketing is every advertiser's holy grail, and Facebook effectively owns the Web's social graph at the moment, it should get some credit for the potential value of owning a better mousetrap.  (Though, despite Facebook's best attempts -- see Beacon -- to Hoover value out of your and my relationship networks, the jury's still out on whether and how they will do that.  For perspective, consider that a $50 billion valuation for Facebook means investors are counting on each of today's 500 million users to be good for $100, ignoring future user growth.)

e. On the other hand,  Facebook's dominant source of revenue (about 2/3 of it) is display ad revenue, and it doesn't dominate this market the way Google dominates the search ad market (market dominance means higher profit margins -- see Microsoft circa 1995 -- beyond their natural life).  Also, display ads are more focused on brand-building, and are more vulnerable in economic downturns.

4. In conclusion: if Facebook doubles revenues and profits off the numbers I suggested above, Facebook's valuation will more or less track Google's on a relative basis (~25x P/E).  If you think this scenario is a slam dunk, then the current price being paid for Facebook is "fair", using Google's as a benchmark.  If you think there's further upside beyond this doubling, with virtually no risk associated with this scenario, then Facebook begins to look cheap in comparison to Google.

Your move.

Who's got a better take?

Postscript:  my brother, the successful professional investor, does; see his comment below (click "Comments")

OMMA Metrics & Measurement "Modeling Attribution" Panel SF 7/22: Hope To See You There

I'll be moderating a panel at the OMMA Metrics & Measurement Conference in San Francisco on July 22.  

The topic of the panel is, "Modeling Attribution: Practitioner Perspectives on the Media Mix".  Here's the conference agenda page.

The panel description:

How do you determine the channels that influence offline and online behavior and marketing performance?  

How should you allocate your budget across CRM emails, display ads, print advertising, television and radio commercials, direct mail, and other marketing sources? 

What models, techniques, and technologies should you use develop attribution and predictive models that can drive your business? 

Do you need SAS, SPSS, and a PhD in Statistics? 

Does first click, last click, direct, indirect, or appropriate attribution matter – which is best?

What about multiple logistic regression? 

What is the impact of survey and voice-of-the-customer data on attribution? 

Hear from experts who have to answer these questions and tackle these tough issues as they work hard in the field every day for their consultancies, agencies, and brands.

So far, Manu Mathew, CEO from VisualIQ, and Todd Cunningham, SVP Research at MTV Networks, will be participating on the panel as well.

Hope to see you there.  Meanwhile, please suggest questions you'd like to ask the panelists by commenting here.  Thanks!

What's NYT.com Worth To You, Part II

OK, with the response curve for my survey tailing off, I'm calling it.  Here, dear readers, is what you said (click on the image to enlarge it):

(First, stats: with ~40 responses -- there are fewer points because of some duplicate answers -- you can be 95% sure that answers from the rest of the ~20M people that read the NYT online would be +/- 16% from what's here.)

90% of respondents would pay at least $1/month, and several would pay as much as $10/month. And, folks are ready to start paying after only ~2 articles a day.  Pretty interesting!  More latent value than I would have guessed.  At the same time, it's also interesting to note that no one went as high as the $14 / month Amazon wants to deliver the Times on the Kindle. (I wonder how many Kindle NYT subs are also paper subs getting the Kindle as a freebie tossed in?)

Only a very few online publishers aiming at "the general public" will be able to charge for content on the web as we have known it, or through other newer channels.  Aside from highly-focused publishers whose readers can charge subscriptions to expense accounts, the rest of the world will scrape by on pennies from AdSense et al. 

But, you say, what about the Apple Tablet (announcement tomorrow! details yesterday), and certain publishers' plans for it?  I see several issues:

• First, there's the wrestling match to be had over who controls the customer relationship in Tabletmediaworld. 
• Second, I expect the rich, chocolatey content (see also this description of what's going in R&D at the Times) planned for this platform and others like it to be more expensive to produce than what we see on the web today, both because a) a greater proportion of it will be interactive (must be, to be worth paying for), but also because b) producing for multiple proprietary platforms will also drive costs up (see for example today's good article in Ad Age by Josh Bernoff on the "Splinternet"). 
• Third, driving content behind pay walls lowers traffic, and advertising dollars with it, raising the break-even point for subscription-based business models. 
• Fourth, last time I checked, the economy isn't so great. 

The most creative argument I've seen "for" so far is that pushing today's print readers/ subscribers to tablets will save so much in printing costs that it's almost worth giving readers tablets (well, Kindles anyway) for free -- yet another edition of the razor-and-blade strategy, in "green" wrapping perhaps.

The future of paid content is in filtering information and increasing its utility.  Media firms that deliver superior filtering and utility at fair prices will survive and thrive.  Among its innovations in visual displays of information (which though creative, I'd guess have a limited monetization impact) is evidence that the Times agrees with this, at least in part (from the article on Times R&D linked to above):

When Bilton swipes his Times key card, the screen pulls up a personalized version of the paper, his interests highlighted. He clicks a button, opens the kiosk door, and inside I see an ordinary office printer, which releases a physical printout with just the articles he wants. As it prints, a second copy is sent to his phone.

The futuristic kiosk may be a plaything, but it captures the essence of R&D’s vision, in which the New York Times is less a newspaper and more an informative virus—hopping from host to host, personalizing itself to any environment.

Aside from my curiosity about the answers to the survey questions themselves, I had another reason for doing this survey.  All the articles I saw on the Times' announcement that it would start charging had the usual free-text commenting going.  Sprinkled through the comments were occasional suggestions from readers about what they might pay, but it was virtually impossible to take any sort of quantified pulse on this issue in this format.  Following "structured collaboration" principles, I took five minutes to throw up the survey to make it easy to contribute and consume answers.  Hopefully I've made it easier for readers to filter / process the Times' announcement, and made the analysis useful as well -- for example, feel free to stick the chart in your business plan for a subscription-based online content business ;-)  If anyone can point me to other, larger, more rigorous surveys on the topic, I'd be much obliged.

The broader utility of structuring the data capture this way is perhaps greatest to media firms themselves:  indirectly for ad and content targeting value, and perhaps because once you have lots of simple databases like this, it becomes possible to weave more complex queries across them, and out of these queries, some interesting, original editorial possibilities.

Briefly considered, then rejected for its avarice and stupidity: personalized pricing offers to subscribe to the NYT online based on how you respond to the survey :-)

Postscript: via my friend Thomas Macauley, NY (Long Island) Newsday is up to 35 paid online subs.

What's NYT.com Worth To You?

Via Chris Schroeder's  (@cmsschroed) RT of Henry Blodget (@hblodget), the news of the NYT's decision to start charging (again) for content.

Blodget's prior analysis suggested this might be worth ~$100 million per year  (my deduction based on his math) to NYT Co.  If a tenth of its 130M monthly unique visitors end up being "heavy users" that pay, 4 bucks a month gets them ~$600 million annually (13m * $4 * 12 months = $624 million).  (Seems high; better data anyone?)

What's it worth to you?  See what some folks had to say in the chart below.  Please take this survey to add your perspective, and let your friends know about it:

Note: I removed one response of "1 million articles for free, willing to pay $0 thereafter" because it messed up the display, but am mentioning it here for full disclosure. And to the respondent, thank you for participating!

Postscript: conclusions and analysis

Fly-By-Wire Marketing

One of the big innovations used in the F-16 fighter jet was the "fly-by-wire" flight control system.  Instead of directly connecting the pilot's movements of the control stick and the rudder pedals to the aircraft's control surfaces through cables (for WWI-era biplanes) or hydraulics, the pilot's commands were now communicated electronically to an intermediate computer, which then interpreted those inputs and made appropriate adjustments. 

This saved a lot of weight, and channeling some of those weight savings into redundant control circuits made planes safer.  Taken to its extreme in planes like the B2 bomber, "fly-by-wire" made it possible for pilots to "fly" inherently unstable airplanes by leaving microsecond-by-microsecond adjustments to the intermediate computer, while the pilot (or autopilot) provided broader guidance about climbs, turns, and descents.

Now we have "fly-by-wire marketing".

A couple of days ago I read Daniel Roth's October 19 article on Wired.com titled "The Answer Factory: Fast, Disposable, and Profitable as Hell", describing Demand Media's algorithmic approach to deciding what content to commission and publish.  The article is a real eye-opener.  While we watch traditional publishers talk about turning "print dollars into digital dimes", Demand has built a $200 million annual revenue business with a $1 billion valuation.  How?  As Roth puts it, "Instead of trying to raise the market value of online content to match the cost of producing it — perhaps an impossible proposition — the secret is to cut costs until they match the market value."  More specifically,

Before Reese came up with his formula, Demand Media operated in the traditional way. Contributors suggested articles or videos they wanted to create. Editors, trained in the ways of search engine optimization, would approve or deny each while also coming up with their own ideas. The process worked fine. But once it was automated, every algorithm-generated piece of content produced 4.9 times the revenue of the human-created ideas. So Rosenblatt got rid of the editors. Suddenly, profit on each piece was 20 to 25 times what it had been. It turned out that gut instinct and experience were less effective at predicting what readers and viewers wanted — and worse for the company — than a formula.

I'm currently in situations where either the day-to-day optimization of the marketing process is too complex to manage fully through direct human intervention, or some of the optimizations to be performed are still sufficiently vague that we can only anticipate them at a broader, categorical level, from which a subsequent process -- perhaps an automated one -- will be necessary to fully realize them.  I also recently went to and blogged about a very provocative MITX panel on personalization, where a key insight (thanks to Scott Brinker, Co-founder and CTO of ion Interactive) was how the process to support personalization needs to change as you cut to finer and finer-grained targeting.  So it was with these contexts in mind that I read Roth's article, and the question it prompted for me was, "In a future dominated by digital channels, is there a generic roadmap for appropriate algorithmic abstractions of marketing optimization efforts that I can then adapt for (client-) specific situations?" 

That may sound a little out there, but Demand Media is further proof that "The future's already here, it's just not evenly distributed yet."  And, I'm not original in pointing out that we've had automated trading on Wall Street for a while; with the market for our attention becoming as digital as the markets for financial securities, this analogy is increasingly apt.

So here are some bare bones of what such a roadmap might look like.

Starting with end in mind, an ultimate destination might be that we could vary as many elements of the marketing mix as needed, as quickly as needed, for each customer (You laugh, but the holodeck isn't that far away...), where the end result of that effort would generate some positive marginal profit contribution. 

At the other end of the road, where we stand today, in most companies these optimization efforts are done mostly by hand.  We design and set campaigns into motion by hand, we use our eyes to read the results, and we make manual adjustments.

One step forward, we have mechanistic approaches.  We set up rules that say, "Read the incoming data; if you see this pattern, then make this adjustment."  More concretely, "When a site visitor with these cookies set in her browser arrives, serve her this content." This works fine as long as the patterns to be recognized, and the adjustments to be made, are few and relatively simple.  It's a lot of work to define the patterns to look for.  And, it can be lots of work to design, implement, and maintain a campaign, especially if it has lots of variants for different target segments and offers (even if you take a "modular" approach to building campaign elements).  Further, at this level, while what the customer experiences is automated, the adjustments to the approach are manual, based on human observation and interpretation of the results.

Two steps down the road, we have self-optimizing approaches where the results are fed back into the rule set automatically.  The Big Machine says,  "When we saw these patterns and executed these marketing activities, we saw these results; crunching a big statistical model / linear program suggests we should modify our marketing responses for these patterns in the following ways..."  At this level, the human intervention is about how to optimize -- not what factors to consider, but which tools to use to consider them.

I'm not clear yet about what's beyond that.  Maybe Skynet.  Or, maybe I get a Kurzweil-brand math co-processor implant, so I can keep up with the machines.

The next question you ask yourself is, "How far down this road does it makes sense for me to go, by when?"  Up until recently, I thought about this with the fairly simplistic idea that there are single curves that describe exponentially decreasing returns and exponentially increasing complexity.  The reality is that there are different relationships between complexity and returns at different points -- what my old boss George Bennett used to call "step-function" change.

For me, the practical question-within-a-question this raises is, for each of these "step-functions", is there an version of the algorithm that's only 20% as complex, that gets me 80% of the benefit?  My experience has been that the answer is usually "yes".  But even if that weren't the case, my approach in jumping into the uncharted territory of a "step-function" change in process, with new supporting technology and people roles, would be to start simple and see where that goes.

At minimum, given the "step-function" economics demonstrated by the Demand Medias of the world, I think senior marketing executives should be asking themselves, "What does the next 'step-function' look like?", and "What's the simplest version of it we should be exploring?" (Naturally, marketing efforts in different channels might proceed down this road at different paces, depending on a variety of factors, including the volume of business through that channel, the maturity of the technology involved, and the quality of the available data.  I've pushed the roadmap idea further to help organizations make decisions based on this richer set of considerations.)

So, what are your plans for Fly-By-Wire Marketing?

Postscript: Check out "The Value Of The New Machine", by Steve Smith in Mediapost's "Behavioral Insider" e-newsletter today.  Clearly things are well down the road -- or should be -- at most firms doing online display and search buys and campaigns.  Email's probably a good candidate for some algorithmic abstraction.

#Adobe + #Omniture: Further Thoughts ( #Analytics )

I've been following the Web Analytics Forum on Yahoo! -- David Simmons' ideas here are especially thoughtful -- and I listened to the Q3 Adobe con call.  Plus last night at Web Analytics Wednesday in Cambridge I had a chance to talk about it with VisualIQ CTO Anto Chittilappilly and Visible Measures' Tara Chang.  Here are some further ideas based on what I've read and heard so far:

• Rich media -- video, interactive ads, etc. -- are a growing piece of the Internet experience. (Google's rumored acquisition of Brightcove reflects this.)  (The flip side: Semphonic CEO Gary Angel writes that "It's taking a long time, but HTML is dying.")
  
• Since they're growing, tracking user interactions with them effectively is increasingly important, not just on the Internet but across platforms, and as the CIMM challenge to Nielsen suggests, not yet well addressed.
  
• User tracking in rich media platforms like Flash is more granular and more persistent than cookie-based tracking (David Simmons explains how and recommends Eric Peterson's Web Site Measurement Hacks for more).
  
• But, support for event-based tracking of users' interactions in rich internet media in existing web analytics platforms is in its infancy (though vendors say otherwise).
  
• So, publishers want tighter, simpler integration of event-based tracking.
  
• In the con call, Adobe CEO Shantanu Narayen mentions the framework "Create - Deploy - Optimize" as a way to understand their overall product roadmap vision.
  
• Adobe has the "Create" (e.g., Photoshop, this part of their business is ~60% of their revenues) and "Deploy" (e.g., Flash Server / Flash Player) pieces covered.  But "Optimize" was still uncovered, up until this announcement.

That's the product strategy logic.  The financial logic:

• Adobe is too dependent on the "Create" leg of their stool, and hasn't been able to monetize the "Deploy" piece as much as they might have hoped -- removing  license fees from the Open Screen Project is one recent example of this limitation.  So they're betting that "Optimize" has legs, and that buying Omniture in this economic climate at ~5x revenues is good timing.
  
• Adobe's traditional software-licensing business model has gotten crushed year to year.  Omniture's revenues are >90% recurring subscriptions based on a SAAS model.  Adobe revenues (~$3B) are 10x Omniture's but the enhanced value proposition of the A-O integration and cross-selling through Adobe's sales force will accelerate O's growth.  Over the next 2-3 years, this will help to reduce the volatility of A's revenues / revenue growth.

What's ahead?  One direction, as I've previously discussed, is that the workflows associated with "Create-Deploy-Optimize" are increasingly complex, and that platforms that support these workflows in a simple, more integrated way will become important to have.  Managing these processes through hairballs built with Excel spreadsheets scattered across file servers just won't cut it.

Postscript: Eric Peterson's take.  And more from him.  The gist -- not so high on Omniture's relative value to customers and the experience of working with them, and not sure why Adobe did this.  Anil Batra has some interesting ideas for product directions that emerge from the combination.

Adobe + Omniture: Pragmalytically Perfect Sense

Big news (via, in my case, Eric Peterson's "Web Analytics Forum" on Yahoo!): Adobe's buying Omniture. 

Simple logic:

• Adobe makes great tools for developing custom dashboards and other data visualization apps.  I know because in one engagement this summer, we've worked with a terrific client and another (also terrific) engineering firm to build a Flex-based prototype of an advanced predictive analytics application.  But prototyping is easy, tying a front end to a working, real-world analytics data model is much harder.
• Omniture leads the pack of web analytics platform vendors, who all have more features and capabilities in their left pinkies than many of us could dream of in six lifetimes.  But exposing mere mortals to the interfaces these leading firms provide is like showing kryptonite to  Joe / Jane Superexecutive.  As analytics get more complex, it's even more important to focus on key questions and expose only the data / views on that data that illuminate those key questions.
• So if you believe that that this web / digital / multichannel analytics thing has legs, then putting these two firms together and working both ends to the middle faster than might otherwise have happened is a smart thing to do.
• The other reason to do this is to anticipate the trend in "custom reporting" and "advanced segmentation" capabilities in the "lower-end" analytics offerings (e.g., GA) from folks like Google.  I've been using these capabilities recently, and they get you a meaningful way, eroding the value of higher-end offerings on both the front (Adobe) and middle-back (Omniture) ends.