The session was well-attended and the panelists didn't disappoint. Across the board they provided a consistent cross-section of the sophistication and energy that characterizes life 2 SDs the right on the ecommerce success curve.

My notes and observations follow. But first, courtesy of Jeff, a quiz (answers at the end of the post):

1. Name the person, company, and city that originated the web-based shopping cart and secure payment process?

2. Name the person, company, and city that originated affiliate marketing on the web?

3. Name the largest email marketing firm in the world, and the city where it's headquartered?

Jeff opened by asking each of the panelists to talk about how they drive traffic, and how they try to distinguish themselves in doing so.

Brian described (my version) what his firm does as "performance marketing in the long tail", historically for education-sector customers (for- and non-profit) but now beyond that category. What that means is that they manage bidding and creative for 2 million less-popular keywords across all the major search engines for their customers. Their business is entirely automated and uses sophisticated models to predict when a customer should be willing to pay price X and use creative Y for keyword Z to reel in a likely-profitable order. The idea is that the boom in SEM demand has driven prices way up for popular keywords, but that there are still efficient marketing deals to be mined in the "long tail" of keyword popularity (e.g.,structured collaboration").

Niraj noted that there's an increasing returns dynamic in the SEM channel that raises entry barriers for upstarts and helps firms like CSN preserve and expand their position.  Namely, as firms like his get more sophisticated about conversion through scale and experience, they can afford to pay higher prices for a given keyword than smaller competitors can, and can reinvest in extending their SEM capabilities.  CSN now has a 10-person search marketing team within its total staff of 500. Since SEM is, to some degree, a jump-starter for firms that don't yet have a web presence sufficient to drive traffic organically, this edge is a powerful competitive weapon.  CSN is up to $200 million in annual revenues, and now manages the online furniture stores for folks like Walmart.

Scott sounded a different note, with similar results.  Shoebuy has focused more on cultivating its relationship with its existing customers and on Lifetime Value -- including referrals.  This focus has had a salutary effect on SEO, allowing them to rely less on SEM as it gets pricier.  Last year Shoebuy experienced double-digit top line growth and hit 8M uniques for December's shopping season, while realizing its lowest marketing expense as a percentage of sales since 2002.  They've continued to plow the savings into a better overall customer experience.  One way Shoebuy guides this reinvestment is through extensive use of Net Promoter-based surveys.  They keep the surveys brutally simple:  1)"Were you satisfied?" 2)"Whould you shop with us again?" 3)"Would you recommend us?".  Then they calculate the resulting NP scores to different things they try in their marketing mix, to give them a more nuanced insight than the binary outcome of an order can provide.

Tom described how while Mall Networks' traffic is "free" -- it all comes from their loyalty program partners' sites (e.g. Delta Skymiles website awards redemption page) -- they still have to jockey for Mall Networks' placement on those pages. (Though Tom was too polite to say so, the processes for deciding who goes where on popular pages is often a blood sport and ripe in most organizations for a more structured, rational approach.)

Former Molecular founder and CEO Ralph Folz asked about display -- is that making a comeback?  Brian indicated the lack of performance and the lack of placement control through ad networks made that a highly negative experience.  He did note that they are now experimenting with participation in real-time-bidding through ad exchanges for inventory that ad networks make available, sometimes for time windows only a hundred milliseconds long.  Jeff reinforced the emergence of "RTB" and mentioned MIT Prof. Ed Crawley's Cambridge-based DataXu (which Flybridge has invested in) as a leader in the field.

Affiliate marketing came up next.  Tom explained the basics (in response to a question): each of the 600 stores in Mall Networks stable pays Mall Networks, say for example, a 10% commission on orders that come through Mall Networks.  Mall Networks gives a chunk to the members of various loyalty programs that shop through it -- say 3-5% of the value of the order; some goes to the loyalty programs themselves, as partial inducements for sending traffic to Mall Networks, and the rest goes to Mall Networks to cover costs and yield profits.

All the other panelists include affiliates in their marketing mix, and all appeared satisfied to have them play a healthy role.  Niraj specifically mentioned the ShareASale and Google Affiliate networks.  Jeff asked about everyone's frenemy Amazon; the answers were uniformly respectful: "they're a tough competitor, but they build general confidence and familiarity with the ecommerce channel, and that's good for everyone."  Niraj noted the 800 lb. gorilla nature of their category dominance: "They're at $20m and NewEgg is the next biggest pure play at $2B.  They're a fact of life. We just have to be better at what we focus on."

Someone in the audience raised email.  All of the panelists use it, with lists ranging from millions to hundreds of millions of recipients in size.  They noted that this traditional pillar of online marketing has now gotten very sophisticated.  In their world, they look well beyond top line metrics like open- and clickthrough rates to root-cause analysis of segment-based performance.  Re-targeting came up, and Niraj noted that for them, email and re-targeting weren't substitutes (as some have seen them) but in fact played complementary roles in their mix.  (Jeff explained re-targeting for the audience: using an ad network to cookie visitors to your site, and then serving them "please come back!" ads on other sites in the network they go to after they've abandoned a shopping cart or otherwise left your site.  A twist: serving ads inviting them to *your* site after they've abandoned one of your competitors' sites.  Hey, all's fair in love, war, and ecommerce...).  A common theme:  unlike most of the rest of the world, email teams at these leading firms are tightly integrated with other channels' operators to better integrate the overall experience, even to the point of shared metrics.

What about social?  Scott: "Building community is key for us.  We run contests -- "What are you hoping will be under your tree this Christmas?" -- to stimulate input from our customers.  And, while we have social media coordinators, many people here participate in channels like Twitter in support of our efforts."  Niraj: "Our PR team came up with a 'Living Room Rescue' contest which we did in partnership with [a popular] HGTV host [whose name escaped me -- C.B.].  We got six thousand entries; we used a panel of professional decorators to narrow the list to a hundred, and then used social voting to choose a winner.  We publicized the contest, and it took on a life of its own, as local papers tried to drum up support for their local [slobs -- my word, not Niraj's].  While we couldn't / didn't measure conversion directly from this campaign, our indirect assessment was that it had a great ROI."  Jeff observed that social's potential seems greater when the object of the buzz is newsworthy.

It was a short leap from this to a question about attribution analysis, the simultaneous-dream-and-nightmare-du-jour for web analytics geeks out there.  Brian was surprisingly dismissive.  In his experience (if I understood correctly), he's seeing only up to 20%, and usually only 5-10% of order-placing customers touch two or more properties they source clicks from, across the broad landscape they cover, across a time frame ranging from a day to a month long.  "In the end, only a couple of dollars would shift from one channel to another if we did attribution analysis, so in general it's not worth it."  We chatted briefly after the panel about this; there are large ticket, high-margin exceptions to this rule (cars).  I need to learn about this one some more, it surprised me.

Mobile!  Is it finally here?  Scott reports that 6-9 months ago *customers* finally began asking for it (as opposed to having it pushed by vendors), so now they have a Shoebuy.com iPhone app.  Jeff noted that customers are rolling their own mobile strategies -- some folks are now going into (say) Best Buy, having a look at products in the flesh, then checking Amazon for the items and buying them through their iPhone if the price is right.  So, your store is now Amazon's showroom.  If you can't find something, or didn't even know you wanted it, but happen to stray near a store carrying it, location-based services will push offers at you -- and the offers may come from competitors.  (Gratuitous told-you-so here.)  Niraj:  "Say you're in Home Depot.  You want a mailbox.  Their selection is 'limited' [his description was more colorful]. We have 300 to choose from.  Wouldn't you want to know that?" Jeff:  Soon we'll also see the death of the checkout line: you'll take a picture of the barcode on the object of your desire, your smartphone will tell the store's POS system about it, and the POS system will send back a digital receipt you can show someone (or in the future, something) on your way out of the store. 

With all these channels in use, I asked how often they make decisions to reallocate investments across (as opposed to within) them -- say from search to email, as opposed to from keyword to keyword.  Brian: "Every day, each morning.  Some things -- like affiliate relationships -- may take 3-4 days to unwind.  But the optimization is basically non-stop."  Later we talked about the parallels with Wall Street trading floors.  For him, the analogy is apt.  Effectively he's a market-maker, only the securities are clicks, not stocks.  It's now reflected in their recruiting: many recent hires are former Wall Street quants.

A final note: The cultures in these shops are intensely customer-focused, flat, and data-driven.  Scott reads *every one* of the hundreds of thousands (yes you read right) of customer survey responses Shoebuy gets each year.  He also described the enthusiasm with which their customer service team embraced having all company communications to customers end with an invitation to email senior management with any concerns.  Niraj described CSN's floor plan:  500 people, no offices.  Everyone in the company takes a regular turn in customer service.  Everyone has access to the firm's data warehouse.  Brian told us about a digital display they have up in their offices showing hour-by-hour, source-by-source performance.  They also recently ran a "Query Day" in which everyone in the company -- including sales, finance, HR -- got training in how to use their databases to answer business questions.  Tom described that they “watch the cash register every minute, hour, day during the Christmas shopping season.”

This was a terrific session, and I've only captured half of it here.  Further comments / corrections / observations very welcome.

Quiz Answers:

1. MIT Prof. David K. Gifford, Open Market, Cambridge

2. Tom Gerace, BeFree, Cambridge

3. Constant Contact, Waltham

(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 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.

Another question concerned how measurable they find / are trying to make their marketing, and what that's meant to their business.  Scott described DD as very analytically driven, telling a story about how insights have transformed their business.  he described how they had observed that 92% of their orders were "ship-to-someone-other-than-buyer".  This helped them conclude that they weren't really a bakery, as the founders perceived, but a gift retailer.  So, they stopped taking pictures of the cakes, and started taking pictures of the packages for their catalogs! (Very memorable "data is the new creative" example, IMHO.)

The panelists were asked about the degree to which they are pursuing finer-grained measurement, and how much they are using testing.  Interestingly, they suggested that there were both cost and utility limits to what they could do.  Charlie noted that being two layers away from the customer made measurement beyond profit per case-equivalent a very expensive proposition for them. As for testing, Scott noted that with most of their sales concentrated in a very tight seasonal window, testing carried high risks that didn't rule it out, but made them more conservative about what to try.  Nonetheless, he noted that given the ease of fielding some tests they are now able to adjust what they do within this tight holiday window if they start very early -- right after Thanksgiving this year, for example.

I asked what campaigns by others they found memorable and gave them ideas for their own marketing.  All three panelists mentioned good ones. I liked Frank's best: he described how, before and during a major snowstorm, Volvo Boston had sent email weather advisories and reminders about topping up on windshield fluid.  Great example of indirect brand building through a useful service.

Afterward I chatted briefly with Paul Regensburg, President of Rain Castle Communications, which recently helped Unica re-launch its brand.  Paul noted a slightly less breathless tenor about "new channels" among the panelists than you'd expect if all you read were the trades.  We agreed that it's always different to hear directly from practitioners, especially when you ask them about everything they're grappling with rather than any particular channel effort or campaign.  The calibration was really useful.

(Finally, congratulations to Myles and his volunteer colleagues for the great work they've done building the Boston chapter to be the fourth largest of 78 in the country.  Last night's event drew well over 100 people, not bad for a frosty January weeknight.  Myles noted that 30% of ticket sales for the event
had come from their viral "Tweets For Seats" Program.)

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

In the past few days I've been following the news about the failed attempt to blow up Northwest 253 on Christmas Day, and the finger-pointing among various agencies that's followed it.  More particularly, I've been thinking less about whose fault it is and more about how social media / collaboration tools might be applied to reduce the chance of a Missed Connection like this.

A lot of the comments by folks in these agencies went something like, "Well, they didn't tell us that they knew X," or "We didn't think we needed to pass this information on."  What most of these comments have in common is that they're rooted in a model of person-to-person (or point-to-point) communication, which creates the possibility that one might "be left out of the loop" or "not get the memo".

For me, this created a helpful context for understanding how Google Wave is different from email and IM, and why the difference is important.  Google Wave's issue isn't that the fundamental concept's not a good idea.  It is.  Rather, its problem is that it's paradigmatically foreign to how most people (excepting the wikifringe) still think.

Put simply, Google Wave makes conversations ("Waves") primary, and who's participating secondary.  Email, in contrast, makes participants primary, and the subjects of conversations secondary.  In Google Wave, with the right permissions, folks can opt into reading and participating in conversations, and they can invite others.  The onus for awareness shifts from the initiator of a conversation to folks who have the permission and responsibility to be aware of the conversation.  (Here's a good video from the Wave team that explains the difference right up front.)  If the conversation about Mr. Abdulmutallab's activities had been primary, the focus today would be about who read the memo, rather than who got it.  That would be good.  I'd rather we had a filtering problem than an information access / integration problem.

You may well ask, "Isn't the emperor scantily clad -- how is this different from a threaded bboard?"  Great question.   One answer might be that "Bboards typically exist either independently, or as features of separate purpose-specific web sites.  Google Wave is to threaded bboard discussions as Google Reader is to RSS feeds -- a site-independent conversation aggregator, just as Google Reader is a site-independent content aggregator."   Nice!  Almost: one problem of course is that Google Wave today only supports conversations that start natively in Google Wave.  And, of course, that you can (sometimes) subscribe to RSS feeds of bboard posts, as in Google Groups, or by following conversations by subscribing to RSS feeds for Twitter hashtags.  Another question: "How is Google Wave different from chat rooms?"  In general, most chats are more evanescent, while Waves appear (to me) to support both synchronous chat and asynchronous exchanges equally well.

Now the Big Question: "Why should I care?  No one is using Google Wave anyway."  True (only 1 million invitation-only beta accounts as of mid-November, active number unknown) -- but at least 146 million people use Gmail.  Others already expect Google Wave eventually will be introduced as a feature for Gmail: instead of / in addition to sending a message, you'll be able to start a "Wave".  It's one of the top requests for the Wave team.  (Gmail already approximates Wave by organizing its list of messages into threads, and by supporting labeling and filtering.)  Facebook, with groups and fan pages, appears to have stolen a march on Google for now, but for the vast bulk of the world that still lives in email, it's clunky to switch back and forth.  The killer social media / collaboration app is one that tightly integrates conversations and collaboration with messaging, and the prospect of Google-Wave-in-Gmail is the closest solution with any realistic adoption prospects that I can imagine right now.

So while it's absurdly early, marketers, you read it here first: Sponsored Google Waves :-)  And for you developers, it's not too early to get started hacking the Google Wave API and planning how to monetize your apps.

Oh, and Happy New Year!

Postscript: It was the software's fault...

Postscript #2: Beware the echo chamber

A frequent beef about the iPhone is that all apps are accessed from a single-level desktop, and that you have to swipe across several screens to get to the app you want.  (Sometimes, this can be life-threatening, as when a friend launches PhoneSaber, and you're slow on the draw.)  Today we're mostly stuck with this AFAIK, since my cursory research (browsing plus buttonholing some Apple Store folks) didn't reveal any immediate plans to upgrade the iPhone OS to address this.

It's interesting to see how what tribe you're from influences how you'd solve this.  If Microsoft (of yore, anyway) made the iPhone, the solution might likely be some sort of Windows Explorer-type hierarchical folders.  If Google made the iPhone, the answer to this challenge might be Gmail-style labels / tags.  If you come from the Apple/ Adobe RIA world, Expose might appeal to you.

From the business side, my mind runs to the "Why" that will shape the "When" and "How".  Here's a 2010 prediction:  big firms will stop thinking in terms of having one iPhone app, and more in terms of fielding "branded suites" of iPhone apps. 

Let's say you're a media firm, with multiple media properties.  These properties might share a similar functional need solved by a common app, like a reader.  Or, a single media property (say, a men's lifestyle one) might want a collection of lighter-weight, function-specific apps like a wine-chooser, a tie-chooser (take pictures of your ties, then have the app suggest -- via expert opinion, crowdsourcing, or an API for your significant other to code to -- which of your ties might go well with a shirt you see / snap a picture of at the store), and so on.

Without more dimensionality to Springboard, the BigCo app developer has two choices:

• Lard up a single app to do more within the "brand experience" it creates with its iPhone app.  But monolithic apps are slower and less reliable, presumably even if you're using the Tab Bar framework.  Plus, monolithic apps don't expand BigCo's share of the iPhone Springboard desktop, presumably a desirable strategic objective.
• Build multiple apps that get scattered across the Springboard, compromising the "critical mass" feel of the "branded suite" (apps that appear together make more of a brand impression than apps appearing separately, on different screens.  I don't have any data to support this, and you could argue the opposite, that apps scattered across screens provide more frequent brand reminders.  I think folks might be likelier mnemonically to remember "five swipes to the men's lifestyle screen".  Anybody got data?).

The BigCo marketing department has a choice not available to the lowly app developer, however, and that's to write Apple a check.  It's reasonable to expect that we won't all get access to the new "MDS" (Multi-Dimensional Springboard) API BigCo gets.  Today, Apple already price-discriminates among iPhone developers: the Standard enrollment charge is $99, while the Enterprise is $299.  As this platform becomes even more important, and as BigCos want to do more with it, it's reasonable to expect that Apple will get even more creative with its pricing, private or publicly.

So that's the "Why".  As for "When", I'm guessing no earlier than 2011, given Apple's Cathedral-style approach to iPhone development (this might provide an opportunity for Android, BTW). (Thanks for re-tweeting this, @perryhewitt .)

And "How"? I'm betting on an Expose-style interface.  Swipe down to "zoom in" to a single screen, swipe up to move to a "higher altitude" and view multiple screens at once, perhaps with a subtle label or background (brand-appropriate, natch) for each one.

Who's closer to this?  What do you know?

Across the street from Betaworks' offices was this sign:

Hit me like a ton of bricks (no irony).  My gold standard for trying boil down what I'm doing -- and for that matter what anyone else I'm working with is doing:

• Clarity -- it's veal.
• "Promise" -- not just veal: quality veal.
• Accountability -- got a beef?  Talk to Dave.
• Brevity -- more questions?  Knock on #425.

Google has Gmail.  Google allows people to publish profiles.  What if Gmail had a button that allowed me to "recognize" a recipient by linking to his / her public profile when I send an email to him / her?

If I have a public profile and the recipient has one too, by pressing this "recognize" button I would make our relationship "provisionally acknowledged" (like a "friend request"); the link would become "acknowledged" if the recipient agreed.  Further, either side (with mutual agreement) could choose to "publish" this relationship in multiple social nets they participate in: Facebook, LinkedIn, Orkut, or they could even make it fully public.

The more two-way email traffic there is between the two users, the stronger the link is assumed by the service to be.  Note that this wouldn't be scored in a linear way.  Probably some sort of recency and frequency considerations would be involved, just as we had at Contact Networks.

Taking a page out of PageRank (pun partially intended), the scoring algorithm could also consider the popularity of the URLs I associated with my Google profile to consider the "centrality of my node" in the uber-network, and therefore the "value" of my "acknowledgements", when given.  Link-love could be configured by each user to be given by-the-message or by default to different email recipients.  Recipients could also "transfer" this link-love, with permission, to their other web presences (e.g., blogs).

The idea isn't limited to the major mail platforms, either.  Any media firm with an online community has a latent social network that could be defined by the response patterns in forum posts.  Users wouldn't experience the pain and inconvenience of joining YASNS, just a minor modification -- perhaps a welcome one, if accompanied by a little extra valuable information -- to how they interact already in the communities they belong to.  "Activating" such social networks through mechanisms similar to the ones described above would enhance the viral marketing potential of the communities, which would appeal to advertisers.

Since basically everyone uses email, doing this would also "democratize the social graph".  What I mean is that today there are two kinds of networks.  Either they are private -- owned and run by Facebook, LinkedIn, etc. -- or they are "public-but-elite", defined by the link structure of the Web.  In the former case, if amigo ergo sum ("I friend therefore I am"), I exist at Facebook's whim.  In the latter case, only folks who take the time to establish a public web presence and get linked to (say, through a blog, or a social net public profile) exist.  (Reminds me of Steve Martin's excitement at making it into the phone book in The Jerk.)  An open, more inclusive social graph mechanism than either of these currently provides would help bridge the digital divide, among other benefits.

Who's doing this?  The idea isn't entirely original.  Partially relevant: Facebook has just updated its News Feed to consider interactions between users as inputs for how to filter items to each user.  I'm sure this must have occurred to the major portals with email services.  Seems like a natural feature for Google Wave, for example, though I haven't seen it.  Surely (as with Contact Networks) it's also valuable to large organizations to establish "enterprise social networks", inside and beyond. 

Postscript: Gather.com CEO Tom Gerace commented they are working on a patent-pending capability they call PeopleRank that will do what I describe above in the online community section of this post. Google's been thinking about this for at least a year -- how come we haven't heard more yet?

It's an interesting question because people have been nibbling at the problem from a variety of related, and sometimes more general angles.  There have been / still are, as Philip notes, major authentication initiatives, like MS Passport and OpenID.  Yahoo! Pipes (example) provides a powerful means for building aggregation services.  We have RSS readers (though not yet branded readers, which I think are still a good idea).

The most obvious answer would seem to be that there's no practical standard, like RSS, for expressing the data structure of a bboard post in an online community.  Why not isn't clear to me.  Since many communities use a much smaller universe of widely adopted toolkits to support their bboards, you might have imagined a standard might have emerged from these?  After all, we have iCal, why not iPost? 

Another reason might be that publishers are reluctant to allow aggregation of their communities' content.  But this doesn't seem to hold water, since they often allow members to subscribe (and sometimes reply) to posts by email, and otherwise expose their content via RSS.

Or, perhaps it might be that users (members of multiple communities) are reluctant to entrust all of their online community passwords to a single publisher of such a tool, or to set up an OpenID.

Maybe it's because the run-of-the-mill email client is a close-enough substitute.  However, it's an ugly way to read threaded community discussions.  I've been trying Google Wave, which looks better for this.  Turns out you can look at public online community discussions implemented as waves.  Since building a critical mass of accessible content would seem to be a key to Google Wave adoption, maybe what Google (or a third party developer) needs to do is extend its Embedded Wave API to allow publishers to expose / transform their online community activity as / into Waves that can then be read in Google's client?

Philip leaves the marketing to prospective entrepreneurs to whom he's freely offered his idea.  I think the answer to the marketing challenge might lie in addressing some of the issues above.

Postscript: Mozilla Raindrop

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.

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.

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.

Building on ideas I had a while back, it provoked an idea for a web service that would allow a group sponsor to register Twitter feeds (or, for that matter, any kind of feed) from members of the group, do a word-frequency analysis on those feeds (with appropriate filters of course), and then display snapshots (perhaps with a word cloud) of popularity, and trend analysis (fastest-rising, fastest-falling).  You could also have specialized content variants: most popular URLs, most popular tags.  Clicking through from any particular word (or url or tag) you could do a network analysis: which member of the group first mentioned the item, who re-tweeted him or her, either with attribution or without.

The builder of a service like this would construct it as a platform that would allow group sponsors to set up individual accounts with one or more groups, and it would allow these sponsors to aggregate groups up or drill down from an aggregate cross-group view down to individual ones, perhaps with some comparative analysis -- "show me the relative popularity of any given word / content item across my groups", for example.

Twitter already has trending topics, as do others, but the lack of grouping for folks relevant to me makes it (judging by the typical results) barely interesting and generally useless to me.  There are visual views of news, like Newsmap, but they pre-filter content by focusing on published news stories.

An additional layer of sophistication based on semantic analysis technology like, say, Crimson Hexagon's, would translate individual key words into broader categories of meaning from all this, so you could, at a glance, in what ways and proportions your group members were feeling about different things: "Well, it's Monday morning, and 2/3 of my users are feeling 'anxious' about work, while 1/3 are feeling 'inspired'  on vacation."

As for making money, buzz-tracking services are already bought by / licensed by / subscribed to by a number of organizations.  I could see a two-stage model here where group sponsors who aggregate and process their members' feeds could then re-syndicate fine-grained analysis of those feeds to media and other organizations to whom that aggregated view would be useful. "What are alumni of university X / readers of magazine Y focused on right now?"  The high-level cuts would be free, perhaps used to drive traffic.

I got three good answers:

• Brett Zucker (CTO at Bridgeline Software) and Joe Henriques (Regional Director, Sitecore) manned up ;-) and each gave me a number for where they see many organizations today -- Brett said 6-10, and Joe put it at 10-20.  Gross average, but crudely useful nonetheless!
• Andrew Hally (VP Product Marketing and Strategy at Unica) offered that it really depends on the industry, and on the mass-customizability of the product and other elements of the marketing mix. In the credit card biz, for example, you see firms executing against thousands of segments continuously redefined through real-time testing, because it's really easy to change terms and assess response.
• Scott Brinker (Co-founder/CTO at ion Interactive) noted that there are several answers to the question, because the process changes with each order of magnitude of the number of segments you set up to support. Scott described how in his experience, the hardest jump to make is from a handful of segments, supported by a highly manual process for creating offers, to the next order of magnitude, which at minimum requires modularization of marketing mix elements so they can be re-combined easily.  Scott further noted that traditional, primary-research-based segmentation approaches are being replaced by emergent (my word, blame me) segmentations based on testing. 

(Gents, apologies if I misquoted you -- please let me know.)

There's another question embedded in all this, which is Personalization (segmentation) with respect to what?  In other words, you can usefully define different segments for different elements of the marketing mix.  Upscale leisure traveler and a business traveler may get the same luxury hotel room.  But, they may behave differently when it comes to price.  Price-sensitive travelers may have different preferences for using marketing channels to find the best price.  So, in theory, you could have a single segment for product (luxury buyer, for example), two for pricing ("service-focused vs. price-focused"), and yet another two for channel preferences (e.g., "online-dominant" vs. "offline-dominant").  This can make segmentation much more operationally relevant, but it then also puts a premium on coordinating the outputs of these segmentation efforts (for example, if someone's really price-sensitive, you may want to steer them toward lower-cost digital channels for conversion).

Finally, another take-away from all this is to match the technology to the degree and kind (e.g., implicit vs. explicit) of personalization you intend to design a process for.  There's no point in buying a "Hemi-size" personalization engine if you've got a "Yugo-size" gas tank for marketing mix execution.  On the other hand, when the technology for testing an order-of-magnitude jump in segments becomes affordable, maybe it's time to optimize for that capability and flex / redesign the execution capability?

• 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.

• 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.

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.