Octavianworld

Activating Latent Social Networks

This morning via TechCrunch  I read Sean Parker's Web 2.0 Summit presentation materials, in which he says that the future belongs to "network services" that connect people, like Facebook, and not to "information services" that connect us to data, like Google.  My experiences at Contact Networks taught me to think of email patterns as proxies for social networks.  So, the following idea occurred to me.

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?

Philip Greenspun: Online Community Integration

Philip Greenspun kindly asked me what I thought of one of his recent ideas, "Online Community Integration".  I think it's a good one.  So of course the question becomes, "Why hasn't it been done already?"

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

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.

Twitter Idea Of The Day

I just read Clive Owen's piece in on wired.com describing the rise of search engines focused on real-time trend monitoring, as opposed to indexing based on authority.  It's good, short, and I recommend it.

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.

Personalization Is A Process #MITXMT

Last week I went to a MITX panel in Cambridge titled, "Get Relevant:  The Next Generation of Website Personalization".  I asked a question: "In between broadcast-to-the-masses and one-to-one, where in your practical experience is the crossover point (in number of customer segments you define and design for) where returns from finer-grained personalization are exceeded by the complexity of supporting an expanding number of them?" 

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?

Action Analytics Symposium Recap by Barry Dahl

Barry Dahl wrote a prompt and thorough recap of the Action Analytics Symposium hosted by Minnesota State Colleges and Universities and Capella University I participated in last week.

#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.

Action #Analytics Symposium

I'll be in St. Paul, Minnesota on September 21-23, presenting at the "Action Analytics: Setting A National Agenda" Symposium hosted by Minnesota State Colleges and Universities (MNSCU) and by Capella University.  (Notes on the conference to follow.)

My First Google App Engine App

It's not much, but hey, it *is*: http://cesarbrea1.appspot.com/

Please sign politely, I'm still figuring out how to edit the Datastore :-)

Marketing Analytics Management

As marketing goes digital, it becomes more accountable:  targetable, testable, trackable.  This is good -- in theory.  In practice, digital marketing has turned many marketers into sorcerer's apprentices who just can't get ahead of the flood.  It's not a question of a lack of analytic smarts or the availability of technology to support the work. Rather, the challenge is collective coordination.  

Some marketing teams compensate by hunkering down in channel-defined silos: "You do email, I do affiliates, he does, SEM, she does catalogs, those guys run the stores."  But by now most people are hip to the idea that the best, most effective customer experiences optimize efforts to attract, engage, and convert customers across channels.  Unfortunately, most marketing teams, and the ecosystem of agencies, technology vendors, research firms, and consultants that support them, are not organized to integrate analytics and tie them comprehensibly and usefully to the mix-, campaign-, and customer-level decisions such experiences require.  Marketing analytics today are 110V appliances trying to plug into 220V strategic sockets, and what the world needs is a good adapter.  

"Marketing analytics management", to coin a term, needs to move from an ad hoc set of informal activities run by a "priesthood" to a formally-defined and run process that is more broadly accessible.  As with any process, the basic questions are:

• what are the outputs?
• what are the inputs? 
• what happens along the way?  
• who's involved, and how? 
• what resources are needed to run the process?
• how do we evaluate and manage the process?  

Outputs

As a senior retailing executive said to me last fall, "I need my marketing team to think less like accountants, and more like decision makers!"  The point: stop thinking that the process ends with a regular report whose chief virtue is that it's accurate -- or, even, that the process ends with one or more particular trends or insights about customer behavior that we should pay attention to.  

Rather, each and every analytic effort needs to have a keep, change, drop, or add implication for the current marketing plan -- itself defined as the set of decisions (expressed as actions) about whom to target, how (what products, price, promotion, etc.).  In a formal "marketing analytics management" context, this means an explicit, documented two-way association.  Top-down, following explicit logic (expressed through models like the ones suggested by The Pyramid Principle), the marketing plan needs to selectively "commission" analytics efforts to support and monitor documented hypotheses and decisions about what to spend on and how to execute.  Conversely, any ad hoc, bottom-up analytic effort should seek a relevant decision as a "patron" for the effort.  A more complete "outputs data structure" might look like this:

• potential decision (action)
• analytic initiative
• stakes (in dollars, likely multiple fields) 
• deadline for decision (for example, marketing plan due dates, important events, etc.) 

Inputs

For each decision to be made, and for any associated analysis to be done, we have to have:

• data that's easily findable, sufficiently understood, sufficiently clean, and sufficient;
• tools up to the complexity of the analytic task;
• a sense for how accurate and rigorous the answer has to be given the stakes involved in the decision to be made;
• a collective memory of relevant past decisions and analysis supporting them 

First, I think it's much more important to know where data lives, and to understand and trust it, than to have lots of it.  If you have the former two, you can always get more of the last if you need it, whereas the reverse just makes a small problem bigger.  This is especially true as the emerging problem du jour to be solved is integration.  To this end, as I've suggested before, it's important to have a data management framework that includes the following elements:

• a map that shows where all the data lives;
• a dictionary that explains what piece of data means;
• a directory that explains how to get to the data;
• a log (just like the inspection logs in elevators) that documents periodic audits making sure that everything is flowing as it should 

Second, as for tools, I've been on both sides of software vendor pitches, and my preference is always to think "decision-in" rather than "analysis-out".  Today's tools, even the free ones like GA, are quite powerful, veritable swiss-army knives for analytic expeditions.  You can spend a lot of time watching a sales engineer demonstrate everything you can do with them.  It's much better to be able to go into these meetings and say, "Here are the decisions we're trying to make, the analysis we think we need to support that, and the data we can reasonably get and rely on.  How would your tool collect, scrub, store, aggregate, and synthesize to support our decisions?  How long would it take to implement, configure, and modify to do that?  How much of this can we do ourselves?"  If you're going to take this approach, it helps to have:

• a guide that describes how routine analytics get done and associated decisions get made

Third, think creatively about statistical validity.  In a recent pitch to a media firm, we pointed out that by concentrating sampling on customer segments with disposable incomes more likely to make fine-grained targeting worthwhile, and by relaxing confidence levels from 99% to 95% and confidence intervals from 5% to 10%, we could reduce total necessary sample size by 85%, and associated research costs by nearly 40% (a big piece of these are fixed of course).  In short, we focused the bulk of the research to be done on where it would pay off -- thus managing research dollars the same way we would have optimized, say, a PPC campaign based on them.

Finally, it's always tempting to short-change documenting and archiving past decisions and analysis when budgets and timelines are tight.  But that's if you get artificially fancy.  At the cheap extreme, get a Gmail account, set up filters and labels, then have folks send relevant stuff to it.  Voila:

• a library (searchable at minimum, if not indexed) that stores all of the individual bits of research (again: queries, regressions, tests, benchmarks) and conclusions you've reached in the past.

Process

Just as data need to be scoped to be appropriate to the stakes for a given decision, so should a process -- the steps and people necessary to go through to get a decision.  Figure out the deadline for the decision, subtract the days required for the review process, along with the days required to get higher-priority work done, and you're left with the limited time you have to do the necessary design, data collection, analysis, and packaging.  Presto, discipline.

Roles

At dinner recently, IBM Research's Kate Ehrlich observed she's seen three main roles in heavy-duty analytics efforts:

• decision-maker
• subject-matter expert 
• data analyst 

These are often and appropriately distinct people.  But, it's really important that the folks in each of these roles be able and willing to communicate with each other -- that they be know enough and be curious enough to ask good questions, so that their collective sniff tests about the size, process, and conclusions of any analytic effort be accurate ones.  (For me, Avinash Kaushik's Web Analytics: An Hour A Day represents the gold standard for thinking about what the "101" level of understanding might be in that analytic category specifically, but perhaps by analogy more generally as well.)

Resources

A recurring theme behind the points above is that you should know where you're trying to go with your analytic efforts, and scope the work, time, tools, and people involved accordingly.  It's been my experience that there are exponentially diminishing returns and similarly exponentially increasing costs to analyses seeking to explain variances beyond a handful of variables (corroborating case in point: predicting tweet virality), or to cut uncertainty by a few percentage points (as above), or to involve more than a handful (3-5) people in primary roles in a review process. Again, all this isn't meant to dissuade us from such complexity -- it's just to make sure that the stakes make it worth it.  I've also experienced the costs of NOT maintaining a tight ship when it comes to the data framework described above -- not just in terms of assembling data and re-learning lessons, but in terms of missed revenue and margin opportunities when bad data hide underperforming operations.

Management  

So how do you know if you've got a healthy "Marketing Analytics Management" process?  In most places, the associated work and resources are managed as cost centers.  But just as we work to make marketing campaign costs (creative, media) more accountable, it makes sense to work some allocation of the associated research and analytics costs into ROI calculations as well.  Doing so has two important benefits.  First, it associates analysis with decisions.  Winning decisions end up transferring credit to the insights and people that helped to generate them.  Winning decisions that were over-analyzed to the point where results dragged accordingly can also be tracked.  Second, it isolates and illuminates analytics and related resources that aren't associated with any decisions or decision-makers who will commission them.  A corollary requirement to make this work is that allocations for analytics shouldn't necessarily be mandatory.  If a decision-maker can get what he or she needs from other sources, or can successfully fly by the seat of the pants, more power.  But with freedom comes responsibility...

Once again, the point of this post is to suggest to senior marketers some pragmatic "scaffolding" they might adopt to address the sorcerer's apprentice problem.  That way, they don't have to rely on the "Master's" intervention: Mickey may have gotten off with a cute, sheepish look, but in today's business climate things are likely less forgiving for the average executive.

Clunkalytics

This afternoon I listened to an NPR segment on the government's "Cash for Clunkers" program.  It sounds like quite the  goat rodeo.  

A senior researcher at JD Power & Associates in Detroit interviewed for the segment noted that although the program provided incentives sufficient to fund sales of 250,000 cars ($1Bn in incentives at ~$4k/car traded in / retired), his firm's estimates are that only 40,000 sales will be incremental (over and above) what would have otherwise have been sold anyway.  Hmm.  If they're right, a billion dollars to lift sales by 40k cars.  That's $25k for each new, fuel-efficient car sold!  (In fairness, that's actually a lot less porky than a lot of things we hear about.)

Would the government have done better to simply buy 40,000 cars, perhaps for a little less than $25k apiece?  Then it could have run a contest where Americans could enter their most egregious gas guzzlers (via online video, natch) in the hope of winning a replacement, which would have been more fun, and bought the government lots more -- and more positive -- coverage of the program (perhaps giving a whole new meaning to "cap and trade").  

Of course this ignores the benefit of getting the other ~200k gas guzzlers off the road.  But treated as an independent objective, surely there would have been a better mechanism for encouraging drivers who were going to buy anyway to buy north of 20 MPG?

Cynically, one might say that the real beneficiaries of this program aren't auto workers, but the dealers whose glutted lots get cleared (especially since the program can be used to buy "foreign" as well as "domestic" cars -- ironically the folks interviewed on NPR sounded a common refrain: "Trading in my old GMC / Ford for a new Toyota pickup!").  It's hard to believe that under current conditions the dealers will order replacement inventories from the plants sufficient to replace what they sold.  

It's easy to understand why this program was so popular in Congress, since there are dealers in every state.  But if you wanted to make the most of a billion dollar stimulus for the present and the future of the nation, would putting it into the pockets of auto salesmen nationwide have been the best way to go? (It's a serious question, since dealers are often at the center of their communities and do spread a lot of money around, so maybe the multiplier is significant.)

Notwithstanding, I'm not coming at this from an ideological position.  I get the need for a stimulus to ameliorate the recession.  I'm thinking about this in the context of other retail promotion programs we see, many of which have the same inefficient dynamics -- subsidizing sales that would have happened anyway, motivating sales of the wrong products, and making the channel happy for a little while rather than encouraging more lasting customer loyalty.  And since "you manage what you measure", I'm also thinking about how I might have set up an analytic framework to execute the program more effectively.

There is a web site for this program (FWIW, it's using Google Analytics).  To measure how well the program attracts possible customers, perhaps the government could have channeled prospective users of the program through it, to gather information (e.g., pre-existing purchase intent, perhaps in combination with data from a BT network) in exchange for the "coupon".  To measure engagement and conversion, surely -- I can imagine a number of options -- consumers could have been tracked through to dealers.

Then, providing open access to the data and crowd-sourcing suggestions for improving the program would have been cool, and good practice for aspiring web analytics professionals (free job training in a growth category!).  Sadly, but more likely, we'd be filing FOIA requests.  Oh well.

(Re-)Thinking Marketing Silos

I've been wondering lately about the social marketing boom, the timing of its inevitable commoditization (via Adverblog, parodied here, warning -- possibly NSFW) and where its useful boundaries are -- at what point does it stop making sense to talk about a social media campaign per se, and to start thinking about social media elements in integrated campaigns.  (For example, how do you classify a social "forward to a friend" capability in an email campaign?)  The issue is broader of course.  Integrating the "customer experience" is widely acknowledged as a good thing these days across many channels, not just with respect to social media.  

Silos in marketing organizations are widely acknowledged as a principal barrier to realizing this objective.  So, three questions are worth asking.  Why do they exist?  What organizational model would be a better choice?  And, practically speaking, how do we get from A to B?

At an operational level, silos today are most commonly defined by channel.  That is, for example, offline channels vs. online channels (in retail, the folks who run the stores vs. the folks who run the website; in media, the folks who run the print magazines vs. the folks who run "interactive").  Or, within "online", email vs. web site vs. social media.  

These definitions get started because even though what folks share in common may be really important -- knowledge about a customer segment's habits for example -- the specialized expertise  required to mechanically execute campaigns in the medium is very different.  Even when these roles are combined, individuals or small groups may still think "channel first" because of the concentration required to think and execute mechanically.

The channel-defined definitions persist in part because the vendors who supply the tools for those channels have an interest in continuing to fight commoditization with new, complex features rather than with simplicity, which though virtuous, sadly accelerates commoditization rather than staving it off.  (The exception -- Apple -- proves this rule.)  Also, vendors have historically made integration hard, though this is changing.  Further, there is a vendor-sponsored event ecosystem that pulls-channel-defined specialists together more often (and in nicer places, with better schwag) than, say, audience-segment-centric conferences might.  And finally, just as cucumbers rarely survive their encounters with barrels of brine, folks steeped in particular media begin to think differently in ways that shape their abilities to think across channels.  Someone with a lifetime as an auteur of 30-second TV spots may find it harder to think about "conversations".

What alternatives might be better, and when?  If different target customer segments had radically different media usage patterns, you might cut things that way.  Or, if the media used to  attract, engage, and convert customers were similarly distinct, you might slice efforts by stage of purchase funnel.  But things have begun to blend: old folks are using Facebook and Twitter as much as young folks (who increasingly think these media are uncool as a result), and what do you call a retail checkout experience that stays on an affiliate's site?

So, the answer may be ad hoc task force overlays to today's channel-defined silos.  These can be reinforced by experience-wide metrics, analytics, and perhaps even evaluation and compensation frameworks for the members of these task forces.  But perhaps the most important first steps are awareness of what customers experience, a firm's business strategy  for choosing and serving (what products, priced how?) the ones they want, and mutual communication and education about how different folks approach and do their jobs in their respective channels.

To what degree do silos constrain you today? How are you adapting to overcome their challenges, and improve target customers' experiences?

Microsoft-Yahoo Search Deal: And Then There Were Two And A Half

Microsoft and Yahoo! announced today that the former's new Bing search engine would now run the latter's search capability.  And, Yahoo! will sell paid search campaigns for the combined capability.  At least for starters, this means Google owns two thirds + of pro forma search volume (per Comscore), and MSFT+YHOO own the other third.  

Yahoo! had no choice but to accept colonization by Microsoft, as its own search technology was going nowhere.  Similarly, Microsoft had to do this deal with Yahoo because in today's tough market (even paid search is seeing only low single digit growth), it couldn't afford to divide its sales force to fight Yahoo in the face of their shared, colossal foe.

It will be interesting to see how much search volume sticks with / moves to each.  I'm guessing there is a curiosity factor that has Bing volume temporarily high, but that Google will hold serve, since Bing's advantages seem concentrated in some commerce-oriented niches.  (Here's a wacky idea though:  Microsoft could reverse one important structural disadvantage with a pre-emptive bid to embed Bing by default in Firefox, when Mozilla's current deal with Google expires in 2011.  Maybe not so wacky if, following long-term trends, IE's browser share drops close to 50% by then, and Google chooses to focus on Chrome.)

The other reason for this deal is for each side to achieve a critical mass of search volume to usefully inform display ad targeting.  Display ad networks have been a surprisingly profitable business, even in tough times (see also for example NET income for VLCK in Q1 of this year).  And the tip of the spear in the trend toward multi-channel optimization of marketing spend has been attribution analysis between search and display channels ("To what degree is a search triggered by display ad exposure, and how can we better target display ads to behaviorally-defined user segments across a network based on their search history?").  So, this deal would seem to be as much about shoring up each side's profit centers as it would be about huddling for warmth in the paid search world.

Given the complexities of having Yahoo! take over Bing sales and operations (through AdCenter), folks at Google should make hay in the short term, since their opponents will be distracted and the transition will create work for customers that arguably doesn't add much value either.  Hopefully, much of this complexity can be absorbed for advertisers by their search agencies, and hopefully they can get paid for some of that.

Notwithstanding interesting riffs on top of the established platforms, and newcomers that are useful in niches, this consolidation is further proof of the diminishing returns to innovation in search as we know it, that is, indexing and ranking unstructured content.  The next big wave of disruption in this category won't happen until we have radical change in the nature of the stuff being searched -- specifically, the advent of "Semantic Web" standards that will structure the underlying data in useful ways.  

It will be interesting to see how The Big Two And A Half left in search react to the propagation of open standards that could level the search playing field.  If the DOJ wants to fight the Search Trusts, perhaps its money is better spent on seeding and supporting those efforts than undoing what nature hath already wrought.

MITX "What's Next": Mid-Year Digital Marketing Zeitgest Check (#MITXDT)

This morning I went to MITX's "The Current Digital Market and the Outlook for What's Next" event at Microsoft in Cambridge. The event was well-attended.  Before the  panel discussion, I enjoyed talking with Allen & Gerritsen CEO Andrew Graff, who helped us at ArsDigita, and iProspect CEO (and ex-Bain colleague) Rob Murray.  Andrew reported that business was surprisingly strong, and Rob validated trends toward multi-channel attribution analysis and optimization.  I also had the chance to meet Forrester's Shar van Boskirk briefly; the 5-year interactive marketing forecast of spend across display, search, email, mobile, and social she / they released yesterday, projecting digital to grow to 20% of an otherwise flat market, with social growing by a factor of five, sparked a spirited conversation on Josh Bernoff's Ad Age article about it yesterday.

Shar moderated a panel that included Dace Venture's Jon Chait, Fidelity Labs' (and Razorfish alum) Hadley Stern, and Razorfish Client Partner Kristin Marlow.  Jon's got a $75M debut fund focused on consumer marketing solutions; Hadley's job is to develop things for Fidelity customers that will be ready for prime-time in 12-18 months, and Kristin runs the Razorfish relationship with T. Rowe Price.

Shar's summary / synthesis of the panelists' themes:

• the marketing organization's function has been transformed into one of "conversation stewardship"; marketers need to take their efforts to plant and nurture conversations to where audiences are (e.g., syndicate, Hadley's main point), and be prepared to have it be a real 2-way communication (Jon's primary point). (FWIW, maybe "2-way" should be "n-way").
• There's a tight focus on performance right now; investments in data management / integration are taking precedence over investments in new media

Panelist comments I noted:

• Kristin: Interactive marketing budgets are on the sideline, waiting for performance to come back; what is getting spent is very close to conversion, and is very closely watched and analyzed
• Kristin: Quantcast is hot, people like the idea of being able to buy cookies  
• Kristin: Attribution analysis for display v search spending optimization is a growth area 
• Kristin: There's also lots of attention to the "on-domain experience" 
• Kristin: Lots of focus on social, because people think it will be largely free, but it's not
• Hadley: "97% of Fidelity's business is done online" [stunning stat, I'd be very interested to better understand how that's bounded / qualified] 
• Hadley: Given that, his big theme is syndication -- "Fidelity Everywhere" -- building applications for the desktop, iGoogle, iPhones, etc. that allow Fidelity customers to do what they need in the places they are most comfortable, which doesn't always mean coming to the website 
• Hadley: Fluid times -- "Feels like every six months we're trying to make big decisions without much information."
• Jon: Trend is toward 2-way marketing , most cost effective ways to maximize engagement with the right people. Seeing 5-14% CTR on display ads in these media
• Jon: The trend is real -- his portfolio companies serving these needs have had their strongets revenue growth ever since Q4 of last year 
• Jon and Kristin: establishing credible social presence takes commitment -- can't dabble.  
• Jon -- entry price for credible social media effort = $250k-350k/year, sustained effort will be seven figures / year
• Kristin: There's a tradeoff between engagement and scale [Note: my presentation at IBM Research on Monday was partly about how you reconcile the two]
• Shar: there isn't and won't be any fully integrated platform for marketing analytics and execution -- the key is to work with vendors who play nice with each other, through adherence to "open data"  

Questions I wished I'd asked:

• How much of decision-making by clients right now is based on modelling versus testing?
• Given the decline in unit prices for media, as well as lower creative costs, the cost of analytics and tracking for any given campaign are getting bigger -- to what degree are marketers factoring these into campaign ROI? [I'm not seeing that yet, but I suspect it will begin to become an issue in the next 12-24 months.  If in the past the medium was the message, the measure may well be the message in the future -- meaning what each target consumer experiences will be personalized based on complex algorithms drawing conclusions about him or her.]  

What did I miss? 

Caption Contest

Online Surveys & Market Research

Facebook at 250 (Million): What's Next? And What Will Your Share Be?

Facebook announced last week that it had passed 250 million members.  Since no social network grows to the sky (as MySpace experienced before it), it's useful to reflect on the enablers and constraints to that growth, and on the challenges and opportunities those constraints present to other major media franchises (old and new) that are groping for a way ahead.

"Structured Collaboration" principles say social media empires survive and thrive based on how well they support value, affinity, and simplicity.  That is,

• how useful (rationally and emotionally) are the exchanges of information they support?
• how well do they support group structures that maximize trust and lower information vetting costs for members? 
• how easy do they make it for users to contribute and consume information? 

(There are of course additional, necessary "means to these ends" factors, like "liquidity" -- the seed content and membership necessary to prime the pump -- and "extensibility" -- the degree to which members can adapt the service to their needs -- but that's for another post.)

My own experience with Facebook as a user, as well as my professional experience with it in client marketing efforts, has been:

• Facebook focuses on broad, mostly generic emotional exchanges -- pictures, birthday reminders, pokes.  I get the first two, and I admire the economy of meaning in the third.  The service leaves it to you to figure out what else to share or swap.  As a result, it is (for me anyway) <linkbait> only sometimes  relevant as an element in a B2C campaign, and rarely relevant in a B2B campaign </linkbait>
• Facebook roared past MySpace because it got affinity right -- initially.  That is, Facebook's structure was originally constrained -- you had to have an email address from the school whose Facebook group you sought to join.  Essentially, there had to be some pre-existing basis for affinity, and Facebook just helped (re-)build this connective tissue.  Then, Facebook allowed anyone to join, and made identifying the nature of relationships established or reinforced there optional.  Since most of us including me are some combination of busy and lazy, we haven't used this feature consistently to describe the origins and nature  of these relationships.  And, it's cumbersome and awkward to have to go back and re-categorize "friends". (An expedient hack on this might be to allow you to organize your friends into groups, and then ask you which groups you want to publish items to, as you go.)
• Facebook is a mixed bag as a UI.  On one hand, by allowing folks to syndicate blogs and tweets into Facebook, they've made our life easier.  On the other, the popular unstructured communications vehicles -- like the "Wall" -- have created real problems for some marketers.  Structured forms of interaction that would have created less risky choices for marketers, like polls, have come later than they should have and are still problematic ( for example, you can't add polls to groups yet, which would be killer).  And, interacting with Facebook through my email client -- on my PC and on my smartphone -- is still painful.  To their credit, Facebook opened up a great API to enable others to build specialized forms of structured interaction on its social graph. But in doing so it's ceded an opportunity to own the data associated with potentially promising ones.  (Like prediction markets; Inkling Markets, for example, lets you syndicate notices of your trades to Facebook, but the cupboard's pretty bare still for pm apps running against Facebook directly.)

The big picture: Facebook's optimizing size of the pie versus share of the pie.  It can't be all things to all people, so it's let others extend it and share in the revenue and create streams of their own.  Estimates of the revenues to be earned this year by the ecosystem of third party app developers running on Facebook and MySpace run to $300-500 million, growing at 35% annually.  

Them's not "digital dimes", especially in the context of steep declines in September ad page trends in, say, revenues of leading magazine franchises, as well as stalled television network upfronts. But, folks might argue, "Do I want to live in thrall to the fickle Facebook API, and rent their social graph at a premium?"  The answer isn't binary -- how much of an app's functionality lives in Facebook, versus living on a publisher's own server, is a choice.  Plus, there's ways to keep Facebook honest, like getting behind projects like OpenSocial, as other social networks have done. (OpenSocial is trying to become to Facebook's social graph as Linux is to Windows.  Engineer friends, I know -- only sort of.)  And, for Old Media types who don't feel they are up to the engineering tasks necessary, there are modern-day Levi Strausses out there selling jeans to the miners -- like Ning, which just today raised more money at a high valuation.  Still too risky? Old Media could farm out app development to their own third party developer networks, improving viral prospects by branding and promoting (to their suscriber lists) the ones they like in exchange for a cut of any revenues.  In this scenario, content gets added as an ingredient, not the whole main course.  

What is true in the new environment is that reach-based ad network plays surfing on aggregated content won't pay any more.  Rather we have to think about services that would generate more revenue from narrower audiences.  The third-party games created by Facebook app developers referenced above demonstrate how those revenues might stem from value through entertainment.  As we speak, Apple and its developers are earning non-trivial sums from apps.  Phonetag has its hands in folks' pockets (mine included) for $10/month for its superuseful -- albeit non-social -- transcription service.  Filtering for relevant content is a big challenge and opportunity.  Might someone aggregate audiences with similar interests and offer a retail version sourced at wholesale from filtering service firms like Crimson Hexagon?  Looks like Porter Novelli may already be thinking down these lines...

Let's push the math: a winner service by anyone's measure would earn, say, $50M a year. Four bucks a month from each person is roughly $50/ year.  You'd then need a million folks, 1/250th of Facebook's user base to sign up.  Reasonability check -- consider the US circulation of some major magazine titles. 

If your application service is especially useful, maybe you can get $2/month directly from each person.  Maybe you can make the rest up in ecommerce affiliate commissions (a 10% commission on $125 in annual purchases by each person gets you ~$1/month) and ad revenue (the $12 million/year nut would require one dollar per member per month; a $10 CPM would mean getting each of your million users to account for one impression on your service a couple of times per week, more or less, to cover that nut.)

We also have to be prepared to live in a world where the premiums such services earn are as evanescent as mayflies, especially if we build them on open social graphs.  But that's ok -- just as Old Media winners built empires on excellent, timely editorial taste in content, New Media winners will build their franchises on "editorial noses" for function-du-jour, and function-based insights relevant to their advertisers.  And last time I checked, function and elegance were not mutually exclusive.

So, even as we salute the Facebook juggernaut as it steams past Media Beach, it's time to light some design workshop campfires, and think application services that have "Value, Affinity, Simplicity."

Structured Collaboration in Social Media: Design For Analytics (IBM Research Talk Recap)

At Kate Ehrlich's invitation, I gave a one-hour talk yesterday at IBM Research and the Center for Social Software titled "Structured Collaboration in Social Media: Design for Analytics".

The presentation made three main points:

• Social marketing today is limited by the (un-)structure of social media. (And, most marketers accept the constraints of these media as they exist today, and "reasonably" adapt themselves to them.)
• Applying "Structured Collaboration" principles to social media design ("unreasonably" adapting the medium) can expand and improve user engagement.
• These same principles can inform the design of "engagement" for marketing analytics, to make segmentation and targeting easier and more effective.

The presentation included a survey and evaluation of the social marketing landscape today, and described two important shortcomings that limit what marketers can do with it:

• "Off-the-rack" collaboration structures that don't align -- and often work against -- what marketers are trying to accomplish 
• Sample bias given the "1-10-90" nature of participation 

The presentation then described and illustrated "Structured Collaboration" principles, and talked about how marketers like Nike are realizing significant results through initiatives that reflect them.  Then, we discussed how the design of these initiatives can reveal segmentation and targeting insights much more readily than the typical application of marketing analytics to conventional social media.

Finally, the talk offered some existing examples for how to exploit these principles cheaply, as well as some ideas for how several firms could apply them to realize their own versions of what Nike, and more recently others like Fiat, have done.

Thanks once again to Kate, Ethan, and Irene, and to all their colleagues for participating!

Testing Across Your Web Presence: A Conversation With SiteSpect's Eric Hansen

A recurring theme in our work with clients is to cast the analytic net across their web presences, not just their web sites.  For example, let's say you're a retailer and you run a "discounted shipping" promotion, with different levels.  The campaign for this should be evaluated not just in terms of conversion on your web site, but in terms of how different levels and creative versions performed in terms of attracting, engaging, and converting customers in all the places you published them -- display ads, SEM units, emails, your site itself, affiliates' sites, etc.

Implementing a test strategy like this has been hard to date, because you may not control all of the different properties involved in the campaign; even if you do, each channel may have its own testing tool; and, even if you've consolidated things somewhat, the tagging job is still a bear.

So, I was really interested to see this recent announcement of SiteSpect's "URL Tunnel" capability, which allows users of this service to implement integrated tests across multiple properties in their digital ecosystem.  I met  SiteSpect CEO Eric Hansen and CMO Kim King a couple of weeks ago at Web Analytics Wednesday, and had a chance to talk with Eric last week by phone about how this works and about how their customers are getting valuable new insights from this capability.

Here's a simple example of how this works.  Let's say I'm a retailer and I have a gift finder on my site that enables parametric search across my catalog (search by gender, age, price range, etc.).  I build a widget version of this gift finder that can be embedded on affiliates' sites, or published as a display ad unit.  I set up "mywidgets.myretailsite.com" and respond to calls from the syndicated widget code from here.  Then I direct this traffic through SiteSpect's solution (either their "SiteSpect ASP" service or their "Enterprise" appliance running in my data center).  Now I can implement and track multivariate tests on widget variants through SiteSpect.

Eric described how a SiteSpect travel customer has used this.  Before, the travel portal acted as a catalog of destination packages, but booking happened on the private-labeled booking engine / travel shopping cart another travel industry partner provided to them.  From a user experience perspective, when a customer pressed "Book it!", he or she was taken off the travel site to the partner's site, e.g., "booking.partner.com", and the travel operator lost visibility to how different variants of those booking pages performed.  Now, the booking engine is still run by the partner, but it's embedded in pages served from a distinct "partner" subdomain off of the travel operator's site, and routed through a SiteSpect server where test variants are defined, applied, and tracked.  

How does the partner feel about this?  In principle, you might expect them to be concerned about the loss of control.  But in practice, since they weren't going to tweak the travel portal's marketing mix elements themselves, they are thrilled give the travel portal the opportunity for insights that will raise conversion rates, and therefore earn more booking fees for the travel partner.  Plus they may learn stuff that they can generalize for the benefit of other relationships.

From The "Stop Whining About Obscurity And Start Rethinking Content For Usability" Dept.: What's your iCalendar Strategy?

At  a number of events I've been to recently, one common refrain has been "Gee, how is it that no one knows about all the cool stuff that's going on in sector X around here?  We've been working hard to get the word out, but we're still flying under the radar."

Around the same time, I was fiddling with my Google Calendars, trying to integrate them and add additional calendars to them (like weather, holidays, etc.).  I was really surprised to see the scarcity of things already registered with Google that I could add.  There's an option for adding a URL for different organizations' iCalendar feeds, but no way readily apparent to me to search / discover such feeds, either on Google Calendar itself or via a logical search.  

I went to the web sites of some organizations with events series that I like to get to, to see if they offered iCalendar feeds that would place those events on my calendar, so I could have them there as reminders if by chance I could get to them.  So far, no joy.  Reassuringly, I'm not alone with this idea or my curiosity at the lack of feeds, or their discoverability, as this post by Jon Udell suggests.

If you are a publisher with a series of events you'd like people to come to, how can you publish an iCalendar feed?  Here are a number of options for tools to publish calendars on your site that also support publishing an associated iCalendar feed.

I also checked out services like Eventbrite  and Eventful, thinking that these intermediaries logically would publish iCalendar feeds for organizations that publicize and manage individual events through them.  Again, no luck -- maybe I'm missing something?

This idea is not new.

The broader point:  Democratization of publishing tools means content exposed as content will find it harder and harder to get its day in the warm sunlight of user attention.  Smart publishers will think about how they can expose their content in formats and applications that are more tightly tied to how users employ that information.  Many things can be turned into event series -- if you are a recipe site, how about a "meal-of-the-week" series?  If you are the US Government -- or Forbes.com -- where's my iCalendar feed for economics statistics announcements?  Other examples: interest rates integrated with interest rate calculators, apartment rental listings integrated with mapping applications that support getting directions...  what can you think of?