Octavianworld: Cesar Brea's Weblog, continued.

@AMABoston #Marketing #Attribution 9/17 Panel Discussion Guide - Suggestions Welcome!

On September 17, I'm moderating a panel on marketing attribution analysis at a meeting of the American Marketing Association's Boston Chapter.

Here are some questions we've put together to guide the discussion.  What's on your mind?

1. Let’s start with a plain English question: How do you decide where to spend your marketing dollars?

2. What assumptions do you make about the relative effectiveness and efficiency of different options?

3. What are these assumptions based on?

4. So, for me, attribution is a fancy word for giving different marketing investment options -- channels, partners within those channels, creative alternatives -- proper, or at least more proper credit for their impact on a metric of interest, like a lead or a sale.  How would you define it, or improve on this?

5. Earlier you told us about how you do it today, but I’m also interested in your attribution journey.  Where was the organization’s level of attribution sophistication when you got into your latest role?

6. What attribution adjustments did you make first, and next?

7. What was the logic for starting there? How much was it the potential value -- the magnitude of the spend, for example -- and how much was it about feasibility - you could reasonably expect to get the data?

8. How much of the adjustment was based on “top down” statistical modeling of overall spend and impression data, and how much of it was bottom up based on user-level proxies like cookie data?

9. How long did it take to get up and to an interesting insight you could put to work?

10. What worked, and didn’t?

11. What surprised you?

12. How did you balance building the capability and getting results?

13. What kind of results have you gotten so far?

14. What’s next on your roadmap?

15. How far do you think you can push attribution analysis before you hit diminishing or negative ROI?

16. What’s the limiter? Is it data quality? Integration challenges like tracking a user across channels? Is it your ability to put insights to work?

17. Let’s take each of those:

1. We hear a lot about how cookies are dead, and then the death of the cookie has been greatly exaggerated -- how far can you trust cookie based data for attribution purposes?

2. Now that the big platforms -- Facebook, Google -- can track us everywhere, and use that to do cross-channel advertising programs, do you try to beat them, or join them? If you try to beat them, how far can you take cross-channel tracking? When and how does privacy come into play -- what are your bright lines for this?

3. What parts of your attribution insights are then integrated with your marketing automation platforms? What’s still manual?

18. What does managing based on  more sophisticated attribution look like? Who does it? What kind of interfaces and reports does this person or team use?  How often do they review and act on the results?

19. What kind of people and organizational challenges have more sophisticated attribution approaches created? How have you solved them?

20. What advice would you offer to someone with a marketing budget of $1M? $10M? $100M? 

21. What’s the practical technical frontier of what people are doing?

22. How do you keep up?  What resources would you suggest for someone interested in learning more and staying up to date?

Ten Things I Think*: Thoughts on The #Amazon Workplace in @nytimes @jodikantor @DavidStreitfeld

On Saturday the New York Times published a piece by Jodi Kantor and David Streitfeld titled, "Inside Amazon: Wrestling Big Ideas in a Bruising Workplace". Here's the link:

http://www.nytimes.com/2015/08/16/technology/inside-amazon-wrestling-big-ideas-in-a-bruising-workplace.html

The article provoked a large number of comments and showed up in several of the social media feeds I follow. Plus, my business is helping organizations build their capabilities to use data to drive results.  

Let's stipulate that the article presented facts accurately and in balance. Here's ten things I think (*Thanks to @SI_PeterKing). 

1. What a great litmus test for values. But don't listen to what people say, watch what they do.

You live your values every time you shop there, or anywhere else. I'm reminded of the mission statement of ArsDigita, where I once worked:

...Beyond that, we don't worry about corporate culture. We have a certain set of customers. We have a certain set of people. We have a certain set of tools. Discussions or theories won't change any of those things. If any ArsDigita member wants to change ArsDigita, he or she need only add to the customers, add to the people, or add to the tools.

So, shop at Amazon, or don't, or, since life's complicated, change the mix of where you shop, according to some rules that make sense to you. Like, "I'll start by looking for competitively priced and appropriately convenient and accountable alternatives, whose operating and employment practices are more consistent with my beliefs about how to treat people. But if I can't find one, I'll still shop at Amazon."

Or, if you work there, and the current culture doesn't work for you, either accept the fact that it doesn't fit and leave, or work to change it as far as you can.

 

2. Everything's relative.

The article calls out Amazon, but doesn't explicitly contrast it with any other retailers, and examine the trade-off in performance with these retailers: prices for consumers, returns for shareholders -- which of course include many pension funds holding the savings of a wide range of investors. (Since Amazon competes in more categories than retail, and it's an icon of the global economy, it's worth extending this comparison of course, but you have to start someplace.)

Is Amazon an anomalous white-collar sweatshop? Why stop at researching the question? Here's an idea for a motivated someone: create a "Shopper's Bill of Expectations" service.  The service would articulate and promote a set of values / standards / expectations its members would like to see: for example, paternity leave, compliance with environmental regulations and trends, living wage, working conditions, etc. Then it would invite members to subscribe to an auditing service, like Consumer Reports, and invite retailers who agree to comply with the service's expectations to enroll in it. Finally, it would provide some sort of tracking mechanism, manual or automated, so it and its enrolled retailers and members can see how much dollar volume the service sends from members to retailers. There's a huge data play in here, too! I have no objections to this service leveraging that in ways that are transparent and which don't unduly compromise or pressure its core proposition. 

Naïve? Seattle's embraced Fair Trade for coffee-growing campesinos, why not for highly-compensated ecommerce executives? (Uh, well...) 

3. This isn't me.  

Whether I expect emails and texts to be answered at midnight depends on the circumstances -- and they have to be pretty dire.  If someone takes a planned vacation which business conditions support, and thoroughly plans coverage, and says he or she will be out of contact, I respect that choice. If someone has a short-term or longer-term health crisis, I do what I can to support that person as far as practically possible, and then beyond as far as I can.  I don't believe in stack-ranking and firing the lowest decile; I believe in setting and continuously raising performance expectations, being direct and thorough and fair in evaluating people against them, and encouraging anyone who doesn't consistently clear those goals to find something else, with compassion, and endorsement for the stronger things they do. I believe in courtesy and kindness. I believe in teamwork, not beggar-thy-neighbor advancement.

4. This is me.  

I'm objective about performance. I prefer a focus on ends, not means, as long as means are pursued ethically, which = Golden Rule to me. I believe in coaching and development, but I expect hard work and demonstrated progress and positivity and enthusiasm in return. I expect to care, and I expect my colleagues to care as well and as much. And, if I'm asked to be up late busting my behind to help someone under pressure, I do expect him or her to be responsive to late-night emails and texts. I don't care for passive-aggressive cultures whose members play nice superficially but work at cross-purposes behind the scenes; in this regard Amazon's directness and openness to public disagreement seems much healthier.

5. Context doesn't justify, but it matters to the assessment.

Amazon is a product of a super-thin-margin industry. It was founded by a man with the characteristics to succeed in that unambiguous, unprotected environment, and it has thrived by attracting managers with similar profiles. If and when you contrast a culture like Amazon's with some other avatar of corporate humanitarianism, it may be worth a look at the glass-house bulwarks supporting the profitability that enable those practices. These may take the forms of de facto technical standards the firm has established, or a monopoly in its market(s), or legal or regulatory actions -- for example, easy monetary policies -- or hard-won, carefully-cultivated brand strength. Assume the bulwarks weren't there: how would things change? Many successful firms would do well to ask this of themselves.

6. Data-driven, to a fault?

If what gets measured gets managed, we also manage as we measure. I see a spectrum: we ignore data at one end, we are slaves to it at the other end.  Both extremes are dysfunctional. Ignorance is of course not bliss, but in very few cases do we have enough clean, ungamed data to put our faith in it exclusively.  Digital commerce may be one such exception, but employee behavior -- measured as described in the article, and here -- is not.

In most cases data are "Platonic Shadows" for what's happening in the business. To be useful they need to be looked at holistically, and across as much time as is available to distinguish signal from noise. I believe in watching for emergent patterns from machine learning, but also in humans having their heads in the game with hypotheses and explicit sensitivity to biases of many kinds.

7. Development requires opportunity and demands responsibility.

It may be dysfunctional at the margins, but I admire the empowerment Amazon offers its managers to get things done, and its expectation that they will get things done.  There's nothing sadder than well-educated and qualified managers who feel blocked and just go through the motions for a paycheck. Well, actually, there is: employees with extremely limited choices who are ethically and illegally exploited.

I appreciate Amazon's bias for action, and for matching analysis to the stakes and uncertainty associated with a decision.

8. Is this a cult of personality?

What happens beyond Jeff Bezos? Paging John Galt!

9. How do you compete?

Amazon competes on utility and service, tightly defined and realized based on the firm's extreme degree of customer focus. Their managerial ranks thus reflect this.  If you don't want to join them, or can't, or want to leave, then beat them.  This means playing a different game. For example, Amazon doesn't create brands so much as it amplifies them.  Likely you will need to create a brand along an emotional dimension that Amazon under-serves, then partner with Amazon in ways that extend but don't erode it. What opportunities to speak to or reflect someone's actual or desired identity can you reinforce through an online-retail service? Maybe some sort of emo-oriented Edgio blending content and commerce in curatively creative ways?

10. On reflection...

There are older and fouler things than Amazon in the deep places of the world. But Amabots and Amholes at Amazon and beyond, remember: In the end, the love you take is equal to the love you make. 

 

 

 

My Q&A With @chiefmartec's Scott Brinker

One Marketing Analytics Myth? The Perfect KPI... published July 6, 2015

Driving Social Engagement With Sentiment Analysis: Text Analytics Summit West

McGraw Hill VP of R&D and Analytics Al Essa kindly invited me to join him in delivering this workshop in San Francisco on November 3.  Hope to see you there!

Mary Meeker's @KPCB #InternetTrends Report: Critiquing The "Share of Time, Share of Money" Analysis

Mary Meeker's annual Internet Trends report is out.  It's a very helpful survey and synthesis of what's going on, as ever, all 164 pages of it. But for the past few years it's contained a bit of analysis that's bugged me.

Page 15 of the report (embedded below) is titled "Remain Optimistic About Mobile Ad Spend Growth... Print Remains Way Over-Indexed."  The main chart on the page compares the percentage of time people spend in different media with the percentage of advertising budgets that are spent in those media.  The assumption is that percentage of time and percentage of budget should roughly be equal for each medium.  Thus Meeker concludes that if -- as is the case for mobile -- the percentage of user time spent is greater than budget going there, then more ad dollars (as a percent of total) will flow to that medium, and vice versa (hence her point about print).

I can think of  demand-side, supply-side, and market maturity reasons that this equivalency thesis would break down, which also suggest directions for improving the analysis.  

On the demand side, different media may have different mixes of people, with different demographic characteristics.  For financial services advertisers, print users skew older -- and thus have more money, on average, making the potential value to advertisers of each minute of time spent by the average user there more valuable.  Different media may also have different advertising engagement power.  For example, in mobile, in either highly task-focused use cases or in distracted, skimming/ snacking ones, ads may be either invisible or intrusive, diminishing their relative impact (either in terms of direct interaction or view-through stimulation). By contrast, deeper lean-back-style engagement with TV, with more room for an ad to maneuver, might if the ad is good make a bigger impression. I wonder if there's also a reach premium at work.  Advertisers like to find the most efficient medium, but they also need to reach a large enough number of folks to execute campaigns effectively.  TV and print are more reach-oriented media, in general.

On the supply side, different media have different power distributions of the content they can offer, and different barriers to entry that can affect pricing.  On TV and in print, prime ad spots are more limited, so simple supply and demand dynamics drive up prices for the best spots beyond what the equivalency idea might suggest.  

In favor of Meeker's thesis, though representing another short term brake on it, is yet another factor she doesn't speak to directly. This is the relative maturity of the markets and buying processes for different media, and the experience of the participants in those markets.  A more mature, well-trafficked market, with well-understood dynamics, and lots of liquidity (think the ability for agencies and media brokers to resell time in TV's spot markets, for example), will, at the margin, attract and retain dollars, in particular while the true value of different media remain elusive. (This of course is one reason why attribution analysis is so hot, as evidenced by Google's and AOL Platform's recent acquisitions in this space.)  I say in favor, because as mobile ad markets mature over time, this disadvantage will erode.

So for advertisers, agency and media execs, entrepreneurs, and investors looking to play the arbitrage game at the edges of Meeker's observation, the question is, what adjustment factors for demand, supply, and market maturity would you apply this year and next?  It's not an idle question: tons of advertisers' media plans and publishers' business plans ride on these assumptions about how much money is going to come to or go away from them, and Meeker's report is an influential input into these plans in many cases.

A tactical limitation of Meeker's analysis is that while she suggests the overall potential shift in relative allocation of ad dollars (her slide suggests a "~$30B+" digital advertising growth opportunity in the USA alone - up from $20B last year*), she doesn't suggest a timescale and trendline for the pace with which we'll get there. One way to come at this is to look at the last 3-4 annual presentations she's made, and see how the relationships she's observed have changed over time.  Interestingly, in her 2013 report using 2012 data, on page 5, 12% of time is spent on mobile devices, and 3% of ad dollars are going there, for a 4x difference in percentages. In the 2014 report using 2013 data, 20% of time is spent on mobile, and 5% of media dollars are going there -- again, a 4x relationship.  

So, if the equivalency zeitgeist is at work, for the moment it may be stuck in a phone booth. But in the end I'm reminded of the futurist Roy Amara's saying: "We tend to overestimate the effect of a technology in the short term and underestimate its effect in the long term."  Plus let's not forget new technologies (Glass, Occulus Rift, both portable and  large/immersive) that will further jumble relevant media categories in years to come.

(*Emarketer seems to think we'll hit the $30B mobile advertising run rate sometime during 2016-2017.)

 

KPCB Internet trends 2014 from Kleiner Perkins Caufield & Byers

My New Book: #Marketing and #Sales #Analytics

I've written a second book.  It's called Marketing and Sales Analytics: Proven Techniques and Powerful Applications From Industry Leaders (so named for SEO purposes).  Pearson is publishing it (special thanks to Judah Phillips, author of Building A Digital Analytics Organization, for introducing me to Jeanne Glasser at Pearson).  The ebook version will be available on May 23, and the print version will come out June 23.

The book examines how to focus, build, and manage analytics capabilities related to sales and marketing.  It's aimed at C-level executives who are trying to take advantage of these capabilities, as well as other senior executives directly responsible for building and running these groups. It synthesizes interviews with 15 senior executives at a variety of firms across a number of industries, including Abbott, La-Z-Boy, HSN, Condé Nast, Harrah's, Aetna, The Hartford, Bed Bath & Beyond, Paramount Pictures, Wayfair, Harvard University, TIAA-CREF, Talbots, and Lenovo. My friend and former boss Bob Lord, author of Converge was kind enough to write the foreword.

I'm in the final editing stages. More to follow soon, including content, excerpts, nice things people have said about it, slideshows, articles, lunch talk...

Unpacking Healthcare.gov

So healthcare.gov launched, with problems.  I'm trying to understand why, so I can apply some lessons in my professional life.  Here are some ideas.

First, I think it helps to define some levels of the problem.  I can think of four:

1. Strategic / policy level -- what challenges do the goals we set create?  In this case, the objective, basically, is two-fold: first; reduce the costs of late-stage, high-cost uncompensated care by enrolling the people who ultimately use that (middle-aged poor folks and other unfortunates) in health insurance that will get them care earlier and reduce stress / improve outcomes (for them and for society) later; second; reduce the cost of this insurance through exchanges that drive competition.  So, basically, bring a bunch of folks from, in many cases, the wrong side of the Digital Divide, and expose them to a bunch of eligibility- and choice-driven complexity (proof:  need for "Navigators"). Hmm.  (Cue the folks who say that's why we need a simple single-payor model, but the obvious response would be that it simply wasn't politically feasible.  We need to play the cards we're dealt.)

2. Experience level -- In light of that need, let's examine what the government did do for each of the "Attract / Engage / Convert / Retain" phases of a Caveman User Experience.  It did promote ACA -- arguably insufficiently or not creatively enough to distinguish itself from opposing signal levels it should have anticipated (one take here).  But more problematically, from what I can tell, the program skips "Engage" and emphasizes "Convert": Healthcare.gov immediately asks you to "Apply Now" (see screenshot below, where "Apply Now" is prominently  featured over "Learn More", even on the "Learn" tab of the site). This is technically problematic (see #3 below), but also experientially lots to ask for when you don't yet know what's behind the curtain. 

3. Technical level -- Excellent piece in Washington Post by Timothy B. Lee. Basically, the system tries to do an eligibility check (for participation and subsidies) before sending you on to enrollment.  Doing this requires checking a bunch of other government systems.  The flowchart explains very clearly why this could be problematic.  There are some front end problems as well, described in rawest form by some of the chatter on Reddit, but from what I've seen these are more superficial, a function of poor process / time management, and fixable.

4. Organizational level -- Great article here in Slate by David Auerbach. Basically, poor coordination structure and execution by HHS of the front and back ends.

Second, here are some things HHS might do differently:

1. Strategic level: Sounds like some segmentation of the potential user base would have suggested a much greater investment in explanation / education, in advance of registration.  Since any responsible design effort starts with users and use cases, I'm sure they did this.  But what came out the other end doesn't seem to reflect that.  What bureaucratic or political considerations got in the way, and what can be revisited, to improve the result? Or, instead of allowing political hacks to infiltrate and dominate the ranks of engineers trying to design a service that works, why not embed competent technologists, perhaps drawn from the ranks of Chief Digital Officers, into the senior political ranks, to advise them on how to get things right online?

2. Experience level: Perhaps the first couple of levels of experience on healthcare.gov should have been explanatory?  "Here's what to expect, here's how this works..." Maybe video (could have used YouTube!)? Maybe also ask a couple of quick anonymous questions to determine whether the eligibility / subsidy check would be relevant, to spare the load on that engine, before seeing what plans might be available, at what price?  You could always re-ask / confirm that data later once the user's past the shopping /evaluation stage, before formally enrolling them into a plan.  In ecommerce, we don't ask untargeted shoppers to enter discount codes until they're about to check out, right?

Or, why not pre-process and cache the answer to the eligibility question the system currently tries to calculate on the fly?  After all, the government already has all our social security numbers and green card numbers, and our tax returns.  So by the time any of us go to the site, it could have pre-determined the size of any potential subsidy, if any, we'd be eligible for, and it could have used this *estimated* subsidy to calculate a *projected* premium we might pay.  We'd need a little registration / security, maybe "enter your last name and social security number, and if they match we'll tell you your estimated subsidy". (I suppose returning a subsidy answer would confirm for a crook who knows my last name that he had my correct SSN, but maybe we could prevent the brute force querying this requires with CAPTCHA. Security friends, please advise.  Naturally, I'd make sure the pre-chached lookup file stays server-side, and isn't exposed as an array in a client-side Javascript snippet!)

3. I see from viewing the page source they have Google Tag Manager running, so perhaps they also have Google Analytics running too, alongside whatever other things...  Since they've open-sourced the front end code and their content on Github, maybe they could also share what they're learning via GA, so we could evaluate ideas for improving the site in the context of that data?

4. It appears they are using Optimizely to test/ optimize their pages (javascript from page source here).  While the nice pictures with people smiling may be optimal, There's plenty of research that suggests that by pushing much of the links to site content below the fold, and forcing us to scroll to see it, they might be burying the very resources the "experience perspective" I've described suggests they need to highlight.  So maybe this layout is in fact what maximizes the results they're looking for -- pressing the "Apply Now" button -- but maybe that's the wrong question to be asking!

Postscript, November 1:

Food for thought (scroll to bottom).  How does this happen?  Software engineer friends, please weigh in!

 

"How to Engage Consumers in a Multi-Platform World?" See you May 22 @APPNATION bootcamp panel in NYC

Sponsorpay's Global Sales SVP Andy Bibby kindly asked me to join his NYC Internet Week APPNATION panel on Wednesday, May 22 2:15-3p at 82 Mercer.  Hope to see you there, watch this space for a recap of the conversation.

@nathanheller #MOOCs in The New Yorker: You Don't Need A Weatherman

The May 20th 2013 edition of The New Yorker has an article by Vogue writer Nathan Heller on Massive Online Open Courses (MOOCs) titled "Laptop U: Has the future of college moved online?"  The author explores, or at least raises, a number of related questions.  How (well) does the traditional offline learning experience transfer online?  Is the online learning experience more or less effective than the traditional one? (By what standard? For what material?  What is gained and lost?)  What will MOOCs mean for different colleges and universities, and their faculties?  How will the MOOC revolution be funded?  (In particular, what revenue model will emerge?)

Having worked a lot in the sector, for both public and private university clients, developing everything from technology, to online-enabled programs themselves, to analytic approaches, and even on marketing and promotion, the article was a good prompt for me to try to boil out some ways to think about answering these questions.

The article focuses almost exclusively on Harvard and EdX, the 12-school joint venture through which it's pursuing MOOCs.  Obviously this skews the evaluation.  Heller writes:

Education is a curiously alchemical process. Its vicissitudes are hard to isolate.  Why do some students retain what they learned in a course for years, while others lose it through the other ear over their summer breaks?  Is the fact that Bill Gates and Mark Zuckerberg dropped out of Harvard to revolutionize the tech industry a sign that their Harvard educations worked, or that they failed?  The answer matters, because the mechanism by which conveyed knowledge blooms into an education is the standard by which MOOCs will either enrich teaching in this country or deplete it.

For me, the first step to boiling things out is to define what we mean by -- and want from -- an "education".  So, let's try to unpack why people go to college.  In most cases, Reason One is that you need a degree to get any sort of decent job.  Reason Two is to plug into a network of people -- fellow students, alumni, faculty -- that provide you a life-long community.  Of course you need a professional community for that Job thing, but also because in an otherwise anomic society you need an archipelago to seed friendships, companionships, and self-definition (or at least, as scaffolding for your personal brand: as one junior I heard on a recent college visit put it memorably, "Being here is part of the personal narrative I'm building.")  Reason Three -- firmly third -- is to get an "education" in the sense that Heller describes.  (Apropos: check this recording of David Foster Wallace's 2005 commencement address at Kenyon College.) 

Next, this hierarchy of needs then gives us a way to evaluate the prospects for MOOCs.

If organization X can produce graduates demonstrably better qualified (through objective testing, portfolios of work, and experience) to do job Y, at a lower cost, then it will thrive.  If organization X can do this better and cheaper by offering and/or curating/ aggregating MOOCs, then MOOCs will thrive.  If a MOOC can demonstrate an adequately superior result / contribution to the end outcome, and do it inexpensively enough to hold its place in the curriculum, and do it often enough that its edge becomes a self-fulfilling prophecy -- a brand, in other words -- then it will crowd out its competitors, as surely as one plant shuts out the sunlight to another.  Anyone care to bet against Georgia Tech's new $7K Master's in Computer Science?

If a MOOC-mediated social experience can connect you to a Club You Want To Be A Member Of, you will pay for that.  And if a Club That Would Have You As A Member can attract you to its clubhouse with MOOCs, then MOOCs will line the shelves of its bar.  The winning MOOC cocktails will be the ones that best produce the desired social outcomes, with the greatest number of satisfying connections.

Finally, learning is as much about the frame of mind of the student as it is about the quality of the teacher.  If through the MOOC the student is able to choose a better time to engage, and can manage better the pace of the delivery of the subject matter, then the MOOC wins.

Beyond general prospects, as you consider these principles, it becomes clear that it's less about whether MOOCs win, but which ones, for what and for whom, and how.  

The more objective and standardized -- and thus measurable and comparable -- the learning outcome and the standard of achievement, the greater the potential for a MOOC to dominate. My program either works, or it doesn't.  

If a MOOC facilitates the kinds of content exchanges that seed and stimulate offline social gatherings -- pitches to VCs, or mock interviewing, or poetry, or dance routines, or photography, or music, or historical tours, or bird-watching trips, or snowblower-maintenance workshops -- then it has a better chance of fulfilling the longings of its students for connection and belonging.  

And, the more well-developed the surrounding Internet ecosystem (Wikipedia, discussion groups, Quora forums, and beyond) is around a topic, the less I need a Harvard professor, or even a Harvard grad student, to help me, however nuanced and alchemical the experience I miss might otherwise have been.  The prospect of schlepping to class or office hours on a cold, rainy November night has a way of diluting the urge to be there live in case something serendipitous happens.

Understanding how MOOCs win then also becomes a clue to understanding potential revenue models.  

If you can get accredited to offer a degree based in part or whole on MOOCs, you can charge for that degree, and gets students or the government to pay for it (Exhibit A: University of Phoenix).  That's hard, but as a variant of this, you can get hired by an organization, or a syndicate of organizations you organize, to produce tailored degree programs -- think corporate training programs on steroids -- that use MOOCs to filter and train students.  (Think "You, Student, pay for the 101-level stuff; if you pass you get a certificate and an invitation to attend the 201-level stuff that we fund; if you pass that we give you a job.")  

Funding can come directly, or be subsidized by sponsors and advertisers, or both.  

You can try to charge for content: if you produce a MOOC that someone else wants to include in a degree-based program, you can try to license it, in part or in whole.  

You can make money via the service angle, the way self-publishing firms support authors, with a variety of best-practice based production services.  Delivery might be offered via a freemium model -- the content might be free, but access to premium groups, with teaching assistant support, might come at a price.  You can also promote MOOCs -- build awareness, drive distribution, even simply brand  -- for a cut of the action, the way publishers and event promoters do.  

Perhaps in the not-too-distant future we'll get the Academic Upfront, in which Universities front a semester's worth of classes in a MOOC, then pitch the class to sponsors, the way TV networks do today. Or, maybe the retail industry also offers a window into how MOOCs will be monetized.  Today's retail environment is dominated by global brands (think professors as fashion designers) and big-box (plus Amazon) firms that dominate supply chains and distrubution networks.  Together, Brands and Retailers effectively act as filters: we make assumptions that the products on their shelves are safe, effective, reasonably priced, acceptably stylish, well-supported.  In exchange, we'll pay their markup.  This logic sounds a cautionary note for many schools: boutiques can survive as part of or at the edges of the mega-retailers' ecosystems, but small-to-mid-size firms reselling commodities get crushed.

Of course, these are all generic, unoriginal (see Ecclesiastes 1:9) speculations.  Successful revenue models will blend careful attention to segmenting target markets and working back from their needs, resources, and processes (certain models might be friendlier to budgets and purchasing mechanisms than others) with thoughtful in-the-wild testing of the ideas.  Monolithic executions with Neolithic measurement plans ("Gee, the focus group loved it, I can't understand why no one's signing up for the paid version!") are unlikely to get very far.  Instead, be sure to design with testability in mind (make content modular enough to package or offer a la carte, for example).  Maybe even use Kickstarter as a lab for different models!

PS Heller's brilliant sendup of automated essay grading

Postscript:

The MOOC professor perspective, via the Chronicle, March 2013

Book Review: Converge by @rwlord and @rvelez #convergebook

I just finished reading Converge, the new book on integrating technology, creativity, and media by Razorfish CEO Bob Lord and his colleague Ray Velez, the firm’s CTO.  (Full disclosure: I’ve known Bob as a colleague, former boss, and friend for more than twenty years and I’m a proud Razorfish alum from a decade ago.)

Reflecting on the book I’m reminded of the novelist William Gibson’s famous comment in a 2003 Economist interview that “The future’s already here, it’s just not evenly distributed.”  In this case, the near-perfect perch that two already-smart guys have on the Digital Revolution and its impact on global brands has provided them a view of a new reality most of the rest of us perceive only dimly.

So what is this emerging reality?  Somewhere along the line in my business education I heard the phrase, “A brand is a promise.”  Bob and Ray now say, “The brand is a service.”  In virtually all businesses that touch end consumers, and extending well into relevant supply chains, information technology has now made it possible to turn what used to be communication media into elements of the actual fulfillment of whatever product or service the firm provides.  

One example they point to is Tesco’s virtual store format, in which images of stocked store shelves are projected on the wall of, say, a train station, and commuters can snap the QR codes on the yogurt or quarts of milk displayed and have their order delivered to their homes by the time they arrive there: Tesco’s turned the billboard into your cupboard.  Another example they cite is Audi City, the Kinnect-powered configurator experience through which you can explore and order the Audi of your dreams.  As the authors say, “marketing is commerce, and commerce is marketing.”

But Bob and Ray don’t just describe, they also prescribe.  I’ll leave you to read the specific suggestions, which aren’t necessarily new.  What is fresh here is the compelling case they make for them; for example, their point-by-point case for leveraging the public cloud is very persuasive, even for the most security-conscious CIO.  Also useful is their summary of the Agile method, and of how they’ve applied it for their clients.

Looking more deeply, the book isn’t just another surf on the zeitgeist, but is theoretically well-grounded.  At one point early on, they say, “The villain in this book is the silo.”  On reading this (nicely turned phrase), I was reminded of the “experience curve” business strategy concept I learned at Bain & Company many years ago.  The experience curve, based on the idea that the more you make and sell of something, the better you (should) get at it, describes a fairly predictable mathematical relationship between experience and cost, and therefore between relative market share and profit margins.  One of the ways you can maximize experience is through functional specialization, which of course has the side effect of encouraging the development of organizational silos.  A hidden assumption in this strategy is that customer needs and associated attention spans stay pinned down and stable long enough to achieve experience-driven profitable ways to serve them.  But in today’s super-fragmented, hyper-connected, kaleidoscopic marketplace, this assumption breaks down, and the way to compete shifts from capturing experience through specialization, to generating experience “at-bats” through speedy iteration, innovation, and execution.  And this latter competitive mode relies more on the kind of cross-disciplinary integration that Bob and Ray describe so richly.

The book is a quick, engaging read, full of good stories drawn from their extensive experiences with blue-chip brands and interesting upstarts, and with some useful bits of historical analysis that frame their arguments well (in particular, I Iiked their exposition of the television upfront).  But maybe the best thing I can say about it is that it encouraged me to push harder and faster to stay in front of the future that’s already here.  Or, as a friend says, “We gotta get with the ‘90’s, they’re almost over!”

(See this review and buy the book on Amazon.com)