Octavianworld: Cesar Brea's Weblog, continued.

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 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)

My New Book: Pragmalytics

I've written a short book.  It's called "Pragmalytics: Practical Approaches to Marketing Analytics in the Digital Age".  It's a collection and synthesis of some of the things I've learned over the last several years about how to take better advantage of data (Big and little) to make better marketing decisions, and to get better returns on your investments in this area.  

The main point of the book is the need for orchestration.  I see too much of the focus today on "If we build It (the Big Data Machine, with some data scientist high priests to look after it), good things will happen."  My experience has been that you need to get "ecosystemic conditions" in balance to get value.  You need to agree on where to focus.  You need to get access to the data.  You need to have the operational flexibility to act on any insights.  And, you need to cultivate an "analytic marketer" mindset in your broader marketing team that blends perspectives, rather than cultivating an elite but blinkered cadre of "marketing analysts".  Over the next few weeks, I'll further outline some of what's in the book in a few posts here on my blog.

I'm really grateful to the folks who were kind enough to help me with the book.  The list includes: Mike Bernstein, Tip Clifton, Susan Ellerin, Ann Hackett, Perry Hewitt, Jeff Hupe, Ben Kline, Janelle Leonard, Sam Mawn-Mahlau, Bob Neuhaus, Judah Phillips, Trish Gorman Clifford, Rob Schmults, Michelle Seaton, Tad Staley, and my business partner, Jamie Schein.  As I said in the book, if you like any of it, they get credit for salvaging it.  The rest -- including several bits that even on the thousandth reading still aren't as clear as they should be, plus a couple of typos I need to fix -- are entirely my responsibility.

I'm also grateful to the wonderful firms and colleagues and clients I've had the good fortune to work for and with.  I've named the ones I can, but in general have erred on the side of respecting their privacy and confidentiality where the work isn't otherwise in the public domain.  To all of them: Thank You!

This field is evolving quickly in some ways, but there are also some timeless principles that apply to it.  So, there are bits of the book that I'm sure won't age well (including some that are already obsolete), but others that I hope might.  While I'm not one of those coveted Data Scientists by training, I'm deep into this stuff on a regular basis at whatever level is necessary to get a positive return from the effort.  So if you're looking for a book on selecting an appropriate regression technique, or tuning Hadoop, you won't find that here, but if you're looking for a book about how to keep all the balls in the air (and in your brain), it might be useful to you.  It's purposefully short -- about half the length of a typical business book.  My mental model was to make it about as thick as "The Elements of Style", since that's something I use a lot (though you probably won't think so!).  Plus, it's organized so you can jump in anywhere and snack as you wish, since this stuff can be toxic in large doses.

In writing it amidst all the Big Data craziness, I was reminded of Gandhi's saying (paraphrased) "First they ignore you... then they fight you, then you win."  Having been in the world of marketing analytics now for a while, it seems appropriate to say that "First they ignore you, then they hype you, then you blend in."   We're now in the "hype" phase.  Not a day goes by without some big piece in the media about Big Data or Data Scientists (who now have hit the highly symbolic "$300k" salary benchmark -- and last time we saw it, in the middle part of the last decade in the online ad sales world, was a sell signal  BTW).  "Pragmalytics" is more about the "blend in" phase, when all this "cool" stuff is more a part of the furniture that needs to work in harmony with the rest of the operation to make a difference.

"Pragmalytics" is available via Amazon (among other places).  If you read it please do me a favor and rate and review it, or even better, please get in touch if you have questions or suggestions for improving it.  FWIW, any earnings from it will go to Nashoba Learning Group (a school for kids with autism and related disorders).

Where it makes sense, I'd be very pleased to come talk to you and your colleagues about the ideas in the book and how to apply them, and possibly to explore working together.  Also, in a triumph of Hope over Experience, my next book (starting now) will be a collection and synthesis of interviews with other senior marketing executives trying to put Big Data to work.  So if you would be interested in sharing some experiences, or know folks who would, I'd love to talk.

About the cover:  it's meant to convey the harmonious convergence of "Mars", "Venus", and "Earth" mindsets: that is, a blend of analytic acuity, creativity and communication ability, and practicality and results-orientation that we try to bring to our work. Fellow nerds will appreciate that it's a Cumulative Distribution Function where the exponent is, in a nod to an example in the book, 1.007.

 

 

A "Common Requirements Framework" for Campaign Management Systems and Marketing Automation

In our "marketing analytics agency" model, as distinguished from a more traditional consulting one, we measure success not just by the quality of the insights and opportunities we can help clients to find, but on their ability to act on the ideas and get value for their investments.  Sometimes this means we simultaneously work both ends to an acceptable middle: even as we torture data and research for bright ideas, we help to define and influence the evolution of a marketing platform to be more capable. 

This raises the question, "What's a marketing platform, and a good roadmap for making it more capable?"  Lots of vendors, including big ones like IBM, are now investing in answering these questions, especially as they try to reach beyond IT to sell directly to the CMO. These vendors provide myriad marketing materials to describe both the landscape and their products, which variously are described as "campaign management systems" or even more gloriously as "marketing automation solutions".  The proliferation of solutions is so mind-blowing that analyst firms build whole practices making sense of the category.  Here's a recent chart from Terence Kawaja at LUMA Partners (via Scott Brinker's blog) that illustrates the point beautifully:

 

MARKETING TECHNOLOGY LUMAscape from Terence Kawaja

 

Yet even with this guidance, organizations struggle to get relevant stakeholders on the same page about what's needed and how to proceed. My own experience has been that this is because they're missing a simple "Common Requirements Framework" that everyone can share as a point of departure for the conversation.  Here's one I've found useful.

Basically marketing is about targeting the right customers and getting them the right content (product information, pricing, and all the before-during-and-after trimmings) through the right channels at the right time.  So, a marketing automation solution, well, automates this.  More specifically, since there are lots of homegrown hacks and point solutions for different pieces of this, what's really getting automated is the manual conversion and shuffling of files from one system to the next, aka the integration of it all.  Some of these solutions also let you run analysis and tests out of the same platform (or partnered components).

Each of these functions has increasing levels of sophistication I've characterized, as of this writing, into "basic", "threshold", and "advanced".  For simple roadmapping / prioritization purposes, you might also call these "now", "next", and "later".

Targeting

The simplest form of targeting uses a single data source, past experience at the cash register, to decide whom to go back to, on the idea that you build a business inside out from your best, most loyal customers.  Cataloguers have a fancy term for this, "RFM", which stands for "Recency, Frequency, and Monetary Value", which grades customers, typically into deciles, according to... how recently, how frequenty, and how much they've bought from you.  Folks who score high get solicited more intensively (for example, more catalog drops).  By looking back at a customer's past RFM-defined marginal value to you (e.g., gross margin you earned from stuff you sold her), you can make a decision about how much to spend marketing to her.  

One step up, you add demographic and behavioral information about customers and prospects to refine and expand your lists of folks to target.  Demographically, for example, you might say, "Hey, my best customers all seem to come from Greenwich, CT.  Maybe I should target other folks who live there."  You might add a few other dimensions to that, like age and gender. Or you might buy synthetic, "psychographic" definitions from data vendors who roll a variety of demographic markers into inferred attitudes.  Behaviorally, you might say "Let's retarget folks who walk into our store, or who put stuff into our online shopping cart but don't check out."  These are conceptually straightforward things to do, but are logistically harder, because now you have to integrate external and internal data sources, comply with privacy policies, etc.

In the third level, you begin to formalize the models implicit in these prior two steps, and build lists of folks to target based on their predicted propensity to buy (lots) from you.  So for example, you might say, "Folks who bought this much of this product this frequently, this recently who live in Greenwich and who visited our web site last week have this probability of buying this much from me, so therefore I can afford to target them with a marketing program that costs $x per person."  That's "predictive modelling".

Some folks evaluate the sophistication of a targeting capability by how fine-grained the target segments get, or by how close to 1-1 personalization you can get.  In my experience, there's often diminishing returns to this, often because the firm can't always practically execute differentiated experiences even if the marginal value of a personalized experience warrants it.  This isn't universally the case of course: promotional offers and similar experience variables (e.g., credit limits) are easier to vary than, say, a hotel lobby.  

Content

Again, a simple progression here, for me defined by the complexity of the content you can provide ("plain", "rich", "interactive") and by the flexibility and precision ("none", "pre-defined options", "custom options") with which you can target it through any given channel or combination of channels.

Another dimension to consider here is the complexity of the organizations and processes necessary to produce this content.  For example, in highly regulated environments like health care or financial services, you may need multiple approvals before you can publish something.  And the more folks involved, the more sophisticated and valuable the coordination tools, ranging from central repositories for templates, version control systems, alerts, and even joint editing.  Beware though simply paving cowpaths -- be sure you need all that content variety and process complexity before enabling it technologically, or it will simply expand to fit what the technology permits (the same way computer operating systems bloat as processors get more powerful).

Channels

The big dimension here is the number of channels you can string together for an integrated experience.  So for example, in a simple case you've got one channel, say email, to work with.  In a more sophisticated system, you can say, "When people who look like this come to our website, retarget them with ads in the display ad network we use." (Google just integrated Google Analytics with Google Display Network to do just this, for example, an ingenious move that further illustrates why they lead the pack in the display ad world.)  Pushing it even further, you could also say, "In addition to re-targeting web site visitors who do X, out in our display network, let's also send them an email / postcard combination, with connections to a landing page or phone center."

Analysis and Testing

In addition to execution of campaigns and programs, a marketing solution might also suport exploration  of what campaigns and programs, or components thereof, might work best.  This happens in a couple of ways.  You can examine past behavior of customers and prospects to look for trends and build models that explain how changes and saliencies along one or more dimensions might have been associated with buying.  Also, you can define and execute A/B and multi-variate tests (with control groups) for targeting, content, and channel choices.  

Again, the question here is not just about how much data flexibility and algorithmic power you have to work with within the system, but how many integration hoops you have to go through to move from exploration to execution.  Obviously you won't want to run exploration and execution off the same physical data store, or even the same logical model, but it shouldn't take a major IT initiative to flip the right operational switches when you have an insight you'd like to try, or scale.

Concretely, the requirement you're evaluating here is best summarized by a couple of questions.  First, "Show me how I can track and evaluate differential response in the marketing campaigns and programs I execute through your proposed solution," and then, "Show me how I can define and test targeting, content, and channel variants of the base campaigns or programs, and then work the winners into a dominant share of our mix."

A Summary Picture

Here's a simple table that tries to bundle all of this up.  Notice that it focuses more on function than features and capabilities instead of components.  

 

 

What's Right For You?

The important thing to remember is that these functions and capabilities are means, not ends.  To figure out what you need, you should reflect first on how any particular combination of capabilities would fit into your marketing organization's "vector and momentum".  How is your marketing performance trending?  How does it compare with competitors'?  In what parts -- targets, content, channels -- is it better or worse? What have you deployed recently and learned through its operation? What kind of track record have you established in terms of successful deployment and leverage from your efforts?  

If your answers are more like "I don't know" and "Um, not a great one" then you might be better off signing onto a mostly-integrated, cloud-based (so you don't compound business value uncertainty with IT risk), good-enough-across-most-things solution for a few years until you sort out -- affordably (read, rent, don't buy) -- what works for you, and what capability you need to go deep on. If, on the other hand, you're confident you have a good grip on where your opportunities are and you've got momentum with and confidence in your team, you might add best of breed capabilities at the margins of a more general "logical model" this proposed framework provides.  What's generally risky is to start with an under-performing operation built on spaghetti and plan for a smooth multi-year transition to a fully-integrated on-premise option.  That just puts too many moving parts into play, with too high an up-front, bet-on-the-come investment.

Again, remember that the point of a "Common Requirements Framework" isn't to serve as an exhaustive checklist for evaluating vendors.  It's best used as a simple model you can carry around in your head and share with others, so that when you do dive deep into requirements, you don't lose the forest for the trees, in a category that's become quite a jungle.  Got a better model, or suggestions for this one?  Let me know!

Wanted: Marketing Analytics Director, Global Financial Services Firm (Mid-Atlantic) # Analytics

I've been working with a global financial services firm to develop its marketing analytics / intelligence capability, and we're now building a highly capable team to further extend and sustain the results and lessons so far.  This includes a Marketing Analytics Director to lead a strong team doing advanced data mining and predictive modeling to support high-impact opportunities in various areas of the firm.  Here's the job description on LinkedIn.  If you are currently working at a large marketer, major analytics consulting firm, or advertising agency, and have significant experience analyzing, communicating, and implementing sophisticated multi-channel marketing programs, and are up for the challenge of leading a new team in this area for a world-class firm in a great city, please get in touch!

#Google Search and The Limits of #Location

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

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

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

Start with the search query:

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

Then graft on a little string to depersonalize the query:

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

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

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

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

 

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

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

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

First stop: Web Search Help.  The critical part:

Q. Can I turn off location-based customization?

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

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

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

 Exactly!  So I tried to game the system:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

"Is Every Query Local Now?" 

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

Heavy.

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

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

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

 

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