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!”
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.
Facebook's Sponsored Stories feature is one of the ad targeting horses the firm's counting on to pull it out of its current valuation morass (read this, via @bussgang).
Sponsored Stories is a virality-enhancing mechanism (no jokes please, that was an "a" not an "i") that allows Facebook advertisers to increase the reach of Facebook users' interactions with the advertisers' brands on Facebook (Likes, Check-ins, etc.). It does this by increasing the number of a user's Facebook friends who see such engagements with the advertisers' brands beyond the limited number who would, under normal application of the Facebook news feed algorithm, see those endorsements.
Many users are outraged that this unholy Son-Of-Beacon feature violates their privacy, to the point that they sue-and-settle (or try to, oops).
What they are missing perhaps is the opportunity to "surf" an advertiser's Sponsored Stories investment to amplify their own self-promotion or mere narcissism.
Consider the following simple example. Starbucks is / has been using this ad program. Let's say I go to Starbucks and "check in" on Facebook. Juiced by Sponsored Stories (within the additional impressions Starbucks has paid for), all of my Facebook friends browsing their news feeds will see I've checked in at Starbucks (and presumably feel all verklempt about a brand that could attract such a valued friend).
Now, what if I, savvy small business person, comment in my check in that I'm "at Starbucks, discussing my <link>NEW BOOK</link> with friends!" I've pulled off the social media equivalent of pasting my bumper sticker on Starbucks' billboard.
I need to look more closely into this, but as I understand it, the Sponsored Stories feature can't today prevent users from including negative feedback in their brand engagements, where such flexibility is provided for. So if they can't handle the negative yet, it may still be that they can't prevent more general forms of off-brand messaging.
I'm sure others have considered this and other possibilities. Comments very welcome! Meanwhile, I'm off to Starbucks to discuss my upcoming NEW BOOK.
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!
Paul Simon wrote, "Every generation throws a hero at the pop charts." Now it's Marissa Mayer's turn to try to make Yahoo!'s chart pop. This will be hard because few tech companies are able to sustain value creation much past their IPOs.
What strategic path for Yahoo! satisfies the following important requirements?
Yahoo!'s company profile is a little buzzwordy but offers a potential point of departure. What Yahoo! says:
"Our vision is to deliver your world, your way. We do that by using technology, insights, and intuition to create deeply personal digital experiences that keep more than half a billion people connected to what matters the most to them – across devices, on every continent, in more than 30 languages. And we connect advertisers to the consumers who matter to them most – the ones who will build their businesses – through our unique combination of Science + Art + Scale."
What Cesar infers:
Yahoo! is a filter.
Here are some big things the Internet helps us do:
Every one of these functions has an 800 lb. gorilla, and a few aspirants, attached to it:
Um, filter... Filter. There's a flood of information out there. Who's doing a great job of filtering it for me? Google alerts? Useful but very crude. Twitter? I browse my followings for nuggets, but sometimes these are hard to parse from the droppings. Facebook? Sorry friends, but my inner sociopath complains it has to work too hard to sift the news I can use from the River of Life.
Filtering is still a tough, unsolved problem, arguably the problem of the age (or at least it was last year when I said so). The best tool I've found for helping me build filters is Yahoo! Pipes. (Example)
As far as I can tell, Pipes has remained this slightly wonky tool in Yahoo's bazaar suite of products. Nerds like me get a lot of leverage from the service, but it's a bit hard to explain the concept, and the semi-programmatic interface is powerful but definitely not for the general public.
Now, what if Yahoo! were to embrace filtering as its core proposition, and build off the Pipes idea and experience under the guidance of Google's own UI guru -- the very same Ms. Mayer, hopefully applying the lessons of iGoogle's rise and fall -- to make it possible for its users to filter their worlds more effectively? If you think about it, there are various services out there that tackle individual aspects of the filtering challenge: professional (e.g. NY Times, Vogue, Car and Driver), social (Facebook, subReddits), tribal (online communities extending from often offline affinities), algorithmic (Amazon-style collaborative filtering), sponsored (e.g., coupon sites). No one is doing a good job of pulling these all together and allowing me to tailor their spews to my life. Right now it's up to me to follow Gina Trapani's Lifehacker suggestion, which is to use Pipes.
OK so let's review:
Well, let's look at this a bit. I'd argue that a good filter is effectively a "passive search engine". Basically through the filters people construct -- effectively "stored searches" -- they tell you what it is they are really interested in, and in what context and time they want it. With cookie-based targeting under pressure on multiple fronts, advertisers will be looking for impression inventories that provide search-like value propositions without the tracking headaches. Whoever can do this well could make major bank from advertisers looking for an alternative to the online ad biz Hydra (aka Google, Facebook, Apple, plus assorted minor others).
Savvy advertisers and publishers will pooh-pooh the idea that individual Pipemakers would be numerous enough or consistent enough on their own to provide the reach that is the reason Yahoo! is still in business. But I think there's lots of ways around this. For one, there's already plenty of precedent at other media companies for suggesting proto-Pipes -- usually called "channels", Yahoo! calls them "sites" (example), and they have RSS feeds. Portals like Yahoo!, major media like the NYT, and universities like Harvard suggest categories, offer pre-packaged RSS feeds, and even give you the ability to roll your own feed out of their content. The problem is that it's still marketed as RSS, which even in this day and age is still a bit beyond for most folks. But if you find a more user-friendly way to "clone and extend" suggested Pipes, friends' Pipes, sponsored Pipes, etc., you've got a start.
Check? Lots of hand-waving, I know. But what's true is that Yahoo! has suffered from a loss of a clear identity. And the path to re-growing its value starts with fixing that problem.
Good luck Marissa!
In May 2007, Microsoft paid $6 billion to buy aQuantive. Today, only five years later, they wrote off the whole investment. Since I wrote about this a lot five years ago (here, here and here), it prompted me to think about what happened, and what I might learn. Here are a few observations:
1. 2006 / 2007 was a frothy time in the ad network market, both for ads and for the firms themselves, reflecting the economy in general.
2. Microsoft came late to the party, chasing aQuantive (desperately) after Google had taken DoubleClick off the table.
3. So, Microsoft paid a 100% premium to aQuantive's market cap to get the firm.
4. Here's the way Microsoft might have been seeing things at the time:
a. "Thick client OS and productivity applications business in decline -- the future is in the cloud."
b. "Cloud business model uncertain, but certainly lower price point than our desktop franchise; must explore all options; maybe an ad-supported version of a cloud-based productivity suite?"
c. "We have MSN. Why should someone else sit between us and our MSN advertisers and collect a toll on our non-premium, non-direct inventory? In fact, if we had an ad network, we could sit between advertisers and other publishers and collect a toll!"
5. Here's the way things played out:
a. The economy crashed a year later.
b. When budgets came back, they went first to the most accountable digital ad spend: search.
c. Microsoft had a new horse in that race: Bing (launched June 2009). Discretionary investment naturally flowed there.
d. Meanwhile, "display" evolved: video display, social display (aka Facebook), mobile display (Dadgurnit! Google bought AdMob, Apple has iAd! Scraps again for the rest of us...). (Good recent eMarketer presentation on trends here.)
e. Whatever's left of "traditional" display: Google / DoubleClick, as the category leader, eats first.
f. Specialized players do continue to grow in "traditional" display, through better targeting technologies (BT) and through facilitating more efficient buys (for example, DataXu, which I wrote about here). But to grow you have to invest and innovate, and at Microsoft, by this point, as noted above, the money was going elsewhere.
g. So, if you're Microsoft, and you're getting left behind, what do you do? Take 'em with you! "Do not track by default" in IE 10 as of June 2012. That's old school medieval, dressed up in hipster specs and a porkpie hat. Steve Ballmer may be struggling strategically, but he's still as brutal as ever.
a. $6 Big Ones is only 2% of MSFT's market cap. aQuantive may have come at a 2x premium, but it was worth the hedge. The rich are different from you and me.
b. The bigger issue though is how does MSFT steal a march on Google, Apple, Facebook? Hmmm. video's hot. Still bandwidth constrained, but that'll get better. And there's interactive video. Folks will eventually spend lots of time there, and ads will follow them. Google's got Hangouts, Facebook's got Facetime, Apple's got iChat... and now MSFT has Skype, for $8B. Hmm.
a. Some of the smartest business guys I worked with at Bain in the late 90's (including Torrence Boone and Jason Trevisan) ended up at aQuantive and helped to build it into the success it was. An interesting alumni diaspora to follow.
b. Some of the smartest folks I worked with at Razorfish in the early 2000's (including Bob Lord) ended up at aQuantive. The best part is that Microsoft may have gotten more value from buying and selling Razorfish (to Publicis) than from buying and writing off the rest of aQuantive. Sweet, that.
c. Why not open-source Atlas?
Sorry for the buzzwordy title of this post, but hopefully you'll agree that sometimes they can be useful to communicating an important Zeitgeist.
I'm working with one of our clients right now to develop a new, advanced business intelligence capability that uses state-of-the art in-memory data visualization tools like Tableau and Spotfire that will ultimately connect multiple data sets to answer a range of important questions. I've also been involved recently in a major analysis of advertising effectiveness that included a number of data sources that were either external to the organization, or non-traditional, or both. In both cases, these efforts are likely to evolve toward predictive models of behavior to help prioritize efforts and allocate scarce resources.
Simultaneously, today's NYT carried an article about Clear Story, a Silicon Valley startup that aggregates APIs to public data sources about folks, and provides a highly simplified interface to those APIs for analysts and business execs. I haven't yet tried their service, but I'll save that for a separate post. The point here is that the emergence of services like this represent an important step in the evolution of Web 2.0 -- call it Web 2.2 -- that's very relevant for marketing analytics in enterprise contexts.
So, what's significant about these experiences?
Readers of Ralph Kimball's classic Data Warehouse Toolkit will appreciate both the wisdom of his advice, but also today, how the context for it has changed. Kimball is absolutely an advocate for starting with a clear idea of the questions you'd like to answer and for making pragmatic choices about how to organize information to answer them. However, the major editions of the book were written in a time when three things were true:
Together, these things made for business intelligence / data warehouse / data management efforts that were longer, and a bit more "waterfall" and episodic in execution. However, over the past decade, many have critiqued such efforts for high failure rates, mostly in which they collapse of their own weight: too much investment, too much complexity, too few results. Call this Planned Data Modeling.
Now back to the first experience I described above. We're using the tools I mentioned to simultaneously hunt for valuable insights that will help pay the freight of the effort, define useful interfaces for users to keep using, and through these efforts, also determine the optimal data structures we need underneath to scale from the few million rows in one big flat file we've started with to something that will no doubt be larger, more multi-faceted, and thus more complex. In particular, we're using the ability of these tools to calculate synthetic variables on the fly out of the raw data to point the way toward summaries and indeces we'll eventually have to develop in our data repository. This will improve the likelihood that the way we architect that will directly support real reporting and analysis requirements, prioritized based on actual usage in initial pilots, rather than speculative requirements obtained through more conventional means. Call this Organic Data Modeling.
Further, the work we've done anticipates that we will be weaving together a number of new sources of data, many of them externally provided, and that we'll likely swap sources in and out as we find that some are more useful than others. It occurred to me that this large, heterogenous, and dynamic collection of data sources would have characteristics sufficiently different in terms of their analytic and administrative implications that a different name altogether might be in order for the sum of the pieces. Hence, the Extrabase.
These terms are not meant to cover up a cop-out. In other words, some might say that mashing up a bunch of files in an in-memory visualization tool could reflect and further contribute to a lack of intellectual discipline and wherewithal to get it right. In our case, we're hedging that risk, by having the data modelers responsible for figuring out the optimal data repository structure work extremely closely with the "front-end" analysts so that as potential data structure implications flow out of the rubber-meets-the-road analysis, we're able to sift them and decide which should stick and which we can ignore.
But, as they say sometimes in software, "that's a feature, not a bug." Meaning, mashing up files in these tools and seeing what's useful is a way of paying for and disciplining the back end data management process more rigorously, so that what gets built is based on what folks actually need, and gets delivered faster to boot.
Hi folks, I need a favor. I need 200 subscribers to this blog via Google Currents to get Octavianworld listed in the Currents catalog. If you're reading this on an iPhone, iPad, or Android device, follow this link:
If you are looking at this on a PC, just snap this QR code with your iPhone or Android phone after getting the Currents app.
Here's what I look like on Currents:
What is Currents? If you've used Flipboard or Zite, this is Google's entry. If you've used an RSS reader, but haven't used any of these yet, you're probably a nerdy holdout (it takes one to know one). If you've used none of these, and have no idea what I'm talking about, apps like these help folks like me (and big media firms too) publish online magazines that make screen-scrollable content page-flippable and still-clickable. Yet another distribution channel to help reach new audiences.
So Facebook's finally filed to do an IPO. Should you like? A year ago, I posted about how a $50 billion valuation might make sense. Today, the target value floated by folks is ~$85 billion. One way to look at it then, and now, is to ask whether each Facebook user (500 million of them last January, 845 million of them today) has a net present value to Facebook's shareholders of $100. This ignores future users, but then also excludes hoped-for appreciation in the firm's value.
One way to get your arms around a $100/ user NPV is to simply discount a perpetuity: divide an annual $10 per user cash flow (assumed = to profit here, for simplicity) by a 10% discount rate. Granted, this is more of a bond-than-growth-stock approach to valuation, but Facebook's already pretty big, and Google's making up ground, plus under these economic conditions it's probably OK to be a bit conservative.
Facebook's filing indicated they earned $1 billion in profit on just under $4 billion in revenue in 2011. This means they're running at about $1.20 per user in profit. To bridge this gap between $1.20 and $10, you have to believe there's lots more per-user profit still to come.
Today, 85% of Facebook's revenues come from advertising. So Facebook needs to make each of us users more valuable to its advertisers, perhaps 4x so to bridge half the gap. That would mean getting 4x better at targeting us and/or influencing our behavior on advertisers' behalf. What would that look like?
The other half of the gap gets bridged by a large increase in the share of Facebook's revenues that comes from its cut of what app builders running on the FB platform, like Zynga, get from you. At Facebook's current margin of 25%, $5 in incremental profit would require $20 in incremental net revenue. Assume Facebook's cut from its third party app providers is 50%, and that means an incremental $40/year each user would have to kick in at retail. Are each of us good for another $40/year to Facebook? If so, where would it come from?
My guess is that Facebook will further cultivate, through third-party developers most likely, some combination of paid content and productivity app subscription businesses. It's possible that doing so would not only raise revenues directly but also have a synergistic positive effect on ad rates the firm can command, with more of our time and activity under the firm's gaze.