Octavianworld

Congratulations @marissamayer on your new #Yahoo gig. Now what? Some ideas

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?

• Solves a keenly felt customer / user / audience / human problem?
• Fits within but doesn't totally overlap what other competitors provide?
• Builds off things Yahoo! has / does well?
• Fits Ms. Mayer's experiences, so she's playing from a position of strength and confidence?
• As a consequence of all this, will bring advertisers back at premium prices?

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:

• Find
• Connect
• Share
• Shop
• Work
• Learn
• Argue
• Relax
• Filter

Every one of these functions has an 800 lb. gorilla, and a few aspirants, attached to it:

• Find -- Google
• Connect -- Facebook, LinkedIn
• Share -- Facebook, Twitter, Yahoo!/Flickr (well, for the moment...)
• Shop -- Amazon, eBay
• Work -- Microsoft, Google, GitHub
• Learn -- Wikipedia, Khan Academy
• Argue -- Wordpress, Typepad, [insert major MSM digital presence here]
• Relax -- Netflix, Hulu, Pandora, Spotify
• Filter -- ...

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:

• Valuable unsolved problem for customers / users: check.
• Fragmented, undominated competitive space: check.
• Yahoo! has credibly assets / experience: check.
• Marissa Mayer plays from position of strength and experience: check.
• Advertisers willing to pay premium prices, in droves: ...

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!

 

 

 

Wonderfully #Pragmalytic Multi-Channel Attribution Advice From @avinash via @visualiq

Via my friends at VisualIQ, this wonderful post from Avinash Kaushik on doing multi-channel attribution and mix optimization in the real world.  Plus a really rich set of conversations in the comments. My summary of his advice (reassuringly consistent with my own experiences with "pragmalytic" approaches):

• Start by solving for specific attribution / optimization use cases you face in the real world, not the more general form of the challenge.  He names three dominant ones he sees: "O2S -- Online to Store", "AMS -- Across Multiple Screens", and "ADC -- Across Digital Channels"
• Use multiple analytic techniques to compensate for imperfect data that any one technique might rely on.  For example, if there are holes or quality problems with your data, supplement it with controlled tests
• Don't cop out, but accept that there are no perfect answers, just better ones, and that you should bias toward acting on acceptably imperfect information and learning and improving based on actual experience

Absolutely terrific stuff here, gets even better on the third and subsequent reads.

#Microsoft Writes Off #aQuantive. What Can We Learn?

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. 

6. Perspective

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.

7. Postscripts:

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?

Organic Data Modeling in the Age of the Extrabase #analytics

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:

• You needed to organize information more thoughtfully up front, because computing resources to compensate for poor initial organization were less capable and more expensive
• The number of data sources you could integrate were far more limited, allowing you to be more definitive up front about the data structures you defined to answer your target questions
• The questions themselves, or the range of possible answers to them, were more limited and less dynamic, because the market context was so as well

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.

Question for Search Marketing / Digital Content Experts #SEO #SEM #CPC @seomoz

As I understand Google SEM CPC pricing, Google considers not only how much the marketer is willing to pay, but also the quality of results, in determining what price puts you in what slot.

So, to a certain degree, your SEO efforts influence your SEM cost.

SEO these days is about:

• relevant content
• relevant links to that content from authoritative sites
• social relevance of the content
• content freshness
• other stuff, like site speed

These things have readily measurable attributes:

• organic search results position
• PageRank
• followers / likes / retweets etc.
• feed update frequency
• response time

So logically, one question you might as a search marketer ask yourself is whether you're globally optimized across your SEO and SEM investments.  Should your next dollar go to pay for a click, or to develop and promote and present content that will rise higher directly, and indirectly lower your SEM CPC?  This is not an academic question, as collectively SEM and SEO are today big, front-and-center elements of many firms' marketing efforts.

One way to answer this question is to build a model that, for a range of keywords, predicts CPC (as a dependent variable) from different values of the measurable attributes of SEO mentioned above, and perhaps others.

I'm curious about whether anyone's seen or done analysis like this?  My quick search on "Optimizing SEO vs. SEM" yielded this interesting SEOMoz result": http://www.seomoz.org/blog/the-disconnect-in-ppc-vs-seo-spending .  I think this great article misses however that most of the leverage in SEO comes not from the narrow definition of the term but from developing and promoting great content.

#SXSW Trip Report Part 2: Being There

(See here for Part 1)

Here's one summary of the experience that's making the rounds:

 

 

I wasn't able to be there all that long, but my impression was different.  Men of all colors (especially if you count tattoos), and lots more women (many tattooed also, and extensively).   I had a chance to talk with Doc Searls (I'm a huge Cluetrain fan) briefly at the Digital Harvard reception at The Parish; he suggested (my words) the increased ratio of women is a good barometer for the evolution of the festival from narcissistic nerdiness toward more sensible substance.  Nonetheless, on the surface, it does remain a sweaty mosh pit of digital love and frenzied networking.  Picture Dumbo on spring break on 6th and San Jacinto.  With light sabers:

 

 

Sight that will haunt my dreams for a while: VC-looking guy, blazer and dress shirt, in a pedicab piloted by skinny grungy student (?) Dude, learn Linux, and your next tip from The Man at SXSW might just be a term sheet.

So whom did I meet, and what did I learn:

I had a great time listening to PRX.org's John Barth.  The Public Radio Exchange aggregates independent content suitable for radio (think The Moth), adds valuable services like consistent content metadata and rights management, and then acts as a distribution hub for stations that want to use it.  We talked about how they're planning to analyze listenership patterns with that metadata and other stuff (maybe gleaning audience demographics via Quantcast) for shaping content and targeting listeners.  He related for example that stations seem to prefer either 1 hour programs they can use to fill standard-sized holes, or two- to seven- minute segments they can weave into pre-existing programs.  Documentary-style shows that weave music and informed commentary together are especially popular.  We explored whether production templates ("structured collaboration": think "Mad Libs" for digital media) might make sense.  Maybe later.

Paul Payack explained his Global Language Monitor service to me, and we explored its potential application as a complement if not a replacement for episodic brand trackers.  Think of it as a more sophisticated and source-ecumenical version of Google Insights for Search.

Kara Oehler's presentation on her Mapping Main Street project was great, and it made me want to try her Zeega.org service (a Harvard metaLAB project) as soon as it's available, to see how close I can get to replicating The Yellow Submarine for my son, with other family members spliced in for The Beatles.  Add it to my list of other cool projects I like, such as mrpicassohead.

Peter Boyce and Zach Hamed from Hack Harvard, nice to meet you. Here's a book that grew out of the class at MIT I mentioned -- maybe you guys could cobble together an O'Reilly deal out of your work!

Finally,  congrats to Perry Hewitt (here with Anne Cushing) and all her Harvard colleagues on a great evening!

 

 

 

#SXSW Trip Report Part 1: The Journey

Arrive Houston late.  Some lady steers my Hertz car out of space 125, toots cheerfully, and is off. (Wonder how she'll persuade the guard: "Yes, I am Cesar Brea...")  I arrange a replacement, and promptly get lost somewhere near Hobby.  

Later:  looking up from the hotel lobby floor is like looking down a shaft on the Death Star.  Thirty stories of beige-brown cantilevered soul-crushing sameness.  Can't sleep.  Accept insomnia, opt for double-header dystopia: the HBO Julianne Moore / Ed Harris / Woody Harrelson docu-drama Game Change about Sarah Palin, then Repo Men.

Morning.  I-290 West, toward Austin; it's  moonsooning.  Vaguely Quixotic: a "Dry-Force Water Removal" van blasts past me doing seventy.  Cattle line up near the road, backsides to the storm, in the bovine manner. 

Who says frontier towns are dead?  They're just spread out more, reflecting today's faster horses.  No horseshoes, but plenty of brake shoes. First Church of Such-and-Such -- still here.   Saloons? Gringo's Tex Mex, with "Latino Fusion".  Doc's specialized, or maybe just re-branded to game insurance billing -- "Drive-in Gynecology Clinic" (really).  Depending on local laws -- or lack of them -- gentlemen's clubs = brothels by another name.  Fireworks - pawn - gold - boots - tack - guns - ammo.  Plus, still plenty of 'tude:

 

I cross the Brazos.  (Always wanted to say that.)

"We got all your outdoor needs." Even if those extend to giant welded roosters:

 

 

Obligatory BBQ stop. Chopped BBQ sandwich, slaw, jalapeno at the Lost Pines BBQ in Giddings. Highly recommended for friendly service and great food:

 

 

 

 

McDade: two chihuahuas play by the road.

They jump into the traffic.

Doing sixty, I swerve and miss.

The tractor-trailer behind me doesn't.

 

Crosses, mostly singly, sometimes in bunches: "Have you found Jesus?"

Austin, 30 miles: "Do you know Linux?"

 

 

Part 2: Being There 

Please Help Me Get Listed On The #Google #Currents Catalog. And Please ReTweet!

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:

http://www.google.com/producer/editions/CAow75wQ/octavianworld

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.  

Thank you!

#Facebook at 100 (Almost)

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.

 

 

 

 

Controlling for Impression Volatility in Digital Ad Spend Tests @DataXu

I've recently been involved in evaluating the results of a matched market test that looked at the impact of changes in digital advertising spend by comparing test vs. control markets, and by comparing differential lift in these markets over prior periods (e.g., year on year).  One of the challenges involved in such tests is significant "impression volatility" across time periods -- basically, each dollar can buy you very different volumes of impressions from year to year.  

You can unpack this volatility into at least three components:  

• changes in overall macro-economic conditions that drive target audiences' attention,
• changes in the buying approach you took / networks you bought through, due to network-specific structural (like what publishers are included) and supply-demand drivers (like the relative effectiveness of the network's targeting approach)
• changes in "buy-specific" parameters (like audiences and palcements sought).  

Let's assume that you handle the first with your test / control market structure.  Let's also assume that the third is to be held constant as much as possible, for the purposes of the test (that is, buying the same properties / audiences, and using the same ad positions / placements for the tests).   So my question was, how much volatility does the second factor contribute, and what can be done to control for that in a test?

Surfing around I came on DataXu's March 2011 Market Pulse study.  DataXu is a service that allows you to buy across networks more efficiently in real time, sort of like what Kayak would be to travel if it were a fully automated agent and you flew every day.  The firm noted a year-on-year drop in average daily CPM volatility from 102% to 42% from May 2010 to February 2011 (meaning I think the average day to day change in price across all networks in each of the two months compared).  They attributed this to "dramatically increased volume of impressions bought and sold as well as maturation of trading systems".  Notwithstanding, the study still pointed to a 342% difference in average indexed CPMs across networks during February 2011.  

A number this big naturally piqued my interest, and so I read into the report to understand it better.  The top of page 2 of the report summary presents a nice graph that shows average monthly indexed CPMs across 11 networks, and indeed shows the difference between the highest-priced and the lowest-priced network to be 342%.  Applying "Olympic scoring" (tossing out highest- and lowest-priced exchanges) cuts that difference to about 180%, or roughly by half -- still a significant discrepancy of course.  Looking further, one standard deviation in the whole sample (including the top and bottom values) is about 44%.  Again, though perhaps a bit less dramatic for marketers' tastes, still lots.

(It's hard to know how "equivalent" the buys compared were, in terms of volumes, contextual consistency, and audience consistency, since the summary doesn't address these.  But let's assume they were, roughly.)

So what? If your (display) ad buys are not so property-specific / audience-targeted that run-of-network buys in contextual or audience categories are OK, future tests might channel buys through services like DataXu and declare the buys "fully-price-optimized" across the periods and markets compared, allowing you to ignore +/- ~50% "impression volatility" swings, assuming the Feb 2011 spreads hold.

However, if what you're buying is very specific -- and only available through direct purchase, or one or two specialized networks at most -- then you ignore factor 2, trust the laws of supply and demand, and assume that you've bought essentially the same "attention" regardless of the difference in impressions.

I've asked some knowledgeable friends to suggest some perspectives on this, and will pass along their ideas.  Other feedback welcome, especially from digital advertising / testing pros!  Oh and if you're really interested, check out the DataXu TC50 2009 pitch video.