Octavianworld

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.

Wanted: Deputy Director, #Digital #Communications @Harvard #University to work with @perryhewitt

My friend Ms. Perry Hewitt is Harvard University's Chief Digital Officer.  Harvard's hiring a deputy to work with her. This is a great job for anyone who wants to combine working on cool stuff for a great boss at an iconic institution, and who has the chops for that opportunity.  If that's you, read more and apply here, and please tell Perry I sent you.

Caveman BI

Many client project workplans I intersect have  the line item "Determine KPIs".  Without further context to frame this work, folks typically end up with something that:

• is (too) narrow, functionally
• is impermanent -- KPI reporting and analysis lasts only as long as the effort itself is sustained
• is disconnected from / uncoordinated with other, interdependent parts of the business

Now, consider the view from the analytic teams supporting efforts like these.  When multiple such requests come in, it's left to these teams to prioritize them, and reconcile them semantically and logistically.  This is hard.

To deal with these challenges, many BI (Business Intelligence) tool vendors provide pre-built "business models" that suggest a set of KPIs based on a more fundamental concept of the operations they reflect.  These can be useful, but they're not free, and it's a lot of work to retro-fit them to your business.  This is expensive.

Whether you use a vendor model as a point of departure, or roll your own, I've found it useful to have a basic "Caveman BI Model" that serves three purposes:

• it provides a common language to the folks involved in the KPI development effort
• it provides a broader context on how KPIs fit into the business, so folks don't lose the forest for the leaves
• it starts simple, so your team can work its way up to the complexity you need, rather than trimming back from the complexity it's given by a pre-existing model

The Caveman BI Model starts with a plain-English, noun-verb description of what the business does that needs to be understood better:

"We use resources to create products that we sell to customers with content through channels."

Generic and esoteric maybe, but in one sentence we've described the entities of the business that we need to understand better:

"We use resources to create products that we sell to customers with content through channels."

Put another way, this one sentence describes "things I need to know about to run the business".

Next, we use metrics to describe how (well) we're managing the entities.  Metrics have the following progression of development:

• a raw count: "we have n customers"
• a synthetic measure, commonly a quotient, produced from more than one count: "$x in sales for #y in customers"
• an index that allows us to compare a raw count or a quotient longitudinally (against past performance), cross-sectionally (say, against competitors or industry averages), or as a variance vs. a budget / plan / forecast

Now, each of these entities has dimensions that allow us to "slice" metrics by to dig into the business a bit.  For example:

• resources would include capital, people, raw materials, and fixed assets, each of which might have their own characteristics (equity vs. debt; hourly vs. salaried; source; etc.)
• products could be characterized according to needs served (say, a disease condition for a pharmaceutical), or an underlying technology or legal framework that makes it possible (say, "Forty-Act" in the mutual fund business)
• customers can have a number of properties that allow us to segment them usefully; these might include wealth, how big their relationship with us is, geography, gender, age, etc.
• content would include "campaigns" that use different marketing assets, themes, or promotions
• channels covers distinctions like "direct sales" vs. "distributors", or "TV" vs. "digital"

(Oversimplifying: Ralph Kimball's BI bible, The Data Warehouse Toolkit, calls metrics "facts" and suggests constructs called "fact tables" and "dimension tables" that relate facts and dimensions to each other.  Put another way, what are the different dimensions that we have to slice each fact by in order to understand the business?  "Sales to customers by product and customer geography" would be an example of an intersection we might want to know about.)

These dimensions in turn have hierarchies.  Geography, for example, has "city, state, country".  Some hierarchies are helpfully scalar:  second-minute-hour-day, for example.  But, hierarchies are often a big problem because they don't reconcile neatly.  For example, weeks don't divide neatly into months; some industry data may be sourced from places that use different hierarchies, making apples to apples comparisons hard.

That's pretty much it.  Summing up:

• what do we want to know about? entities
• how do we want to measure it? metrics
• how do we need to slice this measurement? dimensions
• how do we roll up / roll down these slices? hierarchies

So how do we define and distinguish KPIs  from the myriad metrics we could choose?  One rule of thumb I've applied is to choose metrics that start and end a process.  For example, "qualified leads" are an output of a marketing process that become an input to a sales process.  A transaction is then the end of the sales process.  Each of these could be a KPI; a "synthetic" KPI might compare leads to sales as a "conversion rate".

If you're charged with coming up with KPIs and then provisioning them for decision-makers (who may include you too!), I'd suggest you start by asking someone to explain what the "Caveman BI Model" is for your business, using this framework as your interview guide.  If you find that such a model has not been expressed per se, or doesn't exist, take a pass through creating one, maybe by trying to tie some of the key numbers you see used a lot back into the framework.

Again, the purpose of this post isn't to be the last word in Business Modeling, but rather to share a basic approach you can carry around in your head -- and in common across your heads -- that's been useful to me in the past.  Please let me know what you think!

 

Sponsored Occupations :-)

NYT.com had an interactive poll / visualization today taking readers' pulse on the Occupy protests.  Browsing through the usual left-right snarks and screeds, and on the heels of a recent stroll through one of the protest sites, it occurred to me that we're missing a chance to think beyond the politics of the movement, to the economic opportunity it represents.

Think of the protest sites as outdoor ad inventory.  This inventory is in great locations -- in the hearts of the world's financial districts, with lots of people with very high disposable incomes to see your ads every day, all day, right outside their windows -- the same people that fancy watchmakers pay the WSJ big bucks to reach.  

Yet currently, this valuable inventory is currently filled with PSAs...

...Or it goes begging altogether:

So it dawned on me: "Sponsored Occupations" -- the outdoor ad network that monetizes protest movements!  This concept meets several needs simultaneously:

• One stated objective of the movement is to "Make Them Pay".  The concept creates a practical mechanism for realizing this goal.
  
  
  

 

• Events and guerilla marketing in premium locations without a permitting process -- an advertiser's dream!
• Plus, sponsors could negotiate special perks, like keeping the protesters from "Going all Oakland" (just heard that term) on their retail stores.
• Cash-strapped municipalities can muscle a cut of the publishers' share, turning what's today a drag on public resources (police, etc.) into a money-maker.

There's another important benefit.  This idea is a job creator.  After all, the network needs people to pitch the "publishers" at each location, and sales folks to recruit the advertisers, and staff to traffic the ads, keep the books, etc.  Politicians right and left could fold this into their platforms immediately.

Finally, for the entrepreneur who starts it all, there's the chance to Sell Out To The Man -- at a very attractive premium!  And, for the protesters who back the venture, and get options working for it, a chance to cash out too, just like the guys they're protesting.

After all, Don't Get Mad, Get Even.

 

Postscript in Rolling Stone: "How I Learned to Stop Worrying and Love the OWS Protests". Plus some thoughtful suggestions here.

 

 

 

#MediaPost Future of Media Conference Trip Report

Today I attended Media  Magazine's / MediaPost.com's "Future Of Media" conference in NYC.  NYU hosted the event at its Kimmel Center overlooking Washington Square; after lots of recent events in midtown it was nice to be in the Village for a change, especially on a sunny early-fall day.  To the fortunate folks living in the condo one block south:  admiring your rooftop garden and the sweet library below made for a great conversation starter at breakfast!

Ken Fadner, Joe Mandese, and the MediaPost team assembled a great panel.  Josh Quittner framed his kickoff question with what seems to be the Ur-point of departure for all recent conferences:  Apple's latest announcements.  "Is the future simply "three screens" -- small / smartphone, mid-sized / tablet & PC, large / TV?"  Very quickly, the responses tumbled out. "Yes!  And more: wearables!  Thermostats! Location-based marketing (plus ça change...)! Voice!  Device-based payments! (Biometric access to our devices, for greater security!)  More fragmentation..."

Someone -- I think it was David Verklin -- replied that the answer is to "follow the consumer".  At first it seemed like a bland answer.   But of course it's really the only way you can get a grip on where things are going:  think users and use cases.  Which users, and which of their macro life events and the micro use cases that go along with those, represent the biggest pots of potential value to act as muses for media innovators?  Someone else -- Bob Carrigan? -- suggested that in shaping experiences to address those users and use cases, "convenience trumps quality."  Echoing William Gibson's iconic "The future's already here, it's just not evenly distributed," Maria Luisa Francoli noted that, for example, we're already seeing mobile devices used as a vehicle for payments in Africa.  Riffing on that theme, Beth Comstock predicted that we'd see lots more investment in user experience over raw features in the coming two years.  

Now, the role of the media in shaping what's possible seems even more significant today than 47 years ago.  So Josh naturally segued to ask about what firms would dominate the landscape in the coming years:  "Certainly Apple, Amazon, Google, and Facebook -- but who else?"  On the one hand, the panel had no specific, answers: no candidate from the MSM world, no mention of Microsoft (stunning, since we still spend so much time with their software), no golden child(ren) from the Web's Third Wave (though Groupon and Zynga did get shout-outs at different points later in the conversation).   On the other, their comments together pointed out that Josh's Big Four all aspired to be platforms that allow others -- ten million, by Bob Pittman's counting I think -- to transform features into experiences that solve for the users and use cases mentioned earlier.  One question left unaddressed is how much of the "profit pool" in this vision of media futures goes to the API builders versus the API users.  From my perspective, it looks like "the stack" metaphor for software strategy now generalizes well to media too.

In the spirit of "You manage what you measure," one of the audience questions had to do with the metrics that will guide our progress toward media's future.  There's consensus, as voiced by Brian Monahan, that The Age of Sampling is passing, and The Age of Big Data is upon us.  David Verklin told a good story (even if it sounded slightly apocryphal, and his math seemed slightly off): "CNBC ratings recently went down 15%.  Why?  Two guys turned 55.  What could that possibly mean??  Well, the CNBC ratings panel tracks men 25-54 with certain other characteristics.  The panel had 32 people.  Two of those guys turned 55..."  Swimming briefly against the tide, Bob Pittman counselled "Some of this metrics stuff goes the wrong direction, you gotta help clients think like marketers.  When I ran Six Flags, I told my park managers, 'You have unlimited marketing budgets -- just give me a return.'"   Maria Luisa Francoli disagreed, pointing to attribution analysis as the way forward.  Pittman replied, "True, but that tends to drive you to direct marketing, "  She naturally countered, pointing out attribution analysis' whole premise is to put brand and direct investments on a common currency.   (For me, it's not whether you pursue greater accountability, but how...)

Josh: "Is social networking a fad?" Consensus answer: the genie's out of the bottle, the challenge is that social media is "broad but not very deep" in terms of how relationships are conducted and maintained there. David to Josh: "How many 'friends' do you really have?"  For me, behind this question, crucial to media firms and advertisers trying to capitalize on viral dynamics, is the question of how each of us "filter" the digital world, and of better filters as a worthy avenue toward media futures (the subject of the post I contributed to the conference blog).

What did I miss?  What's your synthesis?  Again, thanks for a great session.

 

#MediaPost "Future of Media" Conference in NYC Today

I'm at Media Magazine's / MediaPost's "Future of Media" conference at NYU's Kimmel Center today. (I contributed this post to the conference blog last week.)  Please say hi!  Trip report to follow.

Trip Report: Nashoba Learning Group 50 Mile Bike-a-thon / Saturday, July 23 #autism

This past weekend Cadel Evans rode to glory.  So did I.  Let's review the bidding:

• Cadel hammered until his quads burned.  I only had to tap lightly to achieve the same effect, and it took me many fewer miles.  Edge to Cesar.
• He struggled on L'Alpe d'Huez, with only two more fans (worried-looking at that) to urge him ahead than I had on L'Alpe d'Old Bay Road (Bolton, Massachusetts).   No one photographed my agony and stuck it on Flickr.  Another pour moi.
• To help him recover, Cadel had paid soigneurs massage his aching thighs.  I worked my cramps out myself.  Tossup I think, depending on whether you're frugal, or into the whole spa thing.
• On the winner's podium, a lovely model handed him his stuffed lion.  When I rolled in, my lovely wife handed me a turkey sandwich.  It was a good sandwich, and Cadel's a little old for toys like that, no?  Advantage me.
• Upon his victory, his domestiques cheered him.  At the end of my ride, William gave me a big smile and asked me to jump in the pool with him.  No contest.

Thank you once again to all our wonderful friends who were so generous again this year.  Regardless of whether your primary motivation was to support an incredibly worthy school, or to see me ride 50 + miles (including a wrong turn in West Concord) in 90-degree heat (and we both know who you are), we are very grateful!

Please sponsor me! Nashoba Learning Group 50 Mile Bike-a-thon / Saturday, July 23

Hello friends,

This Saturday, July 23rd, I mount my trusty steed once again to ride in Nashoba Learning Group's 50-mile bikeathon to help raise money for the school.  Many of you have been extremely generous in years past.   Nan and I are grateful to you for that and proud to have seen your gifts help to build such a wonderful organization.  Since 2003, when Will started as one of the first three students, the school's up to 90 kids with a much larger waiting list, and it's widely recognized to be at the very front of the list of models for how children with autistic spectrum disorders can make the most progress toward their potential.

Saturday is forecasted to be very hot, just like 2007's ride.  I am a little older and fatter (so close to "fitter", yet so far).  So, I hope once again we can count on your support, in any amount!  Donation information:  please visit the Nashoba Learning Group website donation page http://www.nashobalearninggroup.org/donations.htm .  Look for "2011 Bikeathon / Walkathon" in the drop down list if you wish to use a credit card.  Otherwise, you can send a check to:

Nashoba Learning Group
10 Oak Park Drive
Bedford, MA  01730
Attention:  Kathy Romeo

Thanks so much!

Cesar

 

Analyzing #Analytics

Take a look at this chart:

Here's what I see:

• interest bumping along to '06
• growing interest through '08 (Competing on Analytics appears March 2007)
• hype-y growth in interest through '10
• wildly uneven interest so far this year
• growing interest in the early part of each year
• summer "blahs" (Who Googles "analytics" on the beach? Don't answer that...)
• bump in interest in the fall, when business plans featuring "analytics" are developed and presented
• quiet Decembers

What do you see?  Are we watching "analytics" jump the shark?  More likely, we've chowed down on it as much as we can, and are now digesting.  Further interest will likely be less at the general conceptual level, and more at the "how do I get there" level.  Hence "Pragmalytics" (Part II here).

Stay tuned for more.

The Marketing Funnel is a Brita Water Filter #OMMAMetrics @judah @schmults

Something you hear a lot these days is, "The 'Marketing Funnel' concept's dead.  It's just not clear what's replaced it."  At the recent OMMA Metrics conference in NY, IBM/Unica's Yuchun Lee described its successor as some sort of "spaghetti".  Judah Phillips had an excellent article in yesterday's Online Metrics Insider (Thanks to Rob Schmults for pointing me to it!) in which he suggested a "tumbler" metaphor and "seeking-shopping-sharing" structure for what we now do.

Here's my entry into the rename-the-funnel sweepstakes: The Brita Water Filter.

Let's consider our requirements for a metaphor to succeed the "funnel":

1. We still have a "current" whose power combines the "push" of customer needs and desires with the "pull" of companies with products that could satisfy those. ("I have testosterone, Porsche makes 911s.")  To me, that still makes "linear" metaphors useful.

2. For me, "Attract", "Engage", and "Convert", plus sometimes "Retain" -- or variants thereof, like Judah's, still work for me as basic stage descriptors.  What's changed is that channels have exploded in number, and audiences have fragmented as they use different ones.  So a good metaphor will describe a journey that is less predictable in both flow path and rate. (It's probably also useful to use stage descriptors that reflect the customer's perspective, not the marketer's -- "Awareness", "Consideration", "Purchase", plus sometimes hopefully "Loyalty" and  "Advocacy" -- but they're not quite as punchy.  Nonetheless, you get the point.)

3. The channel system that lies between nascent demand and final purchase is, these days, much more replete with advice that educates folks as they flow through, qualifying and intensifying final demand.  The metaphor we use has to describe the "chemistry" that happens in these intermediate spaces and interactions.  And, the behaviors we observe in these stages should inform, as Judah suggests in his article, how we market afterward.

4. Over time, however, the system in between gets clogged with a lot of junky information, or becomes technologically obsolete, so you need to refresh or replace your presences in this system on a regular basis.  The metaphor has to anticipate this need as well.

5. The operational and analytic processes for marketing within this flow are higher-tech than a simple funnel.

OK, so let's look at the Brita filter:

1. Water still generally flows through in a linear fashion; and, not all of it flows through at once.

2. As water flows through the charcoal however, it breaks up into much smaller droplets, that flow through at different rates and and less predictable paths.

3. The interaction of the water with activated charcoal only got rid of some impurities; Brita's engineers learned to add an ionic coating to get some minerals out of the water too.

4. Ideally, you change it now and then to keep its performance up.

5. It's higher-tech than a funnel (and more expensive too, but the benefits are sometimes worth it).

So, you say, "Cute but esoteric -- how is this conceptualization useful?"

1. For me, it helps me to validate frameworks I'm familiar with, like the Channel Pathways (TM) framework we used years ago at Monitor / Marketspace, or the similar "Wiggly Line" chart Akin Arikan and Don Peppers describe in Multichannel Marketing.  

2. It also helps me to extend them, by considering differential flow rates through discrete paths, and to focus on what we can learn from interactions at one stage and channel that might inform what we do in subsequent, different ones.

3. It helps to remind me that when populating and managing any such framework, when it gets too complex or overly-"excepted" with rules, I might be better off replacing it.  

4. It helps me to remember that at best I can only be probabilistic and not deterministic in my understanding of "customer flow dynamics", and that what's important is to be explicit about probability levels so that group decisions can be helped by a shared understanding of these.

5. Finally, it's oddly memorable (to me anyway), and the filter's specific properties help me remember the requirements better.

So, what do you think? Please answer the poll below, and comment with questions / alternatives.

 

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#Watson Wins #Jeopardy! The Singularity Is Near(er): Are You Ready for Computational Engine Optimization ("CEO")?

Saw the news (though missed the show) that IBM's Watson won on Jeopardy.  Interesting to see this and other articles call out Watson's "stumble" -- as though they expected perfection, which is a milestone in itself.  Here's a great explanation of "what went wrong". 

There are two notable things to me about this development / achievement.

The first is to ask whether this puts us ahead or behind Ray Kurzweil's schedule for 2019 (as predicted in 1999).  (Really worth reading his predictions, since we're within shouting distance!  What would you "keep / change / drop / add"?)  

The second is a little closer in.   Given the pace of this development, what does it mean for us as humans / users / consumers / citizens on the one hand, and as marketers / investors, etc. on the other -- from "now" to, say, "two years out"?

Imagine for example that in two years, IBM provides access to a more generalized form of Watson as a cloud-based API.   What might you, as a person or as a business or other organization, do with a service that can understand speech, parse meanings, and optimize spending and investment recommendations based on how sure it is of the answer?

Cesar: "Watson, our lease is up soon, can you suggest some available space options nearby that would make sense for a business like Force Five Partners?"

Watson: "Cesar, here are five choices, with suggestions for what you should be paying for each, based on what I can find out right now..."

A stretch?  Apple's integrated Wolfram Alpha - based support into the Siri app for the iPhone now.  Try asking Siri, out loud: "What is the market capitalization of Goldman Sachs, divided by the US population?"   Answer back to me, in three seconds (iPhone 3GS / AT&T):

(Cross-check: Wolfram Alpha direct. Or,  GS on Google Finance / US Population on Google Search.)

 (FWIW, this hits 3 of 4 criteria in a prediction framework I suggested nearly six years ago.)

Wow.  We had barely figured out SEO, when we got slammed with SNO -- Social Network Optimization (as well as the frozen kind)!  Now we have to figure out Computational Engine Optimization?  (Confusingly, natch, "CEO" -- you read it here first!)  How do I optimize for "What inexpensive steakhouses are nearby?"   How do we even think about that?  

(Possible direction: Semantic Web Optimization -- "SWO", of course.  Make sure you are well tagged-for, and indexed-by, the data stores and services where the terms "inexpensive", "steakhouse", and "nearby" would be judged.  Or, in plain English: if Wolfram Alpha looks to Yelp to help answer this question, make sure your restaurant's entry there is labeled as a steakhouse, has an accurate address, and is accurately price-rated as "$".  Whatever gaming ensues,  just don't blame IBM / Apple / Wolfram /(Google too) for going for the mega-cheddar.)

It's trite to say that change is accelerating as technology develops.  ("We're only in the second inning!")  Some dismiss this (as Arthur C. Clarke said, we always overestimate the impact of technology in the short term, but underestimate it in the long term).  But, if you doubt, this chart is worth a look.  And then think about the degree to which "social" and "mobile" are now reinforcing, amplifying, and accelerating each other...

(Insert shameless commercial:) What are you doing to help your organization keep up? 

 

Lifetime Learning

A lovely Saturday:

A perfect day for some refreshment:

Studying http://philip.greenspun.com/teaching/rdbms-iap-2011

Why?  (And, why now?)  Relational databases and SQL have been around for forty years.  Yet, no reasonable business person would disagree that:

1. it's useful to know how to use spreadsheet software, both to DIY and manage others who do;

2. there's much more information out there today;

3. harnessing this information is not only advantageous but essential;

4. more powerful tools like database management systems are necessary for this.

Therefore, business people should know a little bit about these more powerful tools, to continue to be considered reasonable.

Great Moments in 4G Smartphone Marketing: "These Go To 11"

Announced yesterday:

I've long felt that Nigel Tufnel would make a great pitchman. Maybe his day has come?

 

#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. 

 

Filtering Solutions: @darwineco Now Offering Free Themed Editions

With the world awash in content, filtering is the great problem of the age for all of us.  Arguably, the online social revolution is just a means to this end -- our friends are just another way we filter the information we choose to consume.  

Early in 2010 I wrote about Darwin Ecosystem, an advanced new filtering service for helping you pick out news themes and trends emerging from the digital content fire hose.  Bill Ives (@billives) posts here about new "themed" editions of this service, which, though scoped down in terms of the sources each spiders, are both full-featured and free to try.

My earlier post explains the service and its significance in detail, but you might just try it first, then go back and read the manual!

Facebook at Fifty (Billion)

Is Facebook worth $50 billion?  Some caveman thoughts on this valuation:

1. It's worth $50 billion because Goldman Sachs says so, and they make the rules.

2. It's worth $50 billion because for an evanescent moment, some people are willing to trade a few shares at that price. (Always a dangerous way to value a firm.)

3.  Google's valuation provides an interesting benchmark:

a. Google's market cap is close to $200 billion.  Google makes (annualizing Q32010) $30 billion a year in revenue and $8 billion a year in profit (wow), for a price to earnings ratio of approximately 25x.

b. Facebook claims $2 billion a year in revenue for 2010, a number that's likely higher if we annualize latest quarters (I'm guessing, I haven't seen the books).   Google's clearing close to 30% of its revenue to the bottom line.  Let's assume Facebook's getting similar results, and let's say that annualized, they're at $3 billion in revenues, yielding a $1 billion annual profit (which they're re-investing in the business, but ignore that for the moment).  That means a "P/E" of about 50x, roughly twice Google's.  Facebook has half Google's uniques, but has passed Google in visits.  So, maybe this growth, and potential for more, justifies double the multiple.  Judge for yourself; here's a little data on historical P/E ratios (and interest rates, which are very low today, BTW), to give you some context.  Granted, these are for the market as a whole, and Facebook is a unique high-growth tech firm, but not every tree grows to the sky.

c. One factor to consider in favor of this valuation for Facebook is that its revenues are better diversified than Google's.  Google of course gets 99% of its revenue from search marketing. Facebook gets a piece of the action on all those Zynga et. al. games, in addition to its core display ad business.  You might argue that these game revenues are stable and recurring, and point the way to monetizing the Facebook API to very attractive utility-like economic levels (high fixed costs, but super-high marginal profits once revenues pass those, with equally high barriers to entry).

d. Further, since viral / referral marketing is every advertiser's holy grail, and Facebook effectively owns the Web's social graph at the moment, it should get some credit for the potential value of owning a better mousetrap.  (Though, despite Facebook's best attempts -- see Beacon -- to Hoover value out of your and my relationship networks, the jury's still out on whether and how they will do that.  For perspective, consider that a $50 billion valuation for Facebook means investors are counting on each of today's 500 million users to be good for $100, ignoring future user growth.)

e. On the other hand,  Facebook's dominant source of revenue (about 2/3 of it) is display ad revenue, and it doesn't dominate this market the way Google dominates the search ad market (market dominance means higher profit margins -- see Microsoft circa 1995 -- beyond their natural life).  Also, display ads are more focused on brand-building, and are more vulnerable in economic downturns.

4. In conclusion: if Facebook doubles revenues and profits off the numbers I suggested above, Facebook's valuation will more or less track Google's on a relative basis (~25x P/E).  If you think this scenario is a slam dunk, then the current price being paid for Facebook is "fair", using Google's as a benchmark.  If you think there's further upside beyond this doubling, with virtually no risk associated with this scenario, then Facebook begins to look cheap in comparison to Google.

Your move.

Who's got a better take?

Postscript:  my brother, the successful professional investor, does; see his comment below (click "Comments")

Analytics Commons Post in Google Analytics Blog Today @analyticscommns @linchen @perryhewitt #analytics

Our Analytics Commons project (which I previously wrote about here) got written up in a post on the Google Analytics blog today. ( Thanks to Nick Mihailovski at Google, and to Perry Hewitt at Harvard!  And of course to my partners Lin and Kehan at New Circle Consulting!)

Extending Marketing Integration to Agencies: The "Agency API" @rwlord #rzcs

My friends at Razorfish kindly invited me to their client summit in Boston this week.  It was a great event; they and their clients are working on some pretty cool stuff.  Social is front and center.  Close behind: lots of interesting touch / surface computing innovations (Pranav Mistry from the MIT Media Lab really blew our minds). 

In his opening comments Wednesday, Razorfish CEO Bob Lord made the point that for modern marketing to be effective it's got to be integrated across silos of course; but, further, that this has to extend to having agencies work together effectively on behalf of their clients, as much as clients responsible for different channels and functions need to themselves.

I've been wondering about this as well recently, and Bob's comments prompted me to write up some notes.

One observation is that *if* marketers are addressing agency collaboration, they usually start with an *organizational* solution that brings agencies together from time to time.   While this is great, it's insufficient.  To make entities like these effective, it helps to lay a foundation that *registers* and *reconciles* different aspects of what agencies have and do for their clients.  This foundation, realized through a simple, cheap tool like Basecamp if not the marketer's own intranet system, could include:

• a data registry.  Agencies executing campaigns on behalf of their clients end up controlling data sets (display ad impressions and clicks, email opens, TV ratings, focus group / panel results) that are crucial to understanding the performance of marketing investments, but are typically beyond the scope of what IT's "enterprise data architectures" encompass.  I'm not suggesting that agencies need to ship this data to their clients; rather, that they simply register what's collected, who's got it, and how a client can get it if needed.
• an insight registry.  Agency folks crunch data into fancy powerpoints bearing the insights on which campaign decisions get made.  It would be very helpful if these decks were tagged and linked from the appropriately-permissioned online workspace.
• a campaign registry.  Think http://adverblog.com, only for the marketer's own (and perhaps direct competitors') campaigns of any stripe.  A place to put creative briefs and link to campaign assets / executions that implement them.

These approaches are simple, cheap, and "many hands make light work".  Implementing them collectively as a marketer's "Agency API" would help marketers and agencies to "reconcile" their work, in the following ways:

• discover campaign conflicts and integration opportunities
• unpack possible disagreements into manageable bites to resolve -- data conflicts, insight conflicts, analytic technique conflicts, creative brief conflicts
• better prepare in advance of "interagency council" meetings

Of course the registries are not a panacea for inter-agency "issues" that a marketer needs to sort through, but they help to make any problematic issues more transparent and straightforward to focus on.  

Please take the poll below.  Reactions / suggestions welcome from folks with relevant experience!

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One Tough Neighborhood

Via Windows Vista on my laptop ("Puny human, I laugh at your feeble attempts to tame me!"):

Analytic Commons Project

With inspiration and encouragement from @perryhewitt, New Circle Consulting and Force Five Partners have launched the Analytics Commons Project (http://analyticscommons.com).  Here's the pitch:

Web analytics is a relatively new field that is evolving very quickly. Fortunately, it's been our experience that the community of web analysts is welcoming, vibrant, and very willing to share. The Web Analytics forum on Yahoo! is a wonderful example of this.

Analytics Commons is an effort to improve on this sharing by structuring it a bit. With structure, we can make relevant knowledge a little easier to find, and we can also make it easier to vet the expertise and reliability of the source of that knowledge. (The new Web Analytics Association Certification program is another good step in this direction.)

In designing Analytics Commons, we also decided to start by focusing on a specific form of analytics knowledge, rather than trying now to architect some general information architecture about the field that could capture all its (quickly changing) variety. In particular, we noticed:

• Google Analytics is ubiquitous.
• We're happy users of it.
• It recently added the ability to share Advanced Segments and Custom Reports.
• While GA has an Apps Gallery that features third-party creations, there is currently no public registry of such shared reports that we're aware of.
• But, there does seem to be pent-up demand for sharing reports.
• And, we had a specific itch we needed to scratch ("Target Towns", more on that below) that would help us Keep It Real.

We also figured we would start with something that would be within our ability to actually get done. Our ambition for this initiative doesn't stop here, however. So, the service also provides a way for visitors and users to suggest feedback to shape the vision and path for getting there.

So how does it work? If we've done our job well, it's hopefully self-evident. You register on the Analytics Commons site, and tell us a little about yourself, ideally through links to places where you keep your description up to date (e.g., LinkedIn, Twitter, etc.). If you've got a report to contribute, you get the url for it by clicking on the "Share button" in Google Analytics' Custom Reports or Advanced Segments sections from a GA profile in which you have access to them. Then, you add the url to our service and tag and describe what you've shared. If you need a report, you search for it on our service. If you find and try a report, all we ask is that you rate and comment on it to tell us how well it matched what you needed. Hopefully, discussions about each report will happen on our service, but if you want to connect privately with a report contributor, we've made room in our registered user profiles for folks to provide contact information if they wish. If you don't find what you were looking for, we let you store the search on our service, and if something matches in the future, we'll send you an email with the search results. If you want, you can subscribe to a weekly email listing new reports that have been added to our service, or get an RSS feed of the same.

The service is free to its users. Our privacy policy is simple: everything here is public, except your registration email if you choose not to share that. We won't share that with anyone, period. If you share a report, we assume you have the authority to do that. If you comment on a report, please be polite and constructive. We reserve the right to moderate comments, and to ban anyone who posts material we deem to be inappropriate or offensive

We saved some space on our pages for advertising / sponsorship, to help cover the server bills. If you're interested, please contact us.

Questions? Suggestions? contact us if you wish at help@analyticscommons.com.

About "Target Towns"

In our work for a client, we observed the following:

• They target wealthy customers.
• Wealth is highly concentrated in the US.
• Wealthy people are highly concentrated in a few towns.

Therefore, we thought it would be useful to track traffic and behavior from these "Target Towns".

We tried to construct an Advanced Segment for "Target Towns" through the GA UI. It didn't appear to support what we had in mind. So we asked for help. Avinash Kaushik, Nick Mihailovski, Judah Phillips, and Justin Cutroni all helped us with a piece of the puzzle (Thank You all!). In the end, the answer turned out that we needed to use the GA API. But the API also had limits on how much information you could hit it with in a single query. So we figured we needed a service that would pass the towns ("Dimensions") about which you wanted information ("Metrics") past the API sequentially, and then would aggregate and present the results in a usable form.

Then we thought: "This is a report many people are likely to need!" So, the "Target Towns" service seemed like it would be a good candidate to help seed our Analytic Commons initiative.