About

Cesar A. Brea bio at Force Five Partners

     

Subscribe

Get new posts by email:

RSS

Subscribe via
Google Currents

84 posts categorized "Analytics"

April 16, 2014

My New Book: #Marketing and #Sales #Analytics

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

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

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

January 17, 2014

Culturelytics

I'm working on a book. It will be titled Marketing and Sales Analytics: Powerful Lessons from Leading Practitioners. My first book, Pragmalytics, described some lessons I'd learned; this book extends those lessons with interviews with more than a dozen senior executives grappling with building and applying analytics capabilities in their companies. Pearson's agreed to publish it, and it will be out this spring. Right now I'm in the middle of the agony of writing it. Thank you Stephen Pressfield (and thanks to my wife Nan for introducing us).

A common denominator in the conversations I've been having is the importance of culture. Culture makes building an analytics capability possible. In some cases, pressure for culture change comes outside-in: external conditions become so dire that a firm must embrace data-driven objectivity. In others, the pressure comes top-down: senior leadership embodies it, leads by example, and is willing to re-staff the firm in its image. But what do you do when the wolf's not quite at the door, or when it makes more sense (hopefully, your situation) to try to build the capability largely within the team you have than to make wholesale changes?

There are a lot of models for understanding culture and how to change it. Here's a caveman version (informed by behavioral psychology principles, and small enough to remember). Culture is a collection of values -- beliefs -- about what works, and doesn't: what behaviors lead to good outcomes for customers, shareholders, and employees; and, what behaviors are either ignored or punished.

Photo (16)

Values, in turn, are developed through chances individuals have to try target behaviors, the consequences of those experiences, and how effectively those chances and their consequences are communicated to other people working in the organization.

Photo (15)

Chances are to  culture change as reps (repetitions) are to sports. If you want to drive change, to get better, you need more of them. Remember that not all reps come in games. Test programs can support culture change the same way practices work for teams. Also, courage is a muscle: to bench press 500 pounds once, start with one pushup, then ten, and so on. If you want your marketing team to get comfortable conceiving and executing bigger and bolder bets, start by carving out, frequently, many small test cells in your programs. Then, add weight: define and bound dimensions and ranges for experimentation within those cells that don't just have limits, but also minimums for departure from the norm. If you can't agree on exactly what part of your marketing mix needs the most attention, don't study it forever. A few pushups won't hurt, even if it's your belly that needs the attention. A habit is easier to re-focus than it is to start.

Consequences need to be both visible and meaningful. Visible means good feedback loops to understand the outcome of the chance taken. Meaningful can run to more pay and promotion of course, but also to opportunity and recognition. And don't forget: a sense of impact and accomplishment -- of making a difference -- can be the most powerful reinforcer of all. For this reason, a high density of chances with short, visible feedback loops becomes really important to your change strategy.

Communication magnifies and sustains the impact of chances taken and their consequences. If you speak up at a sales meeting, the client says Good Point, and I later praise you for that, the culture change impact is X. If I then relate that story to everyone at the next sales team meeting, the impact is X * 10 others there. If we write down that behavior in the firm's sales training program as a good model to follow, the impact is X * 100 others who will go through that program.

Summing up, here's a simple set of questions to ask for managing culture change:

  • What specific values does our culture consist of?
  • How strongly held are these values: how well-reinforced have they been by chances, consequences, and communication?
  • What values do I need to keep / change / drop / add?
  • In light of the pre-existing value topology -- fancy way of saying, the values already out there and their relative strength -- what specific chances, consequences, communication program will I need to effect the necessary keeps / changes / drops / adds to the value set?
  • How can my marketing and sales programs incorporate a greater number of formal and informal tests? How quickly and frequently can we execute them?
  • What dimensions (for example, pricing, visual design, messaging style and content, etc.) and "min-max" ranges on those dimensions should I set? 
  • How clearly and quickly can we see the results of these tests?
  • What pay, promotion, opportunity, and recognition implications can I associate with each test?
  • What mechanisms are available / should I use to communicate tests and results?

Ask these questions daily, tote up the score -- chances taken, consequences realized, communications executed -- weekly or monthly. Track the trend, slice the numbers by the behaviors and people you're trying to influence, and the consequences and communications that apply. Don't forget to keep culture change in context: frame it with the business results culture is supposed to serve. Re-focus, then wash, rinse, repeat.  Very soon you'll have a clear view of and strong grip on culture change in your organization.

September 11, 2013

Book Review: "Building A Digital Analytics Organization" by @Judah Phillips #analytics

I originally got to know Judah Phillips through Web Analytics Wednesdays events he organized, and in recent years he's kindly participated on panels I've moderated and has been helpful to my own writing and publishing efforts. I've even partnered with some of the excellent professionals who have worked for him. So while I'm biased as the beneficiary of his wisdom and support, I can also vouch first-hand for the depth and credibility of his advice. In short, in an increasingly hype-filled category, Judah is the real deal, and this makes "Building The Digital Analytics Organization" a book to take seriously.

For me the book was useful on three levels. One, it's a foundational text for framing how to come at business analysis and reporting. Specifically, he presents an Analytics Value Chain that reminds us to bookend our analytic efforts per se with a clear set of objectives and actions, an orientation that's sadly missing in many balkanized corporate environments. Two, it's a blueprint for your own organization-building efforts. He really covers the waterfront, from how to approach analysis, to different kinds of analysis you can pursue, to how to organize the function and manage its relationships with other groups that play important supporting roles. For me, Chapter 6, "Defining, Planning, Collecting, and Governing Data in Digital Analytics" is an especially useful section. In it, he presents a very clear, straightforward structure for how you should set up and run these crucial functions. Finally, three, Judah offers a strong point of view on certain decisions. For example, I read him to advocate for a strongly centralized digital analytics function, rooted in the "business" side of the house, to make sure that you have both critical mass for these crucial skills, as well as proximity to the decisions they need to support.

These three uses had me scribbling in the margins and dog-earing extensively. But if you still need one more reason to pull the trigger, it helps that the book is very up-to-date and has a final chapter that looks forward very thoughtfully into how Judah expects what he describes as the "Analytical Economy" to evolve. This section is both a helpful survey of the different capabilities that will shape this future as well as an exploration of the issues these capabilities and associated trends will raise, in particular as they relate to privacy. It's a valuable checklist, to make sure you're not just building for today, but for the next few years to come.

Here's the book and the review on Amazon.

September 01, 2013

#MITX Panel: Analytically Aligned Decision Making in the Multi-Agency Context

I moderated this panel at the Massachusetts Innovation and Technology Exchange's (mitx.org)"The Science of Marketing: Using Data & Analytics for Winning" summit on August 1, 2013.  Thanks to T. Rowe Price's Paul Musante, Visual IQ's Manu Mathew, iKnowtion's Don Ryan, and Google's Sonia Chung for participating!

 

June 16, 2013

Organizing for #Analytics - Seven Considerations

We're now in the blood-sugar-crash phase of the Analytics / Big Data hype cycle, where the gap between promise and reality is greatest.  Presenting symptoms of the gap include complaints about alignment, access to data, capacity to act on data-driven insights, and talent.  This September 2012 HBR blog post by Paul Barth and Randy Bean of NewVantage Partners underscores this with some interesting data.

Executives' anxiety about this gap is also at its peak.  Many of them turn to organization as their prime lever for solving things. A question I get a lot is "How should we organize our analytic capabilities?"  Related ones include "How centralized should they be?", and "What should be on the business side, and what belongs in IT?"  

This post suggests a few criteria for helping to answer these questions.  But first, I'd like to offer a principle for tackling this generally:

Think organization last, not first.

A corollary to this might be, "Role is as role does."  Too much attention today is paid to developing and organizing for analytic capability.  Not enough attention is paid to defining and managing a portfolio of important business opportunities that leverage this capability.  In our work with clients, we focus on building capability through practice and results.  Our litmus test for whether we're making progress is a rule we call "3-2-1": In each quarter, the portfolio of business opportunities we're supporting with analytic efforts has to yield at least three "news you can use" insights, two experiments based on these insights, and one "scaling" of prior experiments to "production", with commensurate results.  (The specific goals we set for each of these varies of course from situation to situation, but the approach is the same.)

Approaching things this way has several benefits:

  • You frame "Analytics" and "Big Data" requirements in terms of what you need to solve specific challenges relevant to you, not in terms of a vendor's list of features;
  • You stay focused on the result, and not the input, so you don't invest past the point of diminishing returns;
  • By keeping cycles short and accountable to this rule, you hedge execution risk and maximize learning;
  • Your talent recruitment, development, and organization are done in the context of explicit opportunities, and thus stay flexible and integrated around concrete business results and not abstract concepts for what you need;
  • The results-oriented management of the capability helps build confidence that the overall ROI expected will be achieved.  Momentum is strategic.

Now, two critiques that can be made of this approach are, first, that it's too ad hoc and therefore misses opportunities to leverage experience beyond each individual opportunity addressed, and second, that it ignores that most people are "tribal" and that their behaviors are shaped accordingly.  So once you've got a decent portfolio assembled and you're managing it along, here are some organizational considerations you can apply to help decide where folks should "live":

  • For the business opportunities you're faced with, how unique is "local knowledge" -- that is, intimate knowledge of the specific market dynamics or operational mechanics that generate the data and shape the necessary analytics -- to each of them?  The more so, the more it will make sense to place your analysts in the groups responsible for those areas.
  • To what extent does the type of analysis you are pursuing require a certain degree of critical mass? It's hard for a single person or even small groups to manage and mine a Big Data capability, and if you sprinkle Big Data analysts throughout your firm to support different groups, you overwhelm each of them and under-serve the opportunity. Plus, each of them ends up with different Hammers Looking For Nails based on the particular tools and techniques they learn, rather than picking the best ones for different jobs.
  • How important is enterprise leverage to the business case for your capability?  If it is, centralizing your analysts so that purchasing efficiencies and idea sharing and reuse are maximized will be more important.
  • Are you concerned about objectivity?  When analysts get embedded deeply with business teams, there's a risk they can "go native", either because they fall in love with the solutions they're part of developing, or because of pressure, subtle and otherwise, to prove these solutions work.  This phenomenon is well-documented in scientific fields, even with peer review, so it's certainly more problematic in business.  
  • Are you, for whatever reason, having trouble keeping your analysts and their efforts aligned with your key priorities? For example, if one group needs to quickly get a product into market to grab its share of a high-growth opportunity, and then evolve it from there, and your analysts work in a group whose norms and objectives are more about "perfect" than "good enough", you may need to move folks, or get different folks in place.
  • How's your analyst-marketer relationship? If they're talking and working together productively, and the interpersonal karma is good, you can worry less about whether their boxes on the chart are closer or further apart.
  • Finally, which of these four "C's" describes the behavior you're trying to encourage: communication, coordination, collaboration, or control?  At the communication end of the spectrum, you just want folks to be aware of each other's efforts.  Coordination, for example, can mean "Hey, I'll be running my test Tuesday, so could you wait until Wednesday?"  Collaboration may require formal re-grouping, but it might only be temporary.  Control can be necessary for effective execution of complex projects.  The more analytic success relies on such control, rather than being satisfied by the "lesser" C's, the more you may solve for that with organization.

In our work we'll typically apply these criteria using scoresheets to evaluate either or both the specific business challenges we're solving for or the organizational models we're evaluating as possible options.  Sometimes we just use "high-medium-low" assessments, and other times we'll do the math to help us stay objective about different ways to go.  The main things are to keep attention to organization in balance with attention to progress, and to keep discussions about organization focused on the needs of the business, rather than allowing them to devolve into proxy battles for executive power and influence.

June 12, 2013

Privacy vs. Security Survey Interim Results #prism #analytics

This week, one of the big news items is the disclosure of the NSA's Prism program that collects all sorts of our electronic communications, to help identify terrorists and prevent attacks.

I was struck by three things.  One is the recency bias in the outrage expressed by many people.  Not sixty days ago we were all horrified at the news of the Boston Marathon bombings.  Another is the polarization of the debate.  Consider the contrast the Hullabaloo blog draws between "insurrectionists" and "institutionalists".  The third was the superficial treatment of the tradeoffs folks would be willing to make.  Yesterday the New York Times Caucus blog published the results of a survey that suggested most folks are fence-sitters on the tradeoff between privacy and security, but left it more or less at that.  (The Onion wasn't far behind with a perfect send-up of the ambivalence we feel.)

In sum, biased decision-making based on excessively simplified choices using limited data.  Not helpful. Better would be a more nuanced examination of the tradeoff between the privacy you would be willing to give up for the potential lives saved.  I see this opportunity to improve decision making alot, and I thought this would be an interesting example to illustrate how framing and informing an issue differently can help.  So I posted this survey: https://t.co/et0Bs0OrKF

Here are some early results from twelve folks who kindly took it (please feel free to add your answers, if I get enough more I'll update the results):

Privacy vs security

(Each axis is a seven point scale, 1 at lowest and 7 at highest.  Bubble size = # of respondents who provided that tradeoff as their answer.  No bubble / just label = 1 respondent, biggest bubble at lower right = 3 respondents.)

Interesting distribution, tending slightly toward folks valuing (their own) privacy over (other people's) security.

Now my friend and business school classmate Sam Kinney suggested this tradeoff was a false choice.  I disagreed with him. But the exchange did get me to think a bit further.  More data isn't necessarily linear in its benefits.  It could have diminishing returns of course (as I argued in Pragmalytics) but it could also have increasing value as the incremental data might fill in a puzzle or help to make a connection.  While that relationship between data and safety is hard for me to process, the government might help its case by being less deceptive and more transparent about what it's collecting, and its relative benefits.  It might do this, if not for principle, then for the practical value of controlling the terms of the debate when, as David Brooks wrote so brilliantly this week, an increasingly anomic society cultivates Edward Snowdens at an accelerating clip.

I'm skeptical about the value of this data for identifying terrorists and preventing their attacks.  Any competent terrorist network will use burner phones, run its own email servers, and communicate in code.  But maybe the data surveillance program has value because it raises the bar to this level of infrastructure and process, and thus makes it harder for such networks to operate.

I'm not concerned about the use of my data for security purposes, especially not if it can save innocent boys and girls from losing limbs at the hands of sick whackos.  I am really concerned it might get reused for other purposes in ways I don't approve, or by folks whose motives I don't approve, so I'm sure we could improve oversight, not only for what data gets used how, but of the vast, outsourced, increasingly unaccountable government we have in place. But right now, against the broader backdrop of gridlock on essentially any important public issue, I just think the debate needs to get more utilitarian, and less political and ideological.  And, I think analytically-inclined folks can play a productive role in making this happen.

(Thanks to @zimbalist and @perryhewitt for steering me to some great links, and to Sam for pushing my thinking.)

May 20, 2013

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

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

May 10, 2013

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

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

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

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

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

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

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

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

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


January 09, 2013

My New Book: Pragmalytics

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

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

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

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

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

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

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

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

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

 

 

October 31, 2012

Today's Data Exercise: The @fivethirtyeight / Intrade Presidential Election Arbitrage #Analytics

(Nerd alert!  You have been warned.)

Unoriginally, I'm a big fan of Nate Silver's fivethirtyeight blog.  I've learned a ton from him (currently also reading his book The Signal and the Noise).  For a little while now I've been puzzling over the relationship between his "Nowcast" on the presidential election and the price of Obama 2012 contracts at Intrade.  Take a look at this chart I made based on the data from each of these sources:

Obama - 538 vs Intrade October 2012

If we look past Obama's disastrous first debate, and look at the difference between the seven-day moving averages of the 538 Obama win probability and the Intrade Obama 2012 contract price, it looks to fluctuate roughly around 10-15 points, call it 12.  Also, looking at the volumes, it looks like the heaviest trading happens roughly around midweek, before Friday.  So if you trust Nate's projections, and unless you've got inside scoop about any big negative surprises to come, the logical thing to do is to buy Obama 2012s tomorrow, with an average probability of clearing $1.20 on each contract (about a 20% gain).

Now for the nerdy part:

First, the easy job: Intrade lets you download historical prices on its contracts.

Next, the harder job: Nate doesn't provide a .csv of his data.  But if you "view source" on his page, you'll see a file called:

"http://graphics8.nytimes.com/packages/html/1min/elections/2012/fivethirtyeight/fivethirtyeight-ccol-top.js"

right after a preceding description "Data URL".

If you take a look at this file, you'll notice it's Javascript-chart-friendly, but as far as for the kind of analysis above, not so much.  The first order of business was to cut out the stuff I didn't want, like the Senate race data, and the forecast part of the presidential polls.  Then, I further whacked out data before 10/1, because I thought examining trends in a more thinly-traded market would be less relevant.

For a little while I fiddled with the Stanford Visualization Group's Data Wrangler tool to reshape the remaining data into the .csv I needed.  It's a powerful tool, but it turned out to be easier in this case to wrangle the file structure I wanted manually:

"date","obama_votes","romney_votes","obama_win_pct","romney_win_pct","obama_pop_vote","romney_pop_vote"

"2012-10-30",298.8,239.2,79.5,20.5,50.4,48.6

"2012-10-29",294.4,243.6,75.2,24.8,50.2,48.8

etc.

Combining the Intrade and 538 data and then plotting the Intrade close and the "Obama win pct" series results in the chart above.