Gilligan on Data by Tim Wilson

Awareness (the company) has a Measuring ROI Around Web 2.0 webinar this Thursday, May 22, at 2:00 PM EDT. That’s heavy on the buzzwords, but it sounds like it might have some interesting information. And, I found out about it thanks to a mention on Twitter from Connie Bensen, who will be leaving her new kayak behind and heading to London and Paris for some R&R, so will be missing the live event herself.

Unfortunately, it partially conflicts with Kalido’s What’s Behind Your BI? webinar, which starts at 2:30 PM EDT, and it conflicts with Fusing Field Marketing and Sales, which Hoover’s and Bulldog Solutions are putting on at 2:00 PM EDT on Thursday as well.

It looks like I’ll be doing some on-demand catch-up after the fact.

Connie Bensen has a Social Media Measurement post that is worth a read. While the post is focussed on measuring social media specifically, she hits on some areas that, all too often, are overlooked when it comes to developing metrics and then reporting on them over time.

The post includes a lot of resources for measuring social media — going well beyond simply web analytics data — as well as a list of examples of things that can be measured. What really struck me, though, was the list at the end of the post of what a community manager’s monthly report should include. First, the fact that it is a monthly report is somewhat refreshing — real-time on-demand reports are way overrated, and really are not practical when it comes to providing the sort of context that Connie describes.

On to Connie’s list of report elements — the bold text is from her list, and the non-bold description is my own take on the item:

• Ongoing definition of objectives — the framework of any recurring report should be the objectives that it is attempting to measure, so I love that this is the first bullet on the list. I would qualify it just a bit — it does not seem right to be making the defining of objectives an ongoing exercise; rather, objectives should be established, reiterated on an ongoing basis (so that everyone remembers why we’re tackling this initiative in the first place), and revisited periodically (objectives can and should change).
• Web analytics — this is the “easy” data to provide on a recurring basis, it’s data that most people are getting comfortable with, and, even though there is a lot of noise in the data, it is still reasonably objective; the key here is to focus on the web analytics data that actually matters, rather than including everything.
• Interaction - Trends in members, topics, discovery of new communities – this is a somewhat community-specific component, but it’s a good one; the “discovery of new communities” actually implies an objective regarding the role of a community manager; what a great metric, though, to drive behavior within the role.
  
• Qualitative Quotes - helpful for feedback & marketing – to broaden this list to beyond reporting for social media, let’s change “Quotes” to “Data;” make the report real by providing tangible, but qualitative, examples of what is going well (or not); reporting on lead generation activity, for instance, can include selected comments that were made by attendees at a webinar — highlighting what resonated with the audience (and what did not).
• Recommendations - Based on interactions with the customers – recommendations, recommendations, recommendations! What is the point of pulling all of this information together if nothing gets done with it? I sometimes like to include recommendations at the beginning of a report — they’re a great way to engage the report consumer by making statements about a course of action right up front.
• Benchmark based on previous report – my preference is to use stated targets (where it makes sense) as the benchmark, rather than simply looking for the delta of the data over a prior reporting period. But, sometimes, that is simply not feasible. Including “here’s the measurement…and here’s the direction it is heading” is definitely a good thing. But, it’s also important to not look at a 2-month span and jump to “we have a trend!”

Having recently relaunched the Bulldog Solutions blog, I’ve got a good opportunity to put Connie’s post into practice. Oh, dear…that’s going to require re-opening the, “What are our objectives for this thing…clearly stated, please?!” Stay tuned…

At the top list of blogs I follow is Jeremiah Owyang’s Web Strategist blog. He posts frequently, with depth, and with insight. However, I was in the midst of a hectic week in Austin when he posted his Analysis of the Zuckerberg Lacy Interview, and, frankly, while the title persisted in a couple of places (not only in his feed, but in my Yahoo! Pipe feed on data from non-data blogs because of the word “analysis” in the title), I’d been pretty much Zuckerberg-Lacy’d out, and I thought this was going to be a “my take on what happened” type of “analysis.” I was wrong.

The one-sentence recap of the actual incident: Sarah Lacy, of Business Week, interviewed Mark Zuckerberg, founder and CEO of Facebook, at SXSW Interactive in Austin, and the crowd, which was none too happy with the way the interview was going, pretty much took over using Twitter as a backchannel of communication. Google it and you can read scads more as well as see video of the event.

Well, I finally got around to looking at Jeremiah’s post, and I’ll be damned if it wasn’t a brief post linking to a really interesting TechnoSocial post: Anatomy of a Mob: The Lacy/Zuckerberg Interview. Kee Hinckley sifted through a bunch of Twitter data to try to get some insight into what really went on through Twitter during the keynote. It’s a fascinating read.

What I want to point out, though, is not so much the results of the analysis, but some pretty darn noteworthy aspects of what went into it.

First, I immediately started wondering how on earth Hinckley figured out which Twitter users were at the keynote. As it turns out, he explains it — recognizing that it’s imperfect, but, by golly, still pretty clever! And, it took a mix of tools, some level of clunky automation (no one likes to do screen scraping), and quite a bit of flat-out manual effort. He revised what he included/excluded as he got into the manual part of the exercise. What’s Noteworthy: the data Hinckley wanted was not easily accessible (the data always requires more prep work than most people realize), and it required some judgment when it came to getting it. That’s stepping out of a formulaic approach to analysis of “pull the data that’s available and present it.”

Second, the visualization. I am almost always opposed to 3D representations of data. Categorically when it’s two dimensions of data presented with “depth” — that’s just silly. But, even when it’s three dimensions presented in three dimensions, more often than not, the result is uninterpretable. Not the case here! Hincklely steps outside of the box to think about ways to effectively visualize the data — much, much more thought than simply “How do I get all of the data displayed?” He even includes two different charts, one with bubbles and one with a color spectrum, of the same data — clearly grappling with how best to show the information clearly (both work, IMHO). What’s Noteworthy: All too often, I see analysts go through all of the hurdles of prepping the data and “running the numbers” only to take shortcuts when it comes to the visual representation of the results. That’s the equivalent of running 25.5 miles of a marathon really hard…and then going home.

Finally, Hinckley puts a lot of text-based interpretation behind his analysis. In this case, he clearly had the question, ran with trying to find the answer, and took responsibility for explaining the whole process and the results. And, he did all three swimmingly! What’s Noteworthy: In many situations, one person is asking the question, while an analyst tries to find the answer. It’s that third area — explaining the process and results — where many analysts decide not to tread. Rather, they “do the analysis” and turn the “results” (mostly the data, including charts) over to the original requestor to interpret and explain. This bothers me. I much prefer to see an analyst actually draw conclusions and provide real context and interpretation. Whether they are expected to or not! It’s up to the original requestor to decide whether to use that information and how. More often than not, it gets used. To good results.

Overall, it’s a fascinating read. Top. Notch. Work!

I realized that my Data Posts from Non-Data Blogs Yahoo! pipe wasn’t working correctly, and when I fixed it, a recent post from Debbie Weil at BlogWrite for CEOs popped up: More on the ROI of Social Media: Return on Influence. Ordinarily, I’m a big fan of Weil’s thoughts, but this one had me wondering if I ought to try to track down some blood pressure medication. Weil by no means invented the phrase (and does not claim to have), “When it comes to social media, ROI really means ‘return on influence,’” but, sadly, she has jumped right on that misguided bandwagon.

Maybe it’s that I was raised in a house where one parent was an engineer and the other was an English major. Maybe it’s because I’ve got a contrarian bent — a slight one (I like “alternative” music but not “experimental” music). For whatever reason, “ROI is return on influence” has stuck in my craw from the first time I heard it. And it still makes me twitch whenever I stumble across a post where someone waxes eloquently about the genius of the phrase.

Weil has a couple of “short answers” for why return on influence makes sense. Her first is that it makse sense “because the return is soft. The benefits of incorporating social media strategies into your marketing are real (and can no longer be ignored) but they’re not normally measured in dollars.” I have no argument with any part of that assertion after the word “because.” Weil points out that the return is soft. So, why isn’t the “return” being replaced in this platitude? “Influence from (social media) investment” I get. And that is something that you should try to measure.

Are you still with me? No one who has picked up this phrase has stopped to think that it doesn’t make sense! If you develop influence in your market, then you will get a return, which may or may not be soft. But, are you trying to measure the return on that influence, or are you trying to measure the influence that you garnered by engaging in social media?

Marketers really are freaked out by the increasing focus on Marketing ROI. That focus is driven by CEOs and CFOs. In my experience, CFOs are pretty sharp people. They get that Marketing is important. What they want is accountability, efficiency, and effectiveness from Marketing. They want to know that the chunk of the company’s budget that is being invested in Marketing is being well-used. Unfortunately, they communicate that imperative in financial terms: “What’s the ROI?” They’re Finance people, folks! What would you expect?

Marketers, rather than getting to the heart of delivering business value — driving improvements in efficiency and effectiveness, and demonstrating results — have instead gone nutso with, “I have to show ROI!” Return on Influence is a headless-chicken response to this belief. And, almost comically, it has resulted in a classic marketing response: “Let’s spin and message it! Let’s talk about how, for Marketing in the social media world, ROI really stands for ‘Return On Influence.’”

Oh, man oh man, what I would pay to sit in the room when a Fortune 1000 CMO proudly rolls out that explanation to the CFO. It completely, utterly, totally, and ridiculously misses the point.

Accountability and continuous improvement, people: the executives in your company are not stupid (if you think they are, then they either are, or they aren’t but you think they are: in either case, find a new company). Understand what you are trying to accomplish with your social media strategy. Is it to build your brand? Is it to engage with your most avid customers? Is it to position your company as being full of cutting-edge thought leaders? Articulate that. Measure whether you are making headway with your efforts.

Am I right?

I’ve been a fairly avid follower and contributor to the webanalytics Yahoo! group for several years now. It’s a Yahoo! group that is almost 4,500 members strong and includes active participation by many of the top minds in the web analytics industry. I actually follow the group via e-mail, which seems awfully old school. As a matter of fact, the WAA Community and Social Media committee (which I’m a new…and not very active member of — Marshall Sponder does a great job of running the committee, and I do feel bad that I don’t help out more!) is trying to figure out how to get the group onto a better platform. There’s a bit of “if it ain’t broke, don’t fix it” discussion on the subject, honestly. And unfortunately. The fact is that I doubt that a majority of those 4,500 people are really embracing social media just yet. And this online community is already awfully vibrant and successful on the current platform.

The Yahoo! group was originally formed by Eric Peterson. As that list grew (Eric passed it over to the WAA a few years ago), Eric got the idea to start up a convention of having a “Web Analytics Wednesday” on the second Wednesday of the month. This would be a designated date for web analytics professionals throughout the world to get together for a few drinks, to network, and to share ideas and challenges. Initially, the organization and coordination of these meet-ups happened directly through the Yahoo! group. But, Eric eventually put up a nice little application on his web site to facilitate these, and they’ve continued to grow.

Several months after moving from Austin to Columbus, I caught two posts in rapid succession on the webanalytics group that were clearly from people in Columbus. A couple of e-mails and a lunch meeting later, and we were hosting the inaugural Web Analytics Wednesday in Columbus! We actually held it on a Tuesday, as the venue we found promised to be less crowded then. We had a dozen people show up, it lasted for over 3 hours, and the overwhelming consensus was that it was worth doing again. Now, we just have to figure out how to structure it!

Unfortunately, one of the key organizers — David Culbertson of Lightbulb Interactive — wasn’t able to make it. But, he did manage to get a nice post up on his blog, including the picture that we took with Jonghee Jo’s camera.

I guess I’m getting old enough that I’m still amazed at the power of the internet to pull together a group of people with a very focussed area of interest. And to make the leap from online to in-person interactions so smoothly no less!

As I’m thinking about how to categorize this post, I’m realizing that, despite the fact that it includes scatter plots, it’s a bit off topic for the sort of things I generally put in this blog.

But, back in December, in an afternoon of frenzied, yet focussed, exposition, I dashed out a 15-page pager on the topic of multidimensional lead scoring. That’s right. 15 pages. But with lots of pictures! It laid out, as clearly as possible, the rationale and “how to” for something we’ve been working on in the R&D labs at Bulldog Solutions for quite some time. It’s something we actually use ourselves…but we hadn’t taken the time to sit down and try to explain it clearly. Of the 8 scatter plots, here’s one that comes mid-way through the explanation and sort of tells the whole story:

The main point of going to a lead score that has two dimensions is to recognize that there are multiple unique facets of your ideal lead. It’s not just whether he/she is the perfect “profile” — a director-level decision maker with budget authority at a company that is $100 million or greater in the medical device industry (for instance) — but it’s also important to determine if they have a clue who you are and have any interest in talking to you! That’s where the “engagement level” comes in.

Your best leads fall in the top right area of the scatter plot — you want to talk to them, and they seem interested in talking to you. That’s it in a nutshell. The paper goes into more step-by-step detail as to what that really means, how to implement it, and what to watch out for. There’s a longer introduction/overview (no registration required) on the Bulldog Solutions web site. To get the full paper, you have to register (or, shucks, shoot me an e-mail and I’ll send it to you directly). An unnamed source (no, not a relative or a current co-worker!) made the following comment about the paper:

Really like the way you structured the approach to the topic and broke it down into steps that are easily internalized as well as actionable and measurable. As always, I am in awe of your writing skills, which made for a enjoyable read. But most of all it provoked some new thoughts on a topic that had fallen of my radar…

That was pretty high praise from an experienced B2B marketer who is a notoriously straight shooter (painfully so, at times).

Now, as I was working on the paper, with Twitter running on my second screen…and jumping over to Facebook periodically…and checking out the various blogs I subscribe to…

I couldn’t help but think about how “B2B lead scoring” fits in with “social media.” Actually, this was more than idle distraction — I’m also working on a project that brings those two concepts together for our own internal operations. On the one hand, “lead scoring” still feels a bit old school. I mean, we’re focussed on watching what people are doing and, basically, pouncing on them with a rabid salesperson when our processes spit out that they’ll be easy prey. Right? Well, not really. By introducting the “engagement level” dimension, we’re actually saying, “We’re happy to keep feeding you useful information. We’re actually motivated to nurture you without a hard sell…until you start poking around on our site and showing that you think we’ve got some credibility.” And, ideally, we’ll also try to snoop out where in the buying process you are and not reach out to you personally until you’re in the middle to later stages.

I guess I see multidimensional lead scoring as a bridge between the past — Marketing gets the leads and tosses them over to Sales to call ‘em up and sell — and the future — hyper-interconnectivity and information sharing among peer groups, where the company’s only option is to have a great product and support their community of potential users with high quality information through whatever channel the users want to consume it and engage with it.

What do you think? Does this make sense, or am I fooling myself?

Jeremiah Owyang has a post on his Web Strategist blog titled Why Your Social Media Plan should have Success Metrics. Based on the URL of the post, it looks like Owyang initially titled the entry “Why Your Social Media Plan should Indicate What Does Success Look Like.” Admittedly, the original title is a bit clunky. But, in the cleanup, he actually oversimplified the main point of his post, which is that it’s important to have some clear idea of why you’re tackling social media and some idea what you’re hoping to get out of it. He includes some examples:

A few examples of what success could look like for you:

• We were able to learn something about customers we’ve never know before
• We were able to tell our story to customers and they shared it with others
• A blogging program where there are more customers talking back in comments than posts
• An online community where customers are self-supporting each other and costs are reduced
• We learn a lot from this experimental program, and pave the way for future projects, that could still be a success metric
• We gain experience with a new way of two-way communication
• We connect with a handful of customers like never before as they talk back and we listen
• We learned something from customers that we didn’t know before

One of the commenters correctly pointed out that none of these examples were “metrics” per se. I say, “Cool!” Owyang’s point is spot on — be clear on why you’re tackling social media. And, you know what? If it’s, “Because I don’t understand it and don’t ‘get’ it and figure the best way to learn is to dive in and do it,” then that’s okay! Of course, if that is the only reason you are dipping your phallanges into social media, then you should also set a target date for when you’re going to evaluate whether you are going to continue — with more focussed objectives — or whether you are going to reduce your focus on it.

The metrics will come. Sometime, they’re not crisp, clean, perfect metrics. That’s okay. I’m a fan of proxy measures, as well as the occasional use of subjective measures. Quantitative measures that aren’t tied to clear objectives, on the other hand, drive me bonkers.

So, what are my objectives with this part of my personal social media experimenting? Very simply, they’re as follows:

• See if I can “do” it — post with some level of substance on a sustained basis
• Give myself an outlet for expressing my opinions and frustrations about data usage (when it’s not appropriate to express them directly to the person who triggered the need for an outlet)
• Learn about blogging technologies

The jury is still a bit out on the first objective, but it’s looking like the answer is, “I can.”

I am clearly hitting the second objective (and will continue to do so).

I’ve become intimate with both Blogger and WordPress, as well as dabbled with Technorati, Feedburner, Yahoo! Pipes, and any number of social networking and social bookmarking platforms, so I’d say I’m well on my way to the third.

I’m not feeling the need to reset my objectives just yet.

Avinash Kaushik just published a post titled History Is Overrated (Atleast For Us, Atleast ForNow). The point of that post is that, in the world of web analytics, it can be tempting to try to keep years of historical data…usually “for trending purposes.” Unfortunately, this can get costly, as even a moderately trafficked site can generate a lot of web traffic data. And, even with a cost-per-MB for storage of a fraction of a penny, the infrastructure to retain this data in an accessible format can get expensive. Avinash makes a number of good points as to why this really isn’t necessary. I’m not going to reiterate those here.

The post sparked a related thought in my head, which is the title of this post: bounce rate is not revenue. Obviously, bounce rate (the % of traffic to your site that exits the site before viewing a second page) is not revenue. And, bounce rate doesn’t necessarily correlate to revenue. It might correlate in a parallel universe where there is a natural law that no dependent variable can have more than 2 independent variables. But, here on planet Earth, there are simply too many moving parts between the bounce rate and revenue for this to actually happen.

But.

That’s not really my point.

What jumped out at me from Avinash’s post, as well as some of the follow-up comments, was that, at the end of the day, most companies measure their success on some form of revenue and profitability. Realizing that there is incredible complexity in calculating both of these when it comes to GAAP and financial accounting, what these two measures are trying to get at, and what they mean, are fairly clear intuitively. And, it’s safe to say that these are going to be key measures for most companies 10, 20, or 50 years from now, just as they were key measures for most companies 50 years ago.

Sales organizations are typically driven by revenue — broken down as sales quotas and results. Manufacturing departments are more focussed on profitability-related measures: COGS, inventory turns, first pass yields, etc.  Over the past 5-10 years, there has been a push to take measurement / data-driven decision-making into Marketing. And, understandably, Marketing departments have balked. Partly, this is a fear of “accountability” (although Marketing ROI is not the same as accountability, it certainly gets treated that way) Partly, this is a fear of figuring out something that can be very, very, very difficult.

But, many companies are giving this a go. Cost Per Lead (CPL) is a typical “profitability” measure. Lead Conversion is a typical “revenue” measure. That is all well and good, but the internet is adding complexity at a rapid pace. Pockets of the organization are embracing and driving success with new web technologies, as well as new ways to analyze and improve content and processes through web analytics. No one was talking about “bounce rate” 5 years ago and, I’d be shocked if anyone is talking about bounce rate 5 years from now.

Social media, new media, Web 2.0 — call it what you like. It’s changing. It’s changing fast. Marketing departments are scrambling to keep up. In the end, customers are going to win…and Marketing is going to be a lot more fun. But we’ve got a lonnnnnnnnng period of rapidly changing definitions of “the right metrics to look at” for Marketing.

While it is easy to get into a mode of too constantly reevaluating what your Marketing KPIs are, it is equally foolish to think that this is a one-time exercise that will not need to revisited for several years.

Oh, what exciting times we live in!

I say, “No.”

And, actually, it’s not just me. More on that in a minute.

I’m going to reference some stuff from wayyyyy back in May 2007 here. I totally missed it when it came out (I’ve got a long list of good excuses), but I recently stumbled across it as I was setting up a Yahoo! Pipe on Data Posts from Non-Data (Marketing) Blogs. More on that to come as I continue to refine it and, hopefully, add it as a resource page on this blog.

What cropped up was a post that I couldn’t possible skip from Brian Carroll titled The Difference Between ROI and Marketing Accountability. Brian Carroll is the author of Lead Generation for the Complex Sale and a really, really sharp mind when it comes to B2B marketing. Turns out, in his post, he was really referencing an exchange that an earlier post had started between he and the Eisenberg brothers, authors of Waiting for Your Cat to Bark. Jeffrey Eisenberg and Brian (Carroll) had an exchange on Brian’s initial post that resulted in a Brian (Eisenberg) article in ClickZ — also titled The Difference Between ROI and Marketing Accountability (I mixed it up a little bit in my title — I’m just a wild and crazy guy that way). That article referenced and linked back to Brian Carroll’s original post, which Jeffrey had commented on: On B2B Demand Generation tools and Lead Generation Dashboards.

Normally, I wouldn’t go so nutso with the links, but the reality is that all three of these posts/articles make some outstanding points.

From Brian Carroll’s original post:

…most sales and marketing professionals recognize that software will not spontaneously generate results, but the allure of easy execution and fast results are difficult to resist. It’s also easy to forget that these systems require a great deal of hands on input and maintenance to be fully appreciated.

Right on! How many times have I heard: “What do you mean the data doesn’t tell us anything? Didn’t we buy all this software so we’d have good data?” Even working at a company that is focused on using data to drive the business, we are constantly playing catch-up as we adjust our processes and try to force people to keep the CRM up to date. (Aside: If you have to force people to do something, it will fail in the long run — and, thus, the data guy gets embroiled in processes and systems).

Jeffrey Eisenberg’s comment on that article:

Measuring the ROI of lead generation isn’t the same thing as full accountability. If marketing is a profitable activity, it still doesn’t mean that what it is communicating to the universe of buyers is building the business. I’ve seen lots of marketers sacrifice early and middle stage buyers because they had to show an immediate ROI on each campaign they ran. Who is accountable for all the potential business they lose by saying the wrong the thing to the right people at the wrong time?

If this was about half as long, I just might consider getting it as a tattoo! Playing off the old axiom of, “No one gets fired for buying IBM,” I’d say, “No one gets fired for following up with a lead too often and too aggressively.” Hmmmm. I don’t think mine is going to get much traction. The problem, though, is that we chase the siren song of accountability through direct measurement and pretty (or ugly) dashboards. It’s sooooo easy to get sucked into logic that goes something like this:

1. We need to be accountable
2. To be accountable, we have to have objective measures
3. Oh, and those objective measures have to be measurable quickly
4. Accountability = things we can measure frequently (and easily)

And so, at the tactical level, we measure open rates, clickthrough rates, registrations, web site visits, bounce rates, and the like.

Bubble up a little higher in the food chain, and we measure leads and qualified leads. And, we pat ourselves on the back for measuring lead conversion (to an opportunity, to revenue, or both).

And that’s what we start chasing. We start looking for ways to tweak our messaging, alter our media spend,  sweeten our calls-to-action, and “tune the machine” to drive more revenue now. But, is that what Marketing is all about? Is that what it should be about? Is this the best ROI that Marketing can deliver over the long term?

I just finished reading Geoff Livingston’s Now Is Gone: A Primer on New Media for Executives and Entrepreneurs. Interestingly, by my count, Livingston only brings up measurement of social media two times in the book, and it’s a vague, passing nod in both cases. Around the time the book came out, though, he tackled the subject with more vigor by starting a meme on the subject. What’s key in his initial thoughts there is that the ROI examples he focusses on are much deeper than short-term lead-to-revenue. They’re examples of companies that have stepped back and, on the one hand, made a little bit of a leap of faith that social media is something they should invest in and, on the other hand, have focussed on measuring things that were unequivocably positives for the company…but not necessarily things that could be tied directly to revenue.

In short: Measurement is good. Accountability is good. “Marketing ROI” is NOT the magical link between the two.

There is a lot of buzz of late regarding Robert Scoble getting knocked off of Facebook as he was testing out Plaxo and, in the process, scraping data from Facebook. The debate that has primarily raged has been around who “owns” our data when we load it into a social media site. I’m pretty sure that the Terms of Use we all blithely accept spell that out fairly clearly. I’m also pretty sure that legalese is largely irrelevant when it comes to the court of public opininion, as Facebook seems to continually rediscover!

Debbie Weil had an interesting take on the situation in her post: The controversial issue of ”data portability” (or what we used to call “privacy”). She makes the point that, “With so many of us living so much of our lives online we are trusting both that our ‘data’ won’t be misused and that it won’t disappear.” We don’t often enough recognize that data portability and privacy, if not directly in conflict, apply pressure in two different directions. Chris Brogan, Jeremiah Owyang, and many, many others have touched on the subject. In Brogan’s case, and in many of the comments on Twitter, the emphasis is on the nuisance factor of having to re-enter the same information in multiple places. Generally, there is some nod to “privacy” — “it needs to be secure, private, with configurable access permissions” — but that gets thrown in almost as an afterthought. On the other hand, it only takes one or two examples of some form of identity theft to give people pause about making their data truly portable. As a matter of fact, an on-going discussion in the world of web analytics is, “How much detail can we — and should we — track and keep on visitors to our sites?” And, when governments get involved, the emphasis is virtually always on ensuring privacy rather than on improving efficiency (in the U.S., HIPAA and CAN-SPAM come to mind immediately).

This is a truly thorny issue, and it comes down to trying to accurately manage personal preferences across multiple interrelated/interconnected systems. On one end of the spectrum, the privacy paranoid person resists sharing any true information whatsoever, and he can aggressively tell sites not to share his information in any way whatsoever — even with him! This poor soul is almost definitely going to give himself high blood pressure, and the shorter life he is going to live is going to be inefficiently lived as he continually puts up barriers that he has to repeatedly climb over. On the other extreme is the person who will openly share even his bank account details because he doesn’t believe it will ever bite him in the ass (we can label this archetype Jeremy Clarkson).

The reality is that 99% of us live somewhere in between these two extremes. Most of us believe that where we have placed ourselves on this spectrum is the obviously logical place to be. And most of us are uncomfortable shifting even slightly from our current position towards either end of that spectrum.

The person who has a finite number of cell phone minutes each month on herplan may fiercely guard that number while freely sharing her home number. Another person may have unlimited minutes and no issues with screening her cell phone calls as they arrive, so may prefer that number as her primary, most public contact channel.

This means any “solution” will have to be highly configurable. Which, sadly, means that it may be cumbersome to manage. And may struggle to get adopted. I’ll continue to keep my fingers crossed that OpenID, The Todeka Project, or some other approach can allow us to personalize our point on the privacy/portability spectrum.