That got me thinking that a partial solution would be to have a Google Analytics profile that only includes organic search traffic. This isn’t a profile that you would use for cross-session analytics, but it’s one that would allow simplified segmentation, reduced cases of sampling, and, perhaps, a more complete data set.

As it turns out, it was pretty simple to set up, and it seems to do the trick.

Step 1: Make a New Profile

Create a new profile under the same web property that you’re using for your site and name it Organic Search Traffic Only:

There’s nothing magic about this. The key is that this is a profile that uses the same web property ID as the profile where you’re running into sampling issues with your SEO analysis. We’re just going to take that same feed of data coming in as visitors visit your site and carve out the subset of that data that is traffic from organic search referrals.

Step 2: Apply an Organic Search Filter

The next (and final) step is to create a filter and apply it to the profile such that only organic search traffic is included.

In the new profile you just created, select the Filters tab and then click New Filter:

From there:

1. Give the filter a name like “Organic Search Referrals”
2. Select Custom Filter as the Filter Type
3. Set the filter as an Include filter
4. Set the Filter Field to Campaign Medium
5. Set the Filter Pattern to “organic”
6. Save the filter

The screen below shows the filter settings:

Step 3: Sit Back and Let the Data Roll In

The profile is only going to include data from the point you set it up going forward. But, it will accurately reflect (to the extent that any web analytics package can accurately reflect this) new versus returning visitors for all time (well, since you initially implemented Google Analytics), because it’s getting that data from the cookie that already exists on users’ machines.

Initially, I saw some odd data on the unique visitors front, which I can semi-intuitively understand…but not quite explain.

Suffice it to say that, once you have the profile up and running for a week or so, you can select the Non-paid Search Traffic segment in your main profile and compare it to the All Visits segment in your new profile, and the numbers will be virtually identical. But, you can now do SEO analysis with a base set of data that only includes search traffic.

Is that handy?Similar Posts:

• Google Analytics = Strawberry?
• Web Analytics Platforms Are Fundamentally Broken
• A Quick Explanation of Sitecatalyst Events for the Google Analytics Power User
• SEO Tips and Thoughts at Web Analytics Wednesday
• Web Analytics Tools Comparison — Columbus WAW Recap Part 2

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So, Why a New Design?

I updated the design for two reasons:

• The old design was starting to wear on me. There were a number of little alignment/layout/wrapping issues that I had never quite managed to fix, even as I tinkered with the blog functionality (for instance, my social icons never quite lined up well). I also figured out last fall that the nested table structure pretty much precluded me from getting the mix I wanted of fixed and liquid elements. In short, a redesign just seemed in order.
• Responsive web design is here. This was more of the direct-tie-to-my-day-job reason for the overhaul. Various sharp people at Resource Interactive have started pushing responsive web design as something that should be actively considered for our clients. As I dug into the topic, I realized that: 1) this blog is a good candidate for a responsive design, and 2) there are some analytics implications to a responsive design, and I needed somewhere to experiment with them.

So, this site is now using a fully responsive WordPress theme.

What Is Responsive Design, Exactly?

As I understand it, responsive design is an “Aha!” that grew out of the increasing need for web sites to function across a wide range of screen sizes and experiences and platforms: laptop monitors, desktop monitors, tablets (iOS and Android), and smartphones (also iOS and Android). The idea is that, rather than having a “desktop site” and a “mobile-optimized site,” you can have “a site” that works effectively on a wide range of devices.

There are two keys to this:

• The site needs to be viewable in different devices – 3 columns that display on a desktop monitor may need to become a single set of stacked content on a smartphone. Or, a list of links in the sidebar on the desktop may need to become a dropdown box at the top of the page on an iPhone.
• The site needs to support the most likely use cases in different devices – this is a stickier wicket, because it forces some strategic thought (and possibly research and testing) to think through what a visitor to your site who is using an iPhone (for instance) is likely looking to do and how that differs from a visitor to your site who is using a desktop.

Both of these are questions that have always been asked when it comes to developing a “mobile-optimized version of the site,” but they’re a bit more nuanced given that responsive design isn’t a “separate site.”

Wow, Tim, I’m Impressed with Your Coding Skills!

Don’t be impressed with my coding skills.

I did a little research and then shelled out $35 to buy the Rising theme. That doesn’t mean there wasn’t a fair amount of tinkering (and more tinkering yet to be done — I certainly have not fallen prey to a need to have the perfect site designed before pushing it live!), but the end result is an improved site. And, more importantly, having a site that actually works well across devices (Try it! Just resize your browser window and watch the sidebar at the right. Or, fire up the site on your smartphone and compare it to your desktop.)

Now, of the “two keys” above, I really focused on the first one. This is a blog, after all. Regardless of what device you’re on, presumably, you’re here to consume blog post content.

I’m still working with the palette (too little contrast between the hyperlink color and the plain text color), the font selection (I’m not in love with it), and the header logo (pulling what strings I can to get a professional to contribute on that front), but I’m reasonably content with the change. Let me know if you have any tips for improving the design (I’m not proud!).

Where Does Analytics Come into All of This?

While I have access to tons of different web analytics accounts across a range of platforms through our various clients, I don’t actually have a great sandbox for trying things out (you would think our company’s site would be a good testbed, but the reality is that there are so many competing agendas for competing resources there that it’s seldom worth the effort). Luckily, this site has built up enough content and enough of a presence to get a few hundred visits a day, which is enough to actually do some tinkering and get some real data as a result.

Here’s my list of what I’ll be toying with over the coming weeks:

• Responsive design analytics — we’ve had “screen resolution” and “device” reporting for years, but responsive design introduces a whole new twist, because it’s truly experience-centric. I’ve done a little digging online and haven’t found much in the way of thinking on this. While I don’t think it’s possible to directly pull CSS media query data into the web analytics platform, it should be possible to use Javascript to detect which responsive layout is being used for any given visitor and then pass that information to the web analytics platform (as a custom variable or a non-interaction event in Google Analytics). And, it should be possible to record when an onresize event occurs. In both cases, using this data to segment traffic to determine if a particular layout is performing poorly or well, as well as how visitors move through the site in these different experiences, seems like a promising thought.
• Facebook Insights for Websites — I’ve had this running for a while, but, as part of another experiment, I switched over from using my Facebook user ID in the meta data to authenticate my ownership of the site to using a Facebook app ID. That’s a better way to go when it comes to “real” sites, and I’m now actually doing some tinkering on some client sites to fully validate what happens, so look for some thoughts on that front in the future.
• Detecting the Facebook login status of visitors to the site – this is some experimentation that is actively in work. It’s the implementation of some code that Dennis Paagman came up with to use Facebook Connect and Google Analytics non-interaction events to detect (and then — my thinking — segment) visitors based on whether they’re logged into Facebook or not at the time of their visit to the site. This seems like it has intriguing possibilities when it comes to determining what types of social  interactions should be offered and how prominently. I’ve hit a minor snag on that front and am hoping Dennis will be able to help get to the bottom of it (see the comments on his blog post). But, if I get it figured out, I’ll share in a post down the road.
• Site performance – anecdotally, it seems like this site is now loading more slowly than it did with the old design. The Google Analytics Site Speed report seems to indicate that is the case, but I don’t feel like I have enough data to be conclusive there just yet. I have signed up for a site24x7.com account, which is a platform we use with some of our clients for a couple of reasons: 1) to see what it reports relative to Google Analytics (it’s a fundamentally different data capture method, so I’m not going to be surprised if the results are wildly divergent), and 2) to get more reliable data if I start playing with changes to reduce the site load time. In hindsight, I wish I’d signed up a month or so ago so I had good pre- and post- data. If I had a nickel for every time I wanted to have had that, I’d be a wealthy man!

In a nutshell (a gargantuan, artificial nutshell, I’ll grant you), I’ve got a backlog of topics, some of which will require some additional experimentation. This blog post, I realize, is almost more of a “to do” list for me than it is a “how to” list for you! Oh, well. They can’t all be winners!

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | 2 comments | Add to del.icio.us
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On Friday, November 19, 2011, the good folk over at Web Analytics Demystified experimented with a new format for a digital analytics conference, dubbed #ACCELERATE. The key features of the event:

• It was entirely free to attendees (it was sponsored by Tealeaf, OpinionLab, and Ensighten)
• It lasted a single day
• It had two distinct presentation formats — a 20-minute format and a 5-minute format

The 20-minute presentations were  in a “10 Tips in 20 Minutes” format on topics that the organizers selected and then recruited speakers to present. The 5-minute presentations were left entirely up to the presenter when it came to topic selection, but they were encouraged to bring a “Big Idea” and make it “FUN.”

I’ve actually found myself doing more reflection on the conference structure, format, and details than I’ve found myself mulling over the content itself. I’d find that troubling if it weren’t for the fact that I picked up a solid set of intriguing and re-usable nuggets from the content. And, I’ve seen a few blog posts already that do a great job of recapping the event:

• Michele Hinojosa’s Top 10 Takeaways plays with the “list of 10″ format of the event by listing three different sets of 10 takeaways (she left off her own session which provided one of the enduring images for me when she plotted the four different “types” of digital analytics jobs — industry, vendor, agency, consultant — on a 2×2 grid that illustrated how the experiences differ; it’s a handy graphical view of the career development guide she spearheaded for the WAA earlier this year)
• Corry Prohens’s review of the event recaps the content session by session (but, of course, left out his own excellent session on how to go about recruiting and hiring the right digital analyst for the job).
• Gabriele Endress recapped the event as well, including a “top 5 learnings” that are spot-on when it comes to the key realities of the dynamic world of digital analytics

I really don’t have much to add to those summaries. The content was great, and I’ve walked away with an array of actions/requests/hopes:

• I’ve secured a copy of June Dershowitz’s presentation and the blog post that inspired it (top geek humor from the event: “?q=<3″)
• I’ve prodded Michele to elaborate on her 2×2 grid
• I’ve been mulling over the vendor-user relationship as described by Ben Gaines (while I have been critical of technology platforms, I also think most vendors with whom I’ve worked closely would put me at least marginally above average on the collaboration/partnership front)
• I’ve re-cemented Justin Kistner in my brain as my go-to resource for all things Facebook
• I’m looking forward to Chicago and fervently hoping that Ken Pendergast (or someone) takes another run at making the case for one of the enterprise web analytics vendors to offer a freemium option (I’ve heard that that’s been bandied about over the years at Adobiture, but it’s never been something they’ve been able to effectively justify)

That’s all of the stuff I’m not going to cover in this post. Instead, I’m going to cover more of a meta analysis of the event — a range of factors that made the event stand out and positioned it for on-going evolution and excellence.

Social Media Integration

Social media was heavily incorporated into the event:

• Twitter-friendliness Part 1 — the event’s name itself — #ACCELERATE — was a ready-made Twitter hashtag. That was clever, as it meant that all Twitter references to the event automatically used Twitter conventions that made the content easy to find, follow, and amplify.
• Twitter-friendliness Part 2 – throughout the day, Eric Peterson encouraged attendees to use both #ACCELERATE and #measure as they tweeted, and there were incentives for participants to tweet (with quality tweets) both before and during the event (with winners selected using Twitalyzer and TweetReach). This had the effect of #ACCELERATE dominating the #measure world for the day (at one point, TweetReach reported that over 70% of all #measure tweets for the day also included #ACCELERATE in the tweets). That meant that no one who is at least nominally following the #measure hashtag could fail to be aware of the event and aware of the fact that it was a very “socially active” conference.
• Twitter-maybe-not-so-friendliness Qualifier — the slightly unfortunate side effect of the “10 tips” presentation format, combined with the tweet encouragement, was that it was really easy to simply tweet the title of each “tip,” which often really weren’t all that useful without listening and re-articulating the presenter’s explanation of the tip. A tweet I saw from a non-attendee asked a good question on that front:

“…most of the #measure tweets today were about #ACCELERATE… but was it always relevant?”

• Post-event buzz bounty – Eric tacked on an incentive for conference attendees to write about (either publicly or privately in an email) their experiences at the event, with the Web Analytics Demystified team being the judges of the “best” write-up. I suspect that will result in a higher number of blog posts than would otherwise have occurred.

Overall, it was a big win on the Twitter front — I haven’t been to a conference that so actively leveraged the platform both for pre-event buzz generation and during-event content sharing (and further buzz generation). See the last section of Michele Hinojosa’s post for more detail on the Twitter activity.

Presentations Functioning on Two Levels

When it came to the presentation structure, the organizers bent over backwards to set the speakers up for success. In his recap of the event, Corry Prohens credited Craig Burgess with the following observation:

“The conference was also a study on presentation styles and techniques. How often do you get to see 26 presentations in a day? It is a rare opportunity to spot trends and take note of what works. In a field where we all have to present what we know (to clients, stakeholders, etc.) this was a big value-add to the digital measurement insights.”

This was an excellent point. Any conference is going to include sessions that stand out as being fantastic, as well as a few sessions that fall flat. One notable exception (qualifying full disclosure: it’s a conference I’ve never attended): TED.  Whether Eric and company consciously drew inspiration from TED or not, I don’t know, but there are two taglines on the TED home page that could easily be applied to the aspirations for #ACCELERATE:

“Ideas worth spreading”

“Riveting talks by remarkable people, free to the world”

By packing so many sessions into a single day and enforcing brevity (out of necessity), #ACCELERATE had a great pace and kept the attendees engaged for the entire event. Presenters were pushed to bring their “A” game to their sessions, both by repeated reminder-admonitions from Eric, as well as by the inclusion of audience-awarded $500 Best Buy gift cards for the top session of each format.

The presentations were set up to effectively convey useful and engaging content. At the same time, the presentations were set up to give the presenters a set of liberating constraints — establishing distinct guardrails for the content that then empowered the presenters to really focus in on the content and the way they communicated it. This benefited the presenters, certainly, by helping them hone the craft of presenting (that was my experience, at least), but it also benefited the audience by exposing them to a large number of presenters in a concentrated period. I hope everyone took away a few useful nuggets that they can incorporate into their own future presentations (internally or at conferences).

I haven’t attended a single conference in the last 18 months where one of the sub-themes of the conference wasn’t, “As analysts, we’ve got to get better at telling stories rather than simply presenting data.” There is real value in a conference that is designed to help analysts develop their storytelling chops.

Audience Participation

Having the audience directly vote for the winning presentation was another innovation from the event. While it is not at all unheard of to have audience-based voting on presentations, the fact that #ACCELERATE put this at the forefront was something new for digital analytics conferences, as far as I’m aware.

OpinionLab’s DialogCentral platform was leveraged to allow real-time voting and feedback on each session as it occurred. I saw a demo of DialogCentral over a year ago, found it intriguing, and then could never remember what it was called or what ever happened to it, so it was good to see it put into action. Any audience member who had a smartphone could quickly navigate to a mobile-optimized site and vote the presentation on a 5-point scale, leave an open-ended comment, and leave contact info if desired.

There were some glitches on that front, in that there were some participants who did not have smartphones (well, 2 or 3), and at least one attendee reported that the system did not work on her Blackberry. Overall, the voting occurred in smaller numbers than I think the organizers hoped, but it was a great idea and it worked perfectly adequately for a first-time attempt.

And…It Was Free

It’s easy to simply rattle off that “free is better” and leave it at that.  As a first-time event, I’m sure the fact that the event was fully sponsor-supported helped make it fill up quickly. The challenge with having a free event is that the registrants have no real skin in the game — it’s easy to sign up first and then figure out if you can actually attend. If you can’t, well, no worries, because it’s no money out of your pocket! Having co-organized Web Analytics Wednesdays in Columbus — also free events — for several years now, I’ve lived with this challenge firsthand. Trying to accurately predict the no-show rate is an art unto itself, which introduces a range of logistical headaches.

At the other extreme from “free,” the major established digital analytics conferences all have hefty price tags, which makes them cost-prohibitive for many potential attendees who are operating in organizations that have extremely limited training and conference budgets (not to mention the personal budgets for analysts who are in between jobs and could really benefit from the networking opportunities at conferences). That, I suspect, leads to misaligned speaker incentives — members of the industry desperately angling for speaking slots so they can reduce the cost of the overall conference attendance rather than because they have something unique and worthwhile to share.

I could totally see #ACCELERATE evolving to have a nominal registration fee — something like $100 would ensure there was a real commitment required by registrants, but it would also make it totally feasible for someone to attend without corporate backing (make it $25 for students, and, heck, provide bartered alternatives where people can blog about the event or get referral credits).

Overall, free is good, and that made the event right-sized — ~300 people was enough to keep a single track, provide plenty of opportunity for worthwhile networking, while also keeping the setting relatively intimate.

I’m looking forward to Chicago!Similar Posts:

• Gilligan Meets Super #ACCELERATE — Recreated

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The instructions we got from Eric Peterson for the Super #ACCELERATE sessions were simple and clear (and reinforced multiple times):

1. NO MORE than 5 minutes
2. One BIG IDEA
3. Have FUN

With that, I noodled on a variety of topics and then decided to use the opportunity to try to bring together a couple of thoughts I’ve had over the past six months to see if I could coherently articulate how they could all play together in an envisioned future.

Several people asked for a reproduction of the presentation, so I’ve recorded it as a video with voiceover (you don’t get the added imagery of me standing behind a podium, but I don’t think that overly detracts from the experience). The video version below is 30 seconds longer than 5 minutes because I’ve added an intro slide and a set of credits that were not part of the actual presentation.

The slides themselves are also posted on SlideShare (no audio included).

I’ll have another post (or maybe two) of reflections on the event. I’ll also be eagerly looking forward to the next #ACCELERATE event slated for April in Chicago.Similar Posts:

• Reflections on the Inaugural #ACCELERATE Conference

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A recent Wall Street Journal article titled Business Schools Plan Leap Into Data covered the recognition by business schools that they are sending their students out into the world ill-equipped to handle the data side of their roles:

Data analytics was once considered the purview of math, science and information-technology specialists. Now barraged with data from the Web and other sources, companies want employees who can both sift through the information and help solve business problems or strategize.

That article spawned a somewhat cranky line of thought. It’s been a standard part of presentations and training I’ve given for years that there is a gap in our business schools when it comes to teaching students how to actually use data. And, the article includes a quote from an administrator at the Fordham business school: “Historically, students go into marketing because they ‘don’t do numbers.’” That’s an accurate observation. But, what is “doing numbers?” In the world of digital analytics, it’s a broad swath of activities:

• Consulting on the establishment of clear objectives and success measures (…and then developing appropriate dashboards and reports)
• Providing regular performance measurement (okay, this should be fully automated through integrated dashboards…but that’s easier said than done)
• Testing hypotheses that drive decisions and action using a range of analysis techniques
• Building predictive models to enable testing of different potential courses of action to maximize business results
• Managing on-going testing and optimization of campaigns and channels to maximize business results
• Selecting/implementing/maintaining/governing data collection platforms and processes (web analytics, social analytics, customer data, etc.)
• Assisting with the interpretation/explanation of “the data” — supporting well-intended marketers who have found “something interesting” that needs to be vetted

This list is neither comprehensive nor a set of discrete, non-overlapping activities. But, hopefully, it illustrates the point:

The “practice of data analytics” is an almost impossibly broad topic to be covered in a single college course.

What bothered me about the WSJ article are two things:

• The total conflation of “statistics” with “understanding the numbers”
• The lack of any recognition of how important it is to actually be planning the collection of the data — it doesn’t just automatically show up in a data warehouse

On the first issue, there is something of an on-going discussion as to what extent statistics and predictive modeling should be a core capability and a constantly applied tool in the analyst’s toolset. Michael Healy made a pretty compelling case on this front in a blog post earlier this year – making a case for statistics, econometrics, and linear algebra as must-have skills for the web analyst. As he put it:

If the most advanced procedure you are regularly using is the CORREL function in Excel, that isn’t enough.

I’ve…never used the CORREL function in Excel. It’s certainly possible that I’m a total, non-value-add reporting squirrel. Obviously, I’m not going to recognize myself as such if that’s the case. I’ve worked with (and had work for me) various analysts who have heavy statistics and modeling skills. And, I relied on those analysts when conditions warranted. Generally, this was when we were sifting through a slew of customer data — profile and behavioral — and looking for patterns that would inform the business. But this work accounted for a very small percentage of all of the work that analysts did.

I’m a performance measurement guy because, time and again, I come across companies and brands that are falling down on that front. They wait until after a new campaign has launched to start thinking about measurement. They expect someone to deliver an ROI formula after the fact that will demonstrate the value they delivered. They don’t have processes in place to monitor the right measures to trigger alarms if their efforts aren’t delivering the intended results.

Without the basics of performance measurement — clear objectives, KPIs, and regular reporting — there cannot be effective testing and optimization. In my experience, companies that have a well-functioning and on-going testing and optimization program in place are the exception rather than the rule. And, companies that lack the fundamentals of performance management that try to jump directly to testing and optimization find themselves bogged down when they realize they’re not entirely clear what it is they’re optimizing to.

Diving into statistics, econometrics, and predictive modeling in the absence of the fundamentals is a dangerous place to be. I get it — part of performance measurement and basic analysis is understanding that just because a number went “up” doesn’t mean that this wasn’t the result of noise in the system. Understanding that correlation is not causation is important — that’s an easy concept to overlook, but it doesn’t require a deep knowledge of statistics to sound an appropriately cautionary note on that front. 9 times out of 10, it simply requires critical thinking.

None of this is to say that these advanced skills aren’t important. They absolutely have their place. And the demand for people with these skills will continue to grow. But, implying that this is the sort of skill that business schools need to be imparting to their students is misguided. Marketers are failing to add value at a much more basic level, and that’s where business schools need to start.

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | 8 comments | Add to del.icio.us
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Nevertheless, the Most Basic of the Basics

A QR code contains data in a black-and-white pixelated pattern. That’s all there is to it. It can store lots of different types of data (only a finite amount, of course), but the most common data for that pattern to store is a URL. For instance, the QR code below stores the URL for this blog post:

Please, DON’T Do What I Just Did!

Here’s the key point to this whole post: the example above is a perfect example of how NOT to generate a QR code.

Two reasons:

• It will not be possible to track the number of scans of the QR code
• The QR code is needlessly complex, which requires a larger, more involved QR code

With the QR code above, the QR code reader on a person’s phone reads the underlying URL and routes the user to the target address:

The problem here is that, if you’re using QR codes in multiple places — printed circulars, product packaging, in-store displays, etc. — and they’re sending the user to the same destination URL, you won’t be able to distinguish which of the different physical placements is generating which traffic to that destination URL.

That’s a problem, because, inevitably, you’ll want to know whether your target users are even scanning the codes and, if so, which codes they’re scanning. It would be one thing if QR codes were inherently attractive and added to the aesthetics of analog collateral. But, like their barcode ancestors, they tend to lack visual appeal. If they’re not adding value and not being used, it’s best that they be removed!

Why, Yes, There IS a Better Way. I’m Glad You Asked.

The QR code below sends the user to the exact same destination (this post):

Notice anything different? For starters, the code itself is much, much smaller than the first example above. That’s nice — it takes up less room wherever it’s printed! Designers will hug you (well, they won’t exactly hug you — they’ll still blanch at your requirement to drop this pixelated box into an otherwise attractively designed piece of printed material…but they’ll gnash their teeth moderately less than if they were required to use the much larger QR code from above).

The trick? Well, this new QR code doesn’t include the full URL for this page. Rather it has a much simpler, much shorter, URL encoded in its pixels:

http://goo.gl/H104m.qr

It makes sense, doesn’t it, that a shorter URL like this one will require fewer black and white pixels to be represented in a QR code format? This URL, you see, was generated using http://goo.gl — a URL shortener. You can also generate QR codes using http://bit.ly. Both are free services and both have a reputation of high availability.

Using some flavor of URL shortener is one of those things consultants and tradesfolk refer to as a “best practice” for QR code generation. What’s going on is that the process relies on an intermediate server-side redirect (of which goo.gl and bit.ly are both examples) to route the user to the final destination URL. This alters the actual user flow slightly so that it looks something like the diagram below:

That adds a little bit of complexity to the process, and, depending on the user’s QR code reader and settings therein, he/she may actually see the intermediate URL before getting routed to the final destination. That’s really not the end of the world, as it’s a fairly innocuous step with a dramatic upside. (Technically, this approach introduces an additional potential failure point into the overall process, but that plays out as more of a theoretical concern than a practical one.)

Why Is This Marginally Convoluted Approach Better?

By introducing the shortened URL, you get two direct benefits:

• A smaller, cleaner QR code (we covered that already)
• The ability to count the number of scans of each unique QR code

This second one is the biggie. To be clear, this isn’t going to distinguish between each individual printout of the same underlying QR code, but it will enable you to, for instance, identify scans of a code that is printed on a particular batch of direct mail from scans that are printed in a newspaper circular.

How is it doing that, you ask? Well, exactly the same way that URL shorteners like goo.gl and bit.ly provide data on how many times URLs created using them were scanned: when the “URL Shortener Server” gets a request for the shortened URL, it not only redirects the user to the full destination URL, but it increments a count of how many times the URL was “clicked” (and, in the case of a QR code, “click” = “scanned”) in an internal database. You can then access that data using the URL shortener / QR code generator’s reporting system.

But Wait! There’s MORE!

Take another look at the full URL that the shortened URL (embedded in the QR code) is redirecting to:

http://www.gilliganondata.com/index.php/2011/10/12/qr-codes-how-they-work-at-least-for-analytics?utm_source=gilliganondata&utm_medium=qr_code&utm_campaign=oct_2011_blog

Notice how it has Google Analytics campaign tracking parameters tacked onto the end of it? That’s a second recommended best practice for QR codes that send the user to web sites that have campaign tracking capabilities. This is just like setting up a banner ad or some other form of off-site promotion or advertising: you control the URL, so you should include campaign tracking parameters on it! This will enable you to look at post-scan activity — did users who scanned the QR code from the product packaging convert at a higher rate on-site than users who scanned the in-store display QR code? You get the idea.

A Final Note on This — Where bit.ly and goo.gl Come Up Short

The upsides to goo.gl and bit.ly QR code generation is that they’re free and have decent click/scan analytics. The downside is that, once a short URL is generated, the target URL can’t be edited (they have their reasons).

Paid services such as the service offered by 3GVision i-nigma both offer solid analytics and allow QR codes to be edited after the short URLs (which the QR codes then represent) are created. This makes a lot of sense, because a printed QR code may stay in-market for a sustained period of time, while the digital content that supports the placement of that code may need to be updated. Or, say that someone creates a QR code and uses a target URL that is devoid of campaign tracking parameters — with a service like 3GVision’s, you can add the tracking parameters after the QR code has been generated and even after it has gone to print (any resemblance to actual situations where this has occurred is purely coincidental! …or so the blogger innocently claimed…). You can’t go backwards in time and add campaign tracking for scans that have already occurred, but you can at least “fix” the tracking going forward.

As is my modus operandi, this has been a pretty straightforward concept with a couple of tips and best practices…and I’ve turned it into a rather verbose and hyper-descriptive post. <sigh> I hope you found it informative.

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | 9 comments | Add to del.icio.us
Post tags: 3GVision, bit.ly, goo.gl, i-nigma, QR codes

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Eric played to a packed house, which he handled with ease:

The evening’s format was simply a “Q&A with Eric Peterson.” Knowing our audience, I was confident that the questions would be good ones, and they were!

I’ve used Twitter as a crowdsourced note-taking tool in the past at events like 2011 eMetrics San Francisco, and it has worked out well. So, for this event, I made sure that our standard event hashtag — #cbuswaw — was included on notecards scattered around the room (along with the username for our speaker — @erictpeterson — and our sponsor — @foresee). I set up a TweetReach tracker ahead of the event based on the hashtag and then just sat back and let the “note taking” begin!

In the end, we had 179 tweets from 49 different people:

For a small networking event in central Ohio, that seemed like plenty of taking of notes! Several attendees were following the stream of tweets and retweeting as various thoughts caught their eyes (counting myself amongst that group), so it’s a reasonable leap, I think, that looking at the “most retweeted” tweets is a quick-and-dirty way to get a  read on what content was most resonant with the in-person audience.

The most retweeted tweets:

Social media was definitely one hot topic, for which Eric had some thoughts about overall maturity and challenges, but he also referred attendees to his partner, John Lovett’s, book on the subject.

There was also a discussion about “standards” for web analytics. Eric had some new and interesting thoughts on that front…but I found out later that he’d been tossing those around in his head for a while and has a draft blog post written on that subject. So, keep an eye on his blog to see if that gets fleshed out.

I honestly don’t remember if it was the social media question or the standards question that led to a discussion of “measuring engagement,” but John Hondroulis managed to dig up Eric’s post from 2007 on the subject and get that shared out to the crowd.

And, the inevitable privacy topic came up, which garnered a few tweets about the WAA’s Code of Ethics.

All in all, it was a fantastic event!Similar Posts:

• Monish Datta Attends Another Web Analytics (Tuesday)
• Old School Online Community Leads to a Dozen Data Geeks and Drinks
• Monish Datta: “It was the best WAW yet!”*
• Monish Datta Learns All about Facebook Measurement
• Columbus Web Analytics Wednesday — Jan 2009 Edition

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | 2 comments | Add to del.icio.us
Post tags: WAW Columbus

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I seldom use overlays for reporting or analysis. The reason isn’t that they don’t have very real usefulness in certain situations, but, rather, because those certain situations are extremely rare in my day-to-day work. As the “page” paradigm — in its basic-HTML simplistic glory — goes the way of daytime soap operas, and as brands’ digital presences increasingly are intertwined combinations of their sites and social media platforms, the number of scenarios where an overlay provides a view of the page that is both reasonably complete and actually useful are few and far between.

That’s a bit broader of a topic than I was aiming to cover with this post, though.

I recently needed to explain to a client why it wasn’t simply a matter of “fixing” the Google Analytics implementation on his site to get the overlays to work properly. I did some digging for documentation that explained the underlying mechanics of GA’s in-page overlays (similar to what Ben Gaines wrote about Sitecatalyst ClickMap a couple of years ago when he was still at Omniture), and…I couldn’t find what I was looking for. This post is trying to be that documentation for the next person who is in the same situation. If you have deeper knowledge of the underlying mechanics of Google Analytics than I have, and I’ve misrepresented something here, please leave a comment to let me know!

Google Analytics <> Sitecatalyst <> ClickTale

There are different ways to capture/present clickmap and heatmap overlays. In order of increasing robustness/usefulness (I’m leaving out a number of vendors because I simply don’t have current knowledge of their specifics):

• Google Analytics, at its core, uses some basic reverse-engineering of page view data to generate its in-page analytics (overlays). It looks nice in their video…but the video uses a very basic site, which doesn’t reflect the reality of most sites for medium-sized and large companies
• Adobe Sitecatalyst gets a bit more sophisticated with its approach, which automatically closes some of the gaps in the GA approach while also allowing for working around a chunk of the challenges that are inherent with overlays; see Ben’s post that I referenced earlier if you want to really get into the details there!
• ClickTale is a solution that was developed from the ground up to provide workable overlays and heatmaps. As such, it takes an even more robust approach — capturing both mouse movements and clicks. The “downside” (in quotes because this is a limitation in theory — not in practice) is that ClickTale does not track all sessions. It samples sessions — still collecting plenty of data to provide you with highly usable data, but business users inevitably get heartburn when they find out that they’re not capturing everything.

Make sense? The point is that there are different ways to skin the overlays cat. This post just covers Google Analytics.

How Google Analytics Figures Out Overlays

For each user session, Google Analytics gets a “hit” for each page viewed during the session, and it records a timestamp for each page view, so it knows the sequence in which pages were viewed in the session. Consider a simple, 3-page site, where the main page (page_A) has links to the other two pages.

 Now, let’s have three visitors come to the site (Visitor 1111, Visitor 2222, and Visitor 3333). All three enter the site on Page_A, but then:

• Visitor 1111 clicks on the link to Page_B and then exits the site
• Visitor 2222 clicks on the link to Page_C and then exits the site
• Visitor 3333 clicks on the link to Page_B and then exits the site

Google Analytics would have captured a series of page views that looked something like this:

With a little sorting and counting and cross-referencing, Google Analytics can figure out that:

• There were 3 visits to Page_A
• The “next page” that two of those visitors went to from Page_A was Page_B
• The “next page” that one of those visitors went to from Page_A was Page_C

That’s how Google Analytics generates the Next Page Path  area of the Navigation Summary report for a page (and, with the same basic technique, this is how the Previous Page Path is generated):

Make sense? Good. So, how does this become in-page analytics? In-page analytics, really, is just a visualization of the Next Page Path data. To do that:

1. Google Analytics pulls up the current version of the page at the URL being analyzed with in-page analytics
2. It compiles a list of all of the “next pages” that were visited (with the number of “next page” page views for each one)
3. It scans the page for the URLs of those “next pages” and then labels each link that references one of those pages with the number of pageviews (and the % of total “next page” page views that the value represents)

Pretty simple, and pretty solid…except when various common situations occur, which we’ll get to next.

Oh, the Many Ways that In-Page Analytics Breaks Down

In-page analytics is problematic when any of the following situations occur on a page:

• A link has a target URL that is not part of the current site (e.g., a link to the brand’s Facebook page or YouTube channel): Google Analytics doesn’t capture the “next page” viewed, so it can’t deduce how many times the link was clicked (Note: a best practice, obviously, is to have event tracking or social tracking implemented in these situations, so Google Analytics can report on how many times the link was clicked…but this doesn’t work it’s way back into in-page analytics overlays)
• A link points to a PDF or file download: this is similar to the previous scenario, in that the “next page” doesn’t execute the Google Analytics page tag; again, even if a virtual page view is captured on the click, that is, technically, different from the actual target URL in the <a href=”…e> that points to the file, so Google Analytics doesn’t make the connection needed to render this on the overlay. In other words, the virtual page view will show up on the Navigation Summary in the Next Page Path list, but it won’t show up on the overlay.
• Multiple links on the page point to the identical next page: because GA uses the URL of the “next pages,” it doesn’t inherently capture which link pointing to the specific next page is the one that was clicked. The standard workaround for this is to force the URLs to be unique by tacking on a junk parameter to the end of the second URL (e.g., have one link point to “Page_B.htm” and the second link point to “Page_B?link=2″). This will make the target URLs unique in GA’s view…but will also make base reporting for Page_B a bit trickier, as there will be two different rows in the Pages report for the same page (if your <title> tags are well-formed, you can work around this by using the Page Titles dimension in the Pages report)
• Links are embedded in “hidden” content, such as Javascript menu dropdowns: this is simply a limitation of the overlay paradigm, in that it is often impossible to make all of the links on a page visible at once. With in-page analytics, as you mouse over areas that make the links appear, the in-page analytics data will appear as well, but it still requires moving all around the page to reveal all of the links to view all of the “next page” data
• Links are embedded in Flash: in-page analytics simply can’t effectively add clicks to links that are embedded in Flash objects
• Links appear to reference the same page: some implementation of DHTML that trigger overlays or other interactive in-page content wind up including something like “<a href=”#”…”, which looks to Google like a link back to the current page. This confuses GA mightily!
• The link is removed from the page: say you run a promo for a week and then take the hyperlinked image off of the page. When you pull up in-page analytics for that week, GA will know that there were a lot of “next page” views to the target for that promo…but it only has the current page for use in generating an overlay, so it won’t know where to overlay the page views for that promo
• The links on the page aren’t spaced far enough apart: this is a practical reality, in that I have never seen an overlay where there aren’t some overlay details that obscure the details for other links that are located in close proximity. Obviously, you’re not going to design your site to be overlay-friendly…so you just have to accept this limitation.

The kicker is that these are not obscure, corner-case scenarios. They’re common occurrences, and they lead to most overlays presenting an incomplete picture of activity that occurs on the page.

A Handful of Additional Thoughts

Google Analytics are seldom useful. To the best of my knowledge, this is neither an area in which Google is investing to make improvements, nor is it an area that seasoned web analysts are really clamoring for updates.

However, overlays have their place, I think. But, they need to be done right, which is something on which ClickTale is focused (Michele Hinojosa wrote a good overview of the platform last year if you want to read another analyst’s perspective).

Related to overlays, although not strictly overlay-ish, is a feature of Satellite by Search Discovery, whereby you can very easily enable tracking of all clicks on unlinked content (how many times have you been on a site where you think clicking on a product image will take you to the product’s page…and it doesn’t take you anywhere at all!). I think this is some ClickTale-ish like functionality, but that may be something of a stretch. It was a nifty concept, though.

So, that’s it on GA’s In-Page Analytics. Understand what it does and how it does it, and you will be able to identify the (extremely rare) situations when it will be useful.Similar Posts:

• All Web Analytics Tools Are the Same (at least when it comes to data capture)
• Web Analytics Tools Comparison — Columbus WAW Recap Part 2
• Web Analytics Tracking on a Facebook Page
• Web Analytics Platforms Are Fundamentally Broken
• Google Analytics = Strawberry?

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | 4 comments | Add to del.icio.us
Post tags: ClickMap, ClickTale, Google Analytics, heatmap, In-Page Analytics, Overlay

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Onto my shareable takeaways from the Google Analytics summit…

CRAZY Coolness Is on the Way

<sigh> This is the stuff where I can’t provide any real detail. But, essentially, the first two hours of the summit were one live demo after another of very nifty enhancements to the platform, some of which are coming in the next few weeks, and some of which won’t be out until 2012. Some of the enhancements fall in the “well…the Sitecatalyst sales folk won’t be able to use that as a Google Analytics shortcoming when they’re a-bashing it” category, and some fall in the “where on earth did they come up with that — no one else is even talking about doing that” category.

Very cool stuff, and with a continuing emphasis on ease of implementation, ease of management, and a clean and usable UI. Clearly, when v5 rolled out and Google emphasized that the release was more about positioning the under-the-hood mechanics for more, better, and faster improvements in the future, they meant it. Agility and a constant stream of worthwhile enhancements are the order of the day.

I Don’t Know My Googlers

Two presenters — both spoke a couple of times, either formally or when called upon from the stage — really stood out. Maybe I’ve just been living in an oblivious world, but I wasn’t familiar with either one:

• Phil Mui, Group Product Manager — Phil is apparently a regular favorite at the summit, and he got to run through a lot of the upcoming features; he’s a very engaging speaker, and he’s both excited about the platform while also in tune (for the most part) with how and where the upcoming enhancements will be able to be put to good use by users
• Sagnik Nandy, Engineering Lead, Google Analytics Backend and Infrastructure — it was a pleasure to listen to Sagnik walk through all manners of how the platform works and what’s coming in the future; the backend is in good hands!

Both of these guys (all of the Googlers, actually) are genuine and excited about the platform. Avinash Kaushik’s passion and thoughtfulness (and healthy impatience with the industry) is alive and well…and entertaining as all get out!

Google Analytics Competitive Advantage

I owe Justin Cutroni for this one, but it was one of the more memorable epiphanies for me. As we chatted about GA relative to the other major web analytics players, he pointed out a fundamental difference (which I’m expanding/elaborating on here):

• Adobe/Omniture, Webtrends, and IBM (Coremetrics and Unica) are all largely fighting on the same playing field — striving to develop products that have a better feature set at a better price than their competition. This is pretty basic stuff, but it requires pretty careful P&L management — R&D investment that, ultimately, pays a sufficient return through product revenue
• Google is playing a different game — their products are geared towards driving revenue from their other products (Google Adwords, the Google Display Network, etc.). That actually makes for a very different model for them — much less of a need to manage their R&D investment against direct Google Analytics income (obviously), as well as a totally different marketing and selling model.

There is a certain inherent degree of commoditization of the web analytics space. With a relatively small number of players, R&D teams are focused as much on closing feature gaps that their competitors offer as they are on developing new and differentiating features. In a sense, Google is more focused on “making the web better” — raising the water level in the ocean — while the paid players are geared solely towards making their boats bigger and faster.

I fervently hope that Adobe, Webtrends, and IBM are able to remain relevant over the long term. Competition is good. But, it may very well be a very steep uphill battle for structural reasons.

Silly Me — I thought Tag Management Was a 2-Player Field

Several of the exhibitors at the conference offer some flavor of tag management. The conference was geared towards Google Analytics, so their focus was on GA, but all of them clearly had the “any tag, any Javascript” capability that Ensighten touts (TagMan is the other player I was aware of, but, due to crossed signals, I haven’t yet seen a demo of their product).

The most impressive of these tools that I saw was Satellite from Search Discovery, which Evan LaPointe presented during Wednesday night’s blitz “app integration” session, and which he showed me in more depth on Thursday morning. In his Wednesday night presentation, Evan made a pretty forceful point that, if we’re talking about “tag management,” we’re already admitting defeat. Rather, we should be thinking about data management — the data we need to support analyses — rather than about “the tag.”

Subtle semantic framing? Perhaps. But, it falls along the same lines of the “web analytics tools are fundamentally broken” post I wrote last month that set off a vigorous discussion, and which wound up being timed such that Evan’s post about web analytics douchiness had a nice tie-in.

In short, Analytics Engine is impressive for its rich feature set and polished UI. Equally, if not more, exciting is the mindset behind what the platform is trying to do — get analysts and marketers thinking about the data and information they need rather than the tags that will get it for them.

In Short, Not a Bad Couple of Days!

The nature of any conference is that there will be sessions and conversations that are either not informative or not relevant to the attendee. That’s just the way things go. If I walk away with a small handful of new ideas, a couple of newly established or deepened personal relationships with peers, and validation of some of my own recent thinking, I count the conference a success. The Partner Summit delivered against those criteria — there were a few sessions I could have lived without, at least one session that wildly under-delivered on its potential, and some looseness with the Day 2 schedule that made it difficult to bounce between tracks effectively. But, overall, it was a #winning event.

 Similar Posts:

• Google Analytics = Strawberry?
• Web Analytics Platforms Are Fundamentally Broken
• A Quick Explanation of Sitecatalyst Events for the Google Analytics Power User
• Working Around Sampled Search Data in Google Analytics
• Web Analytics Tools Comparison — Columbus WAW Recap Part 2

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | 3 comments | Add to del.icio.us
Post tags: Analytics Engine, Avinash Kaushik, Evan LaPointe, Google Analytics, Justin Cutroni

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To give credit where credit is due, the discussion really took off when Eric Peterson posted an excerpt and a link in Google+ and solicited thoughts from the Google+/#measure community. That turned into the longest thread I’ve participated in to date on Google+, and subsequently led to a Google+ hangout that Eric set up and then moderated yesterday.

This post is an attempt to summarize the highlights of what I saw/heard/learned over the past week.

What I Learned about the #measure Community

Overall, the discussion brought back memories of some of the threads that would occasionally get started on the webanalytics Yahoo! group back in the day. That’s something we’ve lost a bit with Twitter…but more on that later.

What I took away about the group of people who make up the community was pretty gratifying:

• A pretty united “we” – everyone who participated in the discussions was contributing with the goal of trying to move the discussion forward; as a community, everyone agrees that we’re at some sort of juncture where “web analytics” is an overly limiting label, where the evolution of consumer behavior (read: social media and mobile) and consumer attitudes (read: privacy) are impacting the way we will do our job in the future, and where the world of business is desperately trying to be more data-driven…and floundering more often than succeeding. There are a lot of sharp minds who are perfectly happy to share every smart thought they’ve got on the subject if it helps our industry out — the ol’ “a rising tide lifts all boats” scenario. That’s a fun community with whom to engage.
• Strong opinions but small egos – throughout the discussion that occurred both on Google+ and on Twitter (as well as in several blog posts that the discussion spawned, like this one by Evan LaPointe and Nancy Koon’s inaugural one and Eric’s post), there were certainly differing points of view, but things never got ugly; I actually had a few people reach out to me directly to make sure that their thoughts hadn’t been taken the wrong way (they hadn’t been)
• 100s of years of experience — we have a lot of experience from a range of backgrounds when it comes to trying to figure out the stickiest of the wickets that we’re facing. That is going to serve us well.
• (Maybe) Agencies and vendors leading the way? – I don’t know that I learned this for sure, but an informal tally of the participants in the discussion showed a heavy skewing towards vendor and agency (both analytics agencies and marketing/creative/advertising agencies) representation with pretty limited “industry” participation. On the one hand, that is a bit concerning. On the other hand, having been in “industry” for more of my analytics career than I’ve been on the agency side, it makes sense that vendors and agencies are exposed to a broader set of companies facing the same challenges, are more equipped to see the patterns in the challenges the analytics industry is facing, and are being challenged from more directions to come up with answers to these challenges sooner rather than later.

These were all good things to learn — the people in the community are one of the reasons I love my job, and this thread demonstrated some of the reasons why that is.

Highlights of the Discussion

Boiling down the discussion is bound to leave some gaps, and, if I started crediting individuals with any of the thoughts, I’d run the serious risk of misrepresenting them, so feel free to read the Google+ thread yourself in its entirety (and the follow-up thread that Eric started a few days later). I’ve called out any highlights that came specifically from the hangout as being from there (participants there were Adam Greco, John Lovett, Joseph Stanhope, Tim Wilson, Michael Helbling, John Robbins, Emer Kirrane, Lee Isensee, Keith Burtis, and me), since there isn’t a reviewable transcript for that.

Here goes:

• Everyone recognizes that a “just plug it in and let the technology spit out insights” solution will likely never exist — the question is how much of the technical knowledge (data collection minutia, tool implementation nuances, reporting/analysis interface navigation) can be automated/hidden. A couple of people (severalpublicly, one privately) observed that we want (digital) analytics platforms to be a like a high-performance car — all the complexity as needed under the hood, but high reliability and straightforward to operate. Pushing that analogy — how far and fast it runs will still be highly dependent on the person behind the wheel (the analyst).
• Adobe/Omniture and Google Analytics had near-simultaneous releases of their latest versions; both companies touted the new features being rolled out…but both companies have stressed that there was a lot more about the releases that were under-the-hood changes that were positioning the products for greater advances in subsequent releases; time will tell, no? And, several people who have actually been working  with SC15 (I’ve only seen a couple of demos, watched some videos, and read some blog posts — the main Omniture clients I support are over a year out from seeing SC15 in production), have pointed out that some of the new features (Processing Rules and Context Data, specifically) will really make our lives better
• There was general consensus that Omniture has gotten much, much better over the years about listening to customer feedback and incorporating changes based on that feedback; there is still a Big Question as to whether customer-driven incremental improvements (even improvements that require significant updates on the back end) will get to true innovation — the “last big innovations” in web analytics were pointed out as being a decade ago (I would claim that the shift from server logs and image beacons to Javascript-based page tags was innovative and wasn’t much older) — or whether “something else” will have to happen was a question that did not get resolved
• Getting beyond “the web site” is one major direction the industry is heading — integrating cross-channel data and then getting value from it – introduces a whole other level of complexity…but the train is barrelling along on a track that has clearly been laid in that direction
• We all get sucked into “solving the technical problem” over “focusing on the business results” — the tools have enough complexity that we count it a “win” when we solve the technical issues…but we’re not really serving anyone well when we stop there; this is one of those things, I suspect, that we all know and we constantly try to remind ourselves…and yet still get sucked into the weeds of the technology and forget to periodically lift our heads up and make sure we’re actually adding value; John Lovett has been preaching about this conundrum for years (and he hits on it again in his new book)
• Marketing/business are getting increasingly complex, which means the underlying data is getting more complex (and much more plentiful — another topic John touches on in his book), which means getting the data into a format that supports meaningful analysis is getting tougher; trying to keep up with that trend is hard enough without trying to get ahead!
• Tag management — is it an innovation, or is it simply a very robust band-aid? Or is it both? No real consensus there.
• Possible areas where innovation may occur: cross-channel integration, optimization, improved conversion tracking (which could encompass both of the prior two areas), integration of behaviora/attitudinal/demographic data
• [From the hangout] “Innovation” is a pretty loaded term. Are we even clear on what outcome we’re hoping to drive from innovation?
• [From the hangout] Privacy, privacy, privacy! Is it possible to educate the consumer and/or shift the consumer’s mindset such that they are informed about why that “tracking” them isn’t evil? Can we kill the words “tracking” and “targeting,” which both freak people out? Why are consumers fine with allowing the mobile or Facebook application access to their private data…but freak out about no-PII behavioral tracking (we know why, but it still sucks)?
• [From the hangout] How did a conversation about where and how innovation will occur devolve into the nuts and bolts of privacy? Why does that happen so often with us? Is that a problem, or is it a symptom of something else?

Yikes! That’s my attempt to summarize the discussion! And it’s still pretty lengthy!

What I Learned about Google+

I certainly didn’t expect to learn anything about Google+ when I wrote the post — it was focusing on plain ol’ web (site) analytics, for Pete’s sake! But, I learned a few things nonetheless:

The good:

• Longer-form (than 140 characters) discussions, triggered by circles, with the ability to quickly tag people, are pretty cool; Twitter sort of forced us over to blog posts (and then comments on the posts) to have discussions…and Google+ has the potential to bring back richer, more linear dialogue
• Google+ hangouts…are pretty cool and fairly robust; we had a few hiccups here and there, but I was able to participate reasonably well from inside a minivan traveling down the highway that had the other four members of my family in it (Verizon 4G aircard, in case you’re wondering); and, as the system detects who is speaking, that person’s video jumps to the “main screen” pretty smoothly. It’s not perfect (see below), but we had a pretty meaty conversation in a one-hour slot (and credit, again, to Eric Peterson for his mad moderation skills — that helped!)

The not-so-good:

• Discussions aren’t threaded, and the “+1″ doesn’t really drive the organization of the discussion — multiple logical threads were spawned as the discussion continued, but the platform didn’t really reflect that, which many discussion forums have supported for years
• Linking the blog post to the discussion was a bit clunky. Who knows what long tail search down the road would benefit from seeing the original post and the ensuing conversation? I added a link to the Google+ discussion to the post after the fact…but it’s not the same as having a string of comments immediately following a post (and if Google+ fizzles…that discussion will be lost; I’ve made a PDF of the thread, but that feels awfully 2007)
• Google+ hangouts could use some sort of “hand-raising” or “me next” feature; everyone who participated in the hangout worked hard to not speak over anyone else, but we still had a number of awkward transitions

So, that’s what I took away. It was a busy week, especially considering I was knocking out the first half of John Lovett’s new book book (great stuff there) at the same time!

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | 3 comments | Add to del.icio.us
Post tags: Google+

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