Tag: webtrends

Analytics Strategy

Web Analytics Platforms Are Fundamentally Broken

Posted on by Eric Peterson

Farris Khan, Analytics Lead at ProQuest and Chevy Volt ponderer extraordinaire, tweeted the question that we bandy about over cocktails in hotel bars the world over during any analytics gathering:

His tweet came on the heels of the latest Beyond Web Analytics podcast (Episode 48), in which hosts Rudi Shumpert and Adam Greco chatted with Jenn Kunz about “implementation tips.” Although not intended as such, the podcast was skewed heavily (95%) towards Adobe/Omniture Sitecatalyst implementations. As the dominant enterprise web analytics package these days, that meant it was chock full of useful information, but I found myself getting irritated with Omniture just from listening to the discussion.

My immediate reply to Farris’s tweet, having recently listened to the podcast, reflected that irritation:

Sitecatalyst throws its “making it much harder than it should be” talent on the implementation side of things, and I say that as someone who genuinely likes the platform (I’m not  a homer for any web analytics platform — I’ve been equally tickled pink and wildly frustrated with Google Analytics, Sitecatalyst, and Webtrends in different situations). I’m also not criticizing Sitecatalyst because I “just don’t understand the tool. ” I no longer get confused by the distinction between eVars, sProps, and events. I’ve (appropriately) used the Products variable for something totally separate from product information. I’ve used scView for an event that has nothing to do with a shopping cart. I’ve set up SAINT classifications. I’ve developed specs for dynamically triggering effectively named custom links. I’ve never done a stint as an Adobiture employee as an implementation engineer, but I get around the tool pretty well.

Given that I’ve got some experience there, I’ve also worked with a range of clients who have Sitecatalyst employed on their sites. As such, I’ve rolled my eyes and gnashed my teeth at the utter botched-ness of multiple clients’ implementations, and, yes, I’ve caught myself making the same type of critical statements that were rattled off during the podcast about companies’ implementations:

  • Failure to put adequate up front planning into their Sitecatalyst implementation
  • Failure to sufficiently document the implementation
  • Failure to maintain the implementation going forward on an on-going basis
  • Failure to invest in the people to actually maintain the implementation and use the data (Avinash has been fretting about this issue publicly for over 5 years)

In the case of the podcast, though, I wasn’t participating in the conversations — I was simply listening to others’ talk. The problem, though, was that I heard myself chiming in. I jumped right  on the “it’s the client’s fault” train, nodding my head as the panel described eroded and underutilized implementations. But, then a funny thing happened. As  I stepped back and listened to what “I” would have been saying, I got a bit unsettled. I realized I’d been seduced by the vendor. Through my own geeky pride at having cracked the nut of their inner machinations, I’d crossed over to vendor-land and started unfairly blaming the customer for technology shortcomings:

If the overwhelming majority of companies that use a given platform use it poorly…shouldn’t we shine a critical light on the platform rather than blaming the users?

I love digital analytics. I enjoy figuring out new platforms, and it’s fun to develop implement something elegantly and then let the usable data come pouring in that I can feed into reports and use for analysis. But:

  • I’ve been doing this for a decade — hands-on experience with a half-dozen different tools
  • It’s what I’m most interested in doing with my career — it beats out strategy development, creative concepting, campaign ideation, and any and every other possible marketing role
  • I’m a sharp and motivated guy

In short…I’m uniquely suited to the space. I’m neither the only person who is really wired to do this stuff nor even in the 90th percentile of people who fit that bill. But the number of people who are truly equipped to drive a stellar Sitecatalyst implementation are, best case, in the low thousands, and, worst case, in the low hundreds. At the same time, demand for these skills is exploding. Training and evangelization is not going to close the gap! The Analysis Exchange is a fantastic concept, but that’s not going to close the gap, either.

There is simply too much breadth of knowledge and thought required to effectively work in the world of digital analytics for a tool to have a steep learning curve with undue complexity for implementation and maintenance. The Physics of the Internet means there are a relatively finite number of types of user actions that can be captured. Sitecatalyst has set up a paradigm that requires so much client-side configuration/planning/customization/maintenance/incantations/prayer that the majority of implementations are doomed to take longer than expected (much longer than promised by the sales team) and then further doomed to be inadequately maintained.

The signals that Adobe is slowly taking steps to merge the distinction between eVars and sProps is an indication that they realize that there are cases where the backend architecture needlessly drives implementation complexity. But, just as the iPhone shattered the expectations we had for smartphones, and the iPad ushered in an era of tablet computing that will garner mass adoption, Adobe has a very real risk of Sitecatalyst becoming the Blackberry of web analytics. Sitecatalyst 15, for all of the excitement Adobe has tried to gin up, is a laundry list of incremental fixes to functional shortcomings that the industry has simply complained about for years (or, in the case of the the introduction of segmentation, a diluted attempt to provide “me, too” functionality based on what a competitor provides).

The vendors have to take some responsibility for simplifying things. The fact that I can pull Visits for an eVar and Visits for an sProp and get two completely different numbers (or do the same thing for instances and page views) is a shortcoming of the tool. We’ve got to get out of the mode of simply accepting that this will happen, that a deep and nuanced understanding of the platform is required to understand the difference, and then gnashing our teeth when more marketers don’t have the interest and/or time to develop that deep understanding of the minutia of the tool.

<pause>

Although I’ve focused on Sitecatalyst here, that doesn’t mean other platforms are beyond reproach:

  • Webtrends — Why do I have to employ black magic to get my analysis and report limits set such that I don’t miss data? Why do I have to employ Gestapo-like processes to prevent profile explosion (and confusion)? Why do I have to fall back on weeks-long reprocessing of the logs when someone comes up with a clever hypothesis that needs to be tested?
  • Google Analytics — Why can’t I do any sort of real pathing? Why do I start bumping up against sampled data that makes me leery…just when I’m about to get to something really cool I want to hang my hat on? Why is cross-domain and cross-subdomain tracking such a nightmare to really get to perform as I want it to?

My point here is that the first platform that gets a Jobs-like visionary in place who is prepared to totally destroy the current paradigm is going to have a real shot at dominating over the long haul. There are scads of upstarts in the space, but most of them are focused on excelling at one functional niche or another. Is there the possibility of a tool (or one of the current big players) really dramatically lowering the implementation/maintenance complexity bar (while also, of course, handling the proliferation of digital channels well beyond the traditional web site) so that the skills we need to develop can be the ones required to use the data rather than capture it?

Such a paradigm shift is sorely needed.

Update: Eric Peterson started a thread on Google+ spawned by this post, and the lengthy discussion that ensued is worth checking out.

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Analytics Strategy, Social Media

Monish Datta: "Justin Kistner KNOWS Facebook Measurement!"

Posted on by Eric Peterson

We had a fantastic Web Analytics Wednesday last night, sponsored by Webtrends with social media measurement guru Justin Kistner providing a wealth of information about Facebook measurement (and Facebook marketing in general).

With almost 50 attendees, we were, as best as I can tell, tied with the largest turnout we’ve ever had. Is “number of attendees” an appropriate success measure? Well, yeah, it is. Even better that the group was super-engaged, and I’ve never had so many people track me down to laud the content (including multiple follow-up requests as to whether I had the deck yet!

Justin was gracious enough to share his presentation, and it’s posted below (click through to Slideshare to download the source PowerPoint):

Facebook Measurement Fundamentals

A handful of pictures from the event:

Mingling/Eating/Drinking

Food, Drink, and Chatting

Justin Launches His  Presentation

Justin Gets Things Rolling

The Late Night Lingerers
(that’s Monish Datta in the middle — a wholly gratuitous reference in pursuit of SEO silliness)

The Late Lingerers

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Analytics Strategy

Webtrends Table Limits — Simply Explained

Posted on by Eric Peterson

A co-worker ran into a classic Webtrends speed bump a couple of weeks ago. A new area of a client’s web site had rolled out a few days earlier…and Webtrends wasn’t showing any data for it on the Pages report. More perplexingly, there was traffic to the new content group that had been created along with the launch showing up in the Content Groups report. What was going on? I happened to walk by, and, although I haven’t done heavy Webtrends work in a few years, the miracle of cranial synapses meant that the issue jumped out pretty quickly (I can’t figure out how to say that without sounding egotistical; oh, well — it is what it is).

Heavy Webtrends users will recognize this as a classic symptom of “table limits reached.” There’s quite a bit written on the subject online…if you know where to look. The best post I found was You need to read this post about Table Limits by Rocky of the Webtrends Outsiders. The last sentence (well, sentence fragment, really) in the post is, “End of rant.” In other words, the post starts AND finishes strong, and the content in between is damn good, too.

What I found, though, was that it took a couple of conversations and a couple of whiteboard rounds to really explain to my colleague what was going on under the hood that was causing the issue in a way that he could really understand. That’s not a knock against him. Rather, it’s one of those things that makes perfect sense…once it makes sense. It’s like reading an analog clock or riding a bicycle (or, presumably, riding a RipStik…I wouldn’t know!).  So, I decided I’d take a crack at laying out a simplistic example in semi-graphical form as a supplement to the post above.

The Webtrends Report-Table Paradigm

First, it’s important to understand that every report in Webtrends has two tables associated with it:

  • Report Table — this is the table of data that gets displayed when you view a report
  • Analysis Table — the analysis table is identical in structure to the report table, but it has more rows, and it’s where the data really gets stored as it comes into the system

Webtrends aggregates data, meaning that it doesn’t store raw visitor-level, click-by-click data and then try to mine through a massive data volume any time someone runs a simple report. Rather, it simply increments counters in the analysis tables. That makes sense from a performance perspective, but can easily lead to a “hit the limits” issue.

Key: neither of these tables simply expands (adds rows) as needed. Both have their maximum row count configured in the admin console. Those limits can be adjusted…but that comes at a storage and a processing load price.

(Now, actually, there are multiple analysis tables for any single report — copies of the underlying table structure populated with data for a specific day, week, or month…but it’s beyond the scope of this post to go into detail there. Just tuck it away as another wrinkle to learn.)

In the rest of this post, I’m going to walk through an overly simplistic scenario of a series of visits to a fictitious site with unrealistically low table limits to illustrate what happens.

The Scenario

Let’s say we have a web site with a series of pages that we’ll call Page A, Page B, Page C,…Page Z. And, let’s say we have our Report Table limit for the Pages report set to “4” (in practice, it’s probably more like 5,000) and our Analysis Table limit set to “8” (in practice, it would be more like 20,000). That gives us a couple of empty tables that look something like this:

Now, we’re going to walk through a series of visits to the site and look at what gets put into the tables.

Visit 1

The first visitor to our site visits three pages in the following order: Page A –> Page B –> Page C  –> <Exit>.

The analysis table gets its first three rows loaded up in the order that the pages were visited, and each page gets a Visits value of 1. If we looked at the Pages report at that point, the Report Table would pull those top 3 values, and everything would look fine:

Visit 2

The next visitor comes to the site and visits 5 pages in the following order: Page B –> Page C –> Page D –> Page E –> Page F –> <Exit>

We’ve now had more unique pages visited than can be displayed in the report (because the report table limit is set to 4). But, that’s okay. After two visits to the site, our Analysis Table would still have a row or two to spare, and the Report Table could pull the top 4 pages from the Analysis Table and do a quick sort to display correctly, using the All Others row to lump in everything that didn’t make the top 4:

If you searched or queried for “Page F” at this point, you wouldn’t see it. It’s there in the Analysis Table, but you’re searching/querying off of the Report Table. That doesn’t mean Page F is lost, though. It just means it has less traffic (or is tied for last) with the last item that fit in the Report Table.

Visit 3

Sequence of pages: Page F –> Page G –> Page H –> Page B –> <Exit>

Following the same steps above and incrementing the values in our Analysis Table, and again looking at a report for the entire period, we see (bolded numbers in the Analysis Table are the ones that got created or incremented with this visit):

Look! Page F is now showing up in the Report Table! Can you see why? Because the Analysis Table has greater row limits, the Report Table can adjust and pick the top-visited pages.

Visit 4

Sequence of pages: Page F –> Page I –> Page J –> Page B –> <Exit>

Here’s where we really start to lose page-level granularity. Our Analysis Table is full, so there are no rows to store Page I and Page J. So, that will add 2 visits to the All Others row in the Analysis Table (while this is a single visit, this is the pages report, and each of those pages received a visit). Our tables now look like this:

Until the Analysis Table gets reset, no pages after Page H will ever appear in a report.

Even if Page I Becomes the Most Popular Page on My Site?

It’s time for a direct quote from the Webtrends Outsider post referenced at the beginning of this post:

Ugly example #1: Your end users contact you wanting to know about traffic to their expensive new microsite.  You know you’ve been collecting the data correctly because you triple-checked the tagging before and after launch.  So you open the Pages report and WebTrends tells you those pages don’t exist.  Those  expensive pages got no traffic at all, apparently.  Knowing how the CEO’s been obsessed with the new microsite, you call in sick indefinitely.

It doesn’t matter if Page I becomes the only page on your site. Until the tables reset, you won’t see the page in your Pages report — it will continue to be lumped into All Others.

And That Is Why…

If you started out on Google Analytics and then switched over to Webtrends you might have noticed something odd about the URLs being captured (I learned it going in the opposite direction): in Google Analytics, the full URLs for each page, including any query string parameters (campaign tracking parameters excluded) are reported by default. In Webtrends, query string parameters are dropped by default. In the case of Google Analytics, you can configure a profile to drop specific parameters, while, in Webtrends, you can configure the tool to include specific parameters.

Why does Webtrends exclude all parameters by default? The table limits is one of the reasons. If, for instance, your site search functionality passes the terms searched for and other details to the search engine using query parameters, the Analysis Table for the Pages report would fill up very quickly…with long tail searches that only received 1 or a small handful of requests.

What to Do?

The most important thing to do is to keep an eye on your table sizes and see which ones are getting close to hitting their limits. If they’re getting close, then consider adjusting your configuration to reduce “fluff” values going in. If that’s not an issue, then you need to bump up your table limits. That may slow down the time it takes for Webtrends to process your profiles, but it will keep you from unpleasant conversations with the business users you support!

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Analytics Strategy

Web Analytics Tools Comparison — Columbus WAW Recap Part 2

Posted on by Eric Peterson

[Update: After getting some feedback from a Coremetrics expert and kicking around the content with a few other people, I rounded out the presentation a bit.]

In my last post, I recapped and posted the content from Bryan Cristina’s 10-minute presentation and discussion of campaign measurement planning at February’s Columbus Web Analytics Wednesday. For my part of the event, I tackled a comparison of the major web analytics platforms: Google Analytics, Adobe/Omniture Sitecatalyst, Webtrends, and, to a certain extent, Coremetrics. I only had five minutes to present, so I focussed in on just the base tools — not the various “warehouse” add-ons, not the A/B and MVT testing tools, etc.

Which Tool Is Best?

This question gets asked all the time. And, anyone who has been in the industry for more than six nanoseconds knows the answer: “It depends.” That’s not a very satisfying answer, but it’s true. Unfortunately, it’s also an easy answer — someone who knows Google Analytics inside and out, has never seen the letters “DCS,” referenced the funkily-spelled “eluminate” tag, or bristled at Microsoft usurping the word “Vista” for use with a crappy OS, can still confidently answer the, “Which tool is best?” question with, “It depends.”

And You’re Different?

The challenge is that very, very few people are truly fluent in more than a couple of web analytics tools. I’ve heard that a sign of fluency in a language is that you actually think in the language. Most of us in web analytics, I suspect, are not able to immediately slip into translated thought when it comes to a tool. So, here’s my self-evaluation of my web analytics tool fluency (with regards to the base tools offered — excluding add-ons for this assessment; since the add-ons bring a lot of power, that’s an important limitation to note):

  • Basic page tag data capture mechanics — 95th percentile — this is actually something pretty important to have a good handle on when it comes to understanding one of the key differences between Sitecatalyst and other tools
  • Google Analytics — 95th percentile — I’m not Brian Clifton or  John Henson, but I’ve crafted some pretty slick implementations in some pretty tricky situations
  • Adobe-iture Sitecatalyst — 80th percentile — I’m more recent to the Sitecatalyst world, but I’ve now gotten some implementations under my belt that leverage props, evars, correlations, subrelations, classifications, and even a crafty usage of the products variable
  • Webtrends — 80th percentile — I cut my teeth on Webtrends and would have put myself in the 95th percentile five years ago, but my use of the tool has been limited of late; I’m actually surprised at how little some of the fundamentals change, but maybe I should
  • Coremetrics — 25th percentile — I can navigate the interface, I’ve dived into the mechanics of the different tags, and I’ve done some basic implementation work; it’s just the nature of the client work I’ve done — my agency has Coremetrics expertise, and I’m hoping to rely on that to refine the presentation over time

So, there’s my full disclosure. I consider myself to be pretty impartial when it comes to tools (I don’t have much patience for people who claim impartiality and then exhibit a clear bias towards “their” tool — the one tool they know really well), but, who knows? It’s a fine line between “lack of bias” and “waffler.”

Any More Caveats Before You Get to the Content?

My goal with this exercise was to sink my teeth in a bit and see what I could clearly capture and explain as the differences. Ideally, this would also get to the, “So what?” question. What I’ve found, though, is that answering that question gets circular in a hurry: “If <something one tool shines as> is important to you, then you really should go with <that tool>.” Two examples:

  • If enabling users to quickly segment traffic and view any number of reports by those segments is important, then you should consider Google Analytics (…or buying the “warehouse” add-on and plenty of seats for whatever other tool you go with)
  • If being able to view clickpaths through content aggregated different ways is important, then you should consider Sitecatalyst

These are more of a “features”-oriented assessment, and they rely on a level of expertise with web analytics in order to assess their importance in a given situation. That makes it tough.

Any tool is only as good as its implementation and the analysts using it (see Avinash’s 10/90 rule!). Some tools are much trickier to implement and maintain than others — that trickiness brings a lot of analytics flexibility, so the implementation challenges have an upside. In the end, I’ll take any tool properly implemented and maintained over a tool I get to choose that is going to be poorly implemented.

Finally! The Comparison

I expect to continue to revisit this subject, but the presentation below is the first cut. You might want to click through to view it on SlideShare and click the “Speaker Notes” tab under the main slide area — I added those in after I presented to try to catch the highlights of what I spoke to on each slide.

http://static.slidesharecdn.com/swf/ssplayer2.swf?doc=webanalyticstoolscomparison-110227152355-phpapp01&stripped_title=web-analytics-tools-comparison&userName=tgwilson

Do you see anything I missed or with which you violently disagree? Let me know!

http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6= http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=

http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6= http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=

http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=
http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=

http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=
http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=

http://b.scorecardresearch.com/beacon.js?c1=7&c2=7400849&c3=1&c4=&c5=&c6=

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Analytics Strategy, Social Media

Web Analytics Tracking on a Facebook Page

Posted on by Eric Peterson

I’ve been on a quest now for several months to crack the code of how to get web analytics tracking on a Facebook fan page. My (and our clients’) desire to do so shines an interesting light on the way that social media has blurred the concept of a “web site.” Back in the day, it was pretty simple to identify what pages you wanted to track: if the user perceived the page as being part of your site, you wanted to track that page with your web analytics software (even if it was an area of your site that was hosted by some other third party that had specialized capabilities like managing job opening, events, or discussion forums).

Social media, and Facebook in particular, is starting to blur those lines. If your company manages a branded fan page on Facebook, and that page is a place through which your customers and target customers actively engage with the brand, isn’t it acting a bit like your web site? Clearly, a Facebook page is not part of your site, but it’s a place on the web where consumers actively engage with brands, both to give and receive brand-related content. It acts a lot like a traditional web site in that regard.

As companies begin to invest more heavily in Facebook pages — both through creative development and staff to engage with consumers who interact with their brand through a fan page — there is an increasing need to have better visibility into activity on those pages. I wrote an entire post on the subject of Facebook measurement back in January, and I’ve had to update it several times since then as Facebook has rolled out changes and as I’ve gotten a bit deeper into the web analytics aspects of that tracking.

Just last week, Webtrends announced some damn slick enhancements to Analytics 9 that allow not only tracking well beyond what Facebook Insights offers, but that also brings in some specific (anonymous) user information so that the traffic can be segmented in useful ways (the post on mashable.com shows some screen captures of the resulting data). I fully expect that Omniture will come out with something comparable as soon as they can, but I don’t think they have that level of tracking yet (if you know differently, please leave a comment to let me know). [Update: Coremetrics announced some new Facebook tracking capabilities shortly after this post was published.]. My one concern with the Webtrends solution is that, as best as I can tell, it requires the tracked pages to use a Facebook application that will pop up an “Allow Access?” question to the user — the user has to indicate this is okay before getting to the content on the page. Lots of applications have this, but, at Resource Interactive, we’ve also had lots of clients for whom we have built very rich and interactive experiences on their fan pages…without requiring anything of the sort. If the access is needed to enable the application to deliver value to the user, then this is fine, and the improved trackability is just scrumptious gravy that comes along for the ride. If the access is needed just for tracking, then I would have to think long and hard about it — data capture should always be between somewhere between excruciatingly minimally visible to the user and not visible at all.

The question, then, is, “What can be tracked unobtrusively, and how can it be done?” This post will attempt to answer that question.

Why Is It So Tricky in the First Place?

Facebook, largely for privacy reasons, locks down what can happen on its pages. It may make your head hurt (it certainly makes mine) to understand all of the cans vs. cannots for different scenarios, but I’ll take a crack at a short list. There are two basic scenarios that a customer might experience as a “tab on a brand’s page:”

  • The brand can add a tab to the page and drop some form of Facebook application into it; in this scenario, iFrames are not allowed, and Javascript cannot be executed
  • The brand can make a separate application, and, on the “application canvas,” they can drop an iFrame, and Javascript can be executed within that iFrame; but, since the application canvas cannot exist “in a tab,” the design for the page has to include tabs to mimic the fan page, which is a bit clunky and raises some other user experience challenges

Okay, so that was easy enough…assuming you’re following the custom tab / application / application canvas terminology. Both of these scenarios allow the embedding of Flash objects on the page.

Facebook doesn’t allow Javascript, but it does allow it’s own similar scripting language, called FBJS (these tabs also use “FBML” rather than HTML for developing the page — it’s similar to HTML but not identical).

What all of this means is that it’s not as simple as “just drop your web analytics page tag on the page” and you’ll get tracking. But that doesn’t mean you’re entirely SOL. This post is almost entirely geared towards custom Facebook tabs — and, really, it assumes that the content on those pages are based on an FBML application.

Tracking Basic Visits and Page Views for a Custom Tab?

We’ve cracked this to varying degrees for two different web analytics tools: Google Analytics and Webtrends. We haven’t had a pressing need to tackle it for anything else, but I’m pretty sure the same principles will apply and we’ll be able to make it happen. In both cases, the approach is pretty much the same — you need to have the FBML and FBJS on the page make an image call to the web analytics program. To pull it off, you do need to have a good understanding of how web analytics tools collect data, which I wrote an extensive post about a few days ago.

In the case of Webtrends, the simplest thing to do is treat the page like a page where every visitor who comes has Javascript disabled in their browser. I’ll cover that later in this post.

For Google Analytics, things are a little dicier because Google Analytics doesn’t have out-of-the-box “noscript” capabilities. You have to figure out all of the appropriate parameter values and then just make a full image call (again, reference the link above for a detailed explanation of what that means). You’re not going to get all of the data that you would get from running the standard page tag (which I’ll touch on a bit more later in this post), but you can certainly get page views and unique page views with a little FBJS work.

Start out by creating a new Google Analytics UA number for your Facebook tracking. This will give you a profile with a new ID of the form: UA-XXXXX-YY. You will have to provide a domain name, but what that domain name is is immaterial — “<brand>.facebook.com” makes sense, but it can really anything you want.

Then, it’s just a matter of figuring out the list of values that you are going to tack on as parameters to the Google Analytics image call (http://www.google-analytics.com/__utm.gif). Below are some tips on that front (refer to the Google Analytics documentation for a deeper explanation of what each parameter is), with the bolded ones being the ones that I’ll discuss in greater detail:

  • utmwv: 4.6.5 (or a newer version — I don’t think it’s critical)
  • utmn: needs to be a random number between 100000000 and 999999999 (more on this in a bit)
  • utmhn:  <brand>.facebook.com (or something else — again, not critical)
  • utmcs: leave blank
  • utmsr: leave blank
  • utmsc: leave blank
  • utmul: leave blank
  • utmje: leave blank
  • utmfl: leave blank
  • utmdt:  the title of the page (whatever you want to call it)
  • utmhid: leave blank
  • utmr: leave blank
  • utmp: a “URL” for the page
  • utmac: the Google Analytics ID you set up (UA-XXXXX-YY)
  • utmcc: __utma%3D1.<session-persistent ID>.1252000967.1252000968.1252000969.1%3B

This is as simple as it gets. Obviously, all of the “leave blanks,” as well as the limited number of “cookie values” being passed, mean that you’re not going to get nearly as rich information for the visitors to this tab (you should be able to just eliminate the “leave blank” parameters entirely from the image call. You will get page views and unique page views, and you can set up goals and funnels across tabs if you want. You can also start getting a little fancier and inserting campaign tracking parameters and other information, but start here and get the basics working first — you can always augment later (and please come back and comment here with what you figure out!).

For the four bolded parameters in the list above, two are ones that you will predefine for the tab itself — they’re essentially static — and two are ones that will require a little FBJS magic to make happen.

Let’s start with the two static ones:

  • utmdt: this is normally the <title> tag for the page that is being visited; you can make it any plain English set of text you want, but you need to replace spaces and other special characters with the appropriate URL encoding
  • utmp: this is the URL for the page; you certainly can navigate to the custom tab in Facebook and use that, but I suggest just making it a faux URL, similar to how you would name a virtual pageview when doing onclick tracking; again, you will need to make this an appropriately URL encoded value (that mainly means replacing each “/” in the URL you come up with with “%2F”)

The two other values require a little more doing, although it’s apparently pretty straightforward with FBJS (if you’re not a Javascript / FBJS jockey, as I’m not, you may need to track down a willing collaborator who is):

  • utmn: the sole purpose of this value is to make the overall GIF request a “new” URL; it’s a random (or, at least, quasi-random) number between 100000000 and 999999999 that should change every time there is a new load of the page
  • utmcc: the main thing you want to do here is generate a value between 1000000000000000000 and 9999999999999999999 that will stay with the visitor throughout his visit to Facebook. The other values in the __utma subparameter of utmcc are various date-stamps; if you want to get fancy, you can try to populate some of those as well; overall, utmcc is supposed to be a set of cookie values that persists on the user’s machine — we’re not actually dropping a cookie here, which means we’re not going to be able to track any of the sorts of “lifetime unique visitors”-dependent measures within Google Analytics (that includes “new vs. returning” visitors — everyone’s going to look like a new visitor in your reporting)

Make sense? I built a spreadsheet that would concatenate values I’d populated for these variables which just isn’t pretty enough to share. But, you just need to tack all of these values together as I described in my my last post and drop that as an image call on your custom tab.

This won’t work for every tab — you can’t do it on your wall or your Info tab or other pre-defined, unformattable tabs, but if you create a new tab and drop an FBML application in it, you can go nuts with this.

[Update: At almost the exact same time that this post went live, an e-mail hit my inbox with a link to a Google Analytics on Facebook post that I failed to turn up during my research (the post is only a week old, and most of my research happened prior to that). This post includes a handy link generator which looks really promising and helpful.]

Tracking Actions within a Tab

Now, suppose you’ve got your custom tab, and you’ve got tracking to the tab working well. But, you’ve dropped some Flash objects on the tab, and you want to track interactions within Flash. You’ve got two options here:

  • Just use the Actionscript API for Google Analytics — as I understand it, this works fine; I’ve also heard, though, that this adds undue weight to the app (35 KB), and that it’s not super-reliable; but, if you or your Flash developer is already familiar with and using this approach, then knock yourself out
  • Manually generate image calls for each action you want to track — this really just means follow the exact same steps as listed in the prior section, but use Actionscript rather than FBJS for the dynamically generated pieces

Because I work with motivated developers, we went the latter route and built a portable Actionscript class to do the heavy lifting.

Presumably, you can also use FBJS to track non-Flash actions as well, depending on what makes sense.

What About Webtrends?

The same principles described above apply for Webtrends. But, Webtrends has an out-of-the-box “<noscript>” solution, so, rather than reverse-engineering the dcs.gif, you can use a call to njs.gif:

http://statse.webtrendslive.com/<your DCS ID>/njs.gif?dcsuri=/<virtual URL for the page>&WT.ti=<name for the page>

(I did confirm that you can leave off the WT.js parameter that is listed in the Webtrends documentation for using njs.gif).

It also seems like it would make sense to tack on a random number in a parameter at the end (such as “&amp;nocache=<random number>”) just to reduce the risk of caching of the image request (similar to what’s described for the utmn parameter for Google Analytics above). I haven’t even asked for confirmation that that would be useful, but it seems like it would make sense, and it’s just a parameter that Webtrends will ignore in the processing.

Chances are, you’ll want to set up a new profile in Webtrends that only includes this Facebook traffic (see my opening ramble about Facebook pages being quasi-web sites), and you’ll probably want to filter this traffic out of your various existing profiles. That may mean you need to think about how you are naming your pages to make for some easy Include and Exclude filter creation.

(Oh, yeah, and the “statse.webtrendslive.com” assumes you’re using Webtrends Ondemand — if you’re running Webtrends software, you’ll need to replace this with the appropriate domain.)

As you’ve probably deduced by now, we haven’t really vetted our “njs.gif” usage…yet, but we’ve gotten a lot of head nods from within Webtrends that this should work. I’ll update this post once I’ve got confirmation, but I wanted to go ahead and get the information published so that someone else can run with it and maybe figure it out in more detail and let me know!

Webtrends also, apparently, allows Actionscript to interact with the Webtrends REST API directly, which, allegedly, is an option for action tracking within Flash on Facebook pages. We haven’t confirmed that, and, in what little looking I did on http://developer.webtrends.com, I didn’t turn up any particularly useful documentation, so either that’s not widely in use, or I’m a lousy user of their search function.

It’s Not as Tough as It Looks…but It’s Not Perfect

This may seem a little overwhelming, but the mechanics are really pretty straightforward once you dive in and start playing with it.

To test your work, you don’t need to actually code up anything — just set up your new profiles (Google Analytics or Webtrends) build up some image request strings, and start hitting them. You can manually swap out the “dynamic” values — even have some friends or co-workers hit the URLs as well. To introduce a bit of rigor, it’s worth tracking the specific image requests you’re using, how many times you hit them and from what browser. That way you can compare the results in your web analytics tool to see if you’re getting what you’d expect. Then you can move on to actually getting the calls dropped into a Facebook page.

Realize, too, that this whole process is a dumbing down of what normally happens when Javascript or Actionscript is used to tell your web analytics tool that someone has visited the page. Your new vs. returning traffic is going to be inaccurately skewed heavily towards “new.” You’re not going to get browser or OS details (much less whether Javascript is enabled or not). But, you will get basic page views and visits/unique pageviews, and that’s something! You’re stepping back into the Bronze Age of web analytics, basically, but that’s better than the Stone Age, and you’re doing it within social media!

I suspect that you can get a little fancier with FBJS and start to get more robust measurement. As a taste of that, we actually got some tracking working on users’ walls in Facebook, which was both wicked and rad (as the cool kids in the 80s would have said):

  • We posted a status update that was, basically, an invitation to click into a Flash object; if the user clicked into it, then a Flash-based box expanded on their wall, and Google Analytics would be passed an image call to record a page view for the activity
  • We also passed in a “utmv” value, which we then used to set up segments within Google Analytics — the idea being that, each time we do one of these status updates will be a separate “campaign,” but our campaign tracking will be through custom segments within Google Analytics — that will enable all of our reporting, including the conversion funnels we set up, to be set up once and then re-used through Google Analytics segmentation

Neat, huh? Or, as we’d say in the rural Texas town where I grew up, it’s slicker than greased baby poop. This is giving us highly actionable data — enabling us to see how people are interacting with these experiences through Facebook and enabling us to try different approaches to improve conversion over time! (To be clear, we’re not capturing personally identifiable Facebook information — exactly who is interacting is still invisible to us, which is as it should be).

Fun stuff. If you’ve given anything along these lines a try (or if you’ve successfully taken a totally different tack), please leave a comment — I’d love to get other options added!

0
Analytics Strategy

All Web Analytics Tools Are the Same (when it comes to data capture)

Posted on by Eric Peterson

I started to write a post on using web analytics tools — Google Analytics, specifically, but with a nod to Webtrends as well — to track traffic to custom tabs and interactive elements on Facebook pages. But, as I started thinking through that content, I realized that I needed to back up and make sure I had a good, clean explanation of a key aspect of the mechanics of page tag-based web analytics tools. I poked around on the interweb a bit and found some quick explanations that were accurate, but that really weren’t as detailed as I was hoping to find.

Regardless of whether you’re trying to track Facebook or not, it’s worth having a good, solid understanding of these underlying mechanics:

  • If you’re a web analyst, understanding this is like understanding gravity if you’re a human being — there are some immutable laws of the internet, and knowing how those laws drive the data you are seeing will open up new possibilities for capturing activity on your site
  • If you’re a developer, then this will be a quick read, but understanding it will make you the hero to both your web analysts and (assuming they’re not glory hogs) the people they support with their analysis, because you will be able to suggest some clever ways to capture useful information

By the end of this post, you should understand both the title and why the URLs I listed below are what make it so:

I’ve been deep under the hood with both Google Analytics and Webtrends for this, but the same principles apply to all tools (because they’re all bounded by the Physics of the Internet). I’m going to talk about Google Analytics the most in-depth, because it has the largest market share (measured by number of sites tagged with it), and I’ll try to call out key differences when appropriate.

Let’s start with a simple picture of how all of these tools work. When a visitor comes to a page on your site, the following sequence of events happens:

Steps 2 and 3 are really the crux of the biscuit, but we need to make sure we’re all clear on the first step, too, before getting to the fun there.

1 – Javascript figures out stuff about the visitor

We all know what Javascript is, right? It’s one of the key languages that can be interpreted by a web browser so that web pages aren’t just static text and images: dropdown menus, mouseovers, and such. But, Javascript also enables some things to go on behind the scenes. The basic data capture method for any tag-based web analytics tool is to run Javascript to determine what page the visitor is on, what relevant cookies are set on the user’s machine, whether the visitor has been to the site before, what browser the visitor is using, what language encoding is set for the browser, the user’s screen resolution, and a slew of other fairly innocuous details. This happens every time a visitor views a page running the page tag. So, great — a visitor has viewed a page, and the Javascript has figured out a bunch of details about the visitor and the page. Now what? It’s on to step 2!

(I realize I’m saying “Javascript” here, and most tools also have Actionscript support for tracking activity within Flash — for the purposes of this post, I’m just going to stick with Javascript, but I’ll get back to Actionscript in my next post!)

2 – Javascript packages that info into a single string of information

The next step is pretty simple, but it’s where the magic starts to happen. Let’s say the Javascript in step 1 had figured out the following information about a visitor to a page:

  • Site = http://www.gilliganondata.com
  • Page title = The Fun of Facebook Measurement
  • Page URL = /index.php/2010/01/11/the-fun-of-facebook-measurement/
  • Browser language = en-us

Converting that info into a single string is pretty straightforward. Let’s start by pretending we’re going to put it into a single row in a pipe-delimited file. It would look like this:

Site (hostname) = http://www.gilliganondata.com | Page name = The Fun of Facebook Measurement | Page URL = /index.php/2010/01/11/the-fun-of-facebook-measurement/ | Browser language = en-us

Now, rather than using the pretty, readable names for each of the four characteristics of the page view, let’s use some variable names (these are the Google Analytics variable names, but the documentation for any web analytics tool will provide their specific variable names for these same things):

  • Site (hostname) –> utmhn
  • Page title –> utmdt
  • Page URL –> utmp
  • Browser language –> utmul

So, now our string looks like:

utmhn = http://www.gilliganondata.com | utmdt = The Fun of Facebook Measurement | utmp = /index.php/2010/01/11/the-fun-of-facebook-measurement/ | utmul = en-us

We used pipes to separate out the different variables, but there’s nothing really wrong with using something different, is there? Let’s go with using “&” instead and eliminate the spaces around equal signs and the delimiters. The single string now looks like this:

utmhn=www.gilliganondata.com&utmdt=The Fun of Facebook Measurement&utmp=/index.php/2010/01/11/the-fun-of-facebook-measurement/&utmul=en-us

Now, we’ve still got some “special” characters that aren’t going to play nice in the Step 3 — namely spaces and “/”s, so let’s replace those characters with the appropriate URL encoding (%20 for the spaces and %2F for the “/”s):

utmhn=www.gilliganondata.com&utmdt=The%20Fun%20of%20Facebook%20
Measurement&utmp=%2Findex.php%2F2010%2F01%2F11%2Fthe-fun-of-
facebook-measurement%2F&utmul=en-us

It looks a little messy, but it’s a single, portable string that has the exact information that was listed in the four bullets that started this section. While it might be painful to reverse-engineer this string into a more reader-friendly format by hand, it’s a snap to do programmatically (which is exactly what web analytics tools do…as we’ll discuss in step 4) or in Excel.

Before we move on, let’s tack one more parameter onto our string. This is something that is actually hard-coded into the Javascript, and it identifies which web analytics account this traffic needs to go to. In the case of this blog, that account ID is “UA-2629617-3” and the variable Google Analytics uses to identify the account parameter is “utmac.” I’ll just tack that on the end of our string, which now looks like:

utmhn=www.gilliganondata.com&utmdt=The%20Fun%20of%20Facebook%20
Measurement&utmp=%2Findex.php%2F2010%2F01%2F11%2Fthe-fun-of-
facebook-measurement%2F&utmul=en-us&utmac=UA-2629617-3

A subtle point: what we’ve really done above is to combine all the information into a single string with a series of “key-value pairs.” In the case of the first variable, the “key” is “utmhn” and the “value” is “www.gilliganondata.com.” Notice that both the key AND the value are included in the string. If you’ve worked with comma-delimited or tab-delimited files, then you might be wondering why the key is included. Why can’t the Javascript always pass in the variables in the same order, and the web analytics server would know that the first value is the hostname, the second value is the title, and so on? There are at least four reasons for this:

  • It just generally makes the process more robust because it reaffirms to the server exactly what each value means at the point the server receives the information; the internet is messy, so hiccups can happen
  • Most “advanced” features when it comes to capturing web analytics data rely on tacking on additional parameters to the master string — by including both the key and the value for every parameter, that fanciness doesn’t have to worry about the order the parameters are passed in, AND it means the custom parameters get viewed/processed exactly the same way that the basic parameters do
  • The “key-value pairs separated by the & sign” are standard on the internet. Go to any online retail site and poke around, and you will see them in the URL. It’s kind of a standard way to transmit a series of variables onto the back end of a web page or image request, and that’s really all that’s going to happen in step 3

We’ve got our string, so now let’s do something with it!

3 – Javascript makes an image request with that string tacked on the end

Somehow, we need to pass that string back to the web analytics server. We do that by making an image call. In the case of Google Analytics that image request is always, always, always exactly the same, no matter the site using Google Analytics:

http://www.google-analytics.com/__utm.gif

Just like we covered in the “online retail site” URL structure discussion at the end of the last section, we’re going to tack some parameters on the end of the __utm.gif request. The standard way to take a base URL and tack on parameters is to add a “?” followed by one or more key-value pairs that are separated by an “&” sign. Lucky for us, the “&” sign is what we used when we were building our string in the last section! So:

http://www.google-analytics.com/__utm.gif

+

?

+

utmhn=www.gilliganondata.com&utmdt=The%20Fun%20of%20Facebook%20
Measurement&utmp=%2Findex.php%2F2010%2F01%2F11%2F
the-fun-of-facebook-measurement%2F&utmul=en-us&utmac=UA-2629617-3

=

http://www.google-analytics.com/__utm.gif?utmhn=www.gilliganondata.com&amp;
utmdt=The%20Fun%20of%20Facebook%20Measurement&utmp=%2F
index.php%2F2010%2F01%2F11%2Fthe-fun-of-facebook-measurement%2F&
utmul=en-us&utmac=UA-2629617-3

Wow, that looks messy, but it just looks messy — it’s actually quite clean! In reality, there are way more than five parameters tacked onto the image request. As a matter of fact, the request above would really look more like this:

http://www.google-analytics.com/__utm.gif?utmwv=4.6.5&utmn=1516518290&amp;
utmhn=www.gilliganondata.com&utmcs=UTF-8&utmsr=1920×1080&utmsc=24-
bit&utmul=en-us&utmje=1&utmfl=10.0%20r45&utmdt=The%20Fun%20of%20
Facebook%20Measurement%20%7C%20Gilligan%20on%20Data%20by%20Tim
%20Wilson&utmhid=1640286085&utmr=http%3A%2F%2Fgilliganondata.com
%2F&utmp=%2Findex.php%2F2010%2F01%2F11%2Fthe-fun-of-facebook-
measurement%2F&utmac=UA-2629617-3&utmcc=__utma%3D116252048.
1573621408.1267294551.1267294551.1267299933.2%3B%2B__utmz%3D
116252048.1267294551.1.1.utmcsr%3D(direct)%7Cutmccn%3D(direct)%7C
utmcmd%3D(none)%3B&gaq=1

You can get a complete list of the Google Analytics tracking variables from Google (if you’re really into this, check out the utmcc value — that actually is a single parameter that includes multiple sub-parameters, which are separated by “%3D” — a URL-encoded semicolon — instead of an “&”; these are the user cookie values, which you can find towards the end of the long string above if you look for it). You can inspect the specific calls using any number of tools. I like to use the Firebug plugin for Firefox, but Fiddler is another free tool, and Charles is the standard tool used at my company. And, there’s always WASP to provide the “clean” view of the parameters (I use WASP heavily…unless I’m trying to reverse-engineer the specific calls being made for some reason).

The Javascript makes a request for that URL. This is the infamous “1×1 image.” Just to sharpen the edges a little bit on some common misconceptions about that image request:

  • The request for the image is what matters — while the 1×1 image will get delivered back, by the time http://www.google-analytics.com actually sends out the image, the page view has already been counted. As a matter of fact, if there was no __utm.gif image, the traffic would still get counted simply by virtue of the fact that the Google Analytics server received the image request. As it happens, some other little user experience hiccups can happen if there’s no actual image, but the existence of the file matters ‘nary at all from a data capture perspective!
  • Yes, you can actually just request the image directly from your browser. Go ahead — here’s the URL as a hyperlink: http://www.google-analytics.com/__utm.gif (yeah, it’s something of a letdown, but now you can say you’ve done it)
  • The image isn’t a 1×1 pixel image so that it’s small and not noticed by the user. If Google got a wild hair to replace the __utm.gif image with a 520×756 pixel image of a psychedelic interpretation of the Mona Lisa…no one would ever see the change (unless they were doing something silly like calling the image directly from their browser as described in the previous bullet). The image gets requested by the Javascript, but it never gets displayed to the user. It’s sort of like a Javascript dropdown menu — the text for the dropdown gets loaded into the browser memory so that, if you mouse over the menu, the text is already there and can be displayed immediately. The __utm.gif request is the same way…except there’s nothing in the Javascript that ever actually tries to render the image to the user

And one more point: While we’ve been talking about “image requests” here, it doesn’t have to be an image request per se. In the case of Google Analytics, it is. In the case of Webtrends, it is, too (the image is called dcs.gif). In the case of other web analytics packages, it’s not necessarily an image request, but it is a request to the web analytics server. What matters is understanding that there are a bunch of key-value pairs tacked on after a “?” in the request, and that’s where all of the fun information about the visit to the page gets recorded and passed.

4 – Web analytics tool reads the string and puts the information into a database

So, the web analytics server has been getting bombarded with the requests from Step 3. Can you see how straightforward it is for software to take those requests and split them back out into their component parts? That’s the easy part. Where the tools really differentiate themselves is how exactly they store all of that data — the design of their database and then how that data is made available for queries and reports by analysts.

Back in the day (and I assume it’s still an option), Webtrends would make the raw log files available to their customers as an add-on service. That was handy — once we understood the basics of this post and the Webtrends query parameters, we were able to sift through for some juicy nuggets to supplement our “traditional” web analytics (these were in the days before Webtrends had their “warehouse” solution, which would have made the same information available).

5 – Web analyst queries the database for insights

Like step 4, this is an area where web analytics tools really differentiate themselves. In the case of Google Analytics, there is the web-based tool and the API. In the case of paid, enterprise-class tools, there are similar tools plus true data warehouse environments that allow much more granular detail, as well as two-way integration with other systems.

Why Understanding This Matters

You’re still reading, so maybe I should have made this case earlier. But, the reason this matters is because, once you understand these mechanics, you can start to do some fun things to handle unique situations. For instance, what do you do if you have Google Analytics, and you want to track activity somewhere where Javascript won’t run (like…um…your Facebook fan page — that’ll be my next post!). Or, more generally, if you’re Googling around looking for ways to address some sort of one-off tracking need, you’ll understand the explanations that you’re finding — these solutions invariably involve twiddling around within the framework described here.

As I read back through this post before publishing it, I was struck by how far into the tactical mechanics of web analytics it is. The overwhelming majority of web analytics blog posts focus on step 5 and beyond — how to use the data to be an analysis ninja rather than a report monkey. Understanding the mechanics described here is a foundational step that will support all of that analysis work. I was incredibly fortunate, early in my web analytics career, to have an opportunity to run the migration from a log-based web analytics package to a tag-based solution. I was triply fortunate that I worked on that migration with two brilliant and patient IT folk: Ernest Mueller as the web admin supporting the effort, and Ryan Rutan, the developer supporting the effort — he was hacking the Webtrends page tag before the consultant who we had on-site to help implement it had finished his first day. Ernest drew countless whiteboard diagrams to explain to me “how the internet works” (those “immutable laws” I mentioned early in this post), while Ryan repeated himself again and again until I understood this whole “image request with parameters” paradigm.

If you’re a web analyst, seek out these types of people in IT. A hearty collaboration of cross-discipline skills can yield powerful results and be a lot of fun. I had similar collaborations when I worked at Bulldog Solutions, and the last two weeks saw the same thing happening at my current gig at Resource Interactive. Those are pretty energizing experiences that leave me scratching my head as to why so many companies wind up with an adversarial relationship between “the business” and “IT.” But THAT is a topic for a whoooollllle other post that I may never write…

0
Analysis, Analytics Strategy, Reporting, Social Media

The Most Meaningful Insights Will Not Come from Web Analytics Alone

Posted on by Eric Peterson

Judah Phillips wrote a post last week laying out why the answer to the question, “Is web analytics hard or easy?” is a resounding “it depends.” It depends, he wrote, on what tools are being used, on how the site being analyzed is built, on the company’s requirements/expectations for analytics, on the skillset of the team doing the analytics, and, finally, on the robustness of the data management processes in place.

One of the comments on the blog came from John Grono of GAP Research, who, while agreeing with the post, pointed out:

You refer to this as “web analytics”. I also know that this is what the common parlance is, but truth be known it is actually “website analytics”. “web” is a truncation of “world wide web” which is the aggregation of billions of websites. These tools do not analyse the “web”, but merely individual nominated “websites” that collectively make up the “web”. I know this is semantics … but we as an industry should get it right.

It’s a valid point. Traditionally, “web analytics” has referred to the analysis of activity that occurs on a company’s web site, rather than on the web as a whole. Increasingly, though, companies are realizing that this is an unduly narrow view:

  • Search engine marketers (SEO and SEM) have, for years, used various keyword research tools to try to determine what words their target customers are using explicitly off-site in a search engine (although the goal of this research has been to use that information to bring these potential customers onto the company’s site)
  • Integration with a company’s CRM and/or marketing automation system — to combine information about a customer’s on-site activity with information about their offline interactions with the company — has been kicked around as a must-do for several years; the major web analytics vendors have made substantial headway in this area over the past few years
  • Of late, analysts and vendors have started looking into the impact of social media and how actions that customers and prospects take online, but not on the company’s web site, play a role in the buying process and generate analyzable data in the process

The “traditional” web analytics vendors (Omniture, Webtrends, and the like) were, I think, a little late realizing that social media monitoring and measurement was going to turn into a big deal. To their credit, they were just getting to the point where their platforms were opening up enough that CRM and data warehouse integration was practical. I don’t have inside information, but my speculation is that they viewed social media monitoring more as an extension of traditional marketing and media research companies that as an adjacency to their core business that they should consider exploring themselves. In some sense, they were right, as Nielsen, J.D. Power and Associates (through acquisition), Dow Jones, and TNS Media Group all rolled out social media monitoring platforms or services fairly early on. But, the door was also opened for a number of upstarts: Biz360, Radian6, Alterian/Techrigy/SM2, Crimson Hexagon, and others whom I’m sure I’ve left off this quick list. The traditional web analytics vendors have since come to the party through partnerships — leveraging the same integration APIs and capabilities that they developed to integrate with their customers’ internal systems to integrate with these so-called listening platforms.

Somewhat fortuitously, a minor hashtag snafu hit Twitter in late July when #wa, which had settled in as the hashtag of choice for web analytics tweets was overrun by a spate of tweets about Washington state. Eric Peterson started a thread to kick around alternatives, and the community settled on #measure, which Eric documented on his blog. I like the change for two reasons (notwithstanding those five precious characters that were lost in the process):

  1. As Eric pointed out, measurement is the foundation of analysis — I agree!
  2. “Web analytics,” which really means “website analytics,” is too narrow for what analysts need to be doing

I had a brief chat with a co-worker on the subject last week, and he told me that he has increasingly been thinking of his work as “digital analytics” rather than “web analytics,” which I liked as well.

It occurred to me that we’re really now facing two fundamental dimensions when it comes to where our customers (and potential customers) are interacting with our brand:

  • Online or offline — our website, our competitors’ websites, Facebook, blogs, and Twitter are all examples of where relevant digital (online) activities occur, while phone calls, tradeshows, user conferences, and peer discussions are all examples of analog (offline) activities
  • On-site or off-site — this is a bit of a misnomer, but I haven’t figured out the right words yet. But, it really means that customers can interact with the company directly, or, they can have interactions with the company’s brand through non-company channels

Pictorially, it looks something like this:
Online / Offline vs. Onsite / Offsite

I’ve filled in the boxes with broad descriptions of what sort of tools/systems actually collect the data from interactions that happen in each space. My claim is that any analyst who is expecting to deliver meaningful insight for his company needs to understand all four of these quadrants and know how to detect relevant signals that are occuring in them.

What do you think?

0
Analytics Strategy, Social Media

Columbus Web Analytics Wednesday Meets #fiestamovement

Posted on by Eric Peterson

Last night was the monthly Columbus Web Analytics Wednesday at Barley’s Smokehouse and Brewpub, and we were fortunate to have Webtrends sponsor for the second time this year! This time, we managed to get it scheduled in a way that lined up with Noé Garcia‘s travel plans, so he wore the dual crown of “Traveled Farthest to the Event” (from Portland, OR) and “Sponsor Representative.” The dual crown looked surprisingly like an empty beer glass:

Noe Garcia of Webtrends

Noe and Bryan Cristina of Nationwide co-facilitated a discussion about going beyond the application of web analytics tools within the confines of the tool itself. The most active discussion on that front was spawned by one of the regular participants in the group who works at a major, Columbus-based online retailer. Not necessarily this guy, but maybe it was him. My lips are sealed.

Monish Datta explains an approach to web analytics

We talked about how web analytics data, tied to order information, and then matched back to offline marketing channels such as printed catalogs, can be very effective at driving marketing efficiency. In the examples that triggered the discussion, as well as from the other participants’ experiences, the consensus was that, while the ideal world would have all of this data hooked together automatically…rolling up your sleeves and tying the data together manually can still yield a substantial payback. Part of the discussion got into volume — for companies that do a lot of direct mail-oriented promotion, using web analytics data to cut the mail volume by even a fraction of a percent (by using that data to better target who does/does not respond to printed mail) can provide significant and quantifiable savings for a company.

I didn’t think I’d ever hear anyone at a WAW say “Zip+4” (that’s shorthand for the 5-digit zip code plus the four additional digits that you see on a lot of your mail)…other than me! But I did! The person who said that may or may not be a different person pictured in the photo above. Again…my lips are sealed!


And…Ford’s Fiesta Movement

Dave Culbertson, a WAW promotional channel unto himself, kicked off an entirely different, but equally intriguing discussion:

Dave Culbertson Expounds

It all started as Dave was driving his Mazda in Grandview a couple of weeks ago. He got quasi-cut off by a 2011 Ford Fiesta two cars ahead of him. That prompted this tweet:

Dave Culbertson's "I just got cut off" tweet

Now, Dave regularly mocks people who promote themselves as being social media gurus/experts/mavens…but he’s one of the most social media savvy marketers I know. He also knows his cars. For one of those reasons (or maybe both) he immediately recognized that the car in front of him was part of Ford’s Fiesta Movement so he nailed a very relevant hashtag with his tweet. As it happened, someone else on Twitter saw the tweet, quickly realized who the likely culprit was, tweeted to her, and she wound up apologizing via Twitter less than an hour after the incident!

Ms. Single Mama's Cut Off Apology

Ms. Single Mama is a popular blogger, and this was the first time that she and Dave met in person. Everyone was curious about her Ford Fiesta agent experience. She obliged us by explaining, and, later, a good chunk of us headed out to the parking lot to see the 2011 Ford Fiesta she is driving for six months:

mssinglemama.com and her 2011 Ford Fiesta

Yes, we had name tags. Yes, the intial group that followed Alaina out to look at her car was entirely male. Yes, all told, about twice as many people as this wound up checking out the car. And, finally, yes, Alaina made a call in the midst of this picture! Andrew (far left) commented that the dashboard looked like the head of a Transformer. He…was right!

Transformer Head

2011 Ford Fiesta Dashboard

Dave even demonstrated his social media hipness by snapping a picture of the vehicle with his iPhone and then tweeting it:

Dave Culbertson iPhones a picture of a 2011 Ford Fiesta

All in all, it was an engaging, informative evening. I’m sure I’ll miss some of the companies that were represented, but they included JPMorgan Chase, Nationwide, Victoria’s Secret Online, Webtrends, Clearsaleing, Bath&Body Works, Cardinal Solutions, Highlights for Children, Rosetta, Foresee Results, Acappella Limited, DK Business Consulting, Lightbulb Interactive…and others! Not. A. Bad. Crowd!

The next WAW will be July 15th. We’re working hard to get our calendar for the rest of the year nailed down, which means we are looking for sponsors and presenters. Please contact me at tim at <this domain> if you are interested on either front.

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Analytics Strategy

A Record-Setting Web Analytics Wednesday in Columbus

Posted on by Eric Peterson

It turns out, as I’ve been looking at my records (read: old blog posts), that this month’s Columbus Web Analytics Wednesday marked our one year anniversary, which means it was our 13th WAW. As we were discussing it last night, we thought next month was the one-year mark, but, in a post from last May, I referenced that gathering as our third, and the Internet just doesn’t lie!

It’s only taken a year, but we just might have hit upon the perfect spot: Barley’s Smokehouse and Brewpub. For a nominal fee, we were able to reserve a room, which gave us some real volume control when we got into the group discussion portion of the evening, and there’s plenty of room for future expansion. As it was, the final headcount was 28 people, which was right around 50% higher than we’ve ever had before. Credit for that goes to:

  • WebTrends, our sponsor for the evening — we finally got some folk to come out who have been meaning to in the past, but haven’t quite made it; some of those people are WebTrends user; unfortunately, due to a scheduling conflict, Noe Garcia, the WebTrends account executive who supported our sponsorship request, wasn’t able to make the trip from Portland for the event.
  • Webtrends, who provided the evening’s speaker/topic — John Defoe, VP of Solution Services and A Bunch of Other Stuff, kicked off a discussion about using web analytics data outside of the web analytics environment; more on that in a bit.
  • The Amazing Blanqueras — we had some repeats who discovered us through past WAW promotions on Columbus Tech Life Meetup site, and we again brought in some fresh faces from the site; as Columbus Tech Life grows, so will WAW!
  • Dave Culbertson — ‘nary a WAW goes by that someone isn’t there because Dave ran into them and encouraged them to attend
  • Twitter — a number of people tweeted about the event, but my unofficial observations put Jenny Wells of TeamBuilder Search as the lead tweeter on that front

Other than that, we ran our usual gamut of promotions, and,presumably,picked up people through those channels as well. I’m sure we picked up a person or two who was Googling Monish Datta and wound up on this site (I’m up in the top 5 results for a Google search for Monish — I don’t think I’m ever going to overtake his LinkedIn profile). Feel free to take a crack about a WAW blog post not having definitive data on traffic sources…

As to the topic, John kicked off the discussion by sharing some examples of WebTrends customers who are using web analytics data beyond the web analytics environment:

  • A motorcycle manufacturer who uses web analytics data to score leads (site visitors) before passing them on to their dealers for follow-up — giving the dealers a prioritized list of who is more likely to be ready to buy
  • A media site that uses web analytics data to do an hourly refresh of the “most popular articles” on its home page (which led to a $2 million uplift in ad revenue, if I heard correctly) — I’ve always wondered how much that sort of functionality gets hit by a feedback loop (an article just barely cracks the “most popular” list, but, then, by being on the list, it gets more clicks and remains there), but I didn’t get a chance to ask
  • A company that uses web analytics data for targeted re-marketing via e-mail — identifying what content a person has viewed and using that to tailor e-mails promoting the same or similar products

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Columbus Web Analytics Wednesday - March 2009

John used those examples as a way to launch discussions of where others are using web analytics data outside of the web analytics environment:

  • I chimed in with my experiences with using web analytics data for lead scoring that combines web activity data with CRM information and then pushes the lead score into the CRM system
  • Scott Zakrajsek briefly explained out Victoria’s Secret uses web analytics data for targeted e-mail re-marketing
  • Bryan Cristina shared a Nationwide: Car Insurance example…that I totally missed (um…they’re my employer; you’d think I would’ve paid more attention there!)

The wrap-up thoughts, I think, could be summarized as follows:

  • Soooooo many companies aren’t even trying to do any of these sorts of things today
  • It won’t be long before these sorts of uses of web analytics data will be a must-have rather than a cutting-edge differentiation opportunity
  • It sounds easy enough, but, when you get down to it, getting different systems to really talk to each other (or to build a layer to pass information back and forth between them in a meaningful way) takes some roll-up-your-sleeves hard work and the tenacity to stick with it until it works
  • Having an engaged executive sponsor is darn near a must to pull these off
  • Having someone driving the project who really, really “gets it” makes things go a lot smoother…but outsourcing is a viable option

Columbus Web Analytics Wednesday - March 2009

If you’re interested in learning more, it’s not too late to book a trip to Vegas for WebTrends Engage ’09! John’s got a whole session on this basic subject on Wednesday, April 8th.

If you’re interested in sponsoring a future Web Analytics Wednesday, drop me a line at tim at gilliganondata.com!

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Analytics Strategy

Columbus Web Analytics Wednesday: March 18, 2009

Posted on by Eric Peterson

We’re a week later than maybe would’ve been ideal, but our sponsors are coming all the way from Portland, Oregon, and I expect it to be worth the wait! WebTrends will be sponsoring the event, and Noé Garcia (Strategic Account Executive) and John Defoe (VP of Solution Services) will be attending in person. John will be presenting and facilitating the topic for the evening: “Web analytics data beyond the web analytics platform.” This is a hot topic in a lot circles and includes, in my mind:

  • Integrating user-level web behavior into a CRM system to provide Sales with greater insight into the interests of specific prospects and customers (this is standard Eloqua functionality…but don’t read that as me thinking Eloqua offers a remotely robust web analytics solution)
  • Using user-level web behavior to score leads (in conjunction with non-web analytics data) to improve the lead qualification and lead nurturing process
  • Closing the loop and using web analytics insights to dynamically drive relevant web content — something that lots of people talk and wave their hands about…but that is extremely hard to actually build in a way that really works

I’m sure there are other aspects of this topic. I, for one, am looking forward to hearing John’s thoughts on the subject.

As to the details:

When: Wednesday, March 18th at 6:30 PM
Where: Barley’s Smokehouse and Brewpub (1130 Dublin Road, Columbus, OH)
I’m looking forward to the new venue. We continue to struggle to find a place that has a suitably decent food and drink menu, is suitably centrally located, and is  suitably non-noisy for us to be able to handle a presentation and discussion. Dave Culbertson, one of the co-organizers of the event, suggested Barley’s, and two other organizers responded enthusiastically (with the e-mail equivalent of a V8-style self-applied palm to the forehead), so I’m optimistic.
I’m hoping we have a good turnout!
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Analytics Strategy, Social Media

"You only get one chance to do it right. Try not to screw it up."

Posted on by Eric Peterson

Thus were the words that subtitled Bryan Cristina’s presentation (PPT) on campaign tracking at the December Web Analytics Wednesday in Columbus last Wednesday, sponsored by CoreMetrics, Analytics Demystified, and SiteSpect at BJ’s Restaurant in Powell.

 Columbus Web Analytics Wednesday - December 2008

When it comes to screwing things up, we certainly had our opportunities:

  • Originally, we had planned on meeting at O’Shaughnessy’s Pub down in the Arena District. After initially being told we were good to meet there, we got bumped by a private party (apparently, a private party that has been occurring for a number of years at O’Shaughnessy’s and that takes over the entire place; it’s understandable, but still a bit irksome).
  • When we started looking for nearby alternatives, we realized the Rockettes were performing at Nationwide Arena that night, which was likely to clog alternate venues. So, BJ’s it was.
  • I forgot my camera. I was 3/4 of the way home to pick it up en route from work to BJ’s, when Twitter came to the rescue — @heatherdee409 shot me a tweet that she had a camera in her purse and we could use that. Thanks, Heather!
  • BJ’s had told us that we would have “the back room.” Unfortunately, that just described a large area, rather than any sort of private/semi-private space.

Thanks, I assume, to some more proactive promotion of the event (Dave Culbertson of Lightbulb Interactive accounted for at least half of the first-timers), we had record attendance. Combine the turnout with the fact that we were in a shared space, and we had less-than-ideal conditions for Bryan’s presentation. He brought a handout (PPT) and managed to semi-holler for a few minutes to quickly walk people through it. That was unfortunate, but I do think we are at least learning that we may have to settle for lesser quality food and a limited beer selection (read: The Spaghetti Warehouse) when we have a presentation.

Nevertheless, the presentation had some great information. And, some great lines that are typical Bryan-funny-caustic:

  • “‘We want to see what people from this campaign do on the site’ is not a goal, it’s an excuse for those who don’t know what they want to measure or for campaigns that have no purpose”
  • (When setting the campaign up) “Never trust anyone, especially yourself”
  • “Know the last possible second you can get things taken care of. People will forget you were excluded from everything until the last minute and will just blame you for being stupid.”
  • “‘It’s not in test, but it will show up in production’ means they have no idea what you’re talking about, don’t care, and none of your tracking tags will ever make it onto your campaigns.”

That’s just a sampling. Good stuff!

We had some first-time attendees I was pretty excited about:

  • Mark Whitman and Jen Wells of TeamBuilder Search — a relatively new recruiting company focussed on interactive talent; I had a good talk with Mark and got him to tentatively agree to do a presentation on building a career in the interactive space at a future WAW.
  • Noe Garcia of WebTrends — all the way from Portland! Bryan and I both go wayyyyyy back with Noe, and, interestingly, had had dinner with him earlier in the year at the same restaurant when he was in town; he’d been hoping that his travel schedule would line up with a Columbus WAW, and it finally did! Noe’s a great guy, and he’s tentatively agreed to have WebTrends sponsor a WAW in the spring and provide a speaker. Unfortunately, Noe was also partway through Super Crunchers, which I thought was a horrible book. We had a good-natured debate at the end of the evening about it and parted on speaking terms.
  • There were a few people I didn’t actually get to speak to, but who were new faces. And, embarrassingly, I had quite a conversation with a gentleman who has a local SEO/SEM firm…and I didn’t capture/record his name or his company! But, he did point me to Laura Thieme of OSU and bizresearch.com, who seems like another good contact for future WAWs.

List of tweeters in attendance who I could identify:

And, finally, I learned that there is apparently a Monish Datta fan page. Unfortunately, I couldn’t find it. So, I’m stuck just linking to Monish’s LinkedIn profile. But, hey, in the process of looking, I realized that last month’s post got me some serious Google Love on “Monish Datta” search results.

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Analytics Strategy

I Wonder If chacha.com Came Up at eMetrics?

Posted on by Eric Peterson

We had a great Columbus Web Analytics Wednesday last night at The Spaghetti Warehouse, sponsored by CoreMetrics, Analytics Demystified, and SiteSpect. As usual, we held it on a Tuesday, and we went double-rebellious by somewhat inadvertently scheduling it to conflict with eMetrics (which lost us Jonghee and Judy) and with a local pool league (which lost us Nicole).

Nevertheless, we had a healthy turnout, albeit it one that had an exact 1:1 ratio of X to Y chromosomes (think about it):

Columbus Web Analytics Wednesday (Tuesday) -- October, 2008

As tends to be the case, we had heavy Coremetrics representation at the event, but we also had a WebTrends company, one company that’s exploring IndexTools, and heavy dabblers in Google Analytics all around.

We didn’t have any formal topic, but both ends of the table had some interesting discussions. On my end, a couple of the big topics were:

  • The importance of web analysts — or any working professional — being passionate about their work and constantly pushing themselves to try new things; this led to a discussion about the importance of “making mistakes” — it means that you’re trying new things, and it means that you’re learning. As theoretical as the summary sounds, this was rooted in experience with specific people in web analytics roles who were not effective in the position
  • The inaninty of the “clickpath” report — turns out that Bryan C. and I are both major anti-fans of classic clickstream reporting (it goes to my “people are cows myth”); single-level paths are much more practical and useful in most cases. We don’t like overlay reports, either, because they are so rife with pitfalls in their interpretation

We also covered a bit about my pending move to Nationwide, where I’ll be an official co-worker of Bryan C., albeit in a totally different department and not really involved with web analytics. More on that move to come — I expect the number of data management posts here to increase as I get more heavily back into that world.

And, on the “entertaining technology” front, Scott introduced us to chacha.com. It’s a free service where you can call or text in any question, and it will text back an answer. Scott demonstrated by calling and asking “Where do babies come from?” The answer that was returned was accurate, succinct, and, yet, would garner a PG rating on the big screen. I tried it this morning by asking “What is web analytics?” with lesser results:

Off-site web analytics refers to web measurement and analysis irrespective of whether you own or maintain a website.

I’m not sure where the “off-site” came in, but the response included a link with the question and answer: http://search.chacha.com/u/DIffZIPz. And, that link includes a link back to the “Source Website,” which was the Web Analytics Association, so, all in all, not bad for a one-minute response to a random question.

And, finally, Monish Datta said my goal should be for this site to show up as the number one Google result for a search on “Monish Datta.” Currently, I’m on the second page of results, lagging well behind the YouTube photo montage of The Monish set to the tune of Runaway. But, I’ll keep working on that.

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