Alistair Croll & Sean Power blog on watchingwebsites.com about web technology, startups and analytics.  They are the co-authors of Complete Web Monitoring (O’Reilly, 2009) and contributors to Web Operations (O’Reilly, 2010).  If you like this post, you’ll probably like the one we wrote about analytics &  the TechCrunch bump.

Facebook recently introduced embedded commenting within websites, under the name Facebook Comments. Does this new model for commenting on posts help, or hurt, site engagement?  To find out, we compared two weeks’ worth of TechCrunch posts; 7 days before and 7 days after the site implemented the Facebook Comments feature.  Data geeks, you can find the source data here.

In order to reduce the outlier effect of posts with very high, or very small, levels of comment —the massively successful, or truly abysmal, ones—we trimmed the lowest and highest 5 percent of results. In this analysis, “All posts” refers to sums and averages on all posts published, whereas  ”average posts” refers to sums and averages on all posts with the 95th and 5th percentile values removed.  By trimming the results in this way, we hope to get a better representation of the effect of Facebook Comments on a typical post.

TL;DR

To be able to fully understand the scope of Facebook Comments, we’re missing a few critical factors which are only available to the owner of the site itself:

Your strong value proposition comes in two forms: your ability to drive users from your own platform to publishers, and your ability to prevent spam and trolling by forcing identity on all comments.  If you can claim the largest publishers, you have a chance at usurping Twitter’s position as the leading means of spreading awareness about a piece of news.

Disclaimer: I’m one of the nominees for the WAA board of directors. Shouldn’t really matter, but consider yourself warned.

This year, 21 nominees are running as nominees for five board of director positions – 1 in Europe and 4 in North America.

I’m running for three major reasons:

• Social Media Measurement: Let’s make sense out of them.  I co-wrote a book on the subject in 2009 with Alistair Croll. I’ve talked to hundreds (thousands?) of practitioners / enthusiasts about the subject. The WAA needs to pursue it’s standardization, advocacy and education in the field.  Yep. I can help that.
• Lean Startup Analytics: It’s time we give more love to the massive startup ecosystem. Startups have completely different needs than companies in the enterprise space. By appealing to them, we can grow our membership base considerably and help educate those that will turn into the mammoth companies of tomorrow.  Think of it as “educating our young”.
• Non-Profit Analytics: Much of the efforts put into strengthening the startup base can equally help non-profits.  By passing along educational material targeted to non-profits, we can empowering them with stronger abilities to  understand where their efforts are successful, and where they’re not.

During the day time, I, eat, poop and sleep data convergence.  We need to be more inclusive of non-traditional streams of measurement. This will ultimately help us grow our member base, and make the organization more relevant in today’s “big data” environment.  This is the central thesis in Complete Web Monitoring.

There are 12 people on the board of directors and the WAA has over 1500 members (updated number from 5000, thanks Eric Peterson).  We have the resources, collective intelligence and drive to make strides in each of these areas.  We simply need a bit of support, encouragement, and direction to make these a reality.  Aaaaand, that’s why I came to the party :).

But frankly, as far as the vote is concerned, I’m confused. It isn’t how I imagined things being. I never imagined that there would be as many existing board members running as there are positions.  The people I’m “running against” aren’t people I want to run against.  I’m supposed to be running against Jim Sterne, the founder of the WAA?

A big part of me wants to give Alex, Dennis, Jim, June and Vicky a second BoD term. Jim puts it so eloquently:

“Aside from being a major cheerleader for the cause, I bring the organizational memory which will help the next Board and our new Executive Director understand why certain decisions were made in the past.”

Another part of me wants to encourage voters to pass the torch on to new candidates armed with fresh ideas and new perspectives.

• John Lovett, analyst extraordinaire, brings an understanding and tenure in the web analytics industry that few nominees can rival.  I encourage the WAA to take the opportunity to work with him closely over the next two years.
• Brendan Hart brings executive metrics knowledge from National Geographic that can greatly benefit our organization from a practitioner point-of-view.
• Eric Feinberg never ceases to amaze me with his candor, knowledge and ability in the field.  He is a great person to talk and work with and would be a big asset to the WAA.
• Steve Jackson, who wrote the book Cult of Analytics, and pushes hard for awareness of the WAA outside of North America.
• And . . . well . . . I’m running and all. :)

I’m not sure which way to go. No matter what, I feel like the Web Analytics Association will gain some great directors, and lose some great candidates.

I’ll probably end up voting for a mix of organizational memory and fresh meat. Either way, the decision will be very hard.

For all of you planning to vote (and even those that aren’t!), I’d love hear your thoughts – either here or on your own blog (if you blog about it, let me know – I’ll add all WAA related posts to the end of this one). By encouraging discussion, I hope that it will help us all come to a conclusion that’s best for the association.

See you on the ballot.

PS: You can see a nominees in action on Twitter by following the Twitter list or finding their individual accounts here.

Here’s a list of blog posts related to the Web Analytics Association Board of Directors vote:

• Vote in the Web Analytics Association Election! by Larry Freed on FREEDyourMind
• WAA Elections: Accountability, Inclusion, and Value by Eric T Peterson on Web Analytics Demystified
• Vote Lovett for the WAA Board of Directors by John Lovett on Web Analytics Demystified
• Big Election Day in Web Analytics Citizenship by Jacques Warren on WAO Marketing

If you haven’t heard yet, the Web Analytics Association is an organization that’s promoting web analytics.  It does so by passing on information to the public, by providing access to best practices, and ultimately connecting individuals, vendors, practitioners and consultants in the field.  This year, the WAA received nominations for their board of directors.  It is accepting 5 positions – 4 based in North America and 1 based in Europe to compliment their existing board.

I’ve put together a Twitter list of all nominees running for the 5 Web Analytics Association board of director positions.  To follow the nominees, simply click on this link, and hit the “Follow this list” button.  Even better, simply add this list if you use Seesmic Desktop, TweetDeck, CoTweet, HootSuite or any other Twitter application.

If you’re interested in following the nominees individually, I’ve included their Twitter accounts below.  Enjoy!

Beth Kanter‘s 53rd birthday is today.  Of the many reasons why we’re big fans of Beth, she’s pioneering the concepts of web-based analytics for the non-profit / charity sector.  If you haven’t seen her blog before and you want to deep dive in her thoughts on metrics, start here.

To celebrate her 53rd birthday, she’s using social media to incite change in the world by sending 53 Cambodian children to school.  Here’s the full description of her birthday wish.  Have a few bucks laying around?  Help her out here.  It’ll help kids go to school in Cambodia!  How cool is that :).

It gets better.  She’ll write about the lessons she learned during the campaign (just like we did for the Beers for Canada), and share insights on the metrics and measurement tactics she used to determine what worked and what didn’t.

As always, Beth continues to be a rock solid voice in the world of non-profits & web.  She’s on our A-list.

Happy birthday Beth!

Google Analytics recently added a new feature, called Alerts. At first glance, it’s an elegant way to show someone when a KPI on their site has changed significantly from what’s expected. It’s baselining, applied to all KPIs — even the ones you’re not looking at.

This is a great idea for folks who forget to check their analytics data, because now they can find out about significant events. It tricks you into being a better analyst. It encourages baselining, segmentation, and thinking about your business. But we think it’s the start of something bigger, once it incorporates the things Google and others know about your online presence.

Details, and some juicy UI mockup speculation, after the jump.

Baselining, even when you didn’t know you should

Beginner web analysts treat analytics as accounting. They use it to report the news, not make the news. It’s only the more advanced analysts that see analytics as a means for optimization, using things like A/B testing to learn whether a change made things better. And to do that, you need a baseline.

The new feature learns what normal is, then shows you deviation. This encourages experimentation: “I tried something new today, and I can see the results.” Google’s already introduced comparative rankings, showing you how you’re doing against others; now, they make it much easier to identify significant changes to your site, even if you don’t know where to look.

Imagine, for example, that you change your website. You don’t see an appreciable shift in traffic volume, so you decide it didn’t have an effect. But hidden in those traffic numbers is the fact that there was an increase in European traffic at the expense of US traffic. The new functionality would show you this, allowing you to tailor content to specific geographies.

Making segmentation easy to try

The new functionality tries to find chunks of traffic that have “broken away from the pack.” It does this for known metrics and segments — such as geographic regions — as follows:

Notice that little “create segment” at the end? It makes it easy to carve out a slice of traffic you should care about, which then means you can start to play and experiment with it. Segmenting traffic is a sign of web analytics maturity, but until recently, it’s been something few people play with. Now, Google Analytics is essentially telling you, “hey, dummy, have a closer look at this.”

You can use custom segments in lots of cool ways–for example, as the analysis above shows, I now know that returning US visitors are more likely to download content from the site, but first-timers aren’t. Once you’ve seen a segment that Google found for you, you’re more likely to create your own because you understand how they work.

Thinking about your business

You can also set up custom alerts within the system to tell you when something’s gone out of kilter. We know lots of companies who use revenue or transactions per second as the first sign that something’s wrong on the website — this is a great top-down approach if you can manage it, because it means everyone in the company is focused on what actually pays the bills.

The new functionality lets you look for specific occurrences even before they happen. Consider @alexbfree‘s recent post on Twitter Retweeting, which got picked up by Dave Winer. You can set up an alert to see if Dave sends you traffic:

Overall, these are excellent enhancements to the product. They’ll improve engagement — because the system will tell you when things are happening, rather than waiting for you to log in. They’ll encourage good behaviors like baselining and segmentation. And they’ll also satisfy the less business-centric, more hobbyist segment that just wants to know when the world is thinking about them.

What I really want: a holistic view

It’ll be more useful (and in keeping with the Complete Web Monitoring philosophy) when it includes other kinds of data:

• A timeline of posts created, based on Feedburner statistics or blog history
• A series of Google Alerts showing when some search criteria on the web is met
• A volume of followers or friends obtained through the APIs of social platforms like Twitter, Facebook, and Flickr
• Performance data from synthetic or real user monitoring
• Voice of the Customer feedback through systems like Kampyle

Here’s an example of what that could be like, for a content creator/blogger.

That’s a pretty intimidating amount of information. Most of it, Google already has; some, we’d get from elsewhere. We borrowed concepts from:

• Bit.ly’s historical views (over a longer time period) with a rollover for individual links on a given day
• Google Labs’ News timeline
• The dashboard of WordPress
• Postrank‘s content scoring system (we spent time with these folks this week)
• Feedburner RSS stats
• Email subscription management stats from a mailing list provider
• Moni.tor.us performance monitoring
• Trendistic‘s timeline graph of Twitter (with a rollover of Outwit.me‘s realtime tag cloud)
• Google Alerts, which come in by mail but could be turned into a timeline with rollovers

Anyone with a bit of time and some spreadsheet know-how can assemble this manually; it could also be done in Greasemonkey with a bit of work, using Google’s new views as the anchor.

Admittedly, this is still “reporting the news” — the real insight comes from observing correlations, such as what kinds of posts increase subscriptions or what news drives follower count. And this is targeted at a specific kind of site (media/community) whereas other businesses more focused on SaaS or e-commerce revenues probably want something that shows productivity or conversion rates.

Unfortunately, there isn’t a lot of money in giving tools to bloggers. We’re a cheap bunch. So while there’s great multivariate testing for online retailers, a content creator has to cobble together many different views and data sources to paint a complete picture.

We’re in Amsterdam this week, presenting at a Measureworks conference on web performance and optimization and attending a Tweetup.

Our host, Jeroen, told us yesterday that since the introduction of GPSes in Amsterdam, traffic accidents in the narrow-streeted city have risen significantly. Many people are focused on their instruments, rather than looking around them. This made me think of some issues I’d seen with web advertising recently that would have been hard to detect through instruments alone, and underscored some of the shortcomings of a purely instrument-driven analytics approach.

With much fanfare, Microsoft launched Windows 7. By many accounts, it’s a good operating system, despite the widely derided launch parties they tried to encourage (which, to be fair, did get people talking about the launch.) The launch involved a massive online ad buy, as well as a new online store for the company. Two aspects of this launch caught my attention: The differences between regional stores, and the state of video advertising.

The differences between regional stores

Here’s how Microsoft is selling its wares in the US. There are five steps from landing page to checkout.

Note that you can click on the cropped images here to see a larger view of the page from which it came, if you want to better understand it in context.

First, there’s an invitation to get Windows 7 on the home page. Decent, and since I want it, I’ll click it.

Okay, I’m given some options, but the one I want most is to buy the thing. The other options are stuff like a store locator, but since this is 2009 and I have an Internet connection, I’m most interested in buying it now.

I get to choose which one I want — there are three versions. I can see pricing. Great, I’ll click “Buy now.”

I can choose some options (full or upgrade version, download or shipped version.) The defaults are okay, so I’ll click “Add to Cart”.

The cart contents are confirmed, and I can check out. I have some bonus stuff — a chance to second-guess myself on not getting the backup, which increases order cart size; a chance to buy more than one; and a chance to tell someone else about the purchase. This is good e-commerce behavior. But I’m just going to buy one for now.

That was what I expected, and I have an order, right? Well, not if you’re in Canada. Let’s try the same thing on the Canadian Microsoft site.

And now, Canada:

I start by clicking “Get it now” on the home page. This isn’t exactly the same as the image on the US site, but it’s the same text.

Okay, I’ve got the same three versions from which to choose.

The price is higher, which is too bad because the Canadian dollar is roughly 1:1 with the US dollar right now, so this is a 20% “not paying attention to International currency rates” tax. But that’s nothing new on the Internet.

I also notice that the “buy now” button is called “shop now” in Canada. I assume that’s just the result of A/B testing and that Canadians prefer “shop” to “buy” for some reason. So I click it.

Here’s what I get. Wait, what?

Instead of confirming options and putting it in my cart, I see a screen showing me the names of stores.

Let’s be clear: Store information is not what I wanted. There’s nothing on this page saying, “we’re sorry, in Canada we don’t sell products online; you’ll have to go to a store.” Forget for a minute the fact that Canadians can’t buy Windows software online — astonishing as that may be. This is a completely jarring workflow. After what felt like a normal shopping cart pattern, I was suddenly given a page that made no sense. When this actually happened, I tried the workflow three times before realizing what had transpired.

If you’re a web analyst at Microsoft, would you see this? Likely not — you’d notice a large number of exits from this page, and assume you’d done your job of informing visitors where they could pick up a copy of your new product. Instead, you are experiencing huge abandonment.

Relying on your instruments too much

As the Canadian Microsoft store example shows, sometimes your instruments won’t show you something plain and obvious; you have to use common sense. A simple message on the final page saying, “we don’t sell software online in Canada” would alleviate the misunderstanding and make the page successful. Microsoft could even have a link explaining why — and track traffic to that link, to gauge Canadian interest in online purchases, or solicit comments that might mitigate an outcry from Canadians trying to convince Redmond they’re ready to buy products over the web.

Let’s talk about video

Another part of the advertising blitz around Windows 7 is the videos they’re embedding into CTV’s player website. This is a site on which Canadians can watch recently-aired programs (similar to Hulu in the US and the BBC iPlayer in the UK.)

The CTV website isn’t very sophisticated. The most egregious problem with the site is that it can’t interleave commercials and content cleanly. Where Hulu switches smoothly between a program and an ad, the CTV site actually pops out of full-screen mode each time there’s a commercial, requiring the viewer to re-maximize the player.

Just how bad is this inability to handle interactions with online video? When Microsoft advertises Windows 7 on the site, they run a static image for thirty seconds inviting the viewer to click on an ad at right.

That’s completely broken. It underscores just how nascent online video advertising is today. The only way that CTV can let Microsoft segment its respondents is to force them to pop out of full-screen, then invite them to click on a static image which can be imagemapped.

To do video advertising properly, advertisers will soon demand:

• A referring tag showing where on the screen the visitor clicked (in case there are several visual components)
• The timecode within the ad (in case the ad covers several features.)
• The programming information (what show was it?) and other facts that can be used for demographic targeting

This stuff is available in more advanced analytics and video delivery platforms. But the fact that Microsoft had to waste 30-second video clips on a static image shows just how far we still have to go before video advertising delivers on its promise.

We had a good discussion about performance and its impact on KPIs like analytics and conversion with Strangeloop this week. Here are the slides, available for download or viewing, on Slideshare.

Do faster web pages mean better business? Definitely. We’ve seen hard evidence from major web operators like Shopzilla, Google, and Microsoft. But what about other websites? How big an impact does performance optimization have on the business metrics of a typical media or e-commerce site?

Here’s some concrete data on how reducing latency changes the key metrics, such as bounce rate, pages per visit, conversion rate, and shopping cart amount. It’s a pretty detailed discussion, but it if you want to understand the ROI of improving web performance on your site, dig in. If you want to read this more easily, here’s a PDF.

Enough theorizing: Page latency affects your business

Years ago, the need for web performance was anecdotal. Researcher Mihaly Csikszentmihalyi had, years before, shown that human beings are more engaged, and more likely to enter “flow states”, when response to their actions is immediate (see this article for more details on flow and web optimization.) But while it seemed like a good idea to make a page load quickly — and companies like Zona Research made headlines with their 8-second rule — we didn’t have any empirical evidence to that effect.

There’s no longer any debate. There’s reliable, reproducible evidence that web page latency is directly tied to the bottom line. At Velocity, Microsoft, Google and Shopzilla made this abundantly clear in a series of awesome presentations: detailed, controlled testing proves that slower pages hurt the bottom line. In Google’s case, adding delay reduces the average number of searches a visitor does each day even after the delay is removed.

Microsoft, using data from Bing, showed that slow pages affect other KPIs:

In other words, if your website is slow you’ll get:

• Fewer search queries per user
• Less query refinement
• Less revenue per visitor
• Fewer clicks, and lower satisfaction
• A longer time for visitors to click something
• Fewer searches per day
• Lower search engine rankings

Ouch.

There’s a great writeup of all this at Artzstudio, and we covered Marissa Mayer’s talk on Bitcurrent. My only complaint was that the sessions all left me with questions about how this applies to smaller, more targeted sites.

So what about sites we mortals run?

Generally speaking, there are a few KPIs that really matter. These measure attention (how many people find out about you), engagement (how much they interact with your site), and conversion (whether they do what you wanted, and how much you benefit from it.) Here’s a simple view of some of those KPIs. Different KPIs matter as visitors turn into customers, buyers, or enrolled users.

In this diagram, inbound traffic either consists of returning or new visitors. Some of those visitors leave (“bounce”) immediately, while others continue on to other pages, spending time on the site, creating content, and viewing ads. On an e-commerce site, a subset of those visitors convert (by doing something we want them to) and there’s a value to those conversions.

The question, then, is how does a faster page load time affect metrics such as these?

Comparing performance optimization in analytics

At Interop Las Vegas, I had dinner with the guys from Strangeloop Networks (disclaimer: I have friends who work there). They make a web acceleration appliance that speeds up page load times. It occurred to us that they were in a perfect position to tie page performance to analytical results, because their appliance could actually modify pages on their way out, on a visitor-by-visitor basis. So if they only optimized some of the visits, and marked them as such, they could later compare the business performance of optimized and unoptimized segments.

The Strangeloop guys ran sprinted with this idea, testing a couple of different types of site, including media and transactional businesses. Here’s what they found, which they’ve agreed to share with us provided we keep certain details confidential.

Total visits

The system was set up to optimize half of the visits and leave the other half untouched. But when they analyzed the results, that’s not how things looked in Google Analytics, where significantly more optimized sessions were recorded. In all, roughly 14,000 visits were segmented within the analytics package; but the number of optimized sessions that were recorded was significantly higher.

This may be due to problems with the analytics scripts running on slower connections, or it may be a sign of increased page abandonment before analytics has a chance to load. Either way, it’s worthy of more study.

Percent of visits from new visitors

Optimization seems to have an effect on the number of new visitors to the site, too, though it’s not clear why this happens.

We’d expect that since new visitors’ browsers have more to load — there’s nothing in their cache yet, and so page load times are higher — there would be fewer aborted visits for new users, which in turn would increase the relative number of new visitors to the site. But that’s not what happened.

Bounce rate and optimization

The next metric we looked at was bounce rate, that is, how many visitors left quickly from the first page they saw. Faster pages delivered a lower bounce rate than slower ones.

This seems intuitive, since when a page loads quickly you’re less likely to leave out of frustration. That extra percent of visitors who stick around turns into more opportunities to sell something or otherwise engage a visitor.

Engagement: Pages per visit and visit duration

Optimization also affects the number of pages a visitor views. When the site is slow, people read fewer pages.

It’s not clear from the pages-per-visit metric alone whether this is because people spend a finite time on a site, and simply get bored after that time, If that were the case, we’d expect a faster-loading page to result in more pages per visit simply because more pages could be loaded before the visitor got bored. A second KPI, Average time on site, clarifies this. Optimized visitors to the site spent 7 minutes more than unoptimized visitors. So it’s not so much the pages per minute of time on the site, but the actual number of minutes, that increases.

This is great for visitor engagement; if you’re running a media site, it also means a chance to deliver more impressions and make more money per visitor.

Impact of optimization on e-commerce results

But what about e-commerce and retail? Strangeloop instrumented a second site in the same way. The beauty of tagging visits up front is that the actual business outcome of that optimization can finally be quantified. In this case, optimization resulted in a 16.07% increase in conversion rates and a 5.50% increase in average order value.

We can’t share the actual conversion rate and order value amounts, but what these numbers do is allow you to actually quantify the ROI of a performance improvement investment.

Want to learn more?

There’s lots of good data behind these results, which we’ll be looking at in more detail in a Webinar on October 8 at 2PM EDT (you can sign up online at bit.ly/perfwebinar). Strangeloop will also have some data on how much performance improvement visitors experienced by then, and you can ask Hooman Beheshti (their VP of Products) and I questions about the experiment if you want to know more.

Some caveats:

I’m always wary of presenting vendor-specific data, because we try to remain impartial. Strangeloop isn’t paying me to talk about this, and I decided to cover it because it’s useful to the web monitoring community and I asked the question in the first place. I’ve reviewed the information fairly closely and have good reason to trust Strangeloop (for one thing, their VP of products, Hooman Beheshti, is a sometime contributor to Bitcurrent and an expert on web performance who reviewed Complete Web Monitoring and gave us detailed feedback.)

Instrumentation happens as follows:

• Every visitor who requests a page gets a segmentation cookie regardless of who they are. Since Strangeloop’s technology takes advantage of certain features in more modern browsers, not every visitor who is accelerated will benefit from the same performance improvements.
• The numbers reported in the analytics package (Google Analytics) are a result of the segmentation cookies seen, which is tied to how many visitors’ browsers made the analytics request.
• The reporting Javascript within the page sometimes can’t find the cookie that was set (this happens about 5% of the time). This could be a consequence of security restrictions, browser limitations, and so on. However, the “cookie not found” errors occur relatively evenly across optimized and unoptimized visitor, so they don’t distort the numbers.
• Google Analytics’ IP filtering was used to block out internal users, which might distort numbers too.

A real time analytics solution lets you see who is currently visiting your website.   You get granular session-level detail (IP addresses, technographic information, geolocation, and sometimes even a username).  They differ from tools like Webtrends, AT Internet and Google Analytics in that they’re not well equipped to deal with trending and goal tracking.

If you run a website, we strongly suggest that you install a real time analytics solution.

Here’s why:

Monitor campaign vitriol/praise spread

Real time tools let you immediately identify where you’re being talked about by giving you up-to-the-second referral reports.  When a piece of your brand reaches escape velocity, understanding who is a part of the escape can be very valuable.

When our article to TechCrunch50 finalists first appeared on the Internet, it started getting featured heavily on news.ycombinator.com.  Without a real-time analytics tool, I never would have thought to check this particular social news aggregator.  But with real-time dashboard in hand, we were easily able to navigate through different sites and blogs, commenting in a timely manner when relevant.  Note that we could also have used a social media listening platform, but there is one key difference:

A social media listening platform (SMLP) looks at social computing sites for a particular keyword and rolls it up to your dashboard when one is found.  A real-time analytics tool looks at your own website and tells you where inbound links are coming from.  In other words, an SMLP will show you where people are talking about you (but not necessarily visiting you), whereas a real-time analytics platform will show you who is visiting you (but not necessarily talking about you).  Complimentary technologies, not mutually exclusive.

In times of community backlash (see this list for examples), real-time tools are indispensable in helping to mitigate social vitriol, by being able to identify and target communities that are talking about your brand.

Track influencers

With referrers in hand, you can drill down a little bit deeper to find individuals that are talking about you.  This can help you tame viral spread by timing when certain press releases or notifications go out.

In our case, we emailed influencers and let them break the news first (ie – Jason Calacanis, Sean Ellis, Dave McClure, Hiten Shah, Eric Ries and a few others).  Once they had picked up the post, we advised TechCrunch that we’d written something that may be of interest to them.  We used a combination of prospective search on twitter with our real-time analytics dashboard to make sure that we had a handle on who had already covered us, and not.  Though the blog post speaks for itself, its credibility was strengthened by having leading entrepreneurs give a headnod in our direction, which ultimately led to our TechCrunch coverage.

Be creepy and spy on your visitors; solicit feedback

An unrelated, creepy, but nonetheless applicable case is using a real-time analytics tools’ ability to inject itself in a user session.  I used Woopra’s “Spy + Chat” functionality for this example:

Though very intrusive, it can be used as a time intensive, but rich voice-of-the-customer tool.

Examples of vendors in the space: GetClicky, Woopra, VisiStat and Site Meter.   Note that GetClicky has given us a free “Pro” account and that we hold a trial VisiStat account, but no company sponsored (or even suggested that we write) this post.  As always, the opinions are ours, and ours alone.

Any other reasons why real time is useful?

For Alistair Croll’s take on the Omniture – Adobe acquisition, click here.

Adobe’s acquisition of Omniture dramatically strengthens Adobe’s ability to optimize and monetize its clients, and Omniture’s ability to pierce real time streaming and online video markets.  Overall,  sentiment for Omniture’s will increase as it is integrated in a strong brand with a proven record of successful acquisition integrations.  The attached picture from the social media listening platform Sysomos shows blog sentiment for Omniture over the last four quarters (Thank to team Sysomos for providing the screenshot).

Expect to see:

Innovation in the RIA client

Integrated analytics and optimization in Rich Internet Applications has been painful, its adoption slow.  We will see a large increase in the ability of Flex and RIA developers to track the core functions of their applications.  This will lead to an increase in monetization strategies in desktop top clients, including pay-as-you-go services and PPC ad based revenue.

Web Analytics, Feedback & Web Interaction Analytics Now Draggable Into Flash/Flex.

Omniture’s Genesis program ensures that external analytics partners will be provided with a framework to integrate their core strengths into RIA / Flex applications.  Flex developers will no longer need to rely on external tools like GetSatisfaction to understand how their users navigate their applications.

Omniture Becomes The Defacto Platform For Streaming Media & Video

In May 2008, according to comScore, viewers watched 12 billion videos online, while viewership on traditional television is falling.  Online video is expected to grow at nearly 40% annually through 2011 (see this Wikipedia article).

Adobe will seek to strengthen its position as the defacto platform for real-time streaming and video analytics.  Omniture’s current lackluster offering will get a considerable amount of focus and attention.  Startups in the video analytics space may become prime acquisition targets.

Adobe A Strong Candidate For Microsoft Acquisition

Adobe continues on its path to compliment Microsoft’s library of application by extending their reach into the new, rich internet.  Microsoft will be eager to get its hands on this complimentary market and integrate it in its existing go-to market web strategy.

Congratulations to Josh & team for this stellar win-win acquisition.