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Posts Tagged ‘sitecatalyst’

What is Universal Tag? (Part 1)

May 16th, 2010

It has been a while since our last blog post. We’ve been busier than ever deploying the Universal Tag on client sites and I’m happy to say that the number of unique client deployments is approaching 50.

There’s more and more buzz around Universal Tagging every day and as a result, we’re getting the same questions from more people than ever: What is the Universal Tag?

In this multi-part series, we’re going to share our thoughts as to what the Universal Tag is and what it’s not.

In this post, we’re going to cover what we see as the first misconception about the Universal Tag.

Misconception: Universal Tag should be used by those that are in the process of switching vendors.

Fact: Based on two years of experience, we can categorically say that this is not the primary benefit that Universal Tag provides to clients.

Universal Tag is about fixing your current implementation.

That’s right. Out of almost 50 deployments of Universal Tag, only two clients have signed up in order to switch vendors. The large majority has no plan to switch vendors. Yet they recognize that their implementation can be vastly improved. Universal Tag provides them a platform to do just that.

The reason is because the Universal Tag provides a simplified platform for tagging compared to traditional vendor tags. It also changes the best practice implementation considerably. Today’s web analytics tools require you to think well in advance about the different types of reports that you want to get from the solution. Only after you have a good understanding of what your reporting needs are can you start the tagging process.

The Universal Tag framework changes this by letting you send generic data and map it to vendor-specific syntax at any time. This changes best practice implementations in that it lets you just send the data. The rest can be handled through the Universal Tag.

Here is a real-life example that helps demonstrate the point.

Example: Product Syntax

Consider a scenario of a product page. Within the page, you’d like to capture several components, including the product name, product size, color, and number of ratings received. As far as reporting is concerned, you’d like to get reports on top products viewed, top colors and sizes viewed, as well as average ratings of products (a numerical report) and a histogram or bar chart report of reviews (how many views for products with rating 1, rating 2, …).

Sounds simple, huh? Lets look at how you would go about implementing this with the two most popular tools on the market: Google Analytics and SiteCatalyst. The examples that we’ll use will be for a cotton shirt, color: white, size: large and a rating of 4.5.

First Google Analytics. We’re going to use custom variables to capture product name, size and color. The challenge here is that not only your developers have to know about the specific syntax, but they should also be aware of the fact that you can have visitor, visit or pageview-based custom variables. Now there’s no such thing as a numerical custom variable in Google Analytics, so you’ll have to use the event tracking feature in order to get your numerical ratings report. The implementation syntax will look something like this

pageTracker._setCustomVar(1,"product view","cotton shirt",3);
pageTracker._setCustomVar(2,"color","white",3);
pageTracker._setCustomVar(3,"size","large",3);
pageTracker._setCustomVar(4,"rating","4.5",3);
pageTrack._trackPageview();
...
pageTracker._trackEvent("product view","cotton shirt","rating","4.5");

Let’s try the same thing with SiteCatalyst. We’re going to assume that we’ll use prop1 and eVar1 for size, prop2 and eVar2 for color, prop3, and eVar3 for rating and event1 as the numerical event used to measure the average rating. The implementation syntax will look something like this:

s.events="event1,prodView";
s.products=";cotton shirt;;;event1=4.5;evar1=large|evar2=white|evar3=4.5";
s.prop1="large";
s.prop2="white";
s.prop3="4.5";

Again, you’re requiring your development team to know what different props, eVars and events are as well as the exact syntax which should be used (for example using lower case evar for merchandising).

Now let’s look at what this same implementation will look like with the Universal Tag. Here’s an example syntax:

yourdata.product="cotton shirt",
yourdata.size="large",
yourdata.color="white",
yourdata.rating="4.5",
yourdata.page_type="product view",

Now what if you wanted to deploy both SiteCatalyst & Google Analytics? No changes. The implementation will be the exact same.

This simplified implementation has several benefits. The one that’s clearly being addressed in this post is that it simplifies implementations and vastly reduces the deployment cycle. Your development team no longer has to master the analytics tool being used and can concentrate on sending the data through the simplified tag. Your business team or analytics department can then translate this data into vendor-specific syntax.

In future posts, we will share some of the other benefits that we’re seeing with the Universal Tag. Stay tuned.

Universal Tag , , ,

Multi-Touch Attribution: Should I be Worried?

March 5th, 2010

Last week at Online Marketing Summit (OMS) I had the pleasure of sitting in a panel of web analytics professionals, along with Eric Peterson, Matt Belkin from Omniture, Amanda Kahlow, Bill Bruno and Enrique Gonzalez from AARP.

First, I need to congratulate the folks from OMS for putting together a great show. There was a record attendance of over 800 professionals covering all areas of online marketing, along with a great lineup of presenters.

During the panel discussions, one of the questions asked was how should businesses deal with multi-touch attributions.

Here’s a sample scenario to help explain the pain point involved:

A visitor is interested in running shoes and conducts a Google search for the term “running shoes”. The visitor is presented a number of search ads from competing vendors such as Nike, Adidas, and others, and decides to check out Nike and Adidas sites. The visitor gets intrigued by the Nike ID line of products and decides to conduct some further research, and even registers for the Nike newsletter. While doing research on third-party sites, the visitor sees a banner ad for the Nike ID site and clicks the banner. Finally a day later the visitor gets an attractive email offer from Nike and ends up buying the shoes.

In this scenario, the visitor has been exposed to three separate campaigns. The “running shoes” search campaign generated the awareness. The banner campaign possibly helped increase awareness and instill further trust in the product and finally, the newsletter sealed the deal. By default, web analytics providers give credit to the last campaign touched by the user. In our example, the newsletter campaign will get the credit, whereas if it wasn’t for the search campaign, the visitor would not have even been aware of the Nike ID line. In fact, two variables that make multi-touch attribution a real challenge are:

  • Number of simultaneous campaigns. If you’re a company running large numbers of campaigns in parallel, you should account for multi-touch attribution
  • Complex or expensive product: the more complex the product, the longer the consideration and therefore the more likely you are to have multiple touch points.

So how does one tackle this challenge? First, for the large companies running many campaigns, there are a number of commercial solutions such as ClearSaleing that help solve this challenge (and a lot more). But what about smaller companies with small budgets using free solutions such as Google Analytics or Yahoo! Web Analytics?

First, we recommend that you investigate if you even have a multi-touch attribution problem. How? Let’s take another look at our example scenario. Two metrics within your analytics solution can give insight into this. They include time to purchase and number of visits prior to purchase (or conversion).

For example, if you use Google Analytics and have e-commerce tracking, you can use the “Visits to Purchase” report to see how many times do visitors come to your site prior to purchasing. If you are a lead generation type web site and have your conversions set up as goals, you can use the default “Visits with Conversions” segment and look at the loyalty report for the segment. In both cases, if most of your conversions come from first-time visitors, then multi-touch attribution is not going to be a problem for you and the rest won’t apply.

However, if you happen to see a big difference between converting visitors and others, then you can build a quick attribution report by following these steps:

  • Create a Javascript that captures your marketing campaign parameters into a persistent cookie
  • The JavaScript should also be configured to append campaign values together, as visitors go from one campaign to the next
  • Push the cookie value into a custom variable  – such as a visitor-centric custom variable in Google Analytics, a session-based custom field in Yahoo! Web Analytics or an eVar in Sitecatalyst.

You now have a simple yet powerful solution for seeing which campaigns your visitors are responding to, but also in what order.

Happy analyzing.

Web Analytics , , ,

Universal Tag Version 2

January 27th, 2010

We are pleased to announce the availability of version 2 of Tealium Universal Tag. The new version provides many new enhancements following several enterprise-level web analytics deployments with large number of platforms, including SiteCatalyst, Omniture Insight, Google Analytics, Yahoo! Web Analytics, Unica NetInsight, Webtrends and Coremetrics, as well as a number of digital marketing solutions such as DoubleClick, Atlas, ForeSee Results and more.

Some of the new functionality include:

  • Improved multi-vendor support: the new version provides a superior method for complex implementations with multiple vendors. For example, non-technical users can map page tag values differently into various web analytics solutions, while also mapping them to their PPC bid management tool.
  • Attribution management: designed specifically for clients using multiple affiliates, version 2 of Tealium Universal Tag has the ability to conditionally send data only to the winning affiliate(s).
  • Multi-currency support: the new version of Universal Tag supports transactions in multiple currencies for digital marketing vendors that do not provide such support by conducting on-the-fly conversions to the supported currency.
  • Universal data capture: this feature allows non-technical users to automatically capture data elements from the page and map them to their web analytics and digital marketing solutions. Examples of such data elements include microformats, meta tags, in-page style elements, query parameters, cookie values, etc.

We’ll be publishing a number of case studies on Universal Tag deployments soon. In the meantime, to see Universal Tag in action, please contact us.

Web Analytics Implementation , , , , ,

A Model for Scoring Content on Media Sites

January 5th, 2010

If you’re a media site, one of the most critical measurement objectives is to assess the success of your content. But how does one go about measuring this? Default web analytics reports often fall short in this area. Let’s take a look at some of the most popular content metrics provided by the analytics solutions.

Page views

This is probably the best out-of-the-box metric for measuring the success of a content. The more the number of page views, the more popular the content. However, relying on this number alone has two potential shortcomings. First, it fails to differentiate between segment traffics. For example, a loyal visitor is more valuable to a content site than someone who visited the site for the first time and will likely never come back. Also, page views alone fail to report the level of engagement on the page. For example, visitors could be clicking an article and spending only a few seconds on it. The quality of traffic should therefore be accounted for.

Time spent on page

This metric clearly adds a new dimension around engagement. The more time visitors spend on the content page the more engaged they are. However, you cannot rely on this metric alone. One key reason is the fact that this metric is not always available to all visitors. For example, if the content page was the only or the last page viewed during the session, then this metric is simply not calculated within popular web analytics solutions (we’ll discuss this in a separate post).

Another shortcoming of this metrics is that like page views, it fails to segment the reports by the quality of visitor (first-time vs. loyal).

And finally, the Time Spent metric alone does not take into account the popularity of the content. For example, an article could be very engaging but only be viewed by a handful of people.

Visitor Loyalty

Web analytics solutions often provide this in context of the overall site traffic and you may have to do some tweaks to your reports to get this, but it’s important to note what percentage of your content is consumed by first-time visitors and what percentage by loyal visitors – visitors that come back to the site. The reason this is important is because in the long run, you may want to create a loyal following and create content that’s tailored to them.

Bounce Rate

This is also one of the most popular metrics within analytics solutions, but media sites should be careful not to over-analyze their bounce rates. As an example, consider a media site with an RSS feed. Through the RSS feed, visitors can see the headlines of new content using their favorite RSS aggregator. If an article looks appealing, they click the link, enter the site, read the content and then leave. That’s a bouncing visit but still a highly qualified traffic, because the visitor has subscribed to the RSS. The visitor loyalty metric indirectly takes care of this shortcoming.

Content Engagement Score

In this post, we’d like to introduce you to a content scoring KPI that we’ve used to help some of our media clients put a monetary value next to their content.

The formula is as follows:

Engagement Score = (Page Views × Avg. Time Spent  × Avg. Loyalty)

Where:

  • “Page Views” is the number of times the page was viewed during the reporting time period.
  • “Avg. Time Spent” in the average number of seconds or minutes spent on the page by visitors.
  • “Avg. Loyalty” is the average number of visits to the site by your visitors (1 for first time visitors, 2 for those who’ve been to the site twice, and so on).

Of the three metrics needed to create this KPI, “Avg. Loyalty” is the most difficult to get, but this can be obtained done using estimates in popular tools. For example, with Google Analytics, you can use the %New Visits metric to estimate the average loyalty. You can use the following formula for this purpose:

Avg. Loyalty = (%New Visits) + 2 * (1 - %New Visits)

What this formula does is that it assigns a score of 1 for each new visitor and a score of 2 for all others, providing a reasonable approximation. You can create a similar model with Yahoo! Web Analytics – see below figure for an example of such report in Google Analytics.

Using this model, pages with the highest traffic, time spent and the most loyal visitors will get the highest scores, which is the desired outcome. You can of course use any analysis tool to create your score. One popular tool is Microsoft Excel, where the score can easily be created and analyzed. See figure below for an Excel example. It shows that our posting for tracking internal campaigns is the most engaging even though it’s an old blog post.

Overall, this model provides a simple KPI for measuring site content, while taking into account the popularity, engagement and the quality of the visitor. It does however have its shortcomings. The primary shortcoming is that it is dependent on cookies. For loyalty to be counted, visitors have to accept cookies. Furthermore as visitors delete cookies, it will impact this KPI. However, it’s fair to assume that visitor cookie deletion is not dependent on their content preference, so you should expect the same rate of deletion across the board.

The metric also depends on time spent reporting, which is not available to all visitors. Having said that, it’s also fair to assume that the time spent by those who view a certain content as their last page should be inline with those who view the content in the middle of the session. After all, the purpose of this model is to provide an approximate score for content engagement and popularity.

You may also be in a mode where loyal visitors are no more valuable than first-time visitors. For example, newer web sites fall into this category. In that case, you can simply omit the “Avg. Loyalty” metric from the formula (or replace it with the value 1).

So there you have it. We welcome your feedback on the model and hope you find it of use.

Happy Analyzing!

Web Analytics, Web Analytics Reporting , , , ,

10 Web Analytics Industry Speculations

September 21st, 2009

It is by now fair to say that everyone was caught off-guard when Adobe announced it’s acquisition of Omniture. There’s also been no shortage of opinions and commentaries about the acquisition: those who like it and those who don’t. By and large, most customers that we’re dealing with are somewhat neutral, as Adobe is a strong company that has successfully integrated the Macromedia products into its offerings. Of course Omniture’s business model is so different than Adobe’s that it remains to be seen how the acquisition goes.

Instead of providing commentary on the acquisition, we decided to take a different approach and provide some speculation (not predictions) about the market to come. Some are outright outrageous and they’re primarily for amusement purposes.

1. PDF Tracking becomes available

With Adobe owning both the PDF standard and the measurement technology of Omniture, tracking PDF usage finally becomes a reality. This will benefit the industry greatly and has been a feature that’s been requested for a long time, but technological hurdles have alaways made it difficult to pull off.

2. Adobe offers free web analytics

If Adobe’s plan is to compete with Google, then it’ll have to offer a free or a very low-cost analytics solution. However, this is unlikely to happen on the SiteCatalyst platform which is both expensive to maintain and difficult to implement and support using a free model. A better choice would be the HBX platform. Could we be seeing HBX making a comeback and being offered for free? If so, how would former HBX customers react?

3. Microsoft buys Webtrends

I have to admit we were expecting to see someone else like Microsoft acquire Omniture. Microsoft has already made an attempt to compete with Google Analytics when it acquired DeepMetrix. Although Microsoft Analytics did not pan out as expected, we still think that Microsoft will enter the analytics space. At this point Webtrends seems to be the most likely candidate for acquisition by Microsoft since Webrends also offers a SaaS product that can be repackaged by Microsoft.

4. SiteCatalyst adopts Flash cookies

We all know the limitations of regular cookies. Flash shared objects provide a more reliable way of measuring unique visitors. The adoption means a more accurate web analytics reporting and a more efficient way to measure uniques. Like all new technologies, Adobe will have to overcome the privacy PR, but if done correctly, the industry will benefit from a proper adoption of the technology.

5. Adobe to acquire an ad serving company

One of the main things that Adobe gets by acquiring Omniture is a diversification in its product line. Adobe’s core offerings have been on the decline for some time now and it needs to enter growing markets. Digital Marketing is one such area, but to compete with Google and Microsoft, it’ll have to offer its own ad serving technology, since Google and Microsoft have both acquired DoubleClick and Atlas respectively.

6. SiteCatalyst 15 UI in Flex

This is more of a wishful thinking. Flex provides a great technology for building application user interfaces. One way to start integrating the technologies is to build the next generation SiteCatalyst UI completely in Flex. There are some upcoming analytics solutions that are built completely in Flex and the technology has proven to provide a great deal of flexibility and customization that otherwise would not be possible in HTML interfaces.

7. SiteCatalyst CS5?

Purely speculative we admit. It would be interesting though to think what a software version would look like. What is likely though would be an interface directly inside Dreamweaver and Flash where designers could see the performance of their content and their effectiveness, allowing them to make quick edits based on data. That’s after all the value proposition that the acquisition is promising to provide.

8. Quark buys Coremetrics

OK, I admit, this is very unlikely, but certainly amusing. For those of you who’ve been following Adobe for years, their top competitor in desktop publishing has been Quark, developers of Quark XPress. Quark still owns a large percentage of the desktop publishing market, but it’s a company on the decline, since desktop publishing is dying. What if Quark decided to diversify? Again, this is pure speculation and mainly entered for amusement purposes.

9. Adobe sells Visual Sciences assets

The big question mark is what’s going to happen to Visual Science (Discover On Premise or Omniture Insight) customers? We haven’t seen much discussion about that specific technology, but the engagements are far too consultative for Adobe to be interested in. It makes more sense for Adobe to sell off that technology to a BI company such as Business Objects, which would be a better fit.

10. Adobe sells Omniture

Key to any acquisition is that there are so many synergies that 1+1=3. Many are still scratching their heads to find the synergies and if Omniture continues to be a completely separate business unit, then it definitely remains to be seen. So what if the synergies don’t exist? If the only value proposition that Adobe is going after is integration of analytics into Dreamweaver and Flash, then the synergies are minimal since you can very easily integrate other analytics into those platforms today. In that case could we see Adobe sell the business unit? Again this is very unlikely because of the premium that Adobe paid for Omniture but as of today, the number of doubters is more than the number of believers.

Web Analytics , , , ,

Social Media Measurement is Here

January 21st, 2009

We’re proud to announce the general availability of Tealium Social Media, a new measurement service for social media and online PR that’s tightly integrated into web analytics. The service is designed for marketing professionals who use social media and online PR as marketing vehicles to generate awareness and demand, and require side-by-side comparison with other marketing channels.

For a review of Tealium Social Media, please visit this blog posting by PR measurement guru, KDPaine.

How does it work? Consider this scenario:

A visitor is in the market for CRM software and comes across a blog comparing various CRM programs. The blog mentions a number of CRM applications that the visitor had no previous knowledge of, including SugarCRM and NetSuite. Because of the great feedback in the blog, the visitor decides to go to SugarCRM by doing a search for “sugar crm” on Google. This leads the visitor to sugarcrm.com, where the visitor requests a personal demo.

With traditional web analytics, this conversion would be attributed to Google. With Tealium Social Media, the conversion will also be attributed to the original blog that started everything.

Tealium Social Media is a web analytics plug-in that is integrated into popular web analytics solutions: Google Analytics, SiteCatalyst, Unice NetInsight, WebTrends, Coremetrics, etc. This means you can get your social media ROI measurement directly inside your existing web analytics account.

Intrigued? Request a demo.

PR Measurement, Social Media, Web Analytics, online marketing , , , , , , , ,

Tealium Additions

March 26th, 2008

Tealium is growing :)

We’re happy to announce the addition of Mike Anderson and Ali Behnam to the Tealium management team. These additions complete our portfolio of high-end Web analytics consultants with experience serving the top internet companies.

Mike was the master implementation consultant, having implemented some of the most challenging sites, including Disney Xtreme Digital, YouTube, Kayak.com, Charter Communications, Vonage, Fox Movies and many more. He’s also authored both the HBX and Visual Sciences page tags. Mike will also be contributing to the Visitor Insights blog in the areas of technical implementation strategies and techniques.

Ali served as Sr. Consultant at WebSideStory / Visual Sciences / Omniture, where he provided end-to-end consulting for the company’s strategic accounts, including Pfizer, Citi, Clear Channel, Hasbro, Guess and many more. He also authored the Visual Sciences e-commerce benchmark. You’ll be seeing Ali’s contribution quite frequently in the Visitor Insights blog.

These additions mean that Tealium is now very well positioned to handle the most complex HBX to SiteCatalyst migrations. Please contact us to find out more about our migration services.

Web Analytics, Web Analytics Implementation , , , , ,