Visitor Insights

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.

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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.

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One way to make web analytics actionable is to break the site into different sections (such as home pages, category pages, etc.) and generate reports specific to those pages/sections. In this post, we’re going to identify some of the most common reports for analyzing home pages.

First, lets start by defining home pages and their goals. The home page is typically the main gateway page for your site. It’s the first impression that your visitors will have of your site. Its role is to showcase your offerings, your value proposition and provide quick access to the most popular or important sections of your site. For this reason, web analytics should help you answer some of the following questions:

• How effective is the home page at directing visitors to product pages?
• Which part of the home page is the most effective?
• Is the home page effective at enticing visitors to learn more?

Based on these, below are some popular web analytics reports for home page analysis along with the explanation:

• Bounce Rate
• Micro Step Conversion Rate
• Conversion Rate
• Acquisition Sources
• Home Page Real Estate

Bounce Rate

The bounce rate is defined as the number of bounces (single page visits) divided by entries. It shows you what percentage of the traffic landing on the page bounces and does not view any other page on the site. It is a reflection of the home page’s ability to retain visitors. Clearly the goal is to make changes to the home page and lower the bounce rate. It’s probably one of the best reports to look for when analyzing home pages. This report is widely available in most web analytics tools such as Google Analytics, Yahoo! Web Analytics and Unica NetInsight.

Micro Step Conversion Rate

Although the ultimate goal of your site is to drive conversions, we recommend micro step conversions as a better way to assess home pages. The goal of your home page is to drive people to your product description pages. It’s at that level that you do the selling. For this reason, when assessing the success of your home page, it should be around its ability to get visitors to those ensuing pages. You can get this in a number of way. Inside tools such as Yahoo! Web Analytics and SiteCatalyst, you can tag your product description pages as events and look at the success of your home page around this event. In Google Analytics, you can create a goal for your product pages, as long as the pages have a consistent nomenclature. If not, you can create an advanced segment for your product pages and look at the home page traffic for the segment. Such metrics can pretty easily be created inside Unica NetInsight and Webtrends.

Conversion Rate

Yes, this should not be your primary report for home page analysis, but you can still use this report as a tie-breaker. For example, if two versions of home have similar bounce and micro step conversion rates, then you can use the overall conversion rate to see if one version does in fact do a better job. Unfortunately, we often see that many people use conversion rate as the primary report for assessing home page effectiveness.

Acquisition Sources

Want to lower your bounce rate? One place to start is by looking at the acquisition sources. You can start with the sources of traffic to your home page and look at their respective bounce rates. Start with referring sources with high bounce rates. Often, you’ll find a messaging gap between the referring sites and your home page. The referring site may be saying something while your home page could be promoting something else. While you cannot optimize your home page for all referring sites, you can start with those with high traffic and high bounce rates and provide messaging on your home page that helps retain this incoming traffic. You’ll typically find that a handful of sites may account for a high percentage of your bouncing traffic.

Home Page Real Estate

To understand the real estate effectiveness, you’ll have to look at the click activity on the page. Rather than looking at all page links, we recommend classifying the link into sections or categories (such as as header, footer, navigation, left box, right box, etc.), and analyzing the activity by such sections. This is different than the default site overlay that you typically get from web analytics tools and requires some additional configuration to get proper reporting. For example, if you’re using Google Analytics we recommend using Event Tracking to track the activity on various sections and links within sections. You can then see how effectively each section and each link gets visitors to product pages and to final conversion.

You can also investigate some of the in-page analytics tools such as CrazyEgg and ClickTale, which do a more thorough job of providing such reports than web analytics tools.

Of course, depending on your business, your reporting needs may vary, but we believe this list should provide a good starting page for optimizing one of your most important pages.

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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!

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Tealium is proud to be a new member of the Yahoo! Web Analytics Consultant Network (YWACN). This certifies Tealium as one of the few organizations to implement and support Yahoo! Web Analytics. It also allows retaining customers to gain access to Yahoo! Web Analytics for free.

Yahoo! Web Analytics is a full-featured, enterprise analytics solution developed as part of the IndexTools acquisition by Yahoo!. It provides detailed segmentation, audience demographics, click-stream and campaign analysis features using a customizable user interface.

Some key features within Yahoo! Web Analytics include:

• Real Time Reports: information provided within minutes of the activity
• Custom Dashboards: create customized dashboards, including KPIs based on needs
• Custom Reports & Custom Fields: custom fields let you collect information beyond that captured by default web analytics. Examples include content date, author, genre, etc.
  With custom reports, you can get more detailed reporting using custom/multiple dimensions and metrics
• Custom Alerts: get automated notifications based on criteria that’s important to your organization
• And more

You can contact us to learn more about Yahoo! Web Analytics or for a live demo.

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