Obviously this is the entire purpose of web analytics. We have all been presented with this type of situation, logged into our favourite web analytics package, and pulled some numbers that show the relative importance of this content. Web analytics has seen growth primarily because it provides an objective view of site utilization in questions like this. While “pet projects” will always come along, these days it’s a lot easier to provide concrete evidence as projects come along.

Believe it or not, web analytics isn’t enough. The art of interpreting web analytics numbers can still lead to subjective decisions… or the wonderfully oxymoronic phrase “anecdotal analytics”. This shouldn’t be possible, should it? Yet how many analysts have looked at “average session length” and “average page views per visit” and prognosticated on the level of engagement with site content? This is sometimes true, sure, but what if the “average searches per visit” metric is 50% of the total page views per visit? Well, this is an entirely different interpretation and renders the original interpretation anything but “complete and objective.”

How do we resolve this and avoid releasing invalid interpretations or taking unnecessary actions? We never expound on our analytics interpretation if we can’t prove our interpretation is accurate. Enter this very “old fashioned” concept of the scientific method. “Scientific” in this case means that the interpretation must be based on empirical, measurable observations. This approach has been around for, oh, 400 years or so. Why not use something that has such a respected, long-standing track record?

Here’s a simplified approach to scientific method:

1. Define the question or inquiry and collect data related to the question
2. Form a hypothesis about how to interpret the collected data
3. Construct an experiment that can test your interpretation and execute the test
4. Collect test results and analyze them, draw conclusions that can form a new, or corollary hypothesis
5. Retest

So let’s take another look at average session length and its relation to average page views per visit. Let’s create a hypothesis that the relation between these two numbers is being caused by excessive use of search. Let’s construct a test by taking the top ten most used search results used and put them on the home page. This doesn’t change the content at all… it simply reorganizes existing content and changes the user experience.

If the search volume per visit substantively decreases then we’re on a lot safer ground saying that content findability was really driving content consumption, not engagement… and if the search volume per visit doesn’t change? It’s harder to draw a conclusion in this case, but we can always repeat the process, define a new hypothesis and test, and we will eventually learn the true relationship between web analytics numbers and understand how they specifically pertain to our website.

So how many times do we have to NOT learn the right answer? Hard to say, but a fable about Thomas Edison comes to mind:

“A young reporter boldly asked Mr. Edison if he felt like a failure and if he thought he should just give up inventing the light bulb. Perplexed, Edison replied, ‘Young man, why would I feel like a failure? And why would I ever give up? I now know definitively over 9,000 ways that an electric light bulb will not work.’”

Learning what is NOT at the root of your web analytics number is just as valuable, if not MORE valuable, than luckily getting the right answer; or getting an answer that someone else in the industry found was true about their website.

• Campaign History
• Search Engine History
• Visit History
• Purchase History
• Custom Visitor Segmentation
• Content Group Unique Visitor Tracking
• Page of Interest Unique Visitor Tracking

There are many ways to use this information, especially when viewed over time. For example, Unilytics provides a product named VHE Distiller that extracts and transforms this information into a regular relational database suitable for data mining or enterprise reporting. It should be noted that VHE is not a data warehouse, per se; nor is it a complete replacement for a full data warehousing product based on visitor information. There are limitations built into the design of the VHE feature that should be kept in mind when using it. This blog presents some of these “gotchas” when using the VHE feature and resulting export files.

Firstly, the shortest period of time supported by the VHE feature is a single day. In other words, you can’t report on per-hour information in the VHE data. For most organizations this isn’t an issue as your average visitor probably doesn’t come multiple times a day, but if they do, you should keep this point in mind. An example: if a visitor comes to you through multiple campaigns in a single day, only the last one of the day will be recorded. So if they click on banner ad “A”, then click on banner ad “B”, then respond to an e-mail and click on a link “C”, the VHE file will report that campaign “C” was the most recent campaign. However, if these same activities occur on three different days, then you can capture all three activities. Point being, there’s a reason the fields in VHE files are called “Most Recent”!

Next, it is highly recommended that you implement the “WT.dcsvid” parameter in your Webtrends implementation. This populates the “External Visitor ID” field in the VHE file. This allows you, for example, to record a visitor’s unique identifier (e.g., login, e-mail address, internal visitor identifier, etc.) which can be used to link VHE data to your line-of-business systems/databases. This is extremely powerful! The “gotcha” for this item is also a “plus”. If your visitors end up having multiple session identifiers they will have multiple entries in the VHE files. You should use the external visitor ID field to aggregate the multiple visitor records if you are aiming to see the information for each visitor to your site. For example, if visitor “Rachel” has cleared her cookies three times (or logs on from multiple computers), she’ll have three rows in the VHE file. If you want to see the total time she has spent on your site you have to add the total from all three records together to get her “total” time on site.

Another item to remember is that visitor segmentation is limited to four fields in a VHE file. For many organizations four segments is not enough. Think about it this way… do you want to know more than age, gender, income level, and education? What if you also want registration status, major features they have used, engagement measures like recency, frequency, latency, and velocity? But there are effective workarounds. You can use advanced IT concepts such as bitmasks, or encoded fields to force more than just four values into the VHE fields. However, it is not easy to switch from “simple” segments (i.e., one segment value in each field) to “advanced” segments using “dense data” approaches like bitmasks and such. Decide up front which approach you are going to use. If you use an advanced encoded approach you should also build in “room to grow.” The means of inserting encoded field values is beyond this blog, but suffice to say that if you think you’re going to track five different values for a single segment, add a few extras to allow room to grow. You’ll save yourself a LOT of headaches by planning ahead.

Another gotcha has to do with the “of interest” fields, which are the “pages” and “content groups” of interest fields. There’s a limit to how many entries will be in these compound fields. The pages of interest field can only contain 20 different values. Content groups of interest has a limit of 10 different values. You can extract more than these limits over time, but in any given VHE file there will only be up to the limit.

Finally, VHE files are subject to table limits. The default limit before table trimming is applied is 1,000,000 visitors. There is also a “hard limit” (i.e., no more records will be written) in the file that is set to 10,000,000 visitors by default. For most organizations this isn’t a problem, but if you are a larger organization, be aware of these restrictions. You can change these values in the profile in the Webtrends administration console if you need more records. Keep in mind that this can require substantially more disk space so don’t pick arbitrarily large numbers for these limits.

Despite all of these “gotchas” the VHE feature is amazingly powerful. Hooking this up with your internal databases and using business intelligence reporting, while keeping in mind the limitations I’ve described, can be a great step to take in maturing your web analytics practice.

And if you have more demanding requirements or want to fully integrate your web analytics data with other marketing silos, Webtrends offers a richer environment. Here’s my list of positive aspects about Webtrends:

1. With Webtrends you own the data and you can pull it out at anytime. This applies equally to hosted or software.
2. Webtrends allows for a java scripted solution in which the collection server is local within your firewall. This is important for firms which do not wish to have corporate data stored outside the organization and particularly not if you don’t want your data in the US and accessible by the US Patriot Act.
3. Webtrends can re-process old log files to reanalyze if you decide you need to make reporting changes
4. Webtrends allows you to store a full backup of your raw log files in java scripted logs or standard log format
5. Webtrends allows you to use log based analytics in addition to JavaScript tagging as separate profiles to track activity. With this you can see search engine spider activity
6. Webtrends provides a Visitor History export function which can allow you to normalize all of your visitor data in a database. This enables you to understand how individuals navigate and interact with your site. This can be tied to email marketing or CRM tools for 1:1 marketing insight
7. Webtrends provides standard ODBC and REST access that allows you to get critical data and analysis outside of the Webtrends reporting engine and into other applications. This enables you to tie Webtrends results to offline data using Excel or your favorite Business Intelligence tool
8. Webtrends provides more detailed custom configuration and reporting
9. Webtrends can tell you what percentage of users are not accepting cookies
10. Webtrends provides content group analysis
11. Webtrends provides IP reports
12. Webtrends provides email and phone support