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Traffic, Visitor, and Customer Analysis – Two Metrics

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I’ve developed two metrics I think are among the most important you can track, no matter what kind of site you have or what the objective of the site is.

They are designed to focus in right on the biggest problem most sites have – getting visitors to go past the first page they see on your site. Underlying these metrics is the idea someone who comes to the site and views just one page was likely a lost opportunity – a pretty fair assumption for most business models on the web.

An important part of these metrics is the way they are constructed – not using “average behavior”, but instead focusing on specific visitor behavior, and screening out “data noise” as much as possible. One note: I’m going to refer to WebTrends reports as I give you the specific info for creating these metrics; it makes sense in an article like this because WebTrends has the most users. But almost every analyzer out there provides the basic info you need to create these metrics.

Percent One-Page Visits

What it is: One Page Visits divided by Total Visits

If I only had time to look at one metric, this would be it. This metric is usually tied to global navigation issues; it literally measures the percentage of visits bouncing off your site like it was Plexiglas (yea, one “s”). Since you often can’t control which pages people enter your site through, you want to make sure if they don’t find what they’re looking for on the first page they hit, they know how to get to the information they want. Navigation is both a design and copy issue, since you can always write hyperlinks into copy that lead to related topics in other site areas.

Hopefully, the analyzer you are using provides the number of One Page Visits. If you are using WebTrends, under Activity Statistics / By Number of Views, you see how many visits had one page, two pages, three pages, etc. I take the One Page Visits and divide by Total Visits, since the visits by number of pages data is defined by a “visit”. The visits (perhaps called “sessions” in your analyzer) number comes from the General Statistics section at the top of a WebTrends report. A visit ends when a certain number of minutes go by between page views for the same visitor; in my case 30 minutes (you can set it to whatever length you want in WebTrends; 30 minutes is a common standard).

Here is why I use visits. It’s the biggest, most reliable number available, so whatever “dirt” there is in it, it’s not as dirty as unique visitors can be, which is complicated by visitor identity issues. I don’t want to complicate things at this level; I want it clean and simple, the most accurate it can be. You could argue visits are inaccurate, because someone at work might only be able to read one page at a time, but might read 3 pages in a day more than 30 minutes apart. This would have the effect of making the metric look worse than it really is.

Yea sure, but compared with the problems you can run into with dynamic IP’s, multiple users of a machine, and so forth, that’s nothing. And I would add, does it really matter? What does that level of hand-wringing get you, is it actionable in any way? Can you do something better if you spent all the time and effort to get the absolutely exact number?

What’s important is the trend, and as long as you use numbers calculated in the same way each time, the trend is actionable. If you have a super tracking system / you are really only interested in tracking authenticated users and you want to use visitors or unique visitors – and this really is important to your objectives – than go right ahead.

Here is what my Percent One Page Visits graph looks like; a detailed explanation follows so you might want to open another browser window and bring this chart up in it:

http://www.jimnovo.com/One-Page-Visits.htm

The trend is generally down, meaning the percentage of visits having only one page is falling. The changes I am making are working – a higher percentage of visits are going deeper into the site because navigation is improving.

What’s quite interesting is the first trend down ending around day 67 then spiking upward. This was the end of optimizing the original site, which was replaced with the new site, which caused a sharp spike upward again. Hey, that redesign was a great idea, right? Not! But over time (and lots of re-writing), I’ve been able to bring it back down. There is a lesson here – do you actually measure the success of design and other changes you make to your site? You should and you can, as long as the metric you are using ties to the objective of your site.

Another interesting feature on this chart is the 2 spikes around day 50 – know what that is? Less targeted advertising. I primarily advertise by buying specific keywords on Google AdWords and Overture, but decided to test some contextual display ads in targeted content areas of About.com (under the Sprinks pay-per-click program).

Huge click through, bogus customers (high one-page visits), ruined my stats – and very expensive. Do you see why tracking this stuff is so important? I don’t have to calculate the ROI on that ad spend to know it’s worse than I normally get – the customer behavior tells me it is. By switching dollars out of About.com back into Google and Overture, I automatically increase ROI – without ever having to calculate it. Again folks, relative measurement (comparing the trends) rather than absolute measurement (calculating the ROI to the last cent) can save you a lot of time and effort. By the way, I don’t think there is anything wrong with Sprinks – the audience is apparently just not right for my b2b site. Might be good for yours; you won’t know until you test and measure.

In the next article, we’ll take a look at one specific metric you can use to track and improve the ability of each page on your site to get visitors to do what you want them to do. If you can’t wait, you can get a free visitor metrics tool that takes numbers from your traffic analysis reports and creates important visitor metrics for you. To go to the download page for this free metrics calculator click here.

Jim Novo has nearly 20 years of experience using customer data to increase profits. He is co-author of The Guide to Web Analytics and author of Drilling Down:Turning Customer Data into Profits with a Spreadsheet. If you want more visitors to take action on your web site, try using the free conversion metrics calculator, downloadable here. If you need to sell more to customers while reducing marketing expenses, get the first nine chapters of the Drilling Down book free at http://www.drillingdownbook.com.

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Traffic, Visitor, and Customer Analysis – Two Metrics
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