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Meaningful Metrics

Meaningful Metrics

February 3, 2007
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" I'm only concerned with the number of unique visitors hitting a specific page and the drop-off in that number, page after page, through your user path "

You often hear webmasters discuss the importance of testing as part of the development cycle, but the context of the discussion is usually in regard to visually evaluating the appearance of a target web page using different browser software, screen resolutions, operating systems and, increasingly, alternative access platforms such as mobile devices.

Less discussed, but just as important, is the issue of testing the site's performance. Now, I'm not talking about how fast your server is or how quickly your web pages load; I'm talking about how effective your web pages are at doing what you intend them to do.

For example, does your join page encourage people to join or make them leave without joining? If you changed it, did it help? How do you really know?

The effectiveness of your individual web pages can likewise be determined by setting "action" goals for them, establishing a baseline and then optimizing the page and measuring the results based on those goals.

These data are quantifiable, measurable points of operational success and as such it can be tested and evaluated as to whether changes helped — or hurt — your operation.

We're not so much dealing with numbers as we are percentages, but we need the numbers to develop those percentages, and those numbers are easily found in your stats.

Speaking of stats, I'm only concerned with the number of unique visitors hitting a specific page and the drop-off in that number, page after page, through your user path.

For example, say the process we wish to test is a paysite tour — measuring its effectiveness and evaluating changes aimed at increasing conversion ratios. To make things simple, let's look at a "minimum clicks to target" mission, where our goal is to have the visitor become a member. The minimum number of clicks, or "steps," that the surfer must take through our site is first mapped out as shown in the graphic above, showing a warning page, splash page and join page.

Surfer Path

Surfer Flow
The arrows represent the flow of surfers and how successive pages filter down the volume of this flow, starting with all of the traffic hitting your warning page, which turns away a certain percentage of those visitors. Once the visitor takes that next step, clicking through to enter your site, he lands on your splash page. In our minimum clicks example, we won't include the substantial impact of multiple tour pages but will focus on a surfer ready to make a purchase and clicking straight through to the join page. This is the path of least resistance, with the fewest number of steps that will accomplish your goal of having the visitor become a member.

Consider that each successive page in your path is another hurdle for the visitor to overcome, and not all of them do, thus the fall-off in traffic the deeper you travel into the site. The reasons folks leave are infinite and include everything from usability issues to their being done with your content — or your offer. By measuring the relative fall-off rates through this path, we can evaluate these pages and their effectiveness.

In our example, knowing the number of unique visitors to hit the first page in our path gives us the maximum traffic pool. Then knowing the number of unique visitors to hit the splash page over the same period of time and subtracting that number from the number that hit the first page will show you the warning page drop-off rate. Make it a percentage. Dealing with percentages is important, as it eliminates the effects of fluctuating website traffic, which makes "number of visitor" comparisons inaccurate.

Now, to accomplish our goal of the visitor becoming a member, the visitor must take another step and click through from the splash page to the join page — something that a certain percentage will fail to do, resulting in a further drop-off in traffic. Your task is to figure out why people aren't clicking through and eliminate the problem, using the data as a tool for measuring success. Likewise, there's a further drop-off between visits to your join page and actual signups. Why? Changing variables and measuring results will let you know when you've figured this out — and when you haven't.

See where I'm going with this? Lots of folks take the "big picture" look at conversion ratios, measuring the traffic they send to a site against the actual signups — and while affiliates only need concern themselves with this view, site owners have the power to change the site's pages and marketing approach. So having a more granular level of data about a site's conversion ratio allows for maximizing a page's effectiveness and thus the site's overall profitability.

So, looking at our test path and having baseline measurements, we can make changes to the warning page and see if those changes helped or hurt, based upon the change in the percentage of users clicking through. Now that more people are seeing it, we can then make changes to the splash page, measuring its effectiveness at sending visitors through to the join page, which in turn is tweaked, with sales volume being the ultimate measure of that page's success.

Of course, many paysites have several tour pages, so these must each be looked at in the same way. Your stats will show the most common paths through your site that surfers are actually taking, and this all needs to be evaluated as well.

By working to eliminate the roadblocks to membership and measuring the effectiveness of your efforts in this way, you'll substantially improve your bottom line — but by how much? You'll have to measure and chart that for yourself. The important thing to remember is that by analyzing stats, you can optimize your sites, if you evaluate the proper metrics.

Natural Fluctuations
Keep in mind, however, that you'll often see natural fluctuations in these figures as well. For instance, when I ran this example test, I looked at some historical data that showed fluctuations of up to plus or minus 10 percent in the drop-off rates without having made any changes to the tested pages. These could be seasonal variations or fluctuations in the source and quality of the traffic. For example, I was able to correlate one of the drops in efficacy to a rise in Chinese traffic — perhaps the language barrier played a role? While that conclusion is a no-brainer, it illustrates some of the factors that a careful analysis of the data at hand can provide.

At the end of the day, the benefits of studying your meaningful metrics are clear, with a more focused approach resulting in increased sales and a stronger bottom line. Work on the way in which you study stats, and you'll be surprised by what you learn about your site.


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