Are your website metrics reliable?

Not only do many websites have unreliable metrics; they’re usually measuring the wrong things.

“75% of the data Web marketers collect are either misleading or inaccurate,” according to MarketingExperiments, a website optimization research company.

According to MarketingExperiments, poor data quality can be due to:

  • Inadequate tracking;
  • Improperly configured measurement tools;
  • Faulty test structure and protocols;
  • Validity threats;
  • Inconclusive results.

However, many websites have an even deeper problem; they are obsessed with volume. You’d be surprised how many web managers measure their success by how many pages were looked at, and/or how many people visited the site.

Every month my website gets about 20,000 visits and about 40,000 page views. These measures are as close to meaningless as any set of measures can be. One thing I do know is that most of these visits come from search engines and most of these visitors leave pretty much straight away. What does that mean?

For many websites, search engine traffic is a great polluter. For most websites, search engines throw huge quantities of useless traffic at the website. The behavior of the small fraction of people you would actually like to track is often smothered under the huge piles of search data junk.

Does anybody actually look at all those log files generated by website traffic analysis software? I’ve never met anyone who had the time and energy to dig into the huge reams of data they spew out and find anything meaningful.

Example: A customer clicks on page A, then leaves after 1 minute. What does that mean? If they stayed for 3 minutes would that have been better? Why? Supposing the person who spends 3 minutes on the page finds it cluttered and full of verbiage?

Example: A customer clicks on page C, then clicks on page M, then goes back to page C, then leaves. What does that mean? Did they think they were going to get something on page M that they didn’t get? Or did they get what they needed on page M, and were simply using the Back button to navigate out of the site?

We need to radically simplify how we measure the success of our websites. Here’s how:
Identify the top three tasks of your website
Give these tasks to your customers and measure whether or not they are able to complete them.

If you’re a university, a top task should be to find a course. Observe potential students as they try to find a course on your website. If you’re a health website, finding out the symptoms for a particular disease is probably a top task. How easy is it to do that? If you’re running an intranet, finding other people is unquestionably a top task. How easy is it to do that?

Web managers can’t spend their days hunched over screens. That is quite simply not management. Metrics are the lifeblood of management. The essence of web metrics is the observation of our customers as they are hunched over their screens. Were they able to quickly do what they came to do? Web metrics can be boiled down to two words: task completion.

Marketing Experiments

 

4 responses


  1. Certainly agree that metrics software can let loose an avalanche of data that almost nobody has time to deal with. And so one challenge is to boil it down to the proverbial bare essentials. From a marketing perspective, that’s what I’m trying to do right now for a presentation later this month. Any recommendations are highly appreciated!

    I’m wondering, for instance, if analytics can’t be of help in noting an absence of task completion. For instance, peole coming to an online inquiry or order from and then leaving without completing it. Might that not signal an intent to complete the form that was killed by initial reaction to the form?

    On another note, I have a university client who just asked my thoughts about the best way to move forward with reviewing and rewriting of 26,000 website pages.

    My first response was simple: review your analytics for the past year and see how many pages you can find that relatively few people are visiting. Make those pages candidates for elimination. With any luck, the result will reduce the scope of the overall project that the client is now hoping to complete in three months.

    A useful element in Google Analytics is the geographic distribution of visitors. That might, for instance, give some insight in turn into how to best target paid search ads for students in an online degree program.


  2. I couldn’t agree more about how so many people are obsessed with meaningless numbers when it comes to web metrics. I see it constantly at my job.

    However, the “task completed” metric and the examples of how easy it is for site visitors to sign up for a course, etc. are just the tip of the usability iceberg. While it’s imperative that the site be usable enough for people to be *able* to complete a task, from the perspective of an e-commerce site with the *task* of selling a product or service, the completion percentage is not likely to mean the site/page is un-usable. That percentage will vary by offer, competition, perceived value, brand image, page layout, amount of information. Optimization testing is going to be the area that gives the big bang for the buck in e-commerce.

    Thanks for all the good info.


  3. Good observations, as usual, Gerry. One of the things that just amazes me, when I discuss this subject with other Web site administrators, is how many of them make no effort filter out and discard traffic from search spiders/webbots. On our site, automated programs account for 20 to 30 percent of our user sessions and 3 to 5 percent of our page views. This is traffic that isn’t even human, yet many sites happily include it in their stats.


  4. I agree that most site metrics in use today are beyond useless. A task-completion focus is certainly more objective and ought to be monitored. But isn’t that too narrow?

    Regarding the university client with 26,000 pages — it’s a rough start. However, looking for pages that are not highly visited may be because of poor design, weak content, and a confusing structure, among other variables. How do we really know what our web visitors are thinking?

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