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06.30.10 How To Read And Use Web Analytics  By Thomas McMahon Web Analytics are a key indicator to the health and performance of any website, but online marketers often get lost in the complexities and details, forgetting how important analytics actually are and why. Analytics can provide a wealth of information but marketers often look at high level indicators such as: top content, bounce rates, entrance sources and keywords without tying it all together. In most cases, there is a tremendous amount of insight that can be used to make smarter marketing decisions, but most companies barley scratch the surface. At the OMS Minneapolis event last week Adam Proehl gave an excellent presentation on analytics failures and successes. I've taken my notes from that presentation and combined them with my own opinions to create this list. 10 reasons why your web analytics are failing: You speak numbers to non-number people. It takes a numbers person to dig though large amounts of analytics data, figure things out, and draw conclusions. However, most people aren't "numbers" people. Many marketers like charts and clear, action orientated data. Charts are good, numbers in red and green help, and so does simplification. Don't present tabular data just because it make sense to you. Try and think about who you're presenting the information to and how they like to consume information. Some people like tables, others like graphs. As online marketers make an effort to understand the audience on the web they're trying to reach, so should they understand the internal audiences that they report results to. The statistics are fuzzy. It's easy to combine different pieces of data and come out with a great conclusion, even if they don't go together. For example, did you know that Michael Jordan and I have a combined total of 6 NBA championships? While that statement is true, the conclusion is a bit skewed. Yes, Michale's 6 plus my 0 do equal 6, the fact is that that I didn't do any of the work for those championships, but I'm still getting the credit as I was included in the statement. In analytics it's important to break out the data so that it makes sense, not just so it looks good. It's easy to combine two pieces of information in ways that make things look really good, but in reality, is something being hidden? The averages are flawed. Averages are great unless there is a major spike or dip. Then they have a tendency to skew the data a bit too much.  Based on the graph above, you could say that we're averaging 1652 people from StumbleUpon a day. But in reality, most days there were less than 50. The big spike just screwed up the average. As quickly as that spike came, it can also disappear and making decisions based on the daily average isn't a best practice. Continue reading this article. About the Author: Thomas McMahon is a SEO Designer for TopRank Online Marketing in Minneapolis, MN. His specialities inlude technical optimization of existing web sites, creating search engine friendly web designs, and blog optimization. He has also created a number of blog marketing tools, WordPress plug-ins and FireFox add-ons. Blog: http://bloggerdesign.com |
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