Analytics

The following is a guest post by Marshall Sponder of WebMetricsGuru.

A few years ago, Google Analytics added benchmarking, allowing site owners to compare their own sites to others in the same category. Of course, many sites fit into more than one category, and to enable benchmarking, you must first enable data sharing from within your analytics settings. Using my art blog, ArtNewYorkCity.com, I wanted to see how well it performs against other blogs in its category.

Selecting the right category is crucial for this exercise – fortunately, Google Analytics has a fairly rich selection to choose from, especially if you drill down a few levels.

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Keyword research as we know it suffers from a major disconnect. On one hand, you have keyword tools, which provide keyword suggestions and associated keyword frequency estimates. On the other hand, you have web analytics applications, which provide you with the actual search queries that are driving traffic to your site, and the true relevancy of your keywords based on real conversion data. But there is no reason for these two types of tools to be divorced from each other. In fact, in my experience, successful keyword research largely depends on your ability to leverage and manage data from both keyword tools and web analytics in a continuous fashion.

Let's take a look at why neither keyword tools nor web analytics alone is sufficient for complete keyword research, and how the future of keyword research involves seamlessly harnessing the best of both worlds.

Bounce rate has become a hot topic in the search community. It's a bit of a buzz word, and if you're doing any sort of SEO or even PPC consulting you'll likely have clients concerned about theirs. In analyzing a site's bounce rate, I like to look at three main factors:

Bounce Rate Factor #1: Traffic Source

Often times the place traffic comes from (search, direct, referral) can have a major impact on bounce rate and can be an indication of irrelevant traffic (some perhaps that you may be paying for). There are a variety of ways you can quickly diagnose problems using analytics packages.

I had a chance to write a guest post for Web Analytics World last week. In it I talked about quickly diagnosing quick jumps or dips in traffic or conversions, and oulined three steps for getting to the bottom of a shift in traffic:

1. Consider the Source
2. Analyze Your Overall Mix
3. Identify Who or What is Sending You Traffic

To learn more about each step and how you can accomplish the above in Google Analytics, check out the post.