Let me be upfront: this post will contain statistics. Not the fun, pithy kind like “60 percent of statistics are made up on the spot,” but actual cold, hard statistical practices.

Joking aside, I’m going to run through some fairly top-level statistical analysis practices that can be employed every day to help make sense of your marketing data, allowing better strategic decisions. And it won’t be painful at all, I promise.

“But I do this already. I’m always analysing my data!” I hear you say incredulously.

This is probably very true — most users of web analytics (Google Analytics, Omniture, et al.) instinctively apply what would formally be known as “descriptive statistics.” For example, you readily identify a spike or a drop in your daily traffic by “eyeballing” a chart; you use averages to quickly assess performance; and you do all sorts of comparisons that help you understand what is happening (and importantly, what you need to do next).

Although a loose adherence to the general principles is fine and workable, I strongly believe that an element of rigour can help take your analysis to the next level. Below, I’ll run through a couple of concepts tied to real-world examples which will hopefully convince you that this is an approach you should be considering.

Read all the details in the rest of my column at Marketing Land.

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