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So You Call Yourself an Analyst? Part 1: Asking the Right Questions Posted: 22 Sep 2010 01:13 PM PDT Posted by JoannaLord Today I am going to talk about something that plagues companies and consultants everywhere--half baked analysis. It's something we've all done at some point, and something a lot of us still do on a regular basis. It's unfortunate because as online marketers we all understand the power of good data mining, but time and time again we revert to generic inquiry, at best, and default report templates. Disclaimer: Origionally I attempted to write about the five steps I follow for solid data analysis in one post, but as I approached my 6th page of content, I realized it may be best to break up into a series.
Yup that's right...cancel that afternoon meeting because you my friend are going to be stoked about data analysis in 3...2..1... Rethinking the Questions So what questions should you be asking? Bigger ones to start. I know they sound uber-top level, but don't roll your eyes just yet. I challenge each of you to write these out and really think about the answers. I think you'll be surprised with what you come (or can't come) up with. I'm going to apply this to SEOmoz as an example.
So now what? You are left with a handful of metrics to investigate. Those metrics should be the base of your analysis efforts. I urge all of you to revisit the reasons why you analyze what you analyze, you'll be surprised to learn that you don't really have a good reason most of the time. After you have your new questions nailed down and you know what metrics you want to analyze, it's time to jump in the data. This is when I highly suggest you fill your coffee cup, or grab another Red Bull. I also support locking your office door, or putting up a "Do Not Disturb, I am Data Mining You Silly Non-Analyst" sign up on your cubicle. Okay anyway...so the main roadmap to solid analysis includes five steps and they are:
At SEOmoz we track our top sections by week, so we can easily identify shifts in the data, and it looks something like this:
You can see we aren't just looking at our homepage, we are looking at our subdomains, our highest trafficked sections. We also are going beyond visitors, we are pulling top-level stats like pages/visit, time on site, bounce rates, etc. This graph goes around to the entire company once a week. This macro level view helps all of us understand the momentum of our site's growth. It helps us easily isolate problem areas so we can address them before they grow into huge "Oh sh*t" moments. Trust me when I say, if you aren't tracking your data at this macro level, you should start today. While it's great to go into work on a Monday and say I want to increase traffic to my blog by 20%, it is a big feat to accomplish. Not only will it take a lot of time conceptualizing, writing and sharing that content, it will also, most likely, be less lucrative than if you took the existing traffic and increased its conversion rate by 5%. That sort of move is done by honing in on data at a micro analysis level. Specifically this is where things like event tracking in Google Analytics and deeper dives into your preferred analytics package come in handy. Everyone has their own approach for micro analysis, but I think a good place to start is see where successful events (downloads, subscriptions, sign-ups, conversions, etc.) are taking place and see if you can come up with common demoninators. If you see that successful pages all have one or more thing in common, you can start testing these on other sections to increase conversions across your whole site. Here is an example of what we pull for SEOmoz:
We can see which tools are performing the best, and analyze those pages to see if we can isolate out page tweaks to roll out across all tool pages. It seems simple, but way too often analysts look into analytics to see how they are doing, and fail to put in the time required to uncover what they could be doing for increased success. You should know, for every single section and user type on your site, what makes it "successful." You need to be tracking these "successes" as closely as you would your visitor count. Well this post got a little long, but I really wanted to give you guys some real examples on how I approach data analysis both at the macro and micro level. Hopefully, you can take some of this and apply it right away. I know we all have our own unique approach to analysis, and I'd love to hear yours in the comments below! Next post I will be talking about the "analyze" step of a solid analysis strategy. That post will hone in on quick ways to figure out what is going wrong. I will talk about some GA features that you can use to make your analysis more effective and less time consuming. So stay tuned!
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