Alright fellow data crunchers, you ready for the final post in our three-post series, "So You Call Yourself an Analyst?" I was really hoping to get this post up last week but instead I was putting the final touches on our new affiliate program (more on that very soon)! For those of you that missed Part 1 or Part 2 of this series, I suggest checking those out before reading this one.

In Part 2, we outlined ways to analyze the data for valuable insights. We walked through GA features, as well as strategies to help you prioritize your analysis efforts. We should all be sitting here with tons of great data just waiting to be used.

Assigning Value to the Data

So what do we do now? Buy ourselves a pumpkin spice latte, and call it a day? No, no, no.  It's our job to take all of this data and figure out how much we are making (or not making) from our efforts. We can begin to create options based on data to help steer our companies down a more profitable path. By assigning value to the data you are now speaking a language everyone understands--money.

You need to explain the current situation in dollars. Too often analysts show up to meetings with great data but they explain it in weekly increases or decreases. This can be hard for people to conceptualize. Instead translate the data into actual dollars. I find the value per visit (VPV) metric to be one of the most overlooked calculations an analyst can use. Let's roll through an example;

Let's say we wanted to know if we should allocate more resources to the Learn SEO section. We would want to know how much each visit, on average, makes us. How do we calculate the value per visit? We pull how many people visited the section last month, and see how many conversions took place. We then multiply the # of conversions by $99 (you can also use your lifetime value if you want) and we get a monetary value the section brough in last month. We take that value and divide by number of visits to the Learn SEO section, and we have a practical data point to report. Looks like this;

Reporting good data with an attached value to your boss or client is only half the battle. The other part is making those numbers compelling. For example, let's say your marketing team needs to know what section of the site they should focus CRO efforts on. You may be inclined to throw that data in excel, but why not throw it in a pie chart? The data really pops, right?

 

When reporting the value of data you should lean on things like bar graphs, pie charts, and ranges of success. Go dramatic folks! You just spent days throwing back caffeinated drinks so you could find data they want to see. Help them see it better.

Last but not least...Take Action!

Now that we have the value nailed down, we can move onto my favorite part--taking action.  Yup you read that right...it is your responsibility to hand the rest of the company with suggested "next steps" {mind explodes!}

Now I want you guys to be honest, how many of you have said something like this,  "If we can increase traffic to this page by 10% we will increase signups by 2% and add X number of dollars to the bottomline?" Ugh, come on people! You have the data. You know how the traffic is getting to that page right now. You know what referrals are increasing, and which are dropping. You know what sections of the site are funneling to that page and which have no way for a visitor to access that page. Make actual suggestions when presenting the data!

You should be saying, "Here are my suggestions for increasing traffic to this page..." and list a number of referring sources for PR to work on, or give a handful of keywords for your SEO to target, etc. These are things you can suggest based on the numbers. You can have confidence that you are suggesting smart moves for your company.

That is what this was all for friends. As analysts we aren't just tasked with making sure everything looks okay, we are also expected to find ways to improve. If you aren't walking away from the data with action items you can get started on, you shouldn't be walking away from the data.

Well that about wraps it up for this series folks. I hope I have helped some of you reevaluate your current analytical process. I know it all seems like quite a bit of work, but trust me when I say time invested in analytics is well spent. Hopefully this series has given you a roadmap to follow, and at the very least motivated you to log in, check it all out, and start analyzing.

Want to start from the beginning and check out Parts 1 & 2? Here they are:

Part 1: Asking the Right Questions
Part 2: Analysis Redefined