Use the Data You Have: Answer Your Questions

As discussed in the Previous Post in this series (Ask the Right Questions), before you set foot in your Analytics suite, you need to have some idea of the questions that you want to answer.

Eventually, when you’re a superstar with your analytics toolbox, you’ll be able to do some exploratory analysis  – jumping in without a hypothesis or a ready understanding of what you’re looking for. For your first steps as a data driven support professional, I’d recommend having your question (or questions) ready to go.

For the purposes of this series, we’ll do a (very) brief overview of navigating through Google Analytics, and a tiny bit on Mixpanel. It’s important that you become a confident and competent practitioner of your particular toolset. If it’s Google Analytics, get certified.

(Here’s mine!)

Let’s consider the hypothesis from our last Post;

If it is true that our customers want plugins for their site, we would expect that “plugins” would be a top search term in our knowledge base. It would also be a top tag in our chat transcripts. It would also come up more frequently than other support topics in our public forums.

Knowing the way that things are arranged at WordPress.com, I can verify or deny each of these pieces with different tools. Tag transcripts I could find from our live chat software provider. I could search the Forum, or do a big text scrape. For the knowledge base piece, I can use Google Analytics, since our documentation is all recorded there.

For the best argument, you’ll want to use all of your opportunities to verify – that way you can be as certain as possible that you’re making the right call.

Let’s open Google Analytics. Once you’re in your site or app’s Dashboard, you’re going to see a LOT of information. On the left hand sidebar you’ll see a number of tabs like this:

Screen Shot 2016-05-18 at 8.20.23 PM.png

For most of the work you’ll be doing, the Behavior tab is your friend – much of the rest of the Analytics suite can be useful for support, but would require maybe more digging than we’re ready for, or possibly would require committing additional code in order to track more nuanced behavior.

Since our question is about customers being interested in plugins, one way for us to check our hypothesis would be to see how traffic our support documentation on plugins compares to other support documentation. We know the URL for that document ( https://en.support.wordpress.com/plugins/ ) , so we want to expand Behavior and head into our Site Content > All Pages

Screen Shot 2016-05-18 at 8.25.35 PM.png

From here we’ll get a top ten listing of our most-visited locations, as well as a breakdown of Pageviews, Unique Pageviews, etc. Like so:

WPCOMGA1

 

OK! Now we’re getting somewhere – I’ve obscured the actual data here, but you can take my word for it that the /plugins/ page is not our most commonly visited support document, with less than 1% of our overall traffic. It is in the top ten, however.

I will note though, that the /com-vs-org/ doc (which describes the offerings of WordPress.com versus self hosted alternative) is highly popular, and for many customers, the difference between WordPress.com and self hosted sites boils down to one thing: access to plugins.

When we take these two documents together, they represent more traffic than every document except /stats/ – but people do so love their Stats. That /plugins/ and /com-vs-org/ taken  together represent the second most visited support document is meaningful, for sure.

We do want to verify that these two documents are in fact related, and what we’re observing here is in fact noteworthy – we can do this in Google Analytics by selecting the Navigation Summary tab at the top, and selecting the /com-vs-org/ page:

WPCOMGA2

Now we’re getting somewhere – in comparing the flow, I see that one of the most common pages folks visit before /com-vs-org/ is /plugins/ – and it’s also one of the most common pages folks visit immediately afterward. I’d take this as sufficient evidence that our hypothesis is supported.WPCOMGA3
It’s highly important that you are careful not to overstate your case – what we can see here is traffic and its flow – we can’t be sure that this is positive or negative, or what impression customers are getting from these documents. It’s clear that there documents are related, and popular, but not necessarily what that means. 

This is why checking several sources and doing a second-level check is important – seeing not only where the traffic totals are, but also how the traffic flows between different pages or stages.

Representing this accurately and researching it thoroughly will help you to state your case accurately. Consider this example, a Mixpanel report of Failed Logins (on the top, in blue) vs. Signed In (successful logins):

Screen Shot 2016-05-18 at 8.40.22 PM.png

Holy moly, we have nearly twice as many failed logins as we do successful ones?! Somebody call the head office, this is a huge problem!

Approach it with curiosity and a desire for verification – imagine, if you fail to logon to an app or service, what’s the first thing you do? You try to log on again, right? Look how this chart changes when we go from “Total” to “Uniques:”

Screen Shot 2016-05-18 at 8.42.10 PM.png

The two have swapped places – yes, 6500 failed logins a day is not great, but it tells a much more measured story, and probably more accurate to your interests.

Answer your questions, but always verify.

The next and final Post in this series will be taking the answers you’ve found, and turning them into convincing arguments. See you soon!

 

 

 

 

 

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