In Part 0, you set some goals and convinced yourself that this whole project was worth the time.
You’ve got targets, so you’ll know if your efforts are working. Let’s do the thing.
The first step is to decide where to focus. You might have some genius ideas on how to improve your About Us page … and you might also have only 50 visitors a month to that page. In which case you should ignore it, right?
But you might also find that all 50 of those visitors end up converting 🤔 … in which case you should not ignore it.
Clearly this stuff can get complicated. It sure would be nice to have a step-by-step guide for where to look and what to do.
👇 Here’s a step-by-step guide for where to look and what to do.
During this process, we’ll be collecting two lists of pages to focus on, along with some notes about why they’re of interest. One will be the “Happy Path” and the other will be the “Sad Path.”
It’s possible that some of these pages will form a coherent “funnel” where it’s logical, or even required, to visit them in sequence.
Not all of them, though. So no need to document order and dependencies between various URLs. No need to craft a complex diagram with arrows and callouts.
Just grab a spreadsheet or open up a simple text document to collect your notes.
Segment and conquer
The visitors who actually sign up are precious. We want to understand everything we can about their behavior.
We also want to understand how their behavior differs from non-converting visitors.
So we’ll apply the built-in “Converters” and “Non-Converters” segments in Google Analytics, if they work for you.
Why the built-in Converters and Non-Converters segments might not work for you
By default, these segments include any visitor who triggered an Ecommerce transaction or fired a Goal. If it’s possible to convert on your website without firing a Goal, do 2 things:
- Make a TODO for later to create Goals for all website actions that make you money
- Create your own, custom “Converters” segment to include all relevant actions (e.g. “Thank You pageviews > 0”), create another custom “Non-Converters” segment (e.g. “Thank You pageviews = 0”), and keep going!
Now that we’re able to look at data for our most valuable visitors, side by side with data for the unwashed masses, we just need a couple reports.
Behavior > Site Content > Landing Pages
Look at data for the last 3 calendar months. Apply the Converters segment.
What’s the top landing page? Add this to your Happy Path list. We’ll start there.
Next up, apply the Non-Converters segment. What’s their top landing page? There begins the Sad Path.
Now you know where most Converters land, and where lots of Non-Converters land.
So … where do they go?
Behavior > Site Content > All Pages
Keep the same time frame. Switch back to the Converters segment.
What pages do all Converters visit? Add them to your Happy Path. (This would be your proverbial funnel.)
What pages do most Converters visit? Add these to your Happy Path.
Now switch segments again.
What pages have tons of Non-Converter traffic, but aren’t on the Happy Path? Add them to the Sad Path. (Your blog will typically fit this description.)
Tying it all together
One valuable insight at this stage of the game is to identify pages that are correlated with, but not required for conversion.
Of course your Converters visit the Contact Us page, if that’s where they convert. And of course only a small proportion of Non-Converters visit the same page.
But when you notice lots of Converters visiting non-essential pages like About Us, or Integrations, or Learn More About Our Bespoke Tesla Engine Monitoring Devices, there may be an opportunity to direct Non-Converters there.
Maybe that content wins people over. Maybe your Converters are just the handful of visitors who manage to stumble across it on their own, and maybe showing it to more visitors would increase conversions 📈.
But we’re getting ahead of ourselves.
At this point, you’ve got a list of pages tied to conversions, and pages where tons of visitors arrive and leave without making you money.
We’ve looked at the data, and we’re almost ready to look at the website itself. But not quite; first we have to understand who these visitors are. More on that in Part 2.