The last time I looked, I could see over 75 different reporting options within Google Analytics. 75! All generated from just the one itty-bitty piece of javascript placed on every web page of your site. With so much to choose from, it can make life confusing – where do you find the actual information you need within this mass of data?
One option would be to start at the top of the navigation list and work your way down, keeping an eye out for anything interesting. But I predict a definite glaze will come over your eyes after 10 minutes. You could always head off to Google to see what reports others suggest as being worth your time. This could be an interesting exercise – but possibly also a real time-waster and completely irrelevant for your situation.
So how do you determine the reporting areas that matter the most to you and your situation? Thankfully there’s a simple answer. It all boils down to defining a “conversion use case” for your website and then wrapping an “analytics net” around it to track its performance. Below are two very basic examples of this approach to illustrate how this can work for you. For each, I have detailed the basics of the use case and then shown the ideal analytics net required e.g. what reports from the 75-pluswould need the most attention.
Theory #1: Simple service-lead generation.
Conversion Use Case
It all starts with prospects using Google to look for what we offer. From there they click on our ranking or paid advertisement and visit our website. Once there they spend 2-3 minutes reading about who we are and what we do. This leads them to fill in our quote request form, which generates an email to our sales team. One of our team will contact them within one business day and make a time to visit them at their home. Job done.
Basic Analytics Net
Webmaster tools will tell us if our rankings within Google’s organic listings are being seen first and then – once seen – clicked. Our Google AdWords account will be linked to ensure any paid advertising is correctly separated out so we can correctly track individual keywords responsible for visitors arriving.
All the usual great engagement website stats such as bounce rate, time on site and pages per visit will tell us what people do when they arrive. For example, do they visit one page and leave? If they stay, where they go and how long do they spend on each page?
Setting up a goal in Google Analytics will help us follow the trail from visitor arrival to the quote request being completed. Plus it will give us a percentage take on those that visit and convert compared to the total visitors.
We also need to capture all the leads into an online tool of sorts – even a basic spreadsheet will work – so we can track the difference between those who complete the quote request and those who our sales team are able to
contact (Just in case there are those who don’t answer their phone or return left messages) And finally, of those who were contacted, we can tell which ones became clients, which leaves us with an overall lead-to-customer conversion rate.
And that’s the simple one!
Theory #2: E-commerce, pure and simple.
Conversion Use Case
Prospects looking for the products we offer will find us on Google. They will see our search listing or our search ad and visit our website. Here they’ll browse the product they’re interested in and possibly the category it sits within. They’ll do this for between 2-3 minutes. They’ll then either choose to purchase this product now – or decide to visit the site again and buy it later.
Once they’ve purchased their desired item, they’ll opt in to receive our email campaigns/offers. These messages are enticing enough to ensure they visit our website more often and spend longer on it each time compared to those who decide not to buy. All this translates into them purchasing more than twice a year.
Basic Analytics Net
The first part of the process is very similar to the situation before. Webmaster tools and a properly linked Google AdWords account will enable us to track all we need at the
start of the journey.
The engagement reports from the last use case will help us here too. Plus, establishing e-commerce tracking within Google Analytics will ensure we can match actual sale values to the orders coming through. So we can see not only the customers but also the products they purchased. We can then build a segment within the updated Google Analytics custom segment application to track visitors who arrive and decide to purchase
after either a single or multiple visits. Another segment will split out customers from prospects so we can see how their onsite behaviour differs.
Our email marketing campaigns will need some extra work too. By directing the links they contain back to the shopping site, we can then split this traffic out as a separate stream for further analysis. So segment #3 will show us
how our email subscribers differ in behaviour from the rest. (We could further categorise these people: segment #3a for email subscribers who are customers and segment #3b for those subscribers who are still just prospects.) This leaves it up to our ordering system to tell us how the purchase frequency changes for those who are opted into our email campaigns compared to those that aren’t. One should be bigger than the other.
As you can see, there are a lot more behaviours required to make a successful e-commerce site work well compared to its lead-generated cousin. Who said it was as simple as placing a shop online then sitting back to count all the money flowing in?! Nevertheless, once the correct analytics net is placed around either site, it will soon be obvious what’s working well and conversely what needs work.
So why not take some time this month to start the process off for your own site? Write out the conversion use case you want your website to achieve. Go ahead and include all the steps that are required – both on-and offline – to create the results you want. Then have a go at creating the right analytics net to wrap around the process. Contact us if you need help.
