Using Data Analytics to Plug the Leaks in Your Conversion Funnel
Guest Post by Andrew McLoughlin for Colibri Digital Marketing
Despite the benefits, many sites and businesses are still not using any sort of data analytics systems to explore user behavior in their site traffic. Effectively flying blind, those sites cheat themselves of potential conversions, fail to improve user experience, and sacrifice their bottom line. But data analytics tools are low-cost, high-return systems that provide a huge wealth of information.
Our digital marketing agency uses them every day for our clients, and it’s shocking that they aren’t in more common use. With just a little time and patience, your business can use data analytics to tighten your conversion funnels and improve user experience. We’re here to show you how.
If you’re not familiar with the term, “data analytics” refers to the process by which information is collected and interpreted. For our purposes, that data might include things like how many visitors a webpage got, at what times of day, from which sort of device, and so on. By comparing two sets of data, patterns can be charted and leveraged.
For instance, a site tends to get most of its traffic on Fridays and Saturdays. It blogs weekly, publishing Friday mornings. There’s a good chance that the increase in traffic results from visitors coming back to read the new blog content. In another example, a site that typically gets hundreds or thousands of visitors per month suddenly drops to single digits. That would indicate a serious problem, maybe with the site’s rankings or with some kind of server-side error.
By exploring trends and correlations, useful insights can be gleaned about the site’s operations, and improvements can be made.
Data generally fall into one of three categories: acquisition, audience, and behavior.
There are a number of different paths by which your site might be found and visited. Broadly, these paths divide into:
Direct traffic refers to those users who either type your URL outright or find it in their history or bookmarks. They visited your site deliberately and weren’t linked to it from some other place.
Organic traffic found your site through a general search query. They searched a keyword or phrase, and your site was provided among the search results.
Social traffic came to your site from a social media platform. It’s technically just a subset of your “referral” traffic, but with social media’s influence becoming more pervasive, it’s useful to keep it distinct from other referrals. If you include a link to your content in a Facebook post, for example, then users who follow that link will get grouped here.
Referral traffic, like social, describes users who followed a link on another site and found themselves on your own page. If a user was somewhere else, first, then it’s considered a referral. This also contains the subcategory of email traffic, which isn’t quite a referral (since you emailed them the link) but is useful for tracking the efficacy of remarketing campaigns or newsletters and the like.
Want to optimize your website for lead conversion and UX?
This section collects data about the people who are actually visiting your site. One user isn’t interchangeable with another. Different demographics, or users with different intentions, may have very different experiences on the same site. This section keeps track of whether a user has been there before (“new” vs. “returning” users), what device and software they were using, their location, other sites they frequent, personal data (if available), and so on.
These are the sorts of insights that will let you target a specific landing page, call to action, or piece of content to a specific type of potential customer. By doing so, you’ll increase engagement, and help those customers advance through your conversion process more efficiently.
Behavior data examines the type and sequence of interactions a user has with your site. Metrics include which pages got the most traffic, time spent on a page, how many pages a user visited in a single session and their order, whether a user completed a goal (like signing up for a newsletter or making a purchase), and so on. This data is most useful for diagnosing problems.
Imagine that one of your pages has an especially high bounce rate (instances of users visiting the page, but leaving your site immediately, without exploring further.) As an outlier, it would be clear that something about that page makes visitors disinclined to continue on your site.
Maybe the content is unengaging, the interface confusing, or the site menu obscured. For whatever reason, if users are unwilling or unable to further explore your site, the page with the high bounce rate will need troubleshooting.
The behavior charts can also be used to spot pain points in your conversion funnel. You might see users filling a shopping cart, but abandoning the process when it comes to entering their shipping information.
Maybe the layout is unintuitive, or incompatible with autofill software, or perhaps users are irritated at being asked for the same information twice (first for their billing address, then again for their shipping.) This sort of problem is relatively common in e-commerce, and more than once it’s been solved with a simple “use same address as billing” toggle.
How Do I Put All of This Information to Use?
Step one: start collecting data. There are a number of tools out there to get you started, but Google Analytics is probably the simplest. In just a few minutes, you can add a small bit of script to each of your pages, and start collecting data. The sooner you start, the better. You’ll need at least a few weeks of data before you can start making useful inferences. The bigger your sample size, the more representative it’s likely to be.
Once you’ve got a sufficient pool of data (the exact sample size will depend on the scope and scale of your particular site) you can start exploring it for trends.
If you aren’t sure where to start, just pull up visual representations of your data and look for the outliers. If you’ve got a stable line, with a huge spike, look for changes in other sections that correlate with it. If you’ve got a spike in traffic for a certain day, look at the previous week and see if there’s a corresponding spike, for instance.
Compare your CTR (click-through rate) against the relative percentages of new and returning users, to see if there’s a pattern. If returning users tend to explore more deeply, double down on your remarketing initiatives (like email, social media, and newsletters).
Basically, it all comes down to this. Data analytics tools give you a record of how your site is being used. If you see something that’s going well, reinvest in it. If you see something going wrong, take steps to correct it.
Observing the way your users interact with your site will alert you to pain points, and holes in your conversion funnel. By restructuring your site’s content to plug those holes, you can better keep your users engaged, delivering a better experience, and driving more conversions. The sooner you start, the sooner you can start improving your site!