March 29, 2022
We love FullStory – that is no secret – but over the dozens of brands that we’ve helped onboard to the FullStory platform to extract CX insights and drive actionable change we hear a common concern:
“I know all the answers are there… but I just don’t know where to start!”
Now let’s not get it twisted… that’s what onboarding training is meant for: teaching people how to get the most out of FullStory and as quickly as possible. But knowledge degrades, teams change, some are more involved than others in onboarding, and FullStory continues to release updates and new features so what was a “no” yesterday can become a “yes” today.
Before we get ahead of ourselves, not covered in this article is the installation and setup part of FullStory. We mean sitting down and mining for insights. For more information on getting FullStory initially set up and configured, checkout out the Installing the FullStory Script help article.
If you don’t already how FullStory works, we'll tell you! FullStory is a software platform that lets you capture and index everything users are doing on your site or app and make those clicks, taps, page visits – good or bad – all indexable through a search and filtering system, plus recreation of those individual sessions made available for pixel-perfect session reply.
Without any further delay, below are the top four great places to start with FullStory to extract CX insights on a regular, ongoing basis.
Getting feedback from your customers, extended teams, or even your own experiences can fuel investigation of FullStory. These one-off points of data can be driven back into FullStory to validate and quantify the severity of issues on said feedback. And sure, these single-source data points might be truly isolated, but it’s a lead worth following and this approach is probably the most straightforward to line them up and knock them down.
Example: you hear from customer service that there are complaints about the complete purchase button not working. Without specifics (which often website users don’t provide), you can go back to FullStory and search for Error Clicks or Dead Clicks to the “Complete Purchase” CTA to quickly observe users experiencing this issue and measure how many people this issue is potentially affecting.
Of course whenever building new searches in FullStory it’s always wise to QA by watching a few sessions to ensure you’ve got the insight that you’re looking for and not some other event.
This is certainly related to the previous approach of starting with feedback, but takes it a step further. Versus spot-testing points of feedback you can use that to build hypotheses and drive your investigation. Hypotheses are educated guesses about your users, their behavior, and why.
Example: You recently launched a new mini cart experience but you had concerns with some of the functionality, specifically hypothesizing that the “change quantity” experience was poor because the buttons were too small (but the design team insisted they would be distracting if any bigger). You can use FullStory to objectively observe users interacting with the mini cart element to confirm or refute your hypothesis – perhaps dead clicks near the change quantity elements or number of users who change quantity in a session but NOT with the mini cart element.
Note you may need to string a few insights together to clearly define your hypothesis, make sure you are using the right data points, and be ready to accept the data to support or refute your hypothesis.
Sometimes you might know you want insights but don’t have specific insights to uncover. Maybe it’s top pages with dead clicks or slowest pages on the site, both of which are great general CX goals that might illuminate new and valuable insights.
Example: You're in UX and overseeing your company’s ecommerce experience, and although beautifully designed, it’s not performing as well as you’d like. You now have a goal of trying to find out more about user behavior and want to get some topline metrics to understand where to start first. You can turn to FullStory to build a Product & UX Dashboard to measure, among other things:
Plus a number of other metrics to paint a picture of user behavior. Then perhaps you can use this hypothesis to build an A/B test to better convey site goal and measure the improvement of the page.
If you're interested in diving a little deeper on this thread, check out our related post on FullStory dashboards tips for ecommerce.
Once you have funnels mapped in FullStory you can identify where the drop-offs are within the funnel, identify the devices associated, common browsers, plugins, or other smoking guns. But likely more impactful would be watching some session replays associated with users who are dropping out of the funnel at the point in question. For customers of FullStory with an Enterprise License you can port your Funnel into Conversions to identify and quantify which exact breakdowns or identified friction events are causing significant impact on conversions. For more on this topic check out FullStory's deep dive on user friction.
Example: Our Ecommerce Funnel shows that the biggest drop is people from the third to the fourth step. By watching sessions we notice a lot of users re-keying phone numbers and a number of validation messages being served. After sending the Ecommerce Funnel to Conversions we can identify that validation error messages associated with the phone number field are negatively impacting conversions with statistical confidence. Next step would be meeting with the development team to identify how we can make the phone number field easier to engage with – allow special characters like “( ) _ - .” and auto-format to accept and clean data entered.
Now, if you’re reading (or re-reading) this post and still need help:
If you’re interested in either or both of the above – or just want to gush over FullStory with us – give us a shout: firstname.lastname@example.org