Food Now* is an online food delivery platform operating in several countries. The company launched a new loyalty program which offers users monthly discounts. After a few months, Food Now’s team reviewed quantitative data, and were disappointed to learn about the 30% drop out between downloading the app and registering for the loyalty program. The team needed to understand the why behind the what and chose UXcam to find out! Food Now’s goal with UXcam to ultimately increase the number of users that sign up for their loyalty program.
Firstly, the team mapped out the steps for a successful signup process :
- Email address successfully entered
- Password successfully entered
A user could fail each of the steps (ex: submitting invalid email or a password with no special character.) To have a deeper understanding of the situation, it was essential to track these events, successful or failed as well as the reason for any failure. The team then then sent custom events to UXCam to track each of the actions.
Here are the custom events this client sent to UXCam:
Once the custom event data was sent to UXCam, the team created a session funnel within UXCam to identify the biggest drop-off area. If you are not familiar with funnels, check out this guide.
The team discovered there was a 30% drop out between email_success and password_success. In other words, users abandon signing up just after they successfully submitted an email and before setting up a successful password. This tells us that the password step is clearly an area of friction for users. The team wanted to dig deeper about the events and event properties in general and watch some recordings. To do so, they used the event analytics feature, filtered for sessions with events and grouped by event name.
The graph confirmed the Password_fail event had the highest number of sessions. The next step was to identify the most common reasons for a password failure. The team once again used the event analytics feature and filtered for Sessions with password_failed and grouped by reason to see what was the most common reason. The graph revealed the most common reason a password failed was because users did not use a special character.
The team watched the recordings and saw users were not entering a required special character in their password and still tried to continue. They missed the error that a special character is required. Therefore the user frustrated quit the app or navigated away without completing the signup flow.
The team removed the required special character requirement and displayed “weak / medium / strong” under the password field to indicate the strength of the password. They also added text of what makes a password strong and highly recommended strong passwords but did not require anything below a “medium” rating.
(Images of the app are for example use only.)
A month later, the team ran the same Event Analytic report, looking at all events for passwords and email submissions, and found that because of this change, the password failure events were reduced significantly. Overall sign-ups improved by 15%