Diving into the Downfall - Metrics and Lessons Learned

A look at metrics and how they influenced decision-making

Goal

Increase our primary metric for success: Percentage of users who fully complete onboarding (out of all the patients who receive invite email)

The Starting Point

We built the entire SuperPay platform in 2 weeks so that we could get the program up-and-running with our 3 “beta tester” providers. I used Retool to track our onboarding rates, and found the following trend after the first month of SuperPay

The founders and I figured that increasing invites sent was the easiest to tackle, which we did through provider incentives + improved messaging. I also personally wanted to improve the drop-off rate in the sign-up flow by making the account creation + payment information as easy as possible, and make us seem as trustworthy as possible since no one wants to put their credit card down on a site that doesn’t feel legit.

3 Months Later...

Takeaways

Key things to note because the chart doesn't say much at first glance:

  • Reduced Sign-Up drop-off from 32% to 6% - My UI improvements worked! Before, about a third of our patients did not complete sign-up (between stages "Sign Up Started" and "Sign Up Completed"), presumably on the payment step. With the payment page improvements, we got that down to just 6%.
  • An increase in "Invites Sent" did not lead to the desired increase in "Sign Ups Started": In fact, the drop-off rate actually got worse RIP. Before, 78% of the patients who received invites started signing up. And though we did dramatically increase our invites sent and our percentage of patients signed up went up, only 57% of invited patients actually started sign-ups (29 of 51%). No matter what we tried, we just couldn't get patients to start signing up, which ultimately led to the project's demise.

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