Ladder’s mission is to protect families by helping people get covered through a policy. Still, we've had setbacks matching everyone with the best policy, which is not great for both our users and Ladder. In 2023, our Machine Learning team developed a model to improve these matches. This project showcases how I designed a solution that utilizes this model.
I led the design of this project from May 2023 to July 2023.
In addition, I worked alongside a UX Content Designer, 2 Engineers, and a Product Manager.
My tasks entailed owning the end-to-end design process, defining product strategy, and pairing with developers.
Ladder has long grappled with the persistent issue of attempting to cover users who don't buy a policy (i.e. convert), leading to high operational costs. Although users opt out of purchasing policies for various reasons, the primary deterrent is the pricing. Essentially, Ladder offers a policy to a user and the user thinks the price is too high and ends up leaving. This creates problems for both our users and Ladder:
Historically, we've always strived to address issues related to conversion and profitability. However, the predictive model developed by our ML team introduced a fresh opportunity for us to devise an entirely new solution.
This predictive model looks at various inputs to make an educated guess about whether a user is likely to buy a policy. Through backtesting we found that a certain cohort of users who decide not to purchase policies are actually costing us money when we consider all the different expenses.