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Luko onboarding flow updates

2024

Product design

Duration

2024

My role

Product design

UR research

Product strategy

Team

Ian Lambert

Farbod Varzandeh

Context

Onboarding is a critical touchpoint where small adjustments can have a big impact on the business top line revenue. While I didn’t design the initial flow, it’s an ongoing project that requires constant refinement. During my time at Luko, I worked on multiple projects to enhance different aspects of our onboarding experience.

Challenges

Whether optimizing for conversion, implementing new legal requirements, or testing new approaches, updating such a sensitive flow is always a challenge. I collaborated closely with user researchers, product managers, legal teams, and developers to ensure a seamless and top-tier experience for our users.

Solution

Let’s take a look at some of the key updates we’ve made to our onboarding experience over the past few months.

Claim history precision

We needed more precise claim history details to refine our pricing model. Previously, customers with past claims were often overpriced due to broad segmentation. By collecting detailed claim types and dates, we improved risk assessment and offered fairer pricing.

Boost signature step clarity and completion

We noticed a 5.5% drop-off between the payment and signature steps, where users completed payment but didn’t finalize their subscription. To address this, we improved clarity by informing users about the signature step earlier in the process and enhancing transparency on the signature page, ensuring a smoother and more intuitive experience.

Collecting building age for flats

To better assess risk for flats, we introduced a question about the building's age, similar to the one already in place for houses. This allows us to more accurately evaluate the property’s risk profile and adjust pricing accordingly.

Add a question for canceled by former insurer

To prevent adverse selection and improve risk assessment, we implemented a question about prior policy cancellations. This helps us identify potentially high-risk individuals, such as those with previous contract terminations due to non-payment, fraud, or claims, ensuring a more accurate evaluation of future customers.

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