Native Mobile App
Pocket Geek Redesign
This was a project I worked on during my time with Assurant, a global provider of insurance and risk management services.
Pocket Geek is a diagnostic and self-help app distributed with mobile device insurance packages. There was a business need to pivot the core focus of the app to the insurance product.
Work within the UI & framework of the existing app
Create a path to in-app insurance purchase
Verify phones were not broken prior to insuring
Onboard 3 unique users
3 unique user types to serve with this redesign
New Users Who purchased insurance at point-of-sale w/device
New Users Who wish to purchase insurance
Rank User Types
The business goal of increasing insurance signups by default force ranked the New Users who wished to purchase insurance to be my primary focus. Of secondary importance would be to bring users who had recently purchased insurance into their plan. The shifting goals of the app put the lowest consideration on legacy users.
Outline a new users path from discovery, through purchase and use
Is aware of their device value
Explores alternatives to device insurance
Provide direction on how to access benefits
Learns of device
Learns that our product is available post-point-of-sale
Collect feedback any time a claim is made
Finds value in the promise we are making
Notices the promise of quick enrollment and easy billing
Explores benefits of the program, and evaluates cost
Decides the program would benefit them and purchases a plan
Compares our offering with competitors
Welcome user into the program and provide policy documentation
I bought a new iPhone and I know it would be really expensive to replace. I would buy insurance for this if I could evaluate the benefits and not listen to an in-store sales pitch.
I don’t want to deal with a call center if I need to make a claim on my device, I would buy insurance for my phone if I knew I could file a claim right on my phone.
To increase sales of mobile device insurance via Pocket Geek we should;
Re-architect the app to emphasize the insurance product
Allow users to purchase new policies in-app
Allow users to access their policy in-app
Build UX to verify devices are not broken before insuring
Gracefully onboard all user personas into the app
Solutions: Phase One
Update the architecture of the current tab navigation to reflect the pivot in focus. After a feature audit I restructured the content into four primary verticals, with the insurance product as the new default focus.
Replace Home Tab
Scan dial and tab would go away in favor of a discoverable and persistent alert counter
Solutions: Phase Two
Create UX for policy access. I sketched six possible paths for users to access their policy in the app, and critiqued them with our product team. We selected a few that could work, and omitted a few that caused new issues or lacked discoverability. Ultimately designing a new tab that would become the home for all policy functionality and the new focal point of the app.
Show a one-time overlay pitching our insurance product
Solutions: Phase Three
Onboard all user segments. This phase of the redesign focused on improving how users were exposed to the benefits of the app. The current registration experience requires several steps, and asks users for personal information before offering any value.
Audit Existing Experience
How to restructure the current content
Solutions: Phase Four
Allow users to purchase insurance within Pocket Geek. Currently sales happen through distributed carriers at point-of-sale, leaving little control or visibility into how our product is pitched and sold, or if it is at all. Allowing users to purchase insurance beyond the checkout for their new device opens a large opportunity for us to reengage potential customers, control the messaging, and present users with information that may not have been available in-store.
Get users to
the right place
Establish a path for users to self segment
Solutions: Phase Five
Build an experience to support certifying devices as they are submitted, accomplished via human and machine review. We created training models to detect when photos were too blurry, too close, or too far and would trigger instant retake requests on those. Images that passed these initial checks were distributed to agents who manually reviewed them for damage. Agents were scored for accuracy allowing visibility into performance and risk management. The initial plans for this new experience included building a dashboard where agents would be assigned images to review, and an admin dashboard where agent quality could be monitored.