Ribbon Cash

Overview

Ribbon enables individual home buyers to compete in the housing market with cash – extending a fair shot for them to win the home they want. Ribbon's platform affords agents the ability to write and submit cash offers, however, the experience was tedious, entirely manual, and not user-centric.

I conducted a UX audit and deconstructed the platform into objects and their relationships in order to better understand how to map the platform to agents' mental models – to meet them where they're at in their home buying process. Considering habits, buying scenarios, and existing, external technologies leveraged by agents, I proposed multiple directions to consolidate, expedite, and enhance the experience for packaging up offers. After electing a direction to pursue, based on scope, speed, and value rank, I designed and tested an elected experience while constantly iterating. This included harvesting in flight offer data, incorporating document services machine learning models, and packaging the two together with Ribbon suggestion models into a seamless, intuitive offer building experience.

Uploading offers in progress, pre-populating and suggesting cash program elections, and managing multiple offers at once were among other features tied into the experience. It's now faster, more intuitive, and extensible – positively impacting Ribbon's NPS scores and decreasing time to offer submission, resulting in more total offer acceptances. ‍

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More detailed information about the research, strategy, execution, and success of this project is available on request.