How might we rethink our online order flow to make booking a move online effortless and accurate?
Our online booking process was an à la carte model which put most of the onus on customers to estimate their move. It was up to a user to figure out how many movers they needed and for how long.
As a tech-enabled moving company, we knew we could do this in a smarter way by asking the right questions upfront and learning from data over time.
Constraints: simultaneous redesign of visual system/pattern library
After gathering input from the team and conducting initial customer surveys, I outlined our collective assumptions around why users might abandon the current order flow—documenting how the redesign should address each concern.
In order to give customers the best move configuration (headcount x duration) recommendations, I first made a reference sheet of options to see what happens when each move exceeds its allotted time. While we had a simple per-man-hour pricing model, there were stiffer monetary repercussions for customers, the higher the headcount. We also needed to consider the operational implications of placing more headcount on a job, thus expediting move duration and vice versa.
I partnered with data science to explore how the new order flow might be instrumented for feeding their early-stage machine learning data model. We also ran analyses on customer satisfaction (NPS) and concession (refunds) data against move time accuracy to understand the correlation in missed expectation-setting.
Using the $1 Prototyping method, I worked quickly to sketch initial flow variations, mapping them out on the walls of our office. I invited engineering, marketing and customer support teams to visit the walls at their convenience and provide input in between formal design reviews.
Explorations for entering inventory using a glyph-based alphabet as a delightful-spin on the most tedious part of booking a move—listing everything in your home:
We partnered with a newly-constructed apartment community to host an event where new residents could participate in testing prototypes and followed by a 15 minute interview about their recent move in exchange for a gift card.
Responsive hi-fidelity designs were prepared for each major breakpoint from 320px mobile to 1440px width desktop. A Sketch–Invision–Zeplin workflow was used for a smooth handoff to engineering.
I worked simultaneously with the frontend team to ensure assets, typography styles, and color palettes used were saved as reusable components and compatible with the new pattern library.