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.
Successful proof of concept:
Challenges to overcome: