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Background
GetDone functions as a work order management tool for Property Management Companies, offering a solution to track, manage, and streamline work orders. The current and predominant do-it-all property management software used by many PMCs has a complex interface and desktop-only experience for their work management feature. This is affecting property managers and technicians causing a disconnect within the team and resulting in prolonged resolution times for tenant issues.
Problem
When GetDone released their original product, it was created, designed, and tested for property managers (the customers). However, it was the end users (the technicians) who spent most of their time in the app, reviewing and completing work orders assigned to them. This realization led to a two-sided problem:
For property managers, the current layout and functionality made it difficult to search for, assign, and manage multiple work orders.
For technicians in the field, the current product wasn't mobile-friendly and catered poorly to their needs, leading to misuse and neglect in updating the software.
Goal
The aim of this redesign was to create a dual-sided product, meeting the needs of both the customers (property managers & supervisors) and addressing the requirements of the end user (the technicians).
Property Managers: Require a tool with enhanced data visualization, improved search capabilities, and the ability to swiftly assign and edit work orders as they are received.
Technicians: Sought a straightforward and user-friendly tool that exclusively provides information on their work orders, offering a quick method to update the status while they are actively resolving the issue in the unit.
Discovery
By analyzing behavior analytic tools such as FullStory and engaging in direct conversations with property managers, supervisors, and technicians who were using the product, I identified specific problem areas within the current product and uncovered diverse needs on both the supervisor and technician sides.
Design
Along with sketching ideas inspired by my feedback notes, I explored design patterns across various SaaS products to observe effective implementations of key principles like prioritization, management, and data visualization in their respective flows.
With each significant iteration of the product, I conducted feedback sessions involving technicians, supervisors, and property managers to collect their thoughts on specific features, layouts, and functionality. The redesign underwent six main feedback rounds, incorporating iterative improvements, leading to a final stakeholder review.
Development
I collaborated early with the development team, creating a detailed information architecture document that outlined features and capabilities, defining the precise scope. I held a handoff meeting where I went through the mocks, annotations, and gave them the design system I had crafted for this project. As development was being done, I held daily sync meetings to identify any blockers or address any questions.
In the quality assurance phase, I actively participated in multiple testing rounds to ensure a polished product before the stakeholder demo. Beyond development, I played a pivotal role in overseeing the rollout plan, contributing to website updates, marketing materials, and onboarding documentation. This holistic approach ensured technical excellence and seamless integration into the broader ecosystem.
Highlighted Features
Advanced analytics
Property managers can quickly access information at a glance, empowering them to make data-driven decisions with powerful insights into work order completion and technician efficiency. These data points and visualizations were designed based on the wants and needs of the property managers.
Mobile First Design For the Tech
The technician spends a significant portion of their day in the field addressing diverse issues. Therefore, they required a mobile solution enabling swift identification of work orders, prompt status updates, and seamless communication with their supervisor.
Smart Auto Routing
We implemented machine learning which triages work orders to technicians based on multiple factors like priority, their skill set, and unit location to optimize their workflow.
Outcomes
01
250% increase in user sessions
02
25% surge in technician productivity
03
Product has since been rolled out to 64 properties (11,776 apartment units)
In the process of redesigning this product, a crucial lesson emerged, emphasizing the importance of not only understanding the customer's needs but also those of the end user. It became evident that while the technician may not be the direct purchaser of the product, they are the primary users, making their experience a pivotal factor. This redesign aimed for a dual-sided solution, ensuring benefits for both property managers and technicians. Despite their shared focus on work order management, it became clear that their specific needs diverged. Recognizing and addressing these distinctions became integral to the success of the redesign, ultimately creating a more inclusive and effective solution for both parties involved.
