Product Initiative
Maintenance Actionable Insight
Role
Design & Project Lead
Background
Launched in 2015, Alfred operates in 52 markets across North America, supporting over 300,000 residents with its personal assistance services. In 2020, Alfred extended its resident-first technology platform, Alfred OS, to give property owners and operators a unified platform to support all aspects of residential living and property management operations.
Challenge
Responding to residents’ maintenance requests is one of the most crucial aspects of building operations. However, although Alfred OS offers native maintenance management solution, 3rd party solutions such as Yardi Voyage and RealPage have historically taken the main stake in maintenance operations. It created a scenario that property users must constantly switch platforms between Alfred OS and 3rd platforms. Our users were frustrated about the inefficiency and potential loss of data that this cumbersome workflow has caused.
In Q2, 2022, we set out to tackle this problem to help unblock maintenance teams using our native solution and increase service efficiency. The initiative is also a crucial part of the strategic plan to enable Alfred to continuously bring property teams’ tech stacks and workflows onto AlfredOS by building a reliable product foundation in 2022.
Discovery
To uncover unmet needs of maintenance operations and identify opportunities for our customers to adopt our native solutions, we decided to conduct a generative discovery to capture critical nuances of users’ pain points, needs, and desires.
Our discovery goals were:
- To understand the needs and goals of our users, particularly when scheduling and managing preventive maintenance work orders.
- Identify our maintenance users’ primary pain points and opportunities for improving their work efficiency when managing preventative maintenance.
To collect both behavioral and attitudinal insight and data, I led the discovery by conducting 8 remote interview sessions with property managers and maintenance superintendents from different market segments and geography. I also visited partner resident properties to conduct contextual inquiries and further understand maintenance operation challenges and daily work routines.
Insights
After affinity mapping and synthesizing all the facts, the significant insights regarding interviewees’ needs and desires regarding their maintenance operations are surprisingly diverse.
Five categories of opportunity are identified: Analytics, Categorization, Inspection, Approval, and Resident Onboarding.
Prioritization
Our initial thought to address the opportunities was to bundle the ones more readily feasible for a potential V2 release and start planning a work pipeline. However, developing large feature bundles can be slow-footed to capture the engagement momentum and risky. To address this concern and create shared understanding, I introduced prioritization frameworks to better evaluate each opportunity.
We first evaluated the strategic alignment of each opportunity with our company goal for this year and high-level product KPI. Then we leveraged the RICE scoring model to consider each opportunity’s reach, impact, confidence, and effort. To better communicate our development approach with engineering team members and promote collaboration early on, a process map based on lean methodology was also introduced to ensure we achieve the desired outcome by continuously learning from users rather than focusing on how much work and output we could produce.
Target Opportunity
After assessing all opportunities with RICE scores and the interview notes, we believed actionable data insights are the most critical element to the success of maintenance operations. Such ability helps property managers proactively identify problems, make better use of labor resources, and work with Owners to budget the financial planning. It’s a mission-critical capability that major property management platforms have overlooked for years.
Solution Process
Sharing Ideas Collaboratively
Solution Workshop
Until this point, all members of this initiative have been through the discovery process. To continue the learning as a team and efficiently work towards a shared outcome, I gathered all team members across Product, Eng, and ProdOps to ideate solutions to address issues. The goal is to consider all perspectives and find a promising solution.
Risk Assessment
I facilitated a workshop to map all risks and open questions against Perceived Value/Importance and Evidence. It turned out our most outstanding fear and concerns are mostly around Desirability, Information details, and Usability. I converted the items that fall under the High Importance & Low evidence segment to a learning backlog.
Design Factor Matrix
Based on all the insights and ideas gathered, I created a framework that took into consideration of all factors, design principles, and competitor analyses of well-adopted maintenance tools in property management.
Initial Design
I decided to set the initial design scope concisely to support the minimum need to simulate an experiment quickly rather than a full deliverable spec. I created experience goals to define the direction of the new actionable insights and related experiences.
Experiment
We have limited resources and help recruit a large group of users to participate in the user testing. I decided to use a mixed methodology that blends contextual interview and usability testing to help validate our riskiest assumptions efficiently. The goal is to gain enough information and confidence to move forward and avoid paralysis analysis.
The result of the experiment validated the solution hypothesis and led us to believe that actionable insights have addressed users’ needs to utilize data to improve their work improvement and resource planning.
Final Solution
Alfred Maintenance Insights
We enhanced the data content, visual details, and information grouping based on the learning of the experiment and usability study. The new experience allows property teams to optimize maintenance efficiencies and provide better transparency to Owners. It’s a mission-critical capability that all major PMS platforms have overlooked for years.
We are confident this is the path to move forward to unlock more customer and business value.
Surface
Maintenance supervisors can now quickly locate essential metrics alongside historical work orders. The mass work order data can be updated to reflect these metrics complementarily when the titles are triggered. Quantitative data, work order details, and qualitative resident comments are closely connected to form a matrix that effectively reflects the efficiency and success of any maintenance operations.
Sense
With contextualized data breakdowns, maintenance supervisors and staff can quickly have a holistic view of the property and promptly identify weaknesses. This unique capability not only enables meaningful maintenance prevention but also allows more accurate budget planning for property owners.
Execute
Supervisors can drill down data from high-level aggregation to individual work orders to fully understand the “Why?” behind each rating and the high-level data. They can now address resident concerns much more efficiently and accurately. The actionable insight further enables customer-centric maintenance operations to elevate resident satisfaction and NPS.
Outcome
Maintenance Insights was released to all property users in Q3 2022. There are noticeable increases in traction metrics, including maintenance staff logins and new maintenance work order creations. The unique capability to inform preventive maintenance and budget planning also enabled us to successfully implemented our native maintenance solution in more than 72 new customer buildings. We are planning to survey resident satisfaction via NPS and further learn about its long-term impact on resident renewal rates.