Pinhome Home Service is an on-demand mobile platform and application that provides various on-demand services that connecting users with professional service partners.

Product Designer
As the product designer, I took the entire ownership and responsibility of this project to run it from initial discovery state to the final concept, Due to its technical complexity, I closely collaborated with the Principal Engineer to ensure the design was both user-centered and technically feasible, balancing user needs with technical constraints.

Hi-Fi Design: Oct 14, 2024 - Nov 11, 2024
Development: Nov 18, 2024 - Des 20, 2024
Release: Jan 06, 2025

The existing order allocation system restricts service partners to a 2.5 KM radius from their designated pinpoint. However, data reveals that nearly 70% of orders require a larger radius of at least 3 KM. Additionally, the allocation pattern is irregular, resulting in unpredictable coverage areas and extending order assignments further from the service provider's initial location. This finding highlights the need to redistribute service partner anchor points to better align with demand and improve order fulfillment efficiency. The initiative is designed to maintain an 80% average booking conversion rate (CVR) despite the reduced radius by optimizing anchor point allocation and refining the dynamic reference for subsequent orders.

User Problem

Users face difficulties in getting a service partner assigned to their bookings, leading to unallocated orders and frequent cancellations.

Service Provider Problem

Unpredictable coverage areas and extended order assignments force service providers to travel longer distances, often to unfamiliar locations, impacting their work efficiency and commitment.

Business Problem

Unfulfilled bookings and cancellations result in lost customer opportunities, directly affecting revenue and overall business growth.

Cities are geographically diverse, requiring a highly granular approach to analysis. However, analyzing events at the most precise level—the exact location where they occur—is both complex and costly.

To improve analytical accuracy and efficiency, a grid system is used to categorize events into hexagonal areas, where data points are assigned to hexagons and stored accordingly. Unlike irregular shapes that complicate analysis, hexagons provide a structured way to assign and store data points. This choice is particularly beneficial in urban environments, where populations are constantly in motion. Hexagons minimize quantization errors that occur as individuals move through the city while also allowing for easy approximation of the radius.

Recognizing the need for a dynamic solution to map demand pockets, ATLAS Map Dashboard was developed as a new product. This tool empowers the Operations team with enhanced mapping capabilities, enabling them to visualize demand distribution and seamlessly adjust Service Providers pinpoints for improved efficiency.

The ATLAS Map Dashboard is a dynamic, location-based visualization tool designed to help operational teams effectively balance supply and demand. It combines real-time data on demand levels, service partner locations, and performance metrics to facilitate data-driven decision-making. Here’s an overview of the key components and features of the dashboard:

Demand Heat Map:
The map displays demand levels across various geographical locations. The demand is represented in varying intensity, with higher demand shown in darker colors. This allows the operational team to quickly identify areas of high demand, enabling them to allocate resources or make adjustments to meet customer needs more effectively.

Service Partner Location Pins:
Each service partner's location is represented by a pinpoint on the map. These pins show where service partners are based, which can be moved or adjusted based on operational decisions. The ability to reposition these location markers provides flexibility, allowing service partners to be better placed in proximity to areas with high demand, optimizing their ability to fulfill orders efficiently.

Balance Supply and Demand:
By visualizing both the demand heat map and service partner locations, the operational team can make real-time decisions on how to balance supply and demand. For example, if demand spikes in a particular area but there are insufficient service partners nearby, the team can move a service partner's location closer to the high-demand area. This helps ensure that service partners are strategically positioned to meet order needs without delays or inefficiencies.

Service Partner Performance Insights:
The dashboard provides an overview of each service partner's performance, both in general and in specific terms. This includes metrics such as:

• Order history: A timeline of the service partner’s completed orders, which can help identify trends or patterns in their performance.
• Location history: Insights into the locations where the service partner has been active, allowing the team to assess how effectively they are covering demand hotspots.
• Efficiency metrics: Time taken to fulfill orders, completion rates, and other KPIs that can help operational teams gauge service partner reliability.

Data-Driven Decision-Making:
The ATLAS Map Dashboard allows operational teams to make informed decisions by combining location-based demand data with service partner performance metrics. This holistic view empowers them to optimize service partner allocation, improve efficiency, and ensure a more seamless supply-and-demand balance.

In essence, the ATLAS Map Dashboard helps operational teams optimize their resources by providing a comprehensive, visual representation of demand, service partner locations, and performance data in one easy-to-use platform. By enabling quick adjustments based on real-time insights, it improves decision-making and ensures a more efficient, responsive service delivery process.

The project challenge for the ATLAS Map Dashboard arise from its niche use case in the small on-demand service market, which lacks external benchmarks. Without industry references, the team had to rely on internal research, collaborating closely with the product team to identify and prioritize operational needs. This required building a tailored solution based on real-world insights and feedback.

City Level View

• The ATLAS Map Dashboard starts with a city-level view divided into uniform hexagonal tiles, using Uber's hexagonal map data for consistent spatial analysis.
• Operational teams can filter by service type, date range, and city, and view demand levels and the number of service partners per hexagon.
• Filters allow users to focus on specific demand levels or highlight unallocated or uncompleted bookings, helping teams quickly identify demand hotspots and optimize service partner allocation.

Edit Service Coverage
The Edit Service Coverage feature provides the operational team with a comprehensive view of service coverage at both city-level and detailed hexagon-level. This allows them to easily manage and adjust the areas covered by each service partner (SP).

• Add or Remove Coverage:
Operational teams can quickly add or remove hexagons from a service partner’s coverage area. The process is straightforward, involving just a few clicks to select the relevant hexagons.
• Location Search:
To streamline the process, a search function allows ops to quickly find specific locations, eliminating the need for manual zooming or scrolling. This enhances efficiency and ensures more precise edits without wasting time navigating the map.

This feature empowers the operational team to make real-time adjustments to coverage areas, ensuring optimal service distribution and quick responses to changing demand.

Map View
From the city level view, the dashboard zooms into a more detailed hexagon-based view. In this view, the dashboard will show the highlight on selected hexagon, booking and allocation insight and the SP performance on selected hexagon. Each view allows the team to drill down into specific data points, making it easier to optimize service partner allocation, monitor performance, and improve decision-making.

• SP Marker View:
This shows the distribution of service partners (SP) within each hexagon, categorized by SP type (1st party or 3rd party). For services with gender preferences, like body massage or home cleaning, the SPs are further divided by gender (male or female) for a more granular view.

• Demand Pocket View:
In this mode, the SP distribution on other hexagons is hidden, highlighting only the booking and allocation insights and SP performance for the selected hexagon. This view helps the operational team focus on demand patterns and performance in specific areas.

• Supply History View:
This shows the historical sources of SP locations that took bookings within the selected hexagon, providing valuable insights into service partner coverage over time.

• Service Coverage View:
This view displays the active service coverage for the selected service location, helping teams assess whether a specific area is adequately covered or if adjustments are needed.

Move Service Provider Location
The Moving SP Location feature allows the operational team to efficiently reallocate service partners (SPs) based on performance and demand. The process starts by using filters to identify SPs with the best overall performance across the map. Once an SP is selected, the team can view detailed performance highlights for that SP.

Relocating an SP is simple: the team clicks on the "Change SP Location" option and selects the new location. The map also displays SP distribution, highlighting areas with overcrowded SPs and areas with underserved demand, guiding the team to make informed decisions on where to move SPs for optimal coverage and performance.

Add and Remove Service Provider Coverage
The Add and Remove SP Location feature allows the operational team to easily adjust a service partner's (SP) coverage area, similar to the edit service function. This functionality is designed to help optimize SP allocation based on demand.

• Add Service Provider Coverage:
The team can select specific hexagons to add to an SP's coverage area, expanding their service reach to new locations.

• Remove Service Provider Coverage:
Similarly, the team can select hexagons to remove from an SP's coverage, streamlining their area of responsibility based on performance or demand.

This simple, intuitive process ensures that SPs are allocated to the right areas, maintaining efficient service coverage.

SP order and Location History
The SP Order and Location History feature provides operational teams with detailed insights into each service partner’s (SP) activity, including both order and location history.

• Order History:
This section tracks the complete order lifecycle, showing the status of each order from offered, assigned, and ongoing to cancelled. This allows the team to monitor SP performance, track order fulfillment, and identify any potential issues or bottlenecks in the service process.
• Location History:
The location history provides a log of each SP's movements, with precise details on the latitude and longitude of their position, as well as the time they were at each location. This helps the team track service partner coverage and movement patterns, ensuring they are optimally positioned to meet demand.

This feature enables a deeper understanding of SP performance, both in terms of orders and location management, providing operational teams with the data they need to make informed decisions.

Although the impact analysis is still to be conducted following the launch of the improved product in early 2025, we anticipate several positive outcomes based on the features and improvements introduced:

1. Improved Operational Efficiency
with streamlined SP location adjustments and real-time demand insights, operational teams can make quicker decisions, reducing delays and optimizing service coverage.
2. Better Resource Allocation
visualizing SP distribution and demand hotspots helps allocate resources more effectively, leading to better coverage and fewer unallocated orders.
3. Higher SP and Booking Optimization
tracking SP performance and location history enables better placement and availability, improving booking conversion rates and reducing cancellations.
4. Scalability for Future Growth
the flexible dashboard supports operational scaling, allowing teams to manage larger areas efficiently as the business grows.

Overall, while formal impact analysis is pending, the early launch is expected to improve the speed, accuracy, and flexibility with which operational teams can balance supply and demand, ultimately driving better service delivery and operational outcomes.

As a product designer, this project emphasized the importance of creating user-centric, data-driven tools that solve real-world operational challenges. Close collaboration with engineers and operational teams was essential to ensure the design met both technical constraints and user needs. The ability to simplify complex tasks and provide contextual flexibility reinforced the need for intuitive interfaces that allow for quick, informed decision-making.

Key takeaways include the value of iterative design and adapting to feedback, the importance of reducing friction in task execution, and balancing precision with clarity in data visualization. This project highlighted the necessity of continuously testing and refining designs to ensure they meet real-world user needs and enhance operational efficiency.