AutoVerify
I collaborated with a cross-functional team to improve the inefficient data mapping process. Together, we developed AV Inventory, an internal tool that empowers non-technical teams to set up data imports. This reduced setup time from 7-20 hours to 0.5-2 hours, freed developers from repetitive work, and allowed onboarding staff to work independently.
AutoVerify provides dealerships with technology to improve their sales process, from merchandising to transaction.
When onboarding new dealerships, AutoVerify must import their data. Each dealership’s data is unique and requires custom mapping. Originally, developers handled this manually. However, this created a bottleneck that slowed onboarding and consumed engineering resources.
Through user interviews, workflow reviews, and stakeholder input, I identified overlapping, conflicting, and technically complex requirements.
Step 1: Design Initial Workflow Based on Stakeholder Input
I began designing the core functionality based on initial discovery. Starting without the detailed technical requirements was beneficial, as it allowed me to ensure the minimum viable product (MVP) had a strong foundation before introducing technical complexity.
Step 2: Introduce Templates to Streamline Workflow
Stakeholder shared that dealerships have overlapping vendors. Thus, I introduced templates; after linking a vendor to the AutoVerify database and defining the data mapping, the settings are saved as a template.
Users can select a template as a starting point and customize it. This reduces setup time, ensures consistency, and forms the foundation for efficient onboarding workflows.
Step 3: Review Technical Documentation for Design Requirements
I reviewed the detailed technical document, which contained existing data import workflows.
These included creating new values based on conditions, applying fallback or default values, and combining multiple conditions for advanced logic.
Understanding this complexity was essential to designing a tool that could handle a wide range of dealership use cases.
Step 4: Organize and Simplify Workflows
I mapped each workflow and identified core operators capable of handling different scenarios efficiently. By grouping similar operations, I reduced redundancy and defined the minimum requirements needed for the design.
Step 5: Design Iterations
I refined the design based on the additional technical requirements. Stakeholder feedback and constant reference to the documentation ensured the design met both usability and functional requirements.
After Figma Make launched, I used it to quickly explore layout variations for complex workflows, particularly around IF conditions. This accelerated iteration and helped refine the guided workflow for clarity and usability.
Multiple iterations occurred throughout the process, which are outlined in the iterations section below.
Add the Ability to Import Data From Various Sources
Streamline How Users Create and Edit Fields
Who Creates and Manages Templates
More Complex Filters
Update the Prototype to Align With New Visual Identity
Step 1: Select Dealership
Users begin by selecting the dealership they are setting up. The dealer code auto-populates, reducing manual entry errors.
Step 2: Select Import Templates
Users pick a default vendor template and can add additional templates if the dealership uses multiple data sources.
Step 3: Data Mapping
As mentioned above, templates store vendor-specific mappings. Once the template is selected, its default mapping is automatically applied. If users choose another vendor sources, the system applies the closest matching mapping based on template settings.
After selecting the correct sources, users can customize each field using four options:
Option 1: Keep the Auto-Mapped Field
Users can use the field as pre-configured in the selected template.
Option 2: Direct Mapping
User can select a different vendor data field manually.
Option 3: Simple Custom Mapping
User can combine multiple vendor fields using basic operators (+, –, *, /).
Option 4: Advanced Logic (IF Statements)
User can apply conditional rules using spreadsheet-style logic:=IF(condition, true_value, false_value)
Step 4: Filtering
By default, all rows are set to “import all.” Users can refine this by defining filters at either the row or global level.
A row is imported only if the combined logic evaluates to TRUE.
Row-Level Logic
Determines whether a single row of data meets the criteria for import:
("Value A" OR "Value B"): Import if either value is present.("Value A" AND "Value B"): Exclude if both values are present.
Global-Level Logic
Combines multiple row-level filters into one expression using a pop-up logic builder. Users can group and prioritize conditions to form complex import rules: (Condition 1 AND Condition 2) OR Condition 3
Step 5: Review and Verify
Lastly, the user reviews and verifies all information to ensure accuracy before completing the dealership setup.
Please note: All data shown in the designs above is fictional to protect company confidentiality.
While final impact data is still being collected, initial user testing shows a dramatic improvement in efficiency. The new workflow not only freed up engineering resources but also significantly reduced setup time:
Importance of User Advocacy
Originally, this was a product/ engineering driven project. I actively involved and advocated for the needs of the tool users, which significantly contributed to the project's success.
Start Simple, Layer Complexity Later
Beginning with a minimal, testable prototype helped me validate the core user flows before introducing technical complexity. This approach created a strong foundation and made complex systems much easier to design.
