AutoVerify

How can we make data import setup faster and easier while meeting user, business, and technical needs?

Connecting Hearts and Wallets: Is It Possible to Redesign Dating Apps for Singles While Supporting Business Profits?

Role

Sole UX Designer and Researcher

Timeline

Aug-Dec 2024 (Initial Design), Aug 2025 (Refinements)

Team

Cross-Functional (Engineering, Product, Support/Onboarding)

Role

Sole UX Designer and Researcher

Timeline

Aug-Dec 2024 (Initial Design), Aug 2025 (Refinements)

Team

Cross-Functional (Engineering, Product, Support/Onboarding)

Role

Sole UX Designer and Researcher

Timeline

Aug-Dec 2024 (Initial Design), Aug 2025 (Refinements)

Team

Cross-Functional (Engineering, Product, Support/Onboarding)

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.

Context

Custom data imports delayed onboarding and relied on developers

Context

Custom data imports delayed onboarding and relied on developers

Context

Custom data imports delayed onboarding and relied on developers

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.

Initial Onboarding

Data Mapping Customization

The onboarding/ support team starts onboarding a new dealership.

A developer customizes data mapping for dealership (7-20 hours).

Initial Onboarding

Data Mapping Customization

The developer customizes data mapping for dealership (an additional 7-20 hours).

The onboarding/ support team facilitates the onboarding process for a new dealership.

Initial Onboarding

Data Mapping Customization

The developer customizes data mapping for dealership (an additional 7-20 hours).

The onboarding/ support team facilitates the onboarding process for a new dealership.

How can we improve this process so that the non-technical onboarding/support team members can complete the onboarding seamlessly, without relying on developers?

How can we improve this process so that the non-technical onboarding/support team members can complete the onboarding seamlessly, without relying on developers?

How can we improve this process so that the non-technical onboarding/support team members can complete the onboarding seamlessly, without relying on developers?

Discovery

Understanding users, business goals, and technical constraints

Discovery

Understanding users, business goals, and technical constraints

Discovery

Understanding users, business goals, and technical constraints

Principles

Bridging the Gap

Principles

Bridging the Gap

Ideate

What is a "Template"?

Ideate

What is a "Template"?

Through user interviews, workflow reviews, and stakeholder input, I identified overlapping, conflicting, and technically complex requirements.

The main challenge was translating the complex technical workflow into an easy-to-use tool while minimizing development complexity.

The main challenge was translating the complex technical workflow into an easy-to-use tool while minimizing development complexity.

The main challenge was translating the complex technical workflow into an easy-to-use tool while minimizing development complexity.

Business

Wanted faster onboarding, reduced dependency on developers, and improved client satisfaction.

Business

Wanted faster onboarding, reduced dependency on developers, and improved client satisfaction.

Business

Wanted faster onboarding, reduced dependency on developers, and improved client satisfaction.

Engineering

Needed a technically feasible solution that minimized complexity while supporting the core use cases.

Engineering

Needed a technically feasible solution that minimized complexity while supporting the core use cases.

Engineering

Needed a technically feasible solution that minimized complexity while supporting the core use cases.

Users (Support/ Onboarding Teams)

Needed a tool that was simple to use yet flexible enough to handle multiple scenarios.

Users (Support/ Onboarding Teams)

Needed a tool that was simple to use yet flexible enough to handle multiple scenarios.

Users (Support/ Onboarding Teams)

Needed a tool that was simple to use yet flexible enough to handle multiple scenarios.

Principles

How I Make Design Decisions That Worked

Principles

How I Make Design Decisions That Worked

Balance Stakeholder Goals

I focused on designing a tool that was easy to use, technically feasible, and met all user needs.

Balance Stakeholder Goals

I focused on designing a tool that was easy to use, technically feasible, and met all user needs.

Balance Stakeholder Goals

I focused on designing a tool that was easy to use, technically feasible, and met all user needs.

Keep It Simple and Iterate Often

I began with a simple, testable prototype and refined it through multiple rounds of feedback. Complexity was introduced gradually to ensure usability.

Keep It Simple and Iterate Often

I began with a simple, testable prototype and refined it through multiple rounds of feedback. Complexity was introduced gradually to ensure usability.

Keep It Simple and Iterate Often

I began with a simple, testable prototype and refined it through multiple rounds of feedback. Complexity was introduced gradually to ensure usability.

Have Strong Design Rationale

I made deliberate decisions that balanced trade-offs across user, business, and technical perspectives.

Have Strong Design Rationale

I made deliberate decisions that balanced trade-offs across user, business, and technical perspectives.

Have Strong Design Rationale

I made deliberate decisions that balanced trade-offs across user, business, and technical perspectives.

Ideate

Translating Technical Complexity into a Simple Experience

Ideate

Translating Technical Complexity into a Simple Experience

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.

Iterations

Refining the Design Based on Feedback

Iterations

Refining the Design Based on Feedback

Iterations

Refining the Design Based on Feedback

Add the Ability to Import Data From Various Sources

1

V1: Single-Source Imports (One Template)

The first version focused on single-source imports to keep the MVP simple.

Users could select one template and then customize the data mapping if needed.

1

V1: Single-Source Imports (One Template)

The first version focused on single-source imports to keep the MVP simple.

Users could select one template and then customize the data mapping if needed.

1

V1: Single-Source Imports (One Template)

The first version focused on single-source imports to keep the MVP simple.

Users could select one template and then customize the data mapping if needed.

Streamline How Users Create and Edit Fields

1

V1: Custom Fields

Initially, users wanted fully custom fields.

While this could handle the most scenarios, the technical complexity was high, and the workflow risked overwhelming non-technical users.

1

V1: Custom Fields

Initially, users wanted fully custom fields.

While this could handle the most scenarios, the technical complexity was high, and the workflow risked overwhelming non-technical users.

1

V1: Custom Fields

Initially, users wanted fully custom fields.

While this could handle the most scenarios, the technical complexity was high, and the workflow risked overwhelming non-technical users.

Who Creates and Manages Templates

1

V1: Users Create Templates

Initially, the onboarding and support team was expected to create all templates to remove developer dependency.

1

V1: Single-Source Imports (One Template)

The first version focused on single-source imports to keep the MVP simple.

Users could select one template and then customize the data mapping if needed.

1

V1: Single-Source Imports (One Template)

The first version focused on single-source imports to keep the MVP simple.

Users could select one template and then customize the data mapping if needed.

More Complex Filters

1

V1: Simple Row Filters

A basic pop-up covered most filtering needs in the MVP.

1

V1: Simple Row Filters

A basic pop-up covered most filtering needs in the MVP.

1

V1: Simple Row Filters

A basic pop-up covered most filtering needs in the MVP.

Update the Prototype to Align With New Visual Identity

1

V1: Original Branding

The first high-fidelity prototype used AutoVerify’s old branding.

1

V1: Original Branding

The first high-fidelity prototype used AutoVerify’s old branding.

1

V1: Original Branding

The first high-fidelity prototype used AutoVerify’s old branding.

Solution

How to Use AV Inventory to Customize Data Mapping

Solution

How to Use AV Inventory to Customize Data Mapping

Solution

The dating app that WANTS you to be in a relationship

AV Inventory is a tool that enables non-technical onboarding and support teams to easily customize and manage data mapping for vehicle data imports.

AV Inventory is a tool that enables non-technical onboarding and support teams to easily customize and manage data mapping for vehicle data imports.

AV Inventory is a tool that enables non-technical onboarding and support teams to easily customize and manage data mapping for vehicle data imports.

Initial Onboarding

Data Mapping Customization

The onboarding/ support team starts onboarding a new dealership.

The onboarding/ support team uses AV Inventory to customize data mapping (0.5-2 hours).

Initial Onboarding

Data Mapping Customization

The user selects the vendor template(s) and customizes the data mapping (0.5-2 hours).

The onboarding/ support team facilitates the onboarding process for a new dealership

Initial Onboarding

Data Mapping Customization

The user selects the vendor template(s) and customizes the data mapping (0.5-2 hours).

The onboarding/ support team facilitates the onboarding process for a new dealership

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.

Impact

From Days to Hours

Impact

From Days to Hours

Impact

From Days to Hours

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:

✏️

71-97%

Time Reduction for Data Mapping Customization

Without AV Inventory (7-20 hours) vs. With AV Inventory (0.5-2 hours)

✏️

71-97%

Time Reduction for Data Mapping Customization

Without AV Inventory (7-20 hours) vs. With AV Inventory (0.5-2 hours)

✏️

71-97%

Time Reduction for Data Mapping Customization

Without AV Inventory (7-20 hours) vs. With AV Inventory (0.5-2 hours)

Reflection

Top 2 Learnings

Reflection

Top 2 Learnings

Reflection

Top 2 Learnings

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.

Interested in learning More About My Process?

I typically share this during coffee chats or interviews. Don't hesitate to reach out!

Interested in learning More About My Process?

I typically share this during coffee chats or interviews. Don't hesitate to reach out!

Interested in learning More About My Process?

I typically share this during coffee chats or interviews. Don't hesitate to reach out!