Improving The Quoting Process
Create a way to generate quotes quickly and organize inquiries
Problem
Curbie’s "Sell a Vehicle" tool generated a high volume of inquiries, which consumed a significant amount of time for our vehicle purchaser. This process involved reviewing applications and creating accurate quotes, shifting attention away from vehicle acquisition—a core business focus.
Solution
We introduced a new feature in the admin panel to centralize inquiries and highlight the ones our team should focus on. We incorporated machine learning to simplify the quoting process, reducing reliance on external software. This allowed our vehicle purchasers to dedicate more time to their primary objective: vehicle acquisition.
Role
I led the design process as the Product Designer, conducting research, crafting wireframes and prototypes, and working closely with developers during implementation.
Team
Product Manager, Product Designer, 6 Developers
Research
Interviews
Collaborating closely with the product manager, we interviewed our teams to identify issues in our current method of providing quotes to customers. During these interviews, we examined how they used external software and explored the reasons behind their software choices.
Interview findings:
In certain cases, there was redundant data entry across multiple software platforms.
Not all inquiries represented vehicles we would want to purchase, leading to time wasted on creating quotes for these vehicles.
This workflow was unsustainable, particularly with the prospect of scalability and the upcoming busy season.
From these interviews, we identified the crucial information needed to generate an accurate quote.
Insight: I knew we needed a way to highlight high-priority inquiries for our purchaser to focus on. These inquiries included customers looking to trade in their vehicles and vehicles that met our criteria for listing on our website.
How might we help the purchaser know which inquiries are high priority?
Competitive analysis
I researched and studied the platforms our team was currently using, analyzing their common features, and identifying best practices. I closely examined competitors to gain insights into their solutions and strategies. This comprehensive analysis allowed us to align our tool with industry standards.
Design
Wireframes
I began with mid-high fidelity designs because I had already developed most of the required components. Given the project's tight deadline, starting at a lower fidelity level was not feasible.
After creating wireframes, I collaborated with stakeholders for a feedback session. Their valuable input led to several changes and additions to enhance the wireframes.
Important features
Table view
We created a centralized table for all inquiries with columns to indicate if the vehicle met Curbie's qualifications or was a trade-in. This made it easier to identify important inquiries, and with convenient filters, users could customize the table to display only what they needed.
Detailed page
The detailed page includes all the vital information required for our purchasers to create precise quotes.
We designed a user-friendly interface with key sections:
Reference Number: a unique reference number for easy tracking.
Submission Date: The date the inquiry was submitted.
Status: Status of each inquiry for effective prioritization.
Customer Information: Essential customer details.
Curbie Criteria: Indicating qualification for website listing or a trade-in.
Vehicle Information: Includes essential details like VIN, year, make, model, trim, mileage, color, and transmission.
Condition: Indicates vehicle state, including accident history, exterior damage, and tire status.
Additional Details: The number of keys, loan or lease status, vehicle location, and a space for team notes.
Quote calculator: Generates a quote based on the vehicle, retail price, reconditioning cost, and profit we want to make on the vehicle.
Quote calculator
The quote calculator is the page's standout feature, saving the purchaser significant time. It eliminates the need for external software and data duplication. Using our machine learning retail pricing tool MARVIN, we can generate accurate quotes. We display the Canadian Black Book trade value to check if our tool meets industry standards. The retail price and profit are displayed as placeholders and can be adjusted as necessary. At the bottom of the calculator, we show when the price generation was created for tracking and reference.
Test
Prototype and User Testing
With the final designs and prototypes, we met with the vehicle purchaser to see if the designs were going to meet their needs. After some usability testing and discussion, we made more refinements.
Implement
Implementation
I collaborated closely with the developers during the implementation phase. I provided detailed specifications for my designs, answered questions, and assisted with styling. Worked closely with the senior developer to create tickets for the developers. I also carefully reviewed the visuals, tested usability, and checked interactions before each release.
Some final designs
Results
- Significant Time Savings: Adding the offer price calculator saved valuable time for the purchaser, operations, and sales teams, allowing them to focus on core responsibilities.
- Efficient Pricing Tool: The offer price calculator for used vehicles streamlined the quoting process and reduced the need for external software, promoting data accuracy and reducing duplication.
- Enhanced Company Efficiency: Improved overall company efficiency by addressing bottlenecks in the quoting workflow, ensuring that resources are allocated more effectively.
- Scalable Internal Tool: The newly developed tool is scalable, accommodating increased demand during peak seasons and future business growth, without overwhelming the team.
What I learned
I spent lots of time designing the date filter for the table view, ensuring it met both visual and functional requirements. However, during development, we found a library that closely matched my design. This experience taught me the value of researching existing solutions before diving into extensive design work. In future projects, I'll proactively collaborate with developers to explore libraries and options for more efficient design and development processes.
