LoanCrate

This project is under an NDA, so only a high-level overview will be shared. Here I conducted in-depth user research on LoanCrate’s Loan Origination System (LOS) to better understand user needs, behaviors, and pain points within the loan approval process. Through contextual inquiry sessions and secondary research, I analyzed how different user roles interacted with the system and synthesized key findings into actionable insights. These insights informed LoanCrate’s product strategy, helping the team refine workflows, improve user experience, and optimize efficiency within their LOS platform.

LoanCrate's LOS

Tools

Aug 2022 - Dec 2022

Miro, Google Sheets, and Grain

Contextual Inquiry, Affinity Diagraming, Analyzing Interviews, Presenting Insights

Duration

Skills

Project Overview

Challenge-LoanCrate is an advanced Loan Origination System designed to streamline the mortgage loan process. Our team was tasked with identifying user needs and pain points to improve the platform's functionality. To achieve this, we were challenged to create a Jobs-To-Be-Done map, allowing us to transform actionable insights into product feature optimizations. This process helped us align design decisions with user goals and business objectives.

Solution- We analyzed user testing sessions and synthesized the data to uncover key insights ranging from low-level usability issues to high-level strategic opportunities.

Role- UX Researcher

Team- 4 Researchers

User Groups

Processor: Collects documents to satisfy conditions

Underwriter: Approves or denies your loan

Post-Closer: Reviews and clarifies documents

Final Deliverable

A complete Jobs-To-Be-Done map of how the LoanCrate team can optimize their LOS through user data

Where Do Our Users Fit into the Current Loan Approval Pipeline?

Market Analysis

To first understand where our users were coming from, we conducted secondary research to analyze their roles and responsibilities within the loan approval pipeline. We examined industry standards, existing workflows, and common pain points in traditional loan approval processes.

By reviewing documentation, case studies, and existing system structures, we answered key questions such as: How do current users operate in their space? What challenges do they face? Where do opportunities for improvement exist?

We then diagrammed both the traditional loan approval process and LoanCrate’s system to compare workflows and identify where users fit within the product. This analysis helped us uncover inefficiencies, highlight potential improvements, and ensure LoanCrate’s integration would enhance the existing processes.

What Are Users Missing to Add to Their Experience?

Contextual Inquiry

After completing our secondary research, we received six contextual inquiry sessions conducted by the LoanCrate team. These sessions gave us valuable insights into how users interacted with the product, helping us identify its strengths and areas for improvement.

The LoanCrate team, having the most direct experience with the product, was able to answer key questions and provide detailed feedback on user behaviors, pain points, and preferences. By analyzing the results of these sessions, we were able to refine our understanding of the users' needs and validate our initial findings. This collaboration helped us identify critical touchpoints where LoanCrate could be optimized for better user experience, ensuring that the final product would align more closely with user expectations and improve their workflow within the loan approval process.

How Did We Analyze These Testing Sessions?

Data Analysis

With the sessions recorded, we moved them into Grain to transcribe the live conversations. This allowed us to capture every detail of user feedback on the LoanCrate system. Once transcribed, we moved all of the raw data into Excel for thorough cleaning and organization. Inside Excel, we also labeled each data entry with an identifiers so we could reference it later in our analysis and presentations to the LoanCrate team.

I focused on analyzing the underwriter sessions in efforts for creating a Jobs-to-Be-Done map specifically for their role, highlighting key tasks, challenges, and opportunities for a smoother workflow.

Image of Grain’s interface that we interacted with

After all the data was organized, we transferred it into Miro to facilitate collaboration and ensure we could effectively synthesize the findings.This process allowed the team to work together in real-time, drawing connections between user behaviors, pain points, and opportunities for improvement.

How Did We Synthesize The Data?

Affinity Diagramming

Our team's main goal was to transform the raw data into actionable insights that would inform the LoanCrate team on focus areas for their product.

We began by organizing the raw data into meaningful categories, identifying patterns and key themes. Next, we started grouping related data points into clusters that reflected user behaviors, needs, and pain points. This was followed by translating these clusters into mid-level empathy statements, helping to humanize the data and provide insights into user motivations and frustrations. At the top of the process, we turned these empathy statements into high-level, actionable insights that could directly inform design decisions.

Each user’s data was analyzed individually, ensuring that the pain points were clearly mapped and structured for clarity. This approach allowed us to present the findings in an easily digestible format, making it simpler for the team to understand user needs and prioritize improvements for the product.

Screenshot 2024-04-01 at 2.11_edited.jpg

With the data synthesized, we presented our findings to stakeholders, highlighting the most important aspects for each user group. I specifically presented my insights on underwriters, ensuring that each high-level takeaway was backed by raw data to clearly show where it originated and why it mattered. This approach helped stakeholders see the direct connection between user pain points and opportunities for improvement, reinforcing the importance of our research in shaping the product.

Impact Statement

Working on this research-heavy project gave me a deeper appreciation for the importance of truly understanding users in the UX research process. By analyzing contextual inquiry sessions and structuring data into actionable insights, I was able to help the LoanCrate team identify key pain points and opportunities for improvement. My work in curating insights for underwriters provided them with a clearer view of their workflow challenges and potential enhancements, directly influencing LoanCrate’s approach to refining their product.

If I were to do this again, I’d assign severity levels to insights during presentations to clearly show their impact, making it easier for stakeholders to prioritize and take action rather than just discussing their importance.

My Takeaways

Data quality matters – Cleaning and organizing raw transcripts made affinity diagramming and analysis much more effective.
Understanding users is key – Contextual inquiry sessions provided deep insights into user workflows and pain points.
Empathy-driven insights are powerful, "I want" statements helped frame user needs in a way that resonated with stakeholders.
Communication with stakeholders is crucial – Leading meetings strengthened my ability to present findings and align teams.
Prioritization improves impact – Communicating the importance of each insight helped stakeholders focus on the most critical issues, making it easier for them to prioritize and take action.