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Resume Parser

Resume Parser

Resume Parser

Resume Parser

User-friendly resume parser tool, designed to help new grads and entry-level consumers improve their resume.

User-friendly resume parser tool, designed to help new grads and entry-level consumers improve their resume.

User-friendly resume parser tool, designed to help new grads and entry-level consumers improve their resume.

User-friendly resume parser tool, designed to help new grads and entry-level consumers improve their resume.

Team

Team

Team

5 Developers

2 Designers

1 PM & 1 Team Mentor

Role

Role

Role

UX Researcher

UI / UX Designer

Product Design

Process

Process

Process

1. Research

2. Ideate

3. Iterate

4. Next Steps

5. Reflect

Tools

Tools

Tools

Figma

Airtable

Google Suite

Slack

Jira

Project Duration

Project Duration

Project Duration

Jan 2024 - Jun 2024

Project Overview

This case study analyzes a project from CodeLab, a professional software development and UX design agency at UC Davis, as the first full-fledged project I worked on in a team. From this project, I gained an invaluable learning experience alongside my passionate team peers. Beyond UI/ UX skills, I exercised the ability to advocate for my design decisions, collaborate in a cross-functional team, and communicate in clarity to bridge the gap between technology and design among my developers. That is why I am beyond excited to take you on this journey through my mind during the design process for this amazing opportunity of a team project. Through this endeavor, I aim to exemplify my unwavering dedication and desire to create impactful human-centered product designs.

This case study analyzes a project from CodeLab, a professional software development and UX design agency at UC Davis, as the first full-fledged project I worked on in a team. From this project, I gained an invaluable learning experience alongside my passionate team peers. Beyond UI/ UX skills, I exercised the ability to advocate for my design decisions, collaborate in a cross-functional team, and communicate in clarity to bridge the gap between technology and design among my developers. That is why I am beyond excited to take you on this journey through my mind during the design process for this amazing opportunity of a team project. Through this endeavor, I aim to exemplify my unwavering dedication and desire to create impactful human-centered product designs.

Target Audience

18-24 year current college students, new college graduates, and entry-level job seeking applicants looking to revise a current resume.

18-24 year current college students, new college graduates, and entry-level job seeking applicants looking to revise a current resume.

Problem

ATS is a modern algorithm system used by hiring managers and companies to streamline their hiring process. This algorithm is used to filter out resumes and track applicant data according to job requirements. Job seekers pursue to optimize their resume to be ATS-friendly for best outcomes. However, many applicants lack effective resume parser tools that can extract their relevant experiences, provide reliable and applicable feedback, and effectively alter their resume for specific job descriptions. By addressing these concerns regarding ATS from a job seeker perspective, Resume Parser aims to establish a friendly, reliable, and effective AI resume parser web browser tool.

ATS is a modern algorithm system used by hiring managers and companies to streamline their hiring process. This algorithm is used to filter out resumes and track applicant data according to job requirements. Job seekers pursue to optimize their resume to be ATS-friendly for best outcomes. However, many applicants lack effective resume parser tools that can extract their relevant experiences, provide reliable and applicable feedback, and effectively alter their resume for specific job descriptions. By addressing these concerns regarding ATS from a job seeker perspective, Resume Parser aims to establish a friendly, reliable, and effective AI resume parser web browser tool.

Solution

The solution is simple: create the best resume parser tool in the span of 24 weeks. Well, it required a bit more planning than that sentence alone. In response to creating an ATS resume parser dedicated for entry-level individuals, I focused heavily on research (market research, user surveys, competitive analysis) to gain an understanding of current resume parsers in the market and empathize with entry-level users. This led me think of how Resume Parser can stand out from current competitors, and to strategize a friendly yet professional design system for the initial visual interface that is efficient, easy to use, and encouraging for user engagement. I then proposed several design iterations and product branding strategies to further better our product from existing parsers and iterate based on user-testing results.

The solution is simple: create the best resume parser tool in the span of 24 weeks. Well, it required a bit more planning than that sentence alone. In response to creating an ATS resume parser dedicated for entry-level individuals, I focused heavily on research (market research, user surveys, competitive analysis) to gain an understanding of current resume parsers in the market and empathize with entry-level users. This led me think of how Resume Parser can stand out from current competitors, and to strategize a friendly yet professional design system for the initial visual interface that is efficient, easy to use, and encouraging for user engagement. I then proposed several design iterations and product branding strategies to further better our product from existing parsers and iterate based on user-testing results.

  1. Research

  1. Research

  1. Research

1.1 - Market Research

My team and I first conducted market research by gaining an understanding and knowledge about what ATS and its market audience is. We learned about the significance and prevalence of ATS used by hiring managers, recruiters, and companies when receiving resumes. When applicants utilize ATS-friendly resumes, it heightens their chance of the resume being recognized in the application process to ATS standards. We found that of all the industries, the tech industry is one of the top industries taking the most advantage of the resume parsing algorithm. We also found that 99% of Fortune 500 Companies use ATS. After gaining this baseline knowledge, we determined a target audience of entry-level individuals.

1.2 - Competitive Analysis

I compiled a spreadsheet to conduct competitive analysis on three competing resume parsers on the internet: Resume Worded, Open Resume, and Affinda. I compared their overall product features and overview, as well as their UI elements on the desktop version of their websites (as seen in the image spreadsheet below).

Key Findings

  1. Resume parsing is mainly split into two categories — parsing based on content-specific information, and parsing based on formatting.

  1. Vertical page division is efficient for home and landing pages, while horizontal page division provides additional information displayed during feedback of a resume parser.

  1. A consistent color theme, clear buttons, and whitespace leads to an intuitive, user-friendly visual interface and adds to product branding.

Overall, this competitive analysis was tremendously useful in helping the team and I determine what features to incorporate, design layout and inspiration for a resume parser, and design system guidelines that were more intuitive than others (the page division being a huge example).

  1. Vertical page division is efficient for home and landing pages, while horizontal page division provides additional information displayed during feedback of a resume parser.

  1. A consistent color theme, clear buttons, and whitespace leads to an intuitive, user-friendly visual interface and adds to product branding.

Overall, this competitive analysis was tremendously useful in helping the team and I determine what features to incorporate, design layout and inspiration for a resume parser, and design system guidelines that were more intuitive than others (the page division being a huge example).

  1. Vertical page division is efficient for home and landing pages, while horizontal page division provides additional information displayed during feedback of a resume parser.

  1. Vertical page division is efficient for home and landing pages, while horizontal page division provides additional information displayed during feedback of a resume parser.

  1. A consistent color theme, clear buttons, and whitespace leads to an intuitive, user-friendly visual interface and adds to product branding.

Overall, this competitive analysis was tremendously useful in helping the team and I determine what features to incorporate, design layout and inspiration for a resume parser, and design system guidelines that were more intuitive than others (the page division being a huge example).

  1. A consistent color theme, clear buttons, and whitespace leads to an intuitive, user-friendly visual interface and adds to product branding.

Overall, this competitive analysis was tremendously useful in helping the team and I determine what features to incorporate, design layout and inspiration for a resume parser, and design system guidelines that were more intuitive than others (the page division being a huge example).

1.3 - User Survey

42 Responses to survey consisting of 19 questions, mixed multiple choice and open-ended questions. Sent out to an audience of college students and new grads.

Featured Questions:

What type of feedback would be most valuable to you from an AI resume parser, and why?

What impressions / qualities do you want to highlight for hiring managers from your resume?

Some examples of AI resume parsed feedback includes suggestions on word impact, reducing repetition, and tailoring your resume to a specific job description. What type of feedback would you expect on your resume when using an AI resume parser?

What kind of challenges do you face when revising your resume?

Key Findings

  1. Expected feedback from a resume parser consisted mainly of: repetition and word impact, including phrasing, action word implementation, and conciseness.

  1. Most valued feedback from an AI resume parser consisted mainly of: tailoring resume to a specific job description.

  1. Ideate

  1. Ideate

2.1 - User Stories

As a User, I want to…

High Priority: See the feedback directly on my resume so that I can gain immediate visible results. Gain different types of feedback on the AI that is not just limited to a specific section in the resume so that I can gain feedback on my whole resume. Choose the type of feedback (content vs. ATS format) that I want to review first so I can focus on revising what is more important to me. Be able to tailor my resume to different job descriptions, so that I can have specific resumes for specific applications.

Medium Priority: Be able to see my past uploaded resumes, so that I can further revise them if needed.

Low Priority: Be able to upload multiple resumes at once.

2.2 - Wireframing

I referred to the key findings in the user surveys, and user stories to design the general flow and initial wireframes of the website flow. I focused especially on ensuring that there were two different parsing result pages — one for ATS formatting and one for content feedback. This ensured that I was meeting user wants and needs of having multiple types of parsing feedback, which is the main focus of Resume Parser.

  1. Iterate

  1. Iterate

3.1 - Design System

We focused on maintaining a friendly yet professional environment with the greens and yellows as part of the color scheme. It was important to adhere to this design system to maintain a consistent design environment, and to make prototyping more efficient for the team.

3.2 - Initial Prototypes

Our initial prototypes began with the home screen, sign in page, and sign up page.

3.3 - Usability Testing

I conducted usability testing on 4 individuals for the above home, sign in, and sign up pages.

Home Page Questions:

How would you categorize the icons seen on the header, where do you believe they would lead?

After reading this page, do you have a good understanding of what a resume parser does?

Sign Up/In Page Questions:

What made completing this task (signing in or signing up) a good experience for you?

Was the language and instructions on the page easy to understand?

What would you rate how simple and clean the interface was? (scale 1-10; 1 being bad, 10 being amazing) What influenced your rating?

Key Findings

  1. Users valued simple and clean visual interface of the website.

  1. It is unintuitive to scroll down in order to sign up, as the person icon in the navbar is not explicit in its function for signing up in addition to being able to sign in.

  1. Next Steps

  1. Next Steps

4.1 - Handoffs & Adaptations

ATS vs. Content Feedback

At the time of handoffs to developers, there were some adaptations to be made based on the project scope and timeline deadlines.

The leftmost page is where users can choose the type of feedback (ATS formatting vs. content) they want from the AI parser. It would lead to either the ATS page (middle) or Content Feedback (rightmost) page. A shift in the project scope for the team's given timeline, and restrictions from the API used by developers led us designers to shift gears to focus on expanding only the Content Feedback page into more specific feedback categories, shown below.

The topmost page is where users can choose the type of feedback (ATS formatting vs. content) they want from the AI parser. It would lead to either the ATS page (middle) or Content Feedback (bottom) page. A shift in the project scope for the team's given timeline, and restrictions from the API used by developers led us designers to shift gears to focus on expanding only the Content Feedback page into more specific feedback categories, shown below.

The topmost page is where users can choose the type of feedback (ATS formatting vs. content) they want from the AI parser. It would lead to either the ATS page (middle) or Content Feedback (bottom) page. A shift in the project scope for the team's given timeline, and restrictions from the API used by developers led us designers to shift gears to focus on expanding only the Content Feedback page into specific sections, shown below.

The topmost page is where users can choose the type of feedback (ATS formatting vs. content) they want from the AI parser. It would lead to either the ATS page (middle) or Content Feedback (bottom) page. A shift in the project scope for the team's given timeline, and restrictions from the API used by developers led us designers to shift gears to focus on expanding only the Content Feedback page into more specific feedback categories, shown below.

4.2 - Final Designs

  1. Reflect

  1. Reflect

5.1 - Learnings

  1. Provided opportunity to conduct first-hand user research and user testing, including competitive analysis, user surveys, and usability testing.

  1. This project required tremendous collaboration, time-management, and team collaboration skills.

  1. First full-fledged project, utilizing five-step design process in the span of 20 weeks.

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Made with ‎✿ — © Roxanne Ruan 2024 WIP

Email

LinkedIn

Made with ‎✿ — © Roxanne Ruan 2024 WIP

Email

LinkedIn

Made with ‎✿ — © Roxanne Ruan 2024 WIP

Email

LinkedIn

Made with ‎✿ — © Roxanne Ruan 2024 WIP