A Chrome extension that empowers job-seekers with insights beyond a job description.
When a candidate decides to take a job, there are so many things they would like to know before committing to a role. What is the culture of the company? How is the management? What are some of their key values? Also, there might be role-specific questions such as, what does the career progression look like? What does the role entail? How is the manager?
While the job search process is filled with uncertainty, it is important to make informed decisions to the best of our abilities.
67% young job-seekers believe job descriptions should include information about salaries, benefits, location, commute time, and employee reviews, as it is difficult and time-consuming to research every company they apply to. Lack of company research affects candidates' chances of getting a job. A study by Linkedin shows 47% of candidates failed job interviews because they didn't have information about the company they applied to.
We had experienced this problem in our job hunt as well, so we started questioning,
How might we help job-seekers with limited work experience learn more about a job opportunity so that they feel confident when applying for a role?
Using our own experiences as job seekers, we first identified two prospective user groups: new grads seeking their first job and career switchers looking to break into a new field.
To validate the existence of the problem, we interviewed five job-seekers and one recruiter. The main takeaway was that our users are looking to get a job while having limited knowledge about the industry, companies, and roles they are pursuing. Not only does this lack of information make it difficult for our users to know what role is the right fit for them, but it also decreases their chances of getting the job.
We also did a heuristic analysis of LinkedIn and Indeed, and mapped the resources already available to job-seekers.
From the research insights, we identified our primary user and their “jobs to be done”. Meet Mike, a job-seeker with limited work experience.
This helped us understand our primary user’s functional, social and emotional needs. The ultimate goal was to get a sense of achievement and feel proud. They wanted to ace the job interview and land a job that would be a right fit for them.
As we started brainstorming potential solutions, we focused on how we can save time while simultaneously providing value for the user. We converted user needs into potential features.
Originally, we planned to present LinkedIn profiles of past/current employees to the user so they can connect and get more information. However, we could not pursue this feature due to technical challenges caused by changes in LinkedIn’s API. Instead, we focused on how we can extract data already available and provide it to the user in a comprehensive and convenient way.
We started sketching ideas using the crazy 8s sketching technique and created low and high fidelity mockups for testing. The initial design had 3 screens (Guide, Links and Preview) as shown below.
Hi-Fi Mockups (first iteration)
Measuring Success and Testing
We conducted six moderated usability tests with a Figma Prototype. The measure of success was defined as:
The response we got was overwhelmingly positive.
"I am currency in the job hunt process and I would love to have this tool, as I go through this process 5-10 times a day - Pragya."
We iterated our designs based on the feedback. The updated design had 3 primary tabs (Links, Preview, People) and a Guide as shown below. The UI was improved to be simpler and more accessible.
The final design scans a job description the user is viewing and presents a list of third-party sites like glassdoor and Level.fyi that are auto-filled with the role and company information.
High-level journey of a request
When a user is on a job details page on Linkedin, they click on the JD+ icon in their browser, and a request is made to fetch information about the company. The returned response is the company website, LinkedIn, glassdoor, levels.fyi, and blind links. The user can then click on any links to learn more about the company.
We attempted to use the LinkedIn API to search for profiles the user could connect with. Halfway through, we realized the people search function had been deprecated, and we needed to pivot our product to web scraping the data required for the application.
Some of our key takeaways were:
We used a prioritisation matrix to determine our minimum viable product(MVP). implementation and decided to start with developing the links tab. As our next step, we will be:
As a team, we learned that we should check the solution’s technical feasibility early on in the process. Despite the unforeseen challenge, we decided to keep going and brainstorm ways to provide value to our users. We also learned that a faster feedback loop was crucial to remove blockers early in the process.