Helping recruiters source and hire high quality candidates faster, saving time and money throughout the hiring process while also helping job seekers find the best fit and move through the hiring process quickly.
Jibe’s CRM (Candidate Relationship Management) is an end-to-end responsive recruiting platform, designed to improve recruiter productivity and convert higher quality candidates through the use of machine learning and profile matching. The platform allows users to create candidate hiring pipelines, manage and match candidate profiles to job requisitions, rate assessments, move candidates through the hiring process (integrated with the ATS), communicate with candidates, invite candidates to apply to jobs, and create/manage recruiting events. The product was designed to automate and simplify the most pivotal recruiter workflows in a simple, easy-to-use, responsive web enterprise solution that lays the foundation for future iterations.
The time spent on hiring directly correlates to the cost to acquire a candidate - the longer it takes to fill a role, the more it costs the company to provide HR and Recruiting resources plus the cost of not having the role filled by not being properly staffed. Our goal was to help recruiters build relationships with candidates, source candidates faster, and move qualified candidates through the hiring process faster resulting in lower costs to the company overall.
The process of redesigning the CRM, iterating on it, and testing usability spanned my entire time with Jibe and iCIMS. Upon first arriving at Jibe, the CRM was a skeleton structure of a product with limited functionality, no design direction, and lacked user adoption. Please view the complete process in the presentation below.
The new redesigned CRM became a leading product with user adoption and functionality that helped users hire higher quality candidates faster. It was the foundation for the Design System that I worked closely with with engineering to establish for faster design and development times and easier iterating. Jibe's design system and CRM's simplified usability helped influence the acquisition of Jibe by iCIMS.
At Jibe, we worked to better understand the job seeker's experiences, problems, and desires so that we could create solutions to help them get hired faster. Here are some questions we asked:
We assessed and tested the quantity of information needed from a job seeker in relation to the highest match rate surfaced. We needed to solve the problem of how to generate relevant high quality results with the smallest amount of information gathered to make the process quick and frictionless.
Our aim on the job seeker side of the platform was to help job seekers find jobs they're qualified for faster and eliminate the long complicated application and hiring process by replacing it with a transparent and simple solution that helps job seekers get matched to the right job, submit an application, and move through the hiring process faster.
The experience is centered around parsing a job seekers resume, extracting relevant information, and matching that information to our client’s current job postings. With an ensemble matching approach, we devised a way for users to easily give us the most valuable information needed to give them jobs they are the best fit for, essentially making the job seekers need to search for jobs or sift through pages of job postings optional. Through many revisions and explorations we arrived at a toggle solution for the Job Search pages, where the user would toggle from “Job Search” to “Job Matching” to enter the matching flow. We concluded that this prominent solution was clear, a familiar UX pattern, and reduced friction and confusion among our users.
Commute Search was a pivotal feature in the job search experience. It solved the problem for many different communities who needed to find jobs based on their location and the commute they'd need to take by car, public transportation, or bike. Allowing users to search and be matched to jobs based on this variable helped job seekers remove a lot of the guess work and find results quickly.
We learned a lot about how different user needs and HR processes can be complicated and conflicting. Ultimately, we were able to create a solution that parsed a job seekers resume, matched them to available jobs, and moved them to submitting an application faster. We were able to help users find more relevant jobs faster and within their preferred commute.