Prototyping The Self-Service Workflow
Robert Half Direct is all about the self-service experience. Research gave me high-level insights on what users wanted out of the experience. I led my team through several iterations of what the self-service experience might look like, conducting moderated usability tests after each iteration. Below, I walk-through the final validated design.
The client user tracking dashboard
The key to achieving a quick, self-paced, self-service workflow is a comprehensive tracking dashboard. Through both research & usability testing, we found users wanted a single pane to manage the flow of the hiring process.
Drag & drop
I designed and tested several different methods to enable client users to linearly manage the hiring process. Laying-out the process vertically, required too much scrolling up and down for the user; so much so, that the scrolling made it hard for the users to maintain their cognitive footing. The solution was to lay-out the process horizontally so that users could see an overview of the end-to-end process in one view, without needing to scroll. I designed a drag-and-drop system, using large drop-zones, to streamline how Client Users move a candidate through their own process. Utilizing the drag-and-drop system eliminates the need for both scrolling, & clicking through auxiliary screens, & makes the single screen flow come to life.
One-step to make an offer
I tested both single and multi-screen process flows that enable the client user to extend an offer. Both research and testing showed me the client user valued a quick & logical method to extend the offer. Through the final usability testing, I found the shorter the offer flow was, the more likely the client user was to immediately post another position. User satisfaction at the end of the offer flow had a direct correlation to increased repeat business, so I knew I needed to make the offer process as quick as possible.
To achieve the quick flow, the offer extension process needed to be a direct extension of the drag & drop system. Through moderated usability testing, I validated the single screen, one-step, drag & drop offer. To make an offer, all the client needs to do is make the final drop into the offer drop zone. Extending an offer is contained in a single popover window. Once the client makes the decision to extend an offer, it takes less than 30 seconds to make, confirm, and send the offer to the candidate.
The candidate dashboard
Candidate Users almost completely equally used both the desktop and mobile experience, so It was important to establish strong parity between the two. In both experiences, the candidates are notified in the main dashboard when they have an interview invitation to review. The clear calls to action, coupled with clear information layout, enable the candidate to quickly review and accept an interview invitation.
A single pane for monitoring
Research told me that Candidate Users, much like Client Users, wanted to be able to monitor their progress through the interview process in a single pane, or single page. I tested multiple designs on the Candidate Users. The validated design makes use of a single pane, with all pertinent information about the selected job opportunity. I designed a segmented progress indicator to ensure Candidate Users always know where they are within the interview process.
Bringing-To-Life The Shortlist With Strategic Data Collection
The RH Direct product is driven by RH Direct AI's ability to match Candidates to Client's open positions. Research told me Client Users wanted to review a limited, high-quality shortlist of potential candidates. The surveys and user interviews gave me a range of anywhere between 5 to 20 candidates as the proper candidate range for the shortlist. I tested the range through usability testing, & ultimately arrived at the magic number of 10 candidates for the shortlist.
The real world problem evidenced during beta & pilot testing. It became clear, the AI Algorithm, driven by the existing Machine Learning Model, was really good at narrowing the shortlist down to 40-150, or so, potential candidates. In its initial state, the technology wasn't capable of producing the high-precision matching, necessary to achieve the user's ultimate goal of 10.
The match precision challenge
High quality match precision is achieved through high quality data. Robert Half Direct AI takes in massive amounts of raw, free-text, through job descriptions & résumés. This is called unlabeled, or unannotated data. AI & Machine Learning is limited in what it can do with unlabeled data. To achieve higher quality predictions, & thus, high-precision matching, the machines need labeled, or annotated data.
Deploying annotation, or data labeling teams is extremely costly, so much so, that it would collapse the viability of the Robert Half Direct product. Not only is it costly, but it isn't easily scaled.
The solution: user-labeled data
My idea was to influence users to label their own data, by hardwiring the mechanism directly into the Robert Half Direct platform itself. Designed & implemented properly, users aren't cognizant of the fact they are labeling data. This meant the method of user-labeled data needed to be quick, with very low effort required. Influencing users to label their own data is not only zero cost, but it's highly scalable, & also enables the users to consistently & systematically QA the data going into the Machine Learning Model.
To ideate the solution, I started with sketching-out the process flow above, as an initial guide.
Influencing client users to label their data
For Client Users, I built-in to the job posting experience one additional data collection prompt: Top 3 Skills. Based on the job title & job description, Robert Half Direct AI displays common/suggested skills. Alternatively, the user has the ability to write-in their own skill tags. These 3 top skills are weighted the most in the Robert Half Direct AI matching algorithm. Additionally, the user is able to designate one skill as 'Must Have', which further increases the algorithmic weight of the skill.
Collecting additional client data to QA the Machine Learning model
Seen in the screen above, the Client User is given a QA point post-interview. This helps to continuously refine the next batch of matches to populate the shortlist.
Influencing candidate users to label their data
I designed two individual prompts, to act as reminders for Candidate Users to consistently update their profiles by adding their skill tags (labeled data). In order to keep the sign-up flow quick and unobtrusive, the first prompt comes in the logged-in experience, a toast notification suggests the Candidate User update their skills. Secondarily, on occasion, as the user works through the experience, they will be met with the second prompt for user-labeled data, the blue modal suggests the user updates their skills. In both cases, the copy reinforces to the user, the effort to update their skills tags will result in better job recommendations/matches.
Collecting user-labeled candidate data
The Robert Half Direct AI suggests skill tags based on the user's résumé & profile. The Candidate User has the ability to add up to 50 skill tags to enhance match precision. In the broader sense, these skill tags are directly cross-referenced to the Client's top 3 skill tags.
Testing & validation
In both testing, & real-world application, the approach to user-labeled data consistently ensures a shortlist of 10 candidates per batch.
Designing For Graceful Degradation
AI isn't perfect; there will always be scenarios where AI doesn't produce the desired result, or is incapable of producing the desired outcome. In those circumstances, it's important the user still has the ability to engage in a powerful job search experience.
The candidate user solution: searching traditional Robert Half job listings
Solving for a degraded candidate experience was simple; we made the decision to enable candidates to search and browse traditional Robert Half job postings. After creating an account, while the candidate waits for Robert Half Direct invitations, they can engage with Robert Half's traditional service offerings by applying to a job posted by a Robert Half Recruiter (on behalf of the client in a traditional search).
The client user solution: browsing close matches
If there are no matches for a Client User to review, instead of making them wait for the next batch of high-precision matches, Robert Half Direct allows them to review close matches, whom are just outside of the given requirements.