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Cleo by Bullhorn

Bullhorn's Machine-Learning-powered Assistant

Objective

Prototype the experience of an assistant and chatbot to help the

workforce industry expedite its workflows

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Skills

Conversation Design, DialogFlow, contextual inquiries, heuristic analysis, information architecture, user interviews, usability testing, user flow, wireframes, high fidelity prototypes

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Role

UX Designer

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Duration

2 Months

BACKGROUND

Bullhorn is a technology company focused on providing Applicant Tracking Systems (ATS) and Customer Relationship Management software (CRM) for the workforce industry.

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In an effort to improve workflows and provide the right data for users at the right time, a Machine-learning and Artificial Intelligence mission and team was created in the summer of 2019. As the UX designer on this team, I was tasked with both designing applications of the machine-learning back-end, and collaborating with the VP of AI and product managers on discovery research. â€‹

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The first product to be built on the AI team became a chatbot, named Cleo, to help users with relevant data and quick actions while on the Bullhorn platform. This initiative was expanded to provide candidate-facing products and mobile products as well. 

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Artificial Intelligence

The AI Mission at Bullhorn is providing an incredible experience for recruiters and candidates built to anticipate their needs by:​

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  • ​Creating automation in key areas of manual effort and focus to increase the work done

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  • Reducing the variability of outcomes: Consistently find, engage and hire high-quality talent â€‹

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  • Reducing the variability of costs: Improve predictability of sourcing cost and time to fill

    • Provide customers strategic insight into their business, markets and organizational strengths​

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The ideal outcome is to make the best recruiters even better, and newest hires more productive, faster.

Key Issues

Many recurring issues in Staffing remain unsolved, but could be addressed by AI-powered solutions. Through user calls and surveys, I identified the top frustrations candidates and recruiters are facing.

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Sourcing

  • Clarity of Requirements

  • Identifying sources and channels for candidate outreach

  • Finding most relevant candidates

  • Controlling cost of acquisition

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Recruiting

  • Ranking Applicants for a job

  • Phone screening matching candidates

  • Determining availability and compensation

  • Scheduling interviews

  • Timely interview feedback

  • Slow offer approvals

  • Repetitive and redundant onboarding

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Strategic Planning

  • Identifying most profitable clients, roles, and locations

  • Trends for in-demand skills and pay rates

  • Benchmarking recruiter time by role

  • Benchmarking level of activity by role

  • Predicting placement revenue

Solutions

Automatic Candidate Screening

In order to find the most relevant candidates for a job, automatic candidate screening allows Cleo to interface with both recruiters and candidates, and allows recruiters to override any result based on their own judgment

Screening Questions

Automatically generated from the job description, weighted for ranking

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Data Auto-Backfill

As Cleo screens candidates, the information received automatically updates both current records and past positions

Data Validity

From the automated screening questions, data is validated and automatically updated in the ATS to guarantee the most relevant information

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Cleo working with Recruiters

Cleo can help recruiters with daily workflows, to maximize efficiency at every step of the process

Add a task via chat, app or voice
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NEXT STEPS

Integrating Machine-Learning Initiatives with Cleo

As the AI team at Bullhorn kicks off new initiatives, integrating them with Cleo to provide a simple, friendly user experience is key to their success

Auto-Match

Automatically matching candidates to jobs and learning what criteria to value over others is a key initiative for the AI team at Bullhorn. As I explore how to experience this technology, I want to ensure Cleo can help prompt users to try it out. 

When a new job is added along with a job description, the data entered is compared to the database to estimate a budget and how many candidates to submit.

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