Hive Consumer
ARTIFICIAL INTELLIGENCE FOR JOB SEEKERS

Overview

ABOUT

Hive is a better way of matching people and jobs. It is a product for consumers to manage job searching to match candidates with positions using artificial intelligence. AI is utilized to match the candidate with the job beyond the words on a resume and understands the meaning behind the words in a resume and job post.

OBSTACLES

• Multi-features, large scale and familiarity with legacy products.

• Establishing viability in a saturated $8+ B recruiting market.

• 6 month timeline including modeling and developing algorithms and models for a large industry.

• The project was reviewed at 6 months and extended for another 6 months.

PROOF OF VIABILITY

Hive was thought to be an enterprise product focusing on recruiting. After a viability study on a competitive model product - we found we needed job candidates - both active and passive for recruiters to use the site. We Pivoted our model to integrate with LinkedIn and other systems for the consumer / job seeker.

SOLUTION

FINAL DESIGN DASHBOARD

STRUCTURE OF THIS PROJECT

Throughout the 6 months of Phase 2 (Consumer) we worked closely with the AI Data Scientists & User Researcher to create artifacts and requirements for the AI workflow and process relevant for job seekers. We worked, tested and reiterated at a very quick pace.

MONTH 1
DISCOVERY AND BUSINESS RESEARCH

In order to compete with the current market we needed to be compelling and different from LinkedIn. It’s social aspect is their biggest advantage and helps attracting passive and active candidates altogether as well as companies and recruiters.

WHAT LINKEDIN IS DOING WRONG?

1. Unwanted approaches from irrelevant recruiters or people wanting to join your network.

2. There is no way of verifying the skills endorsements. 

3. Discovering which settings work to restrict fraudulent accounts from accessing user information is an uphill task.

4. There is no verification process in place to make sure that user profiles are genuine and authentic.

5. Irrelevant ads for users profession, skills and knowledge 

OTHER COMPETITORS

MONTH 2
USER RESEARCH

HOW MIGHT WE ...

Define the primary persona?

Define needs of the job seeker?

PERSONA BUILDING

Wrote a survey of 10 questions about LI and sent it out to a list of 500 active job searchers and people employed within two years of the survey with the help of an agency and a focused database.

From these 2 data sets we came up with our personas Sarah, David and Jay our primary persona.

FINDINGS: USER RESEARCH

JAY has learned through a learning curve where apply to jobs. Some sites cater to offshore recruiters that bombard him with irrelevant jobs. He is experienced in all levels of interviewing, recognizing how to talk and be presentable to a recruiter, a hiring manager and a stakeholder at a company. He well versed in all different career sites. He values company culture, traveling and flexibility to work remotely. His experience working at startups makes him knowledgeable in the volatile job market in the tech industry.

MONTH 3
IDEATION, SKETCHING, WIREFRAMING & SPRINTS

Hive for Job Seekers needed to have a way of effectively showing results beyond keywords. Most sites show irrelevant jobs and very little show how Applicant Tracking Systems parse resumes in order to pre select them for the recruiter. It also needed more support from the Artificial Intelligence in other tasks like creating candidate profiles and communicating with recruiters in an adequate language. It needed to integrate into current best practices already familiar with job seekers.

From previous research my team narrowed down the Job Seeker needs to focus on:

• More relevant job posts and less searching through hundreds of results using Artificial Intelligence.

• Ability to track interviews, interview process and receive feedback for profile improvement.

• Redesigning the job search process using Artificial Intelligence

We needed Hive to be both competitive to LinkedIn as well as an appealing add-on enhancement to LinkedIn. We kept referring back to our competitive landscape and Engagement analysis to guide to our design of Hive Enterprise.

GALLERY OF SKETCHES

FOCUSED SPRINTS (3, 2-WEEK SPRINTS)

RESULTS OF SPRINTS

• User Flows

• Preliminary requirements for job searching

• Site Map

Consumer User Flow After Sprint

Consumer Site Map After Sprint

MONTH 4.5
FINAL DESIGNS AND TESTING

After testing the comps from the Sprint, we realized testing failed because the Consumer / Job Seeker dashboard was too feature heavy. It also didn’t provide any novelty from our competitors. We did some more ideation and modified the designs and retested (images 1, 2, 3 below):

• Job Match Display - was modified to display jobs as soon as candidate enters dashboard.

• We eliminated a section of “Recommended jobs” that were being displayed in pages where they weren’t relevant. (Matches - image 4 below.)

• Job Post - menu contains subcategories from integrations with Glassdoor and Jobscan to display company profile, reviews and how job seeker’s resume scores in an Applicant Tracking System.

• Integrated a calendar to synch with user’s preferred system (Google Calendar or Calendly).

Initial Wireframes

Screens

Design: Job Results with direct access to job and other integrations.

Design: Job Scan integration which allows users to see how their resume is paired with the selected job according to keywords
(method used by most ATS systems).

Calendar integration that syncs with Google Calendar

MONTH 6
FINAL TESTING & HANDOFF TO DEVELOPERS

We designed and built-out the whole product, tested and handed off the files to the development team.
What the Developers received:

• Annotated screens

• Glassdoor, Jobscan and other implementation recommendations

• AI requirements, justification, logic patterns and organizational groups of data

As the product was built, we tested it with job seekers and refined usability and design. We utilized moderated in-person and remote testing with both external and internal (inside a company) recruiters.

CONCLUSION & SUMMATION
+ FINAL THOUGHTS

What I learned from working on this project 

I discovered the differences and possibilities of creating a product for consumer vs enterprise. The consumer interface was unique and required a lot of research and testing in order to create something viable in a saturated market that doesn’t really listen to the consumer needs because a profitable business model is mostly focused on enterprise. 

What you would I have done differently if you knew then what I know now about the project

If we would’ve started with a more simplified approach without the assumption of a feature heavy dashboard, we would’ve advanced further on creating more necessary features. I also would have explored the possibility of customizing the integrations suggested and exploring what combinations of integrations were more suitable for the consumer.

What you would I have done next had you continued with the project.

I would’ve worked on the social aspect of this job market app to attract passive candidates. I would’ve also scaled by targeting candidates from specific industries, one at a time, trying to create a consolidated source of candidates for certain sectors.