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I N T R O D U C T I O N
Uncommon 
Uncommon is a web-based recruiting platform that offers 100% interested and qualified candidates. It provides automated sourcing, screening, and outreach to help recruiters shorten time-to-fill and reduce cost-per-hire.
To comply with my non-disclosure agreement, I have omitted and obfuscated confidential information in this case study. The information in this case study is my own and does not necessarily reflect the views of Uncommon.
My Role 
I was the Lead Product Designer at Uncommon from 6/2017 to 2/2019. The team consisted mostly of engineers and operations, and I was a solo designer. I was responsible for redesigning the entire product, which included research, user experience and visual design.
I collaborated with a product manager, who focused on high-level goals and specifications, front-end engineers, back-end engineers and data scientists to ensure quality and feasibility.
C O N T E X T
Before I Joined 
The idea of Uncommon sparked from the disconnection between employers who want to hire but seldom find a fit and qualified candidates who wish to find a job but often get overlooked.
The previous Uncommon product (built before my time) was a job board aimed to serve candidates. It did so by allowing candidates to match, search, browse and apply to lists of jobs. The product had issues such as sign-ups, onboarding, conversion, etc, and I was initially brought on to help improve the experience.
P R O B L E M
Recruiters' Uphill Battle 
The unemployment rate had hit a 17-year low, and the candidate-driven job market has officially reached all hiring levels, making it tougher than ever for employers to recruit.
Recruiters are burdened by the labor-intensive process of sourcing and screening candidates; the process is expensive, slow and riddled with biases. Even with the best intentions, the tools they use can conflict with their goals and further hinder the growth of their companies.
How might we do the heavy lifting to help recruiters eliminate repetitive labor and focus on interviewing ideal candidates?
H y p o t h e s e s  &  F i n d i n g s
Hypothesis #1
Recruiters prefer to lighten their workload by using A.I. powered tools to help them find qualified candidates.
Findings:
Workload is a real concern, especially when a recruiter is handle 10+ job openings. 
But their main priority is finding the right candidates, even if it takes time. 
They are willing to try new tools to stay competitive but most A.I. powered tools don't consistently produce accurate results.
Hypothesis #2
Recruiters prefer to use the advertising platform that cost the least.
Findings:
Recruiters often have to manage many advertising platforms for each job listing because they want maximum reach. It's about how to efficiently and effectively manage all their campaigns across different platforms. 
Hypothesis #3
Recruiters prefer to contact each qualified candidates with personalized custom message to increase response rate.
Findings:
Recruiters often like to go after large number of qualified passive candidates. In that case, bulk messaging with sequenced messages will yield the best result.
What Success Looks Like 
To make sure our value proposition aligns with our customers' goals, we narrowed our focus to two main metrics:
     1.   Shorten time-to-fill (interview)
     2.  Reduce cost-per-hire
S O L U T I O N
A New Tool Is Born
In a world where finding candidate-fit is a costly rarity, Uncommon is a two-sided recruiting marketplace with human-interpretable matching mechanics that accurately matches recruiters with 100% interested and qualified candidates.
Demystifying The Black Box With Qualifications
For recruiters, machine-learning-based sourcing tools function as what engineers call “black boxes,” proprietary systems that can be viewed in terms of its inputs and outputs, but without any knowledge of internal workings.
Nonetheless, the recruiter’s selections have to be legally defensible to justify how and why the decision was made.
To increase transparency and improve matching by titles, keywords or random algorithm imposed value, we built our system to extract detailed qualifications such as: specific job experience, skill proficiency, education level certification, etc, from each candidate profile.​​​​​​​
These qualifications are "human interpretable" and unbiased. They nest on the dashboard sidebar, and recruiters can play with them and see real-time matches updates in a side-by-side comparison. Recruiters can also verify the exact qualifications that sparked the match in detail.
New Pricing - CPIQ
To reduce cost-to-hire, we introduced a new pricing model called - Cost Per Interested & Qualified (CPIQ).
Instead of paying job boards, search engines, and social networks with cost-per-click (CPC), which is a conflict of interest, we only charge passive candidates who responded positively and qualified active candidates. Recruiters can also manually disqualify a profile directly from the candidate cards.
This aligns perfectly with our goal of delivering quality candidates, and is also in the best interest of our customers. ​​​​​​​​​​​​​​
Automated Outreach
Reaching out to passive candidates with cold emails is another burden, since response rates are low, and the process is repetitive and mundane.
To boost response rate, and alleviate tedious work, an automated outreach system allows recruiters to send bulk emails directly from their email, with templates optimized for response rate. The templates can also be personalized with variants, or effortlessly created from scratch to save for reuse.
The system then tracks the campaign and provide updates and feedback with 'status pills' on each candidate card along the way. This way, recruiters are always kept up-to-date until candidates are ready to engage.
The system then tracks the campaign and provide updates and feedbacks with 'status pills' on each candidate card along the way, so recruiters are always kept up-to-date, until candidates are ready to engage with positive responses.​​​​​​​​​​​​​​
Automatic Bidding Optimization
Another major cost of time and budget comes from managing multiple sourcing vendors. Hence, we partnered with vendors to help recruiters programmatically advertise their job listings to source active candidates.
Using qualifications as the language, we also optimized bidding costs based on defensible merits; recruiters only pay for the candidates who meet all their criteria within the pre-set budget.
This allows more flexibility for recruiters who manage multiple positions; they can manage and allocate budgets for all vendors from one simple screen in a  cinch.
R e s u l t s
A Successful Launch
On February 15th, 2018, we successfully launched Uncommon with $18 million funding in Series A (funded by Spark Capital, Zeev Ventures, and Canaan Partners), received hundreds of sign-ups, and partnered with top technology companies like Lyft, Aflac, Etsy, and more.
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