How the Most Innovative Companies are Transforming Talent Data into Talent Intelligence

This case study shows how Intel leveraged HireScore’s Talent Intelligence capabilities to gain excellent candidates. Athena Karp, the CEO of HiredScore reveals in the case study how the most innovative companies turning “Talent Data” into “Talent Intelligence”. Karp pinpoints how to leverage Talent Intelligence to find the right talent at the right time and cost. The article concludes by showing how Talent Intelligence is not about increasing the amount of information per candidate; it’s about increasing decisions per day. 

Talent In Talent Intelligence

Companies are investing in modern ATSs, CRMs, assessments, virtual assistants, and web sourcing tools with the hope that improved candidate data capture will help recruiters and sourcers recognize and focus on top talent. At the same time, recruitment marketing teams are attracting more applicants than ever before with compelling employer value propositions, programmatic advertising, modern career sites, and application processes as simple as a single click. 

A low barrier to applying, coupled with record unemployment, means the floodgates have opened. Recruiters are expected to sift through hundreds, if not thousands of applicants, for a single position. They are required to collate resumes, application questions, chatbot conversations, complex assessments and games, interview notes, and other candidate information, all while giving each candidate their undivided time and attention and a fair evaluation process. Beyond the importance of candidate experience, recruiters are also responsible for driving and delivering on business goals, including cost, speed, quality, and diversity and inclusion goals. 

See how @TalentTechLabs helped Intel to #source and screen high-quality #candidates using #TalentIntelligence: Share on X

Today’s recruiter isn’t struggling with too little information, the challenge is to identify relevant information. So how are the most innovative companies turning all of that “Talent Data” into “Talent Intelligence”?

Intel’s Sourcing Challenge

At the same time as companies are investing in attracting and organizing candidates, they are also searching for a return on that investment. For most roles, the near-term “value” of talent is ambiguous, so business leaders understandably tend to manage Talent Acquisition to cost-per-hire rather than to the quality of the talent that’s being hired. Innovative TA teams avoid the distraction of cost-per-hire as a driving KPI and instead focus on maximizing the quality of the talent they can acquire at their target cost. 

Intel’s Global Talent Acquisition team understood this challenge when it sought a way to deliver excellent candidates to every manager every time. They started from two simple observations: the primary drivers for people to join and stay at a company are the relationships they have with the people at that company, and nobody is better at building relationships with humans than humans. A small project team brainstormed on how they could give their recruiters twice as much time to build personal relationships with candidates without doubling the size of the organization. Carefully studying the workflows of the global recruiting teams, they saw some patterns that persisted across countries and business units. 

Two-Part Pattern

First, they saw that recruiters typically shared with managers the first five or so qualified applicants rather than the best five qualified applicants. Within a compliance-focused organization, knowing that someone is a “good fit” is not time-saving, recruiters must justify the decisions they make about each candidate based on the qualifications of the job. With sometimes hundreds of applicants, recruiters didn’t have time to manually extract and validate qualifications for every candidate in order to find the best, so they had to resort to a first-in-first-out approach. While FIFO may be objectively “fair” it is certainly a poor process for maximizing the quality of the candidates presented to managers.

Second, despite roughly a hundred applicants per position, only one applicant could ultimately fill it. Since the recruiters were finding the first five candidates, and often high-quality candidates apply later, it was almost certain that some of the best candidates were never looked at and had been rejected and now existed as a dormant record in the ATS and CRM. Despite millions of such candidates being stored in the ATS and CRM, the project team was disturbed to find that these databases were rarely used to proactively source talent.

The problem seemed to be that searching and sifting were usually more time-consuming than simply waiting for more applicants. Where sourcers were running searches the results were typically long unordered lists of candidates that had to be manually reviewed and despite the best efforts of the savviest boolean experts, critical requirements, such as years of experience, were impossible to filter on. 

@TalentTechLabs believes that #TalentIntelligence can help you source, screen, and attract highly-qualified #candidates. Check out Intel's success using Talent Intelligence: Share on X

These two critical bottlenecks led Intel to conduct an extensive search for talent intelligence solutions that could reduce the burden on recruiters to identify the best applicants by providing explainable scoring and mitigate the strain on sourcers by automatically surfacing qualified candidates from both the ATS and the CRM. The Intel project team found in HiredScore a unique solution that could remove both bottlenecks by going beyond a black-box matching algorithm. HiredScore’s proprietary “brain” (a customized-for-Intel unbiased artificial intelligence solution) automatically understood the minimum qualifications buried in Intel’s job requisitions.

This powerful proprietary machine learning and text comprehension technology was able to identify the experience in the candidates’ resumes, determine who satisfied those qualifications, and which candidates were likely to be interested in the role if approached. This application of HiredScore’s talent intelligence led the team to expand the scope of their project and re-evaluate the sourcer/recruiter workflow and capacity models.

Talent Intelligence Changes the Conversation

HiredScore’s proprietary-for-Intel “Brain” was built on three years of de-biased Intel hiring data and was optimized to understand what qualified talent looked like for every job level, location, and business unit. HiredScore has a solution called Fetch that runs automatically at the time a requisition is created in the ATS and searches both the ATS and CRM for qualified candidates that are likely to be interested in the role (a powerful two-side algorithm) in a fair and unbiased way. In this feature, the Intel team saw an opportunity to use automated talent intelligence to enrich the hiring managers’ experience. They updated the recruiting team’s workflow to include a review of the fetched candidates with the hiring manager BEFORE the requisition was posted.

This change had an enormous impact on the intake meetings with hiring managers. With zero effort the recruiter could show up with candidates in hand that had already shown interest in Intel in the past, were known to be qualified, and likely to be interested. This gave the manager an opportunity to re-evaluate the job requirements based on what real candidates looked like and to invite those candidates to apply immediately if they chose. Fetch’s AI is so powerful that Intel saw over 21% of the Fetch leads who were contacted by sourcers/recruiters decided to apply to, versus 1 – 2% of other leading passive talent tools.

Enabled by HiredScore’s talent intelligence, reviewing the fetch results before a req is posted, has yielded some important results for Intel.

Access the full case study by downloading the Report here


Rachel Athena (“Athena”) Karp 
CEO & Founder, HiredScore

Athena Karp is the founder and CEO of HiredScore, an artificial intelligence HR technology company that powers the global Fortune 500. HiredScore leverages the power of data science and machine learning to deliver deep hiring efficiencies, enhance talent mobility, and help organizations adapt for the future of work. HiredScore has won best-in-class industry recognition and honors for delivering business value and transformation in the HR industry. 

Prior to founding HiredScore, Athena was an investor in New York, most recently at Altaris Capital, where she managed and sourced healthcare. Previously, she was an Investment Banker at Bank of America Merrill Lynch, focused on public technology and media companies. Athena is continually active with efforts to alleviate poverty and improve the public education system in West Philadelphia, through Community Education Alliance’s network of charter schools with +1,000 disadvantaged students, focused on preparing youth with skills for the future of work. Athena holds a BSFS in international politics from Georgetown University Walsh School of Foreign Service. She is a member of the 2018 Class of Henry Crown Fellows within the Aspen Global Leadership Network at the Aspen Institute and is a member of the World Economic Forum’s Global Shapers.

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