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3 Ways to Improve Talent Acquisition with an Effective Data Strategy

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Read more about author Sean Thorne.

Recruiting top-tier talent is always a priority, especially as businesses look to staff back up or expand in new directions once the COVID-19 pandemic recedes. It’s a challenge made more formidable as businesses look to expand their talent searches for the dawning age of long-term remote work. A report from iHire showed that 86.2% of U.S. companies are currently hiring, which is a 13.4% increase over 2019. However, 77% expect a shortage of qualified applicants in the coming year. In a challenging labor market, a savvy Data Strategy can make all the difference. 

Currently, only 21% of HR leaders believe their organization uses talent data analytics effectively to inform business decisions. But developments in the commercial data space can help to close this gap for talent professionals by allowing them to leverage more accurate, high-quality B2B, professional, and firmographic data to improve hiring outcomes. Here are three effective strategies to consider: 

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Find the Right People by Searching Selective Characteristics 

How often have you heard a hiring manager say that they absolutely love Employee X and would love to have two more just like her? Now they can. For businesses in a position to rapidly scale, look-alike candidate searches can help to narrow down the list of candidates quickly by finding individuals that share key characteristics with existing top performers. These characteristics may vary across different businesses, but layering qualities like average tenure length, performance ratings, and industry background over the traditional skills-based filters many recruiting tools regularly utilize can help to zero in on a great hire. This data, often sourced from social profiles, resume copy, or other sources of professional data, can be acquired at scale from quality data providers. 

Using this cumulative data, companies can pinpoint what they want in new hires and prioritize those qualities in their job postings and proactive outreach. This data can add dimension and depth to a skills-based search. For example, if a hiring manager is seeking candidates with CRM skills, the pool of CRM-competent candidates is fairly large. However, if current top performers also came to the role with five or more years of experience working in financial services, then adding these additional layers to the search may produce a smaller pool of more promising potential hires. Likewise, candidates with less CRM experience, or experience in less complementary industries, will fall away. 

When considering applicants, enriching their candidate profiles with aggregated data such as candidates’ supervisor feedback, the size of their previous company, and feedback from past employers could further narrow your search, drastically decreasing the time spent scrutinizing profiles and increasing time spent interacting directly with promising candidates. Recruitment applications utilizing enriched data can also contextualize demographics such as location, job titles, past experience, industry, and more.

Expand Data Coverage to Discover Hidden Gem Candidates 

The COVID-19 pandemic has brought many changes to the workplace, and one of the most enduring appears to be the widespread embrace of remote work. For hiring managers, distributed workforces present a challenge and an opportunity. Instead of being limited to their geographic area, employers can cast a far wider net for hard-to-fill roles, increasing talent pool diversity in the process. However, breaking out of your local hiring bubbles requires more data resources than hiring close to home. By leveraging high-quality data sources through talent acquisition platforms, recruiters can widen the field of candidates.

One of our company’s prominent clients is a diversity recruiting and retention platform that connects underrepresented talent to roles in highly visible sectors. Their mission to connect professionals from underrepresented and marginalized groups with employment opportunities was limited by their access to data resources reflecting a global talent pool. They were able to leverage professional data to add millions of new global profiles to their platform, allowing them to identify more professionals from underrepresented and marginalized groups to connect with employment opportunities.

Build Upon and Adapt Candidate and Company Data 

Often, candidates are best understood not only on the basis of their stated skills and history but on the nature and character of the companies and ventures they’ve been a part of. Once a candidate has made it through the initial screening process, it’s still important to verify the information they provided and to add additional context to their resume. 

Furthermore, there is a growing need for tools to utilize and visualize the data to maintain ease of use and efficiency. 

Enriching a candidate profile with company data sourced from a high-quality provider can add this needed texture. By understanding the candidate’s previous employers, their revenue, and operating structure, hiring managers can learn more about the candidate’s ability to adapt to the environment and role they’re seeking. Enriching a profile with company data can help to better understand things like salary expectations and career pathing needs, before the candidate even sets foot in your office, or logs onto your company Slack, as the case may be. The challenge here is managing data at scale and creating the tools to analyze it is a labor- and resource-intensive challenge. 

Onward

The new realities of work and recruiting demand a fresh approach to data. It’s no longer sufficient to identify local candidates with the relevant skills on their resumes. Whether sourcing talent for a fast-growing startup or a monolithic legacy business, new sources of professional data open up new opportunities to source better, more diverse candidates who are more likely to succeed. A savvy Data Strategy can reduce search times, cut down recruiter workload, and boost retention and productivity, all by applying new data layers to the existing candidate pool, enriching applicant records, and expanding the candidate consideration set. 

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