The old job search paradigm for candidates is looking for posted jobs and sending applications.
In the pre- Internet era, a job search was limited to a local network of open positions found in places like newspaper classified ads. A typical job search began by scanning local or national classified pages, applying to a handful of jobs that seemed like a reasonable fit by phone or a mailed resume, and then waiting to hear back from employers.
In the mid-1990s, online job boards led to optimism that technology would make the job search more efficient. For the first time, online sites pulled together all job postings and made them keyword searchable to anyone with an internet connection. It was revolutionary, but it was still the same old paradigm for hiring.
Today, there is a New Paradigm that Promises to Make a Fundamental Change in Hiring
Today’s online job sites are more than just aggregators of job postings. They are employer review sites, career networking sites, and job marketplaces that collect detailed data on all sides of the job market – data on people, companies, skills and jobs.
This new paradigm for job search uses big data and machine learning to cut through the clutter of online job postings and candidates to curate a smaller, smarter set of job recommendations that better fit a candidate.
This is possible by leveraging the vast amount of information on not only jobs and companies, but also job seekers’ personalized skills, education, and other professional experiences), job, company preferences, work locations, and more. Candidates get matched faster with jobs and companies that best fit their skills and preferences and hiring managers get smaller pools of qualified applicants.
Consider eHarmony. It has mastered the ability to connect people based on data using the same strategy behind its business model to tap into the market of people in search of the perfect job, rather than the perfect partner. Elevated careers by eHarmony, requires job candidates to upload a resume and fill out a profile. Profile information is then paired with eHarmony’s matching technology to connect individuals with job matches based on skills, values, and personality. Searches can be conducted based on keywords and other parameters.
It matches job seekers with potential companies based on factors involving culture, personality and skills using a “Compatibility Scorecard”. The scorecard breaks down individual and company values so both can see what they have in common to understand compatibility. Along with eHarmony’s matching technology, the scorecard pairs candidates with job listings.
Time is another issue. Recruiting takes time and money. Replacing a bad hire is expensive, costing from a 3 to 5 times a person’s annual salary, depending on their position and how long they’ve been there.
Job seekers don’t typically like spending a lot of time filling out online job applications. In a recent report, 60% of job seekers said they’d started an online application without finishing it.
Matching jobs to job seekers is complex and there’s a multi-billion dollar recruiting industry devoted to it.
Data-driven recruitment may also increase a company’s performance and boost employee engagement. Information about a candidate’s personality and how the person fits within a particular company’s culture could play a much bigger role in addressing retention, engagement and productivity among employees.
Consider Wells Fargo. It developed a predictive model to help them with selecting qualified candidates for teller and personal banker positions. The company worked with Kiran Analytics to identify qualities that engaged high-performing employees in client-facing positions and looked for those specific qualities in potential candidates. At the end of the program’s first year, new employee retention was up by 15% for tellers and 12% for personal bankers.
Job seekers will soon apply directly for jobs matching search queries, as well as view compatibility information and predicted job satisfaction with a specific organization, helping lead to higher productivity and retention among employees.
Sites like Monster and CareerBuilder don’t pair job seekers with positions based on compatibility, skills, values. They just present candidates with thousands of job openings.
AI is clearly making job seekers’ journeys easier. And, there’s more good news: it’s only going to get better. Growing data sets and continuous AI learning means AI platforms like WorkFusion and Scout Exchange’s will only get smarter and more efficient.
The trick for job seekers to take advantage of all AI has to offer now and in the future. Job searchers need to make sure their resumes accurately align with job performance AND skill sets. You’ll need to feed AI data to be prepared and competitive for all stages of the employment journey.