Elanor is an HR executive at Unicorn marketer. She\u2019s been involved in the recruitment process for six years now. Every year they do a campus drive at the most prestigious college in Chicago. They\u2019re always on the look for a promising candidate for a challenging role as a Digital Marketer. Elanor has been maintaining a spreadsheet of rejected candidates for the same post and logging the reasons for rejection as well.
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The reasons read as follows – lack of analytical and writing skills, poor communication, showed no scope for management abilities, and so on.
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So, the above scenario is a representation of how there\u2019s enough scope for the HR department at Unicorn marketer to make data-backed decisions with predictive analytics. Before we jump into predictive analytics in hiring, let\u2019s quickly define predictive analytics in general.
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Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events<\/em>.<\/em> – <\/em>Wikipedia.<\/em> Let\u2019s get back to the example we began with. For instance, let\u2019s say, 31 candidates, have been rejected in the last six years – future hiring process can certainly be streamlined by taking the right steps to attract the right talent possibly by mentioning it in their hiring announcements – posters, display ads, first calls, interactions, and so on. How do we conclude so? It\u2019s by adopting predictive analytics in the recruitment process i.e utilizing historical data to make predictions about the future.<\/p>\n\n\n\n
<\/p>\n\n\n\nSourcing the right talent<\/strong><\/h4>\n\n\n\n