Published by inostix, on 15/10/2014
To identify the elements of hiring success with HR analytics, you can use several state-of-the-art hiring algorithms these days that can successfully predict the characteristics of applicants who will perform successfully on the job. Predictive HR analytics can provide your firm with significant talent and business advantages, higher hiring efficiency and lower employee churn. Below are a five examples of analytical approaches but in the end the predictive possibilities are virtually unlimited, provided the availability of good data such as: candidate demographics & background data, psychometric data, structured interview data, assessment data, performance data, onboarding evaluation data, training data, etc.
Predictive hiring algorithms will allow you to re-evaluate your current hiring assumptions and raise the prospect of an upswing in profitability through data-based hiring. You will also be able to predict profiles of candidates with a higher risk of leaving your organisation or who are (or will be) performing below standard.
With predictive algorithms you can now use your data from previous campaigns to avoid contacting candidates or using channels that don’t provide a positive response and focus on those that do.
Organisations using predictive algorithms will be able to predict which new hires, based on their profile, are likely to be high-potential employees. This allows you to move them in to your high-potential programs with much greater confidence of success.
A predictive algorithm is able to predict when and why your employer brand strength will increase or decrease, compared to others. Combining external and internal employer brand survey data can increase both the tenure and the productivity of your workforce.
A growing number of organisations are using contingent workforce strategies. Finding the balance between fixed or contingent labour force is complex and decision-making can be improved by using predictive algorithms, identifying hidden patterns and connections that may lead to productivity risks.
Here’s my call to all of you: let’s take the emotion out of the hiring process and replace it with a data-driven approach! Rather than relying on the gut or intuition of hiring managers, businesses can analyse large amounts of data on applicants (some of which may seem irrelevant), using algorithms to test the information against the job criteria. What are you waiting for?
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