Predictive Recruitment: fact or fiction
What is machine learning?
If machine learning was known for a long time, why has it become important right now?
Everything can be calculated or predicted. An insurance company can calculate how likely someone is to cause damage, health insurance can optimize the healthcare premium, psychologists can better analyze the causes of depression, one can convert written text to digital text and Adver-Online can predict how many candidates one can expect for a certain recruitment campaign.
Why an e-recruitment platform? How dit it orginate?
The second source is knowledge of the account managers of Adver-Online. These compile the optimal configuration of distribution channels and check the results of the predictive machine learning models. We do not blindly trust our models and put a lot of effort into validating the results. We use the knowledge of the account managers in such a way that we focus on quality over quantity. Models quantify theoretical constructs, but do not convey any substantial meaning. Because quality is hardly quantifiable, the models focus on quantity instead of quality. So if we determine everything by our models, we will get more candidates, but the quality of these candidates will not be taken into account. By combining the knowledge of the account managers with the predictive power of our machine learning models, we achieve the optimal result.
The third source is Social Media. You cut yourself in the fingers these days if you don’t use social media in your campaigns. We work closely with Wonderkind (formerly Recruitz) to put together optimal social media campaigns. Job boards only feature active job seekers, but almost everyone is on social media. Through active and accurate targeting we can reach the latent jobseeker and in this way attract more, but above all good quality, candidates for the campaign. All data from the social media campaigns have enriched the data of Adver-Online.
Is machine learning and artificial intelligence the holy grail
However, one should never blindly rely on machine learning models. Machine learning and artificial intelligence often only scratch the surface. A good intuitive example is the strongly significant correlation between the number of ice creams sold and the number of child deaths from drowning. If we were to blindly trust the results, we would immediately remove all ice cream from the shelves. However, there is a clear underlying variable here, namely temperature. When the temperature goes up, people eat more ice cream and therfore swim more. This is a very simple example, but errors like this can be less obvious. At Adver-Online we have our managers who can avoid mistakes like this, by merging their business rules with predictive models and actively validating the outcomes.