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 originate?
If you can automate something, you have to do it. Otherwise, you are wasting money. With the e-recruitment platform we automate the process of package composition and we optimize expectations management for the recruiter. The recruiter indicates what kind of person they are looking for and the system shows the optimal campaign composition and the expected results in number of views, candidates and interviews. This is based on three data / information sources:
The first source is legacy data from Adver-Online.
This involves looking at the completed campaigns. This data is used as a source for the expected number of views, candidates and interviews and is predicted with the most recent data and machine learning techniques. All recent and most successful machine learning techniques, such as the Deep Learning Artificial Neural Network technique and the most successful Regression Trees, have been compared.
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.