In our first article of the series we explained the process behind JobsTheWord’s ability to analyse hundreds of millions of tiny snippets of information, in a multitude of different formats extracted from the internet each day, to discover high quality candidates for our clients.
In essence we use a combination of Data Mining, Machine Learning and Artificial Intelligence, all of which have been in prevalent use across a broad spectrum of commercial sectors, but is only just beginning to evolve in the area of recruitment.
Machine learning itself has been around since the 1970s, but the explosion of data now available – human traces generated by individuals using the internet – has meant a terrific surge in anyone and everyone who has the mathematical and scientific knowledge and skills, taking advantage of this and developing software systems to extract datasets and analyse information intelligently.
The velocity at which the Big Data analytics industry is developing is both alarming and exciting, but it does mean we specialists who are creating and managing the software need to be continually reviewing and enhancing our systems to meet the increasingly sophisticated demands of today’s market.
As the benefits of using big data become more and more apparent, the technical skills required for the work we scientists do will become a highly prized commodity and at present the world is ill prepared. A recent study by McKinsey & Co: Big data: The next frontier for competition found the United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.
So where are we up to here at JobsTheWord?
Our Data Science team (who live above our Big Data team) have continued to adapt and design new algorithms that enable our machine learning to carry out more and more sophisticated artificial intelligence, which is at the heart of what makes what we do unique. Understanding a specific candidates’ universe helps us develop levels of interaction, usefulness and engagement not previously possible in the world of recruitment.
We haven’t lost the H2H when communicating messages
Digital communication has meant that H2H [Human to Human] or P2P [Person to Person] is now complementing B2B and B2C. H2H simply means a more human tone of voice is now required with the majority of online communications and recruitment certainly needs to follow suit, especially to find the best hires. This is especially valid when marketing a job – along with the employer brand – the opportunity needs to be communicated effectively to encourage the ideal candidate to apply for a role, something we continue to strive to perfect for our clients.
We’re ‘Up Close and Personal’
Recruitment for the larger, seemingly more impersonal organisations, has shifted over the years in an effort to become more efficient in its management of applications, which means the first encounter a potential passive candidate has is through their ATS systems, introduced into organisations for the management and measurement of their recruitment success.
However, candidates tell us that taking them down the fully automated route is not always the key to the desired outcome, which is why it is vital to ensure that any recruitment process does not get in the way of a great hire. Big Data is changing the face of recruitment by equipping us the power to be up close and personal, so that we never lose track of the fact that candidates are real people, especially when those candidates are passive and need cajoling to apply for a job.
One candidate told us:
My vision of working with people and organisations to improve their lot is to remove complication and bureaucracy and make the job [of applying] easy, inviting and engaging. [Company name withheld] was not; laborious, monotonous and laboured spring to mind. You can’t judge or filter people like this, you won’t find the inspirational, creative people that want to make a difference – they are likely to walk away basing their experience as an indication of the culture – which wasn’t very appealing.
How we help our clients
The development of our machine learning means we can now ‘guesstimate’ attrition rates even down to specific roles and companies. Having listened to our clients’ needs, we can help them build a relevant talent pipeline and avoid the need to continually recruit for vacant roles. It is reassuring for them to know that by using our technology they can engage a group of relevant candidates for roles they need to fill at the drop of the hat anytime in the future.
In addition, through our machine learning, we are now able to understand, deeply, the relationships between key skills and how certain levels of candidate translate this to their CV.
But more importantly, we understand what type of message a candidate wants and will respond to; with standard email marketing all but dead, what delivers results is when a candidate receives a highly relevant, useful, personalised email in their mailbox. It really is the only way to turn passive candidates into quality applicants.
UNDERSTANDING THE TECHNICAL TERMINOLOGY
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
The process of analysing data from different perspectives and summarising it into useful information – information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analysing data. It allows users to analyse data from many different dimensions or angles, categorise it, and summarise the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
A type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed; it focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension – as is the case in data mining applications – machine learning uses that data to improve the program’s own understanding. Machine learning programs detect patterns in data and adjust program actions accordingly.
Applicant Tracking System (ATS)/Candidate Management System (CMS)
A software application designed to help an enterprise recruit employees more efficiently. An ATS can be used to post job openings on a corporate website or job board, screen CVs, and generate interview requests to potential candidates by e-mail. Other features may include individual applicant tracking, requisition tracking, automated CV ranking, customised input forms, pre-screening questions and response tracking, and multilingual capabilities. It is estimated that roughly 50 percent of all mid-sized companies and almost all large corporations use some type of applicant tracking system.