reshufl

You may have already read about our world-leading parser and search and match software. Our data analytics suite takes recruitment and candidate attraction to a whole new level.

Even the best search software is essentially only going to find what you are searching for. For instance, search for a “customer service manager” and you’ll find, (especially with our search), hundreds of potential candidates. But each of these candidates will already be carrying out this role. The incentive you are offering them is perhaps a better location, more pay or a nicer work environment.

What if you could not only find existing customer service managers, but also people whose next job should be a customer service manager? Now their incentive is much higher.

What if you could suggest to your users a way to increase their salary, or to learn a particular skill to gain promotion, or suggest locations where their current job pays better, or even to completely change their job whilst utilising their existing skills?

What if you could pinpoint where there is a shortage or excess of a particular skillset?

What if you could analyze your data and, at the click of a mouse, find out and summarize exactly what you have in your talent pools. How many candidates do we have in the ICT sector? How many software developers do we have in Dayton, OH?

With smarter data insights and big data analytics you can:

Without a proper understanding of your data, you’ll always be groping in the dark. You may be advertising a job when you have the perfect candidate in your own database.

Offering candidates a way forward, promotion, a better salary or even just guidance on what they are best suited to do is the best way to attract new people. It’s what recruitment is all about, it’s what reshufl is all about.

At reshufl we are constant parsing huge volumes of CV/Resumé and vacancy data that allows us to extract rich insights from current market data.

Our huge structured taxonomy of recruitment related terms available through our parser technology feeds directly into both our predictive and recruitment data analytics solutions. By constantly parsing huge volumes of data and using modern artificial intelligence and machine learning techniques, we are on the lookout for new and trending roles and skills around the clock. 

By using our taxonomy of recruitment related terms we are able to extract important concepts from the CV/job which allows us to much more accurately spot trends in data. 

reshufl has a unique advantage when it comes to analysing data and predicting carerr behaviour. For example, without the taxonomy and the compression of terms into concepts, the data one would end up with would be essentially meaningless.

Let’s say you have a million CVs. Each of these CVs has “job pairs”. i.e. candidate went from role X to Y, then from role Y to Z. Without conceptualizing the data you will simply end up with thousands of pairs such as 0.01% of “cust svc asst” go on to be a “customer service manager”, 0.003% of “customer service assistant” go on to be a “member svc mgr”. In theory, given we have over 10k variations of “customer service assistant” and over 10k variations of “customer service manager”, you could have a resultant 100 million predicive role clusters… utterly meaningless.

reshufl predictives gives meaningful insights like “35% of X go on to be a Y” where X represents ALL customer service assistants and Y represents ALL customer service managers.

The constant parsing of huge volumes of data allows us to accurately extract up to date role and skill valuation data based on a rolling timeframe so that these valuations are always fresh.

This insightful information can be fed back to candidates to help them achieve their career goals. It also identifies and can prioritize the most important skills

Based on our data, we can generate real time reports looking at specific aspects of the current or history job market.

For example comparing employment trends for a specific role in a specific area  will allow your business to focus limited resources more effectively.