BIG DATA & RECRUITMENT

Transcription

1 BIG DATA & RECRUITMENT GEERT-JAN WAASDORP, AUGUST 2015

2 TABLE OF CONTENTS FOREWORD 3 From available candidates to a pool with available talent (end 2015) 3 Welcome to the world of big data & recruitment 3 2 WHAT IS BIG DATA 4 From data to recommendation 4 Big data and recruitment 4 3 BUSINESS CASE AVAILABILITY ON LABOR MARKET LEVEL 7 Big data on labor market level 7 Case: How more data leads to more intransparency 8 From data to insights and predictives 9 5 AVAILABILITY ON TARGET GROUP LEVEL 10 Salary and working conditions 10 Mobility drivers 11 Reaching the target group 12 Key data of own recruitment efforts 13 Where are we going? AVAILABILITY ON INDIVIDUAL LEVEL 15 Availability on individual level 15 Where are we going? 16 7 FROM AVAILABILITY TO AVAILABLE TALENT 17 Pools with available potentials 17 CLOSING THOUGHT 18 INITIATORS 19 2

3 FOREWORD 1 Labor market profiles and CVs are richly present on the web, due to the fact that people consciously (and unwittingly) leave their personal information behind, even if those are not related to the labor market. Varying from social media like LinkedInXing/Viadeo and CV databases up to and including consumer panels and dating websites. Profiles are becoming easier to find, easier to be unlocked, linked to each other and enriched by new type of people aggregators. Developments such as the Watson technology from IBM or Workfusion drive unlocking (flex)workers worldwide. In theory, this provides a strong accessibility to profiles globally, however whether these profiles are suitable and workforce is available for talent acquisition is another question. In this paper we scrutinize the availability of a workforce and profiles with the use of big data. 1 Availability translated into terms such as scarcity and recruitment feasibility within a local, national and international labor market. 2 Availability translated into indicators of drivers, mobility and motivation on the labor market as in benefits, salary, media and working conditions. 3 Availability on individual level. There are many concrete developments that indicate whether a person is available. This means that on individual level estimations of profiles can be made; whether a person is actively looking for a job and/or if he is open for a new step in his career. FROM AVAILABLE CANDIDATES TO A POOL WITH AVAILABLE TALENT (END 2015) In this paper we look at big data and recruitment on the level of available profiles. Data is also perfectly useable to classify profiles and workforces based on for example cultural fit, competencies, quality and other dimensions. Dimensions that translate profiles and potential candidates to qualified talent. This step and how to use data for that purpose is the scope for the paper that will come to market by the end of WELCOME TO THE WORLD OF BIG DATA & RECRUITMENT The use of data and new technical developments gives us almost unlimited access to profiles on a global scale, helps us to understand how to find and recruit these profiles more effectively and gives us the opportunity to classify (potential) candidates in qualified talent. The possibilities seem to be (theoretical) unlimited. Some great steps are being made all over the world. We have tried to collect some of the greatest examples. Suggestions and comments are appreciated and can be mailed to Geert-Jan Waasdorp ([email protected]). Enjoy reading this paper, On behalf of Carerix and Intelligence Group 3

4 WHAT IS BIG DATA? 2 The exponential growth of (measuring) data, has clarified the opportunities of using big data. However, big data is not new. The term has existed for over 20 years, but didn t become a hype until McKinsey s publication in 2011: Big data: The next frontier for innovation, competition and productivity. There is no simple way to define big data, except that it does not mean a big amount of data. On Wikipedia the following definition is used: Big data is a broad term for datasets so large or complex that traditional data processing applications are inadequate. In other words, if you can process and modify data with Excel, it is not big data. Many companies have made their own set of rules to determine and define big data. A semantic debate, because it is much more relevant what (big) data can be used for In this paper in relation to recruitment. FROM DATA TO RECOMMENDATION The journey (table 1: hierarchy of recruitment data) starts with having access to data, structured and unstructured zeros and ones that we call information. The next step is to combine and translate data to intelligence. Intelligence can be defined as comprehensive results, for example a population consists of 50% males and 50% females. The third step translates these intelligence to insights. An insight could be that there are relatively more or less females within a certain talent population. The last step translates insights to recommendations and predictions that matter. The best known recommendation is the one that amazon.com or bol.com makes based on your profile and behavior on their website, as well as options that you may or may not see on your screen. In this example a recommendation could exist of certain employer benefits or recruitment channels to more or less attract a certain gender and a prediction about how the population will develop in the next 10 years. BIG DATA & RECRUITMENT The world of big data and recruitment is still in development. The most common form of data is intelligence. With this we mean nicely formatted presentations with research and data with the aim to sell their products and services. Organizations that go beyond that (of which we will provide examples in the next chapters), are mostly what we call single source insights, meaning that organizations only make use of one source of data (single source) from which insights are gained. These parties still have to take a couple steps to enter the world of big data & recruitment. SOURCE TRANSLATION EXPLANATION Data information 0-1 Intelligence comprehensive results 50/50 Insights insights more or less Recommendations recommendations which means Predictives predictions we predict that TABLE 1 I Hierarchy of data 4

5 WHAT IS BIG DATA? 2 The following steps are essential: To interlink multiple data sources To provide relevant insights To provide recommendations To make predictions To offer click & play implementations that provide a better and faster recruitment outcome In 2020, the way employers recruit will be changed dramatically. For certain parts of the process we already use products and services based on data. However, all these products and services have not been yet been integrated into our recruitment processes and systems. It is only a matter of time before we fully integrate those as well. A great implementation of big data would be a recruitment system that can provide expectations about the number of expected applications based on used recruitment channels, working conditions and salary offered to candidates. The same system can also provide recommendations on which employer benefits and recruitment media lead to certain expected results. Broadbean, together with PepsiCo made a first important step with the announcement of their Big Data Analytics Suite. They already operate on insight level. Another good example is the Tamo programme in Carerix. Tamo has been developed by Carerix partner HRlinkIT and is fully integrated into Carerix. Tamo allows all application channels to be registered in detail and connected to specific job applications. The system provides insights to optimize the recruitment process. The next step in this market is to make sure that with one push of the button, all insights can be translated to vacancy texts and publications. The recruitment business is changing rapidly on various independent acting single source parts. These parts will be discussed in the following sections. Keep in mind that data is the catalyzer of these innovations. The size of that (big) data is less important. It is relevant that data provides more and easier access to available and suitable profiles and talent. Source: Broadbean and Tamo Big Data Analytics Suite from HRLinkIT, integrated in Carerix 5

6 BUSINESS CASE We expect that in 2019 recruiters can upload a vacancy to the cloud or make a picture with the recruitmentapp and gain direct insight into: The number of available candidates in their own talent pool The number of available candidates in the market Recruitment feasibility Expected time to hire Expected cost per hire Extent to which working conditions are competitive Extent to which salary is competitive Attractiveness of the vacancy text Sustainability of the function And that we obtain recommendations for: The best ways to recruit (recruitment channels, media) Text changes based on motives and drivers Adding competencies, skills Alternative functions and recruitment opportunities The vacancy will be placed in a navigation screen, where it is possible to use certain buttons to directly see the expected outcomes. For example, what would happen if we would offer an increase of 10% on the salary, an improvement of the vacancy text or an extension of the lead time of the vacancy with 14 days? Based on predictive analyses recruiters can easily see what is realistic for a vacancy and recruitment plan. One click can then modify vacancy texts or a salary offer, start the execution of the recruitment plan and/or invite available candidates. The most exciting part is that all options mentioned above already exist. However, right now they are only available as single source or as stand-alone option within a system, API or as web service. In the following chapters the most important possibilities of those options will be discussed as well as how that will look like in the future. 6

7 LABOR MARKET LEVEL 4 That Google is able to predict an epidemic is one of the classic big data stories. The same story is applicable to the labor market. When the Dutch company Nedcar was close to bankruptcy in February 2012, jobboards in The Netherlands witnessed a strong increase of traffic and CVs from the area of Born, the location where Nedcar is based. Increasing activity of jobsearches from certain areas and/or IP addresses can be traced back to conditions that affect supply and demand in the labor market. When we combine data from the same area with actual job counts in these areas, for example recorded by job aggregators like Indeed or data sources like Jobfeed, AC Nielsen, Wanted Analytics or AnzeigenDaten, we obtain a first draft of the supply and demand trends in a particular region. We can go even more in-depth by looking at traffic through the geo-data on vacancy level and see if specific occupations are more scare or popular in time. In other words, we look on vacancy level where traffic is coming from. It is even possible to analyze from which region/organization the interest originates. This is relevant information for recruiters that use active searching and are looking for passive candidates (also see chapter 6). BIG DATA ON LABOR MARKET LEVEL Activity on the labor market is measurable on vacancy level. Imagine that you could obtain all details from all vacancies in a certain (geographic) market, like Germany or London area; this data could be turned into a real time development of supply and demand. In the United States, Careerbuilder one of the market leaders built a supply and demand portal based on the following data: 51 million active job seekers 135 million profiles/cvs 19 million salary benchmarks Approx. 25 million unique vacancies annually The best thing about the supply & demand portal of Careerbuilder is that they make use of three data sources: CVs, vacancies and traffic on jobboards. The downside is that only Careerbuilder s data is incorporated, which makes the portal theoretically incomplete. However, looking at the volume (big data), their insights are still significant. WantedAnalystics (Wantedanalytics.com) transcends the number of data mentioned before, with a database of 100 million unique vacancies (4 times as much data as Careerbuilder), providing a complete picture of demand within the United States but misses the supplies. Another interesting source, less detailed than the supply & demand portal of Careerbuilder, but global (instead of only the USA) and open, is the Talent Shortage Explorer from Manpower. This tooling provides insights for hard-to-fill vacancies on national level. Example of the supply & demand portal of Careerbuilder 7

8 LABOR MARKET LEVEL 4 USG developed Lami (local labor market insights), an equivalent of the supply & demand portal of Careerbuilder. LinkedIn, ADP and Randstad also provides similar reports with the aim to give intelligence about get supply and demand. Talent Shortage Explorer from Manpower Nowadays, data and tooling is more available and more frequently used by HR- and recruitment managers for its insights into scarcity, recruitment feasibility and availability of potential talent. Even though data is more accessible and complete than ever before (which is a good development), it is often incomplete, single sourced and acting on intelligence level. This results in all kinds of challenges for the interpretations (don t take single source data as valid by definition). The moment that these single sources are combined will open a lot of new opportunities to the future. CASE: HOW MORE DATA LEADS TO MORE INTRANPARENCY Increasingly more is known about scarcity on the labor market in the Netherlands, which consists of a workforce of approximately 8 million people. In this small population more than 10 sources try (and compete) to make the labor market more transparent. Other tooling that provide insights for the current recruitment feasibility and scarcity on the Dutch labor market are the governments werkverkenner of the UWV ( werknemer/solliciteren/test/werkverkenner) and arbeidsmarktkansen.nl of Intelligence Group. Demand and supply are combined in both tooling. Both UWV and Intelligence Group use multiple data sources to get insights so they can provide a more complete image of the labor market. Another frequently used source in The Netherlands is Jobfeed.nl from Textkernel (recently bought by Careerbuilder). At this point we already mentioned seven different sources, but it is just a (important) start. Many regional and sectoral organizations like the leading Brainport Development (region Eindhoven, one of the smartest hubs in the world) have the ambition to develop platforms that combine some of the previously mentioned data with other sources from CBS, DUO, ROA, Google and the Chamber of Commerce. 8

9 LABOR MARKET LEVEL 4 FROM DATA TO INSIGHTS AND PREDICTIVES The beauty of the tools mentioned in previous sections is that they do not only share data and intelligence, but they also provide insights. This means that tools qualitatively show the feasibility and scarcity developments. The downside of these tools is that data is presented in several places, in various ways and with different definitions which contradicts the aim of the tool itself: transparency. The opportunities of big labor market data is developing rapidly. More and more (single source) data is becoming available and combined in different fragmented and competitive initiatives. On the short term this leads to a more intransparent Dutch labor market, but hopefully ends when the winner takes it all. Source: pilot of Brainport Development 9

10 TARGET GROUP LEVEL 5 Availability does not only provide information about whether the target group is actually present in the market and whether they are willing to change jobs (see chapter 6), but also under which conditions they are available. Ranging from key figures such as lead time and hard drivers such as salary, benefits and working conditions up to and including pull factors and mobility drivers. Next to data available in the market, to provide extra insights into under which conditions potential candidates are available, part of this information is also present if not hidden within the systems of the own organization, such as key figures of recruitment. SALARY AND WORKING CONDITIONS The labor market consists of various (traditional) parties specialized in salary and working conditions benchmarks, such as Mercer and Hay Group. However companies as Indeed, Wagerindicator, LinkedIn and glassdoor are catching up by collecting a big amount of data and disclosing this to recruiters. Information on national, regional, municipal and organizational level. A great example is the introduction of Glassdoor.com in Europe that provides more transparency in salaries on corporate level. Also data about working conditions are provided semi-openly more frequently nowadays. Think about sources as collective agreements that are available as open data. Another important development can be seen with freelancers and flexworkers. With aggregated data from Vendor Management Systems (VMS), flexworkers will obtain a lot more actual sight into rates at that time in the market. When a hirer (an organization that allocates an assignment) posts its assignment on an online marketplace (VMS/ATS), he will obtain insights into: The average hourly rate from 10 previous assignments Rates and conditions for similar assignments The development of pricing (as an indication of scarcity) Nowadays, an employer only has a limited view on hourly rates. Online marketplaces that connect supply and demand provide more and more transparent view on pricing. An great example of this is Elance.com. Elance.com Glassdoor.com: benefits of Deloitte 10

11 TARGET GROUP LEVEL 5 MOBILITY DRIVERS Next to financial conditions, other aspects also play a relevant role in enabling potential candidates. Think about the employer brand of an organization, the working atmosphere up to and including substantive issues and training possibilities. In total we recognize three levels of mobility drivers: 1 Critical recruitment factors: Aspects that are important in making a decision about an employer and especially typical for the target group. With these aspects we can easily outline their mobility drivers, like bonus for a sales person. 2 Moving motives: Aspects that are not considered most important, but are typical to the target group. These aspects can be used as supplement to stimulate the target group to move, like the willingness to contribute to others for nurses 3 Preconditions: Very important aspects, but not typical to the target group. For the target group it is natural to see these conditions in a job. If these aspects are not present, the target group will not move like a (good) salary or attractive work. The harder mobility drivers, such as salary and financial aspects, are often seen as preconditions whereas the softer motives, such as culture, leadership and career possibilities, are conclusive in choosing an employer. If the choice has to be made by flexworkers/ freelancers, there is relatively more pressure on the harder aspects. Despite the conclusive character of the softer motivates, little attention is given to this information. The shabby conditions of most vacancy texts illustrates this. A lot of data is available to get insights in mobility drivers, both local as international, information about culture, critical recruitment factors and more. These data sources are not yet integrated with other data or systems. They stand alone, are partially open and free of charge. Take for example the online tooling company compass based on the research of Gert Hofstede ( where you get more insights in the cultural differences between countries. Also in the App Store and in Google Play you will find more and more Apps and companies that provide information about cultural differences between countries. A great tool (free for use) is developed by Trompenaars Hampden-Turner (THT) Consulting (a part of KPMG s People and Change Practice) and called culture for business app with can be downloaded on 11

12 TARGET GROUP LEVEL 5 Differences between culture (and benefits) are important to understand. At the same time there are enormous differences in mobility drivers that need to be used for recruitment purposes. To recruit talent within in a country, you can use data from the Global Talent Acquisition Monitor. An independent and complete research in the 45 important recruitment countries which give insights in behavior, candidate expectations, media and all other data you need to recruit effectively. Useable for local and global mobility purposes. Another source is the data from decoding global talent from The-Network. Data is based on the partners of The Network (jobboards), but free of charge. Both parties also combined their power in the globaltalentbarometer.com which gives interactive insight in global mobility drivers, pull factors, relocation elements and much more. Every target group has its own niches with regard to orientation and media behavior. The choice for the right recruitment strategy is essential. Do you reach active or passive job seekers with the choices you make? Or both? The mix of reaching active and passive job seekers decides the availability of profiles. Example of Globalrecruitmentchannels.com with data from Poland, Greece and Brazil REACHING THE TARGET GROUP Seduce talent with using the right arguments, drivers and cultural aspects is step 1. The next step is reaching them by using the right recruitment and media channels. A Pavlovian reaction is to use LinkedIn, Google, social media or jobboards like Monsterboard, Stepstone and Indeed, but with the help of data from Google Analytics and independent data from the Global Talent Acquisition Monitor, access can be granted to the media and orientation behavior of the target group. The choice of media decides on the access to profiles, for example by using a CV database, social media or own talent pool for recruitment purposes. The choice for the right media decides the extent to which talent is available. Globalrecruitmentchannels.com is the only tool that provides local intelligence in orientation behavior from jobboards to newspapers and recruitment consulting firms, based on specific defines target group in 45 different countries. This global data is already integrated in the recruitment processes of different international recruiting companies and will be integrated in different recruitment software (ATS). That way media data is more and more integrated available within the recruitment process. Companies likes Broadbean also have enormous global databases with mostly data of jobboards that are making this available for their clients, for example in the Big Data Analytics Suite as mentioned before and Carerix/Tamo. 12

13 TARGET GROUP LEVEL 5 KEY DATA OF OWN RECRUITMENT EFFORTS It s critical that an organization has grip and sight on the most important recruitment of a target group, as this information is essential for professional recruitment, expectation management and execution of the right tasks. The best known and used key figures are cost per hire and time to hire and the most figure is quality of hire. Take the numbers from the following example: AVERAGE IT PROFESSIONALS SUPPORTIVE ORGANIZATION ADMINISTRATION Views vacancy Conversion 2,10% 1% 8% Amount applications 8, Amount applications - intermediaries 2,1 8 0 Time to hire (in days) Time to fill (in days) Cost of hire Quality of hire 11% 8% 16% Bad hires 10% 8% 4% Candidate experience 7,1 6,8 6,6 NPS score Most effective channel Referral Intermediaries Own website TABLE 2 I Example of relevant key figures The example illustrates that the organization is performing well, compared to the Dutch average. If we zoom into the target group level we see big differences, almost unrecognizable, in relation to the generic key figures of recruitment. The key figures related to the target group, usually downloaded from recruitment system reports, provide a lot of information about the availability about the individuals in relation to the organization. Which target group is harder or easier to recruit? Can we recruit without help of intermediaries? How attractive is the organization (looking at the conversion rate, NPS and quality of hire)? A lot of the data belong to the management reports of the recruitment department. The next step is to connect own recruitment data (single source) to external data like media, recruitment channels, mobility drivers etc. of the target group. For example: What happens to the conversion rate when vacancy text do not match the mobility drivers of the target group? What is the effect of a 10% increase in salary on the time to hire? Through which recruitment channels can we increase conversion? These analyses belong to higher recruitment mathematics and have a top spot on recruitment managers bucket lists. As shown before, different companies made the first steps forward by connected multiple data sources: Google Analytics, ATS data and external data. The connection enables the option of integrating data in dashboards. External data plays a relevant role on target group level, for example in salary and employer benefits benchmarks and data about working conditions. 13

14 TARGET GROUP LEVEL 5 WHERE ARE WE GOING? There is no lack of recruitment data and intelligence on target group level. Parties such as Glassdoor.com turn this data into insights already, but big developments are taking place within recruitmentand vendor management systems. Aggregated data is downloaded from organizations systems, such as rates, working conditions (hours, salary, retirement and more) and linked to lead time, response and conversion. At the same time it will be possible, at the end of 2015, to upload a vacancy and receive advice about hard and soft aspects in a vacancy text in relation to the moving motives of the target group. When these recommendations are linked to historic data such as lead time and conversions, predictions can be made about the expected recruitment result per recruitment channel. It will take until at least mid-2017 (earliest) But it is definitely part of the big data impact that will change our recruitment business in the next years. 14

15 INDIVIDUAL LEVEL 6 The actual availability on individual level is most important in the end. Especially when the recruitment strategy has a strong sourcing and talent pooling component. The effectiveness of sourcing becomes bigger if you know whether a certain person is available on the labor market. Big data makes it possible to make an educated guess and see whether an individual is available. For example, when someone suddenly changes her/his LinkedIn/social media profile on certain important aspects. In that case the chances are higher that this person is available on the labor market or that he/she is thinking about leaving a current position. The appointment of a new manager, reorganizations or the length of contract are direct indications of availability. Algorithms already exist, using almost 100 different predictors for availability. AVAILABILITY ON INDIVIDUAL LEVEL In the United States we see a couple of new parties with algorithms that predict availability. Gild.com developed a predictor (based on an analyses of 30 million job switchers) that provides a monthly overview of availability on individual level. Or an aggregator such as Entelo (Entelo.com) characterizes candidates as more likely to move. These profiles have a 30% chance above average to switch jobs within three months. Example Entelo.com about availability on individual level Availability is not only calculated on individual level. Joberate.com developed the J-index, which can confirm to what extent employees within a certain organization are looking for another job. Even though analyses are not on individual level, Joberate.com comes close and it is interesting for recruiters to know where to hunt. Much more tooling exists which predicts if a certain candidate will match with the culture of the company or matches the ambitions and potential asked from him or her. This makes from a candidate, the needed talent. A lot of interesting developments are happening now and we will discuss them in the next paper. The scope now is how to get insights in the availability of potential candidates without real contact or input from the person him or herself. Example of Gild.com about availability on monthly level for the labor market 15

16 INDIVIDUAL LEVEL 6 WHERE ARE WE GOING? If there are predictions on individual level like Gild.com and Entelo.com, this obviously refers to predictive data and recommendations. It makes sense that jobboards with CV databases (a CV in a jobboard database is more likely to be actively looking), social networks like LinkedIn, Xing and communities like Stackoverflow will also integrate this in their services to their client or integrate this in the recruitment dashboards. Arrows are pointed towards individuals with the biggest chance on availability first. Other developments (already available in the market) enrich profiles with written articles, blogs, vlogs, video s or interviews. This extra information is important for recruiters and sourcers to find a connection to the approached person. To be more effective in reaching out to these potential candidates, we now see developments such as Crystal (Crystalknows.com) where, based on messages, profile sketches of individuals are made. This enables effective and targeted communication with potential candidates. What should you write in your and what should you definitely not write? Source: Crystalknows.com Next to availability scores other parameters that provide more insight into mobility drivers of individuals will also be defined in the future. Think about the following: Characterization in terms of profiling Mobility drivers Desirable salary Employer benefits Desirable working conditions 16

17 FROM AVAILABILITY TO AVAILABLE TALENT 7 The access to CVs and online profiles seems unlimited, even though these profiles are often incomplete or not completely suitable. This paper demonstrated the extent to which knowledge of profiles are available for the labor market. From recruitment feasibility and the moving motives of targets up to and including the labor market activity on individual level. This paper spoke about a regular profile. However, employers are on average not looking for a regular or average profile. They are looking for talent. Individuals that suit their corporate culture and desired competencies. POOLS WITH AVAILABLE POTENTIALS Nowadays talent pools consist out of pools with available potentials. A collection of people in which the employer is interested and who are also interested in the employer. These persons are registered in a database of an employer and classified based on their functions. Or individuals that joined a (sub) LinkedIn/ Xing Group of an employer, or expressed their interest for a certain employer within Facebook or in other communities. Individuals meet the following criteria: A talent pool usually is not more than a file of available potentials with a content strategy of a couple of annual newsletters. Transforming a pool of available potentials to a pool with available talent. The real talent pool becomes increasingly important and more a reality with the use of big data. This makes sure that you, as employer, have access to an enhanced file with qualified candidates. The following ways help in narrowing down the pool: E-assessments Video Match with internal (big) data Gaming Craftsmanship Cultural fit Our next paper by the end of 2015 will be about this topic and can be downloaded from Interest in the organization Meet the desirable competencies/function demands 17

18 CLOSING THOUGHT While reading this paper you must have thought one of the following things: Awful English (we apologize), yes, sure, old news, what are they talking about? or we are already doing that. Whatever your response was (let me know on the recruitment business is changing rapidly, where data and automation play a big role. It will take over and change the way we execute our job now. In the meantime, we think it is very normal that everybody applies to vacancies online, that we search in hundreds of millions of profiles online for free, that with CV parsing technologies job interviews are automatically uploaded into the database and that with Search! and Match! technology from Textkernel the best match can be found with a database within seconds (see: By measuring media, automated reports of key figures are also becoming more common. Everything that was special 3 years ago, is these days perceived as normal. Not all of what we have written will become reality. Moreover, we did not discuss the actual chance yet, because we simply do not know that yet. One thing is sure, and that is that our game of access to available talent will change. Everything that is not running smoothly will be solved then, such as the link of multiple different data sources. A lot of data that is not disclosed yet will be disclosed within a couple of years when recommendations and predications automatically will be incorporated into daily work. The day you will receive a notification in your eye with the new Google Glass every time you see available talent comes closer and closer... 18

19 INITIATORS Carerix Recruitment Software is a complete online recruitment software system for recruitment and staffing agencies. It provides a single system to post job openings, recruit potential candidates and manage tasks. An extensive database is combined with smart technology to support current and emerging forms of recruitment and staffing activities. By contributing substantively to the realization of this research, Carerix wants to make a practical contribution to the further strategic alignment within the vertical chain or intermediate staff and employer. Carerix BV Rotterdamseweg 183c 2629 HD Delft T +31 (0) [email protected] Intelligence Group is an international research agency specialized in recruitment (marketing) data & insights. ig gathers information about the global labor market, analyze and advice since 2003 and makes her data available by recruitment tooling and integrating her data (Global Talent Acquisition Monitor) in recruitment systems and the recruitment process of international employers. By following trends and making connections between developments in supply and demand, we stay on top of the market with relevant insights to help employers find and attract the best talent in the world. Intelligence Group Marconistraat AK Rotterdam +31 (0) [email protected] 19