[Whitepaper] Recruitment Analytics in a Programmatic Sourcing Era

in a Programmatic Sourcing EraRecruitment Analytics

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About the Author

Allan Schweyer

Allan Schweyer is the Founder of TMLU, Inc. in which he developsand delivers curriculum and conducts human capital management related research for a wide variety of clients including multiple government agencies, the Incentive Research Foundation, Lockheed Martin, and many others.

Allan is a recognized subject matter expert in human capital management. Prior roles include Staffing Subject Matter Expert at HR.com, Presidentand Executive Director of the Human Capital Institute (HCI) and partnerat the Center for Human Capital Innovation.

Allan is the author of the books, Talent Management Systems (Wiley & Sons, 2004) and Talent Management Technologies (HCI Press, 2009) and co-author of the Enterprise Engagement Textbook (2014). Over the past twenty years, he has published extensive articles and white papers in dozens of popular media and industry-specific publications worldwide, including Inc. andThe Economist Magazines. Allan has been recognized as among the “100 Most Influential People in HR and Talent Management.”

Recruitment Analytics in a Programmatic Sourcing Era

Contents

Introduction

Part One: Rethinking Recruitment Advertising

The Data Imperative

A Renaissance for Recruiters

The Future is Now, but the Metrics Aren’t

Figure One: Programmatic Recruitment Advertising Feedback and Learning Loop

Part Two: The New Recruitment Advertising Metrics

Table One: Programmatic Recruitment Advertising KPIs

Conclusions

Footnotes

References

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Introduction

The world of recruitment advertising has changed remarkably in just the past few decades.Only twenty-five years ago jobs were posted on physical bulletin boards at job centers, and listedin newspapers. Today, through real-time, automated bidding engines, algorithms negotiate behind the scenes to determine who will see which job ads, and where and when they will see them. In milliseconds, countless factors are considered, prices negotiated and ads displayed.This occurs every day – thousands of times each minute – across the web. Welcome to the ageof programmatic recruitment advertising.

Despite great changes in the way we advertise jobs, however, recruitment, (aka, “sourcing”) remains the process of generating suitable applicants for job openings. It starts with finding and attracting candidates and ends when those involved in the selection process receive sufficient qualified applicants.1

In sourcing candidates, recruiters rely enormously on recruitment advertising, a big and growing business.2 Conservatively, employers worldwide post about 3.3 million jobs each month 3 contributing to a worldwide annual recruitment marketing spend of at least $10 billion, 4 including approximately $6 billion in the US alone.5

The recruitment advertising market will likely remain a high growth industry for the foreseeable future. The US unemployment rate has stayed at or under 5 percent over the past ten consecutive months according to the US Bureau of Labor Statistics 6 and employers are once again citing skills shortages among their chief concerns.7

Yet, even though the employment market may already favor job seekers,8 few organizations will pour money into recruitment advertising without evidence of returns. Rigid budgetary discipline, a necessity for most organizations during the Great Recession, continues, despitethe recovery.9 Today, decision-makers demand data and evidence of results.10 To furnish this evidence, recruiters should understand and adopt carefully chosen metrics relevant tothe current recruitment advertising landscape and where it is headed.

While a range of staffing and HR metrics inform and influence recruitment advertising,the measures of its effectiveness should align to its own key objectives and be within recruiters’ span of control. Recruitment advertising supports two key objectives: to deliver qualified applicants to a specific job opening (or to fill a talent pool) and to present the organization ina positive light in order to protect and strengthen the employer brand.11 In just the past five years, the metrics, benchmarks, and KPIs used to measure recruitment advertising effectiveness have shifted. This paper explores those changes and recommends a mix of revised and new measures based on the changing landscape of recruitment advertising.


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Part One

RethinkingRecruitmentAdvertising

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The marketing industry has undergone sweeping change since the advent of the world wide web in the 1990s. Recruiters, to their credit, discovered the internet as a medium for advertising before most others. Today, more than 80 percent of recruitment advertising is placed online, making it the largest single component of the digital advertising industry.12

Consumer advertising, by contrast, still utilizes television as its largest advertising medium (online, digital advertising is set to exceed it by 2018).13 Nonetheless, marketers have moved quickly to understand the programmatic and analytical advantages of digital advertising.

Already, as of 2016, automated systems place the majority of digital advertising; 14 a process by which software and algorithms instantly process massive amounts of data to determine what ad content should go where, when, and to whom – and at the best price – to achieve maximum Return on Investment (ROI).15

Not surprisingly, growing numbers of recruiters are embracing programmatic ad placement, and the rich world of data and analytics it makes possible. First movers and fast followers are discovering the advantages inherent in reducing time spent on manual ad placement and in reaching active and passive candidates across thousands of channels; all the while controlling their budgets by paying only for applications received rather than impressions, clicks, or the duration of the posting.16

There is little doubt that recruitment advertising will follow consumer advertising in its rapid elimination of manual ad buying and placement over the next few years.17 With this shift comes the need to re-examine recruitment and sourcing metrics to determine which measures remain relevant and what new metrics, benchmarks, and KPIs recruiters should adopt as they evolve toward a more programmatic, predictive and personalized era of recruitment advertising.


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The Data Imperative

In a 2015 article in Forbes Magazine, HR thought leader Josh Bersin remarked: “Today, forthe first time in the fifteen years I’ve been an analyst, human resources departments are getting serious about analytics. And I mean serious.” 18

There is little choice. Fewer operations today are allowed to run on experience and instinct alone. Leaders have woken up to the fact that even “expert” opinion is only, at best, informed opinion. In 2005, the results of a two-decade-long experiment involving hundreds of experts across dozens of domains revealed that their predictions – concerning events in their own fields – were no better on average than that of the person in the street.19

This reality is illustrated in Michael Lewis’ best-selling book, “Moneyball.” In telling the story ofthe Oakland Athletics (A’s) baseball team and its analytical approach to the 2002 draft, Lewis demonstrates the dramatic improvement that evidence and data make in sourcing talent.The team’s expert scouts failed (like every other scout in baseball had for at least a century)to spot available, low cost talent... talent that data analysis helped identify very quickly.

By combining baseball wisdom with data-driven decision making, the A’s, with the third lowest payroll costs in major league baseball that year, made the playoffs.20 More remarkably perhaps, the A’s tied the New York Yankees – the highest paid team in baseball – for the most winsin baseball that season.21 Today, virtually every Major League baseball team, and many teamsin other professional sports, use a similar data-driven approach to sourcing and evaluating talent.22

In advertising as in baseball, analytics and data-driven decision-making are fast becomingthe way business is done. In 2013, 67 percent of the respondents to a 2014 advertising executive survey said that more than half of their digital efforts are driven by data.23 A 2014 survey involving more than 3,000 marketers revealed that almost everyone anticipates a greater role for data in their advertising efforts in the future and more than two-thirds already consider their marketing operations to be data-driven.24 Where recruiters are concerned, a 2016 survey by Jibe (a recruiting technology provider) found that almost three quarters of recruiters believe analytics are a high priority.25

Of course, much in the world of business, sports and recruiting is subjective and difficultto measure, data analytics offers no panacea for eliminating uncertainty and ambiguity.For recruiters and others, the most insightful and desirable metrics are often the most difficultto measure because they lack precision and definition – case in point, “quality of hire.” Yet, in the real world, measurement isn’t about getting the absolute, right and precise answer. Such absolutes are exceedingly rare outside of controlled, laboratory environments. The goalof measurement, whether in sports, business or recruiting, is to reduce uncertainty and thereby improve decision-making.26


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A Renaissance for Recruiters

Automated recruitment switches the focus from clicks to applicants, thereby more closely aligning key financial metrics with recruitment advertising goals. This aids recruiters in generating the evidence they need to demonstrate value for the money they spend. Real-time, precise tracking allows organizations to instantly shift budget from vacancies that have enough applicants to those that don’t. Moreover, automatic controls that stop the process once the desired number of applicants is reached leave nothing to chance and relieve recruiters fromthe burden of regularly checking click volume and worrying about their exposure to unexpected costs.

Programmatic recruitment advertising shouldn’t concern recruiters with respect to job security. Only computers, algorithms, and software can process the millions of decision points at playin optimizing ad placement, but only skilled recruiters can create the messages that make ads effective. As the laborious, manual work of ad placement and tracking is reduced, recruiters gain time to focus on creative work. More than ever, relationships and trust are key – tailored messages, well-crafted job titles, creative descriptions and thoughtful blogs, articles and posts build rapport and reputation; they give firms an edge in recruiting.27

Recruiters should expand their branding activities by composing and posting to social media sites, monitoring responses, engaging in conversation and building networks.28 These activities – and tracking employee/candidate sentiment on sites like Glassdoor – add value. Again, recruitment advertising and marketing generates applicants for job openings; buildingthe employer brand and reputation makes it easier to attract top talent and desirable applicants.


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The Future is Now, but the Metrics Aren’t

In the early years of the world wide web, job ads were posted for a duration of time at a fixed price. Gradually, job boards proliferated and were joined by aggregators (who distribute one post to many sites) and social media, which gave recruiters another place to seek candidates and advertise jobs online. As technology became available to track users and their actions on job boards and web pages, duration-based pricing gave way to pay-for-performance models such as cost-per-impressions (eyeballs) and then cost-per-click.

These models better satisfied recruiters’ need to distribute their jobs and demonstrate valuefor their investments. It also gave them new recruitment advertising metrics, such as cost-per-impression, click-through rate, and cost-per-click, while adding to the intricacy of existing staffing measures and benchmarks, such as source effectiveness.

Today, web and social media users, including active and passive job candidates, experiencea wide array of online advertising – not only by type but in the personalized manner ads are increasingly presented. Consider that as you move through web pages, auctions involving hundreds or thousands of players occur in real time behind the scenes. In just microseconds,the winner – the highest bidder – earns the right to place an ad in front of you.

According to senior marketing manager, Paul Kluge, “Getting the right message to the right consumer at the right time in the right context to drive a conversion at the lowest price isthe goal of every marketer …” 29 From the perspective of the advertiser, the ad delivered, is (ideally) the right ad, presented at just the right time to get the prospect’s attention and cause them to take the next step in becoming a customer – or, in the case of recruitment advertising, an applicant.

Though programmatic ad buying and predictive analytics don’t yet accomplish this perfectly, they do so more effectively than any person could hope to. The algorithms and software used to place ads learn constantly, and they never forget. As ads are placed, prospects attracted, and applications received, the system receives constant feedback. Algorithms improve, and the predictive abilities of the software deepen.

This is the new age of programmatic ad placement – powered by big data and predictive analytics (See Figure One). And it’s not just the future, it’s the present. Recruitment advertising metrics need to catch up.


1 2 3 4

Set your budget andapplication volume target

Software uses data to findwhere your target candidates

live, work, and play on the web

Your ads are placed across the web at the right place & time

to engage the best candidates

You receive qualityapplicants

10,000+ sites

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Programmatic Recruitment Advertising Feedback and Learning Loop

Figure One:


Part Two

The NewRecruitmentAdvertisingMetrics


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As programmatic recruitment advertising ushers in a new era in staffing, new metrics and KPIs to measure its effectiveness are needed. While some legacy metrics remain relevant, others are needed to bridge the gaps.

The essential recruitment advertising metrics are those that track closely with the supply of applicants and the strength of the employer brand. Programmatic ad buying changes the game such that metrics including click volume, click through rate, cost-per-click, and – to a degree – even source of hire, become less important. Meanwhile, cost-per-applicant (CPA), cost-per-quality-applicant (CPQA) and other related metrics, such as source of quality applications, application conversion rate, and “bounce rate” come to the forefront. In measuring recruitment advertising effectiveness in programmatic ad buying, organizations will become less concerned about the volume of people who see their ads, the number of click-throughs and where their ads are placed, because fees are not calculated against any of these factors. What recruiters will care about are applicants; the volume of applicants first – because programmatic services currently charge by the applicant – and the quality of applicants because, ultimately, that is how sourcing and recruiting effectiveness is assessed.

Even quality of hire metrics are only peripheral to the assessment of recruitment advertising (whether programmatic or not). Again, sourcing ends when those responsible for hiring receive sufficient qualified applicants. The sourcing process can’t impact the hiring process directly.Who gets hired, who gets an interview and whether anyone is hired at all, are subject to a myriad of factors – beyond the control of the recruiter – that may have little or nothing to do with recruitment advertising.


Programmatic ad buying changes the game... metrics including click volume, click through rate, cost-per-click, and – to a degree – even source of hire, become less important. Meanwhile, cost-per-applicant, cost-per-quality-applicant and other related metrics, such as source of quality applications, application conversion rate, and “bounce rate” come to the forefront.

““


Applicants-per-Hire (APH)

How many applicants, on average, are required to fill each position? Collect data for this metric by job and job type. Knowing how many applicants you need, on average, to fill various positions will help you set the right parameters in your programmatic spend (i.e., set the right cut-offs for applicants) so that you don’t overspend.

Using historical data where available, simply divide the number of hires per position bythe number of applicants considered forthe jobs.

Time to Applicants (TTA)

Though quality trumps time, you should know how long it takes from job advertisement to the supply of sufficient applicants. Remember that requisitions can be put on hold and vacancies left open, these decisions are often made outside the recruiter's control. Account for those factors to determine a reliable measure of how long it takes to generate sufficient applicants for specific jobs and job types. Note: if recruiting controls the time between receiving a requisition and placing advertisements, track this metric separately. This is a gauge of recruiter or recruitment process efficiency but is not relevant in assessing programmatic advertising efficiency.

Track the time a position is advertised to the point at which enough applicants have been submitted to cause the advertisement to be closed or suspended. Determine averages by job and job type.

Volume of Applicants by Source (ABS)

Though the source of your applicants is less important in a programmatic environment, you should still develop an understanding of where your applicants are originating if only so that you can eventually construct a more important measure, Source of Quality Applications (see below).

Your programmatic sourcing leads prospects to your career webpage to apply. Do your web statistics tell you where your prospects came from when they clicked through to your page? If so, search your web stats by name of the applicant to determine the source (this can be automated). Alternatively, this metric might come from reports supplied by your program-matic vendor. Request a breakdown of where the applicants you paid for originated (i.e. the specific job board, social media site or web page).

Supporting Metrics Description & Rationale How to Measure

Table One: Programmatic Recruitment Advertising KPIs

Cost-per-Applicant (CPA)

This is what you’re currently paying for inthe programmatic model, so it is a very important metric by default. As more programmatic platforms come on stream, this will be among the most important measures by which you will choose your supplier.

Measuring CPA in programmatic recruitment is straight-forward. Calculate and convertto dollars the time spent by recruiters and any others involved (greatly diminished inthe programmatic approach) plus fees paidto your programmatic vendor, divided by number of applicants received.

Key Metric (Applicants)

Description & Rationale How to Measure

Table One below suggests the current and near future metrics that recruiters should includeamong their KPIs:


Cost-per-Quality-Applicant (CPQA)

Ultimately, you should measure what you’re really aiming for – quality applicants. Even though CPA represents a significant improve-ment over duration, impression-based and even click through metrics, it remains flawed because it measures quantity rather than quality.

First, define “quality.” Remember, a key ingredient of a good metric is one that measures what is within the control of those whose efforts will be assessed usingthe measure. Thus “quality of applicant” should be defined as those applicants who pass a threshold that moves them along in the hiring process. This might mean, for example, those who pass the initial screening: either automated ATS type “knock-out” questions, or a telephone screening interview.

Be careful not to include factors that involve artificial limits. For example, you might havea rule that only four applicants per job are interviewed in-person. If so, being put forward for an in-person interview cannot drive your applicant quality metric.

The best measure of applicant quality may be those who qualify for an eligible pool of talent (for a specific job or others like it). Organiza-tions that maintain eligible talent pools (an important “best practice”) are selective in who qualifies and they don’t restrict numbers (the more, the better). As such, this becomes an excellent means to define “quality of applicant” for recruitment advertising purposes.

When you have your definition, calculate your CPQA by dividing the total of what you spent on applicants by the number of quality applicants received. Do this by position and position type as well.

Time to QualityApplicants (TQA)

See TTA above. For this metric, use your definition of Quality Applicant as a substitute for Applicant.

Track the time a position is advertised tothe point at which enough quality applicants have submitted to cause the advertisement to close. If ads are suspended because enough applicants were received, but re-opened due to a lack of quality applicants, includethe intervening time in your calculation. Determine averages by job and job type.

Key Metric Description & Rationale How to Measure


SourceEffectiveness

In the programmatic recruitment era, source effectiveness remains important but is aimed at programmatic platform providers themselves rather than the thousands of job boards, social media and websites where they place your ads.

Think of programmatic advertising vendors as providers of “recruitment advertising as-a-service.” As more vendors enter the market, the question of source effectiveness switches from the places your ads are served to the service that places them. This brings the benefit of greatly simplifying the tradition-al “source effectiveness” measure that many recruiters use today.

Compare CPQA and TQA among programmat-ic and platform vendors. Choose the vendor with the best combination of CPQA and TQA weighting each accordingly.

Admittedly, this is a near-future metric, nota current one. At present, there are too few recruitment advertising programmatic platform vendors to offer a robust compari-son. Nonetheless, by registering for free trials and conducting assessments on current vendors, you may be able to compare CPQA and TQA among existing suppliers.

In the future, as more providers enter the market, buyers guides and published rankingsof vendors – presumably by CPQA, TQA, and other factors – will emerge. Granted, this may be months or even a year or more away.

Key Metric Description & Rationale How to Measure

Bounce Rate Even though your main concern might be number of applicants, you should also track the ratio of prospects (those who land on your career site) to applicants (those who apply to a position or join your talent pool). Your bounce rate is a good indicator of the effectiveness of your site. For example, do prospects land immediately on a lengthy ATS-driven application (which might be a turn off) or are they given additional compelling information about the organization to encourage the next step? Your Bounce Rate helps gauge the impact of adjustments to your site and the funnel it represents.

Simply divide the number of unique site visitors by applications received to jobs and/or to your talent pool.

“Social MediaMultiplier” (SMM)

While still difficult to measure, word-of-mouth is more visible today because so much of it occurs in the social media. “Likes,” re-tweets, blogs, “friending” on Facebook and many other social media activities have become important sources for referrals and traffic to career websites. Some social media sites offer analytic tools for those concerned with their brand and the reputation of their organiza-tion, and/or the effectiveness of their recruitment marketing. The Social Media Multiplier offers a rough gauge of word-of-mouth about your organiza-tion by tracking the number of people who visit your career page from various social media sites.

To measure, query your career website stats to include a report on how many visitors came to you from social media sites, including Facebook, LinkedIn, Twitter, Pinterest, and others.

Key Metric (Branding) Description & Rationale How to Measure


Net Promoter Score (NPS)

The Net Promoter Score, a widely used measure of customer and employee sentiment, can also be used to gauge broad attitudes about your organization as an employer by asking prospects just one question.

Using your career website, your corporate Facebook page, LinkedIn networks, etc., ask prospects how likely they are to recommend your organization to others as a potential employer based on what they know aboutthe organization. Alternatively, “likes” on Facebook might offer a similar, though cruder metric. Similarly, depending on the size of your organization, Glassdoor.com (see below) surveys employees and ex-employees on their likelihood of referring your organization to a friend.

Glassdoor Rating For large organizations especially, Glassdoor ratings and reviews might yield valuable data and insights for recruiters. As an anonymous, third party site, Glassdoor data is likely to be less biased than an internal survey, particular-ly where raw employee sentiment is sought, and it is also effortless – you don’t need to construct, distribute, collect and analyze a survey.

Though Glassdoor ratings and reviews come from current and past employees, not prospects, it has become the premier place for talent seeking information about organiza-tions they might be interested in working for. As such, recruiters should monitor Glassdoor to track overall ratings and to review comments, which might also guide recruiters in the development of recruitment advertising and marketing content. Moreover, recruiters can and should craft compelling company overviews on Glassdoor and respond to negative and positive reviews. Increasingly, Glassdoor ratings and reviews impact organizations’ employer brands.

Visit Glassdoor.com, enter your organization’s name and receive your aggregate/average rating to date.

More advanced analytical features are available to employers by subscription, but you can access full reviews and see your overall ratings at no cost. You can also view aggregate metrics such as the percentage who would refer your organization to a friend – a variation of the NPS metric described above.

PwC consulting, for example, is currently rated and reviewed 11,000 times, offeringa treasure trove of information withoutthe need for surveying.

Key Metric (Branding) Description & Rationale How to Measure

Return on Invest-ment (ROI) or Return on AdDollars Spent (ROA)

Like ROI (Return on Investment) ROA is a bottom line metric that estimates the efficiency of spending in recruitment advertis-ing. If recruitment (or sourcing) were concerned only with forwarding quality applicants, CPQA might suffice. But because recruiting also entails branding, a broader measure is needed to assess the ROI on overall recruiter activity.

Key Metric (Other) Description & Rationale How to Measure

Divide Net Benefits by Total Costs and multiply by 100 to get percentage ROI or ROA,

Total Benefits – Total Costs(Net Benefits) ______________________________ x 100 = % ROI

Total Costs

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Conclusions

In recruiting and most everywhere else, the emphasis on data and automation brings constant change. Undoubtedly, experience still matters in recruiting because it informs the questions we ask, the data we collect and store, and the way we interpret the results of analysis. Moreover,the new freedoms that programmatic recruitment advertising offers, means that recruiters can spend more time thinking strategically and in creative pursuits that build the organization’s reputation and attract top talent to its doors.

The measures above, while not comprehensive, should aid recruiters as they adopt programmatic recruitment advertising and begin to shift the focus of their work to branding, social media monitoring and networking.


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Footnotes

1 Aswathappa, K. (2007). Human Resource and Personnel Management. Tata McGraw-Hill Education.

2 Borrell. (2014). 2014 Recruitment Advertising Outlook: The Long, Gray Line. Borrell Associates. Borrell Associates; McDonald, J. (2015, January 26). The Warc Blog. Retrieved August 2016, from Recruitment advertising: On the up: https://www.warc.com/Blogs/Recruitment_advertising_On_the_up.blog?ID=2012; ResearchAndMarkets. (2016). 2016 Recruitment Annual Report.

3 Trefis. (2013, January 18). Forbes. Retrieved August 2016, from Forbes Investing: http://www.forbes.com/forbes/welcome/#723c4d7d6054

4 McDonald, J. (2015, January 26). The Warc Blog. Retrieved August 2016, from Recruitment advertising: On the up: https://www.warc.com/Blogs/Recruitment_advertising_On_the_up.blog?ID=2012

5 Borrell. (2014). 2014 Recruitment Advertising Outlook: The Long, Gray Line. Borrell Associates. Borrell Associates; Trefis. (2013, January 18). Forbes. Retrieved August 2016, from Forbes Investing: http://www.forbes.com/forbes/welcome/#723c4d7d6054

6 Bureau of Labor Statistics. (2016, August 16). United States Department of Labor. Retrieved August 2016, from Series title: (Seas) Unemployment Rate: http://data.bls.gov/timeseries/LNS1400000

7 Mosley, R. (2015, May 11). CEOs Need to Pay Attention to Employer Branding. Retrieved August 2016, from Harvard Business Review: https://hbr.org/2015/05/ceos-need-to-pay-attention-to-employer-branding

8 Durham, M. (2014, June 12). Staffing Experts Say It’s A Job-Seekers Market As Businesses Recover From Recession. Retrieved August 2016, from CBS Philadelphia: http://philadelphia.cbslocal.com/2014/06/12/staffing-experts-say-its-a-job-seekers-market-as-businesses-recover-from-recession/

9 Jeffery, M. (2010). Data-Driven Marketing. John Wiley & Sons.; Ip, G. (2016, June 1). Post-Recession Rethink: Growth Potential Dimmed Before Downturn. Retrieved August 2016, from Wall Street Journal: http://www.wsj.com/articles/post-recession-rethink-growth-potential-dimmed-before-downturn-1464803880; Reuters. (2016, February 2). U.S. Companies Are Slashing Investment in 2016. Retrieved August 2016, from Fortune Magazine: http://fortune.com/2016/02/02/capital-spending-2016/


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10 Capgemini. (2013). The Deciding Factor: Big Data & Decision Making. Economist Intelligence Unit, Business Analytics. Economist Intelligence Unit.; Leonard, D., & Nelson, B. (2016, July 14). Successful Predictive Analytics Demand a Data-Driven Workplace. Retrieved August 2016, from Gallup: http://www.gallup.com/businessjournal/193574/successful-predictive-analytics-demand-data-driven-culture.aspx

11 Lacka-Badura, J. (2015). Recruitment Advertising as an Instrument of Employer Branding: A Linguistic Perspective. Cambridge Scholars Publishing.

12 Winkler, R. (2011, January 29). : http://on.wsj.com/gcwaIP. Retrieved August 2016, from The Wall Street Journal: http://www.wsj.com/articles/SB10001424052748703956604576110200601537840; Borrell. (2014). 2014 Recruitment Advertising Outlook: The Long, Gray Line. Borrell Associates. Borrell Associates.; McDonald, J. (2015, January 16). Mediatel Newsline. Retrieved August 2016, from Recruitment advertising: on the up: http://mediatel.co.uk/newsline/2015/01/16/recruitment-advertising-on-the-up/

13 Statista. (2014). Statistics and facts about the Advertising Industry in the United States. Retrieved from http://www.statista.com/topics/979/advertising-in-the-us/

14 Weiser, B. (2014, September 29). Magna Global's New Programmatic Forecasts. Retrieved August 2016, from Media Village: https://www.mediavillage.com/article/magna-globals-new-programmatic-forecasts-by-magna-global/; eMarketer. (2015). Programmatic Advertising 2015. eMarketer.; Morrison, K. (2016, May 9). Why Programmatic is the Future of Digital Display Advertising. Retrieved August 2016, from Social Times: http://www.adweek.com/socialtimes/why-programmatic-is-the-future-of-digital-display-advertising-infographic/639184

15 Rayport, J. F. (2015, June 22). Is Programmatic Advertising the Future of Marketing? Retrieved August 2016, from Harvard Business Review: https://hbr.org/2015/06/is-programmatic-advertising-the-future-of-marketing

16 Holwell, E. (2016, January 6). 2016 Outlook: Three Key Factors in Recruitment Marketing. Retrieved August 2016, from LinkedIn: https://www.linkedin.com/pulse/2016-outlook-three-key-factors-recruitment-marketing-eric-holwell

17 eMarketer. (2015). Programmatic Advertising 2015. eMarketer.

18 Bersin, J. (2015, February 1). The Geeks Arrive In HR: People Analytics Is Here. Retrieved August, 2016, from Forbes: http://www.forbes.com/sites/joshbersin/2015/02/01/geeks-arrive-in-hr-people-analytics-is-here/#805e24c7db38


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19 Armstrong, J. S. (2010). Evidence Based Advertising: An Application to Persuasion. International Journal of Advertising, 30(5), 743-767.

20 Lewis, M. (2004). Moneyball: The Art of Winning an Unfair Game. W.W. Norton & Company.

21 Major League Baseball. (2002). Final Standings 2002. Retrieved August 2016, from MLB.com: http://mlb.mlb.com/mlb/standings/mlb_standings_2002_season.jsp

22 Davenport, T. H. (2014). Analytics in Sports: The New Science of Winning. International Institute for Analytics. SAS Institute Inc.

23 Pal, K. (2015, August). Big Data Influence on Data Driven Advertising. Retrieved August 2016, from KD nuggets: http://www.kdnuggets.com/2015/08/big-data-influencing-data-driven-advertising.html

24 GlobalDMA. (2014). The Global Review of Data-Driven Marketing and Advertising. Accenture, Digital.

25 Jibe. (2016). An Exploration Into the Depths of Recruitment Analytics. Jibe.

26 Hubbard, D. W. (2010). How to Measure Anything: Finding the Value of Intangibles in Business. John Wiley Sons, Inc.

27 Satell, G. (2015, October 30). Content Is Crap, and Other Rules for Marketers. Retrieved August 2016, from Harvard Business Review: https://hbr.org/2015/10/content-is-crap-and-other-rules-for-marketers

28 Headworth, A. (2015). Social Media Recruitment: How to Successfully Integrate Social Media into Recruitment Strategy. Kogan Page.

29 Kluge, P. (2015, April 13). Take Programmatic Display To The Next Level. Retrieved August 2016, from Marketing Land: http://marketingland.com/data-powers-primary-benefits-programmatic-advertising-123404


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References

Armstrong, J. S. (2010). Evidence Based Advertising: An Application to Persuasion. International Journal of Advertising, 30(5), 743-767.

Aswathappa, K. (2007). Human Resource and Personnel Management. Tata McGraw-Hill Education.

Bersin, J. (2015, February 1). The Geeks Arrive In HR: People Analytics Is Here. Retrieved August 2016, from Forbes: http://www.forbes.com/sites/joshbersin/2015/02/01/geeks-arrive-in-hr-people-analytics-is-here/#5d3bfb067db3

Borrell. (2014). 2014 Recruitment Advertising Outlook: The Long, Gray Line. Borrell Associates. Borrell Associates.

Bureau of Labor Statistics. (2016, August 16). United States Department of Labor. Retrieved August 2016, from Series title: (Seas) Unemployment Rate: http://data.bls.gov/timeseries/LNS14000000

Capgemini. (2013). The Deciding Factor: Big Data & Decision Making. Economist Intelligence Unit, Business Analytics. Economist Intelligence Unit.

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