Many leaders focused on talent acquisition are looking at predictive analytics (aka - talent analytics, workforce science) as a way to improve the talent acquisition process.  At a high level, predictive analytics uses historical data and current inputs to make predictions about future behavior. 

In the talent acquisition and management industry, predictive analytics is receiving lots of press, focus, and investment.  Within the industrial organizational psychology community, they might argue that the last 40 years or so have been all about predictive analytics.   The big news is the technology advancements that are enabling predictive analytics to take off.  Some examples:

  • The growth of cloud-based applications is one technology factor enabling the growth of predictive, data-driven models for hiring.  Platforms can be easily connected to create interoperability.  So, now employee lifecycle data can be integrated into one data warehouse.
  •  Data can be captured and stored at a much lower cost with new applications making “big data” easier to manage, process, and analyze.
  • Business intelligence engines are more powerful and cost effective to deploy.

How can predictive analytics benefit your pre-employment hiring process?

Predictive analytics can provide you with three key benefits, among many, to help you improve your pre-employment process.

 1. Predictive analytics will help you improve your quality of hire

Probably the most important use of predictive analytics is to improve quality of hire.  By linking the hiring process to production performance, attrition data, engagement survey information, and other data from the employee lifecycle, models can be developed that will predict the potential future performance of a job candidate.  While big gains may be possible, the continuous adjustments to your hiring model will drive marginal improvement.  Hiring managers need to make sure their hiring models have measurable processes and objective data that are reliable.  Examples include pre-employment assessment results, interview results, engagement survey data, recruiting sources, turnover data, and performance results.  The old adage, “garbage in, garbage out” still applies.

2. Predictive analytics will help you source more efficiently and effectively

Tying recruiting sources into a predictive analytics model can enable hiring managers to improve the source of hire.  For example, hiring managers can trace the hire back to the original hiring source and then link that to quality of hire.  This enables recruiting managers to optimize their recruitment marketing and eliminate poor sources.  In addition, this same approach can be used to evaluate in-house recruiters, third-party recruiting firms, job boards, and other recruiting sources.  Recruiting leaders can significantly improve the return on investment for their recruitment marketing programs and use the data to negotiate better pricing terms.  As a recruiting leader, you now possess significant pricing leverage.  Think of meeting with any job board and sharing your quality of hire and cost of hire index with the job board.  Those sources that don’t make your top tier either will have to reduce their pricing or be cut from your budget.

3. Predictive analytics will help you improve your speed of hire

A third benefit is speed of hire.  As you build your quality of hire models, your understanding of which candidates are the best hires for your company will improve.  You will then be able to deploy tools that serve as leading indicators of potential job performance.  Once a candidate hits your hiring radar and fits your job models, you will be able to move quickly to connect with them and advance them forward.  Because you will have confidence in your hiring model, you will also be able to focus on candidates that are right for your business.

A few things to understand about predictive analytics

Predictive analytics offers tremendous opportunities to use historical data in a way to predict future behavior.  Recruiting leaders need to understand that:

  1. The amount of data involved with predictive analytics requires investment in technology and business intelligence applications to store it and manage it.
  2. Organizations also need to have a team that understands data modeling, technology infrastructure, and the appropriate experience and credentials to develop and review content.  Or, work with teams that can support your modeling needs.
  3. Models need to be built on reliable data that is captured in a consistent way.
  4. Data may not tell the entire story.  For HR purposes, understanding the story around the hiring process is critical. 

Predictive analytics can improve your pre-employment hiring process helping to improve your quality of hire, recruitment sourcing, and speed of hire.  Predictive analytics is not new, just faster, better, and cheaper now.

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0 to 90 day turnover is one of the biggest challenges facing organizations that hire hourly workers. Think of frontline service, sales, and shutterstock_118727401support jobs in contact centers, retail, hospitality, field services, and quick service restaurants.   

While turnover cost may range, FurstPerson has found that many CFOs are comfortable with using a variable cost of $4,500 per term for frontline hourly workers.  So, if you have 500 employees and 50% turnover, attrition costs your company $1.1 million per year. 

Potential Causes of Early Stage Turnover 

Our research and experience has shown that new hires leave during the first 90 days for one or more of the following reasons: 

  1. Candidate recruiting and sourcing strategies are weak and prevent the organization from developing a pipeline of better qualified candidates. For example, too much emphasis is placed on traditional sourcing via newspapers and job fairs. This leads to a job candidate pool of unemployed and underemployed individuals that have a history of job hopping.

  1. The job was not clearly explained to them or was oversold. For example, schedule requirements are glossed over and not clearly explained.  Once the new hire understands the actual requirements, they end up quitting.

  1. The new hire does not have the right work ability to perform the job. For example, they cannot multi-task and then struggle during training leading to either poor training performance or voluntary turnover.  FurstPerson data shows that candidates who struggle with poor work ability contribute significantly to “no call, no show” turnover. 

  1. They have poor prior work habits which leads to poor future work habits. The job candidate’s past behavior and experiences provide an indication of future turnover risk. 

  1. They cannot manage the interpersonal requirements for the job and leave because these requirements are overwhelming.

  1. The new hire’s interests and attitudes do not match the required job preferences resulting in a poor job fit. 

  1. The new hire is assigned to a Supervisor who lacks coaching and leadership abilities and behaviors. 

  1. Hiring process decisions are made based on casual observations or gut feel instead of predictive, data-driven analysis. 

These turnover factors show up at various risk points during the first 90 days of employment.  A systematic pre-employment hiring process focused on each potential cause of early stage turnover can help organizations reduce 0 to 90 day attrition.

                                
Potential Turnover Cause
Employment Risk Period
Solution

Candidate recruiting and sourcing strategies are weak and prevent the contact center organization from developing a pipeline of better qualified candidates.

0 to 30 Days

Shift recruiting sources away from traditional strategies like newspapers and job fairs.  Focus on internet based sources and employee referral programs.  A critical element is how the recruiting team executes on these programs, not just having the program.  You should also consider maintaining an "always-on" recruiting process so you have candidates in your pipeline.

The job was not clearly explained to them or was oversold.

0 to 30 Days

Implement a checklist that is reviewed by the recruiter and the job candidate.  The checklist covers each critical element about the job.  Both the candidate and the recruiter sign it at the conclusion of the visit.  The checklist forces clarity with the candidate.

The new hire does not have the right work ability to perform the job.

0 to 30 Days

A job simulation can help you measure a job candidate's ability to learn and apply new information while completing mock job scenarios.

Not only does a job simulation have a strong degree of face validity, but it can measure competencies (multi-tasking, computer navigation, decision logic) that are hard to evaluate elsewhere.

They have poor prior work habits which lead to poor future work habits.

0 to 30 Days

A biographical data assessment can systematically review competency factors like dependability.  These assessments have been proven to reduce early stage turnover. 

A strong review of work history via an application blank can eliminate those candidates that potentially may not meet your needs.  One caution is to over-emphasize work history at the expense of competencies.  By over-emphasizing work history you can potentially shrink your labor market.

They cannot manage the interpersonal requirements for the job.

31 to 60 Days

A behavioral phone interview, simulation, role-play, or in-person interview should help you evaluate the job candidate against these criteria.

A job simulation can specifically provide the job candidate with an immersive experience of performing the job giving them the chance to exit.

Personality assessments can also help you determine if the candidate has the right interpersonal competencies to be successful on the job.

The new hire's interests and attitudes do not match the required job preferences resulting in a poor job fit.

31 to 60 Days

A job related personality assessment can measure job fit and job preferences.  FurstPerson has found correlations between personality assessments, job tenure, and operational performance.

The new hire is assigned to a Supervisor who lacks coaching and leadership abilities and behaviors.

61 to 90 Days

Spend even more time evaluating your Supervisor candidates against the job requirements to be a Supervisor.  A strong frontline performer doesn't necessarily become a strong team leader.

Person to person role play scenarios can be effective at evaluating candidates for Supervisor roles.  Round out the hiring process by using problem-solving tests and personality assessments.

Hiring process decisions are made based on casual observations, gut feel, or emotions.

0 to 90 Days

Incorporate predictive, data-driven analysis to make initial selection decisions and validate that these models provide incremental improvement in the hiring process.

 

Each of these solutions can help reduce the potential turnover cause in your organization. The collective use of these strategies increases the probability of making the right hire.  

In summary, many HR leaders that employ sizable frontline worker populations accept turnover as just the cost of doing business. Real world applications of key hiring strategies have demonstrated that early stage attrition can be reduced. Reducing 0 to 90 day attrition by 20% or more is not uncommon. The key is to apply best practices embedded in solid research and analytics. The return on investment can be substantial. 

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Towers Watson recently released their 2014 HR Service Delivery and Technology Survey. The Towers Watson survey shows that spending on HR products and technology is increasing for the first time since 2011.

The top three areas for HR technology spending, according to the survey, include:

The investment focus in HR Data and Analytics is notable. Unlike other corporate functions like IT and Operations, HR has not had the analytical tools and decision science to easily quantify investment opportunities.  Technology, through improved software tools and reduced cost of data, is changing this.  For HR leaders responsible for pre-employment hiring and assessment, investing in HR Data and Analytics is important for three reasons:

 

  1. Investing in HR Data and Analytics will help create transparency.  This allows HR leaders to gain visibility into processes and results that used to be murky.  For example, which recruiters deliver the highest candidate quality?  Are the most engaged employees also the best candidates?   Which trainers take similar talent pools (i.e. new hires) and get superior results compared to their peers? The result – HR Leaders will be able to better justify additional investment in HR tools and services.
  2. You will gain improved monitoring by investing in HR Data and Analytics.  HR Leaders will be able to monitor the inputs and outputs to their pre-employment hiring processes and post-hire processes to track progress against benchmarks and be alerted when goals are not met.  For example, monitoring the steps in your fill rate process will allow you to compare the number of candidates meeting your quality of hire thresholds compared to your benchmarks based on historical data.
  3. HR Data and Analytics tools allow you to forecast the future.  Using historical data and current trends, HR leaders can ask “what if” questions to determine potential business impact.  As an example, consider the impact on revenue performance by being able to adjust the pass rate during the assessment process.  With the right analytics portal, you will have candidate data, current scoring model data, and performance data.  Tweaking each of these inputs will allow you to compare the trade-off of a lower pass rate, recruiting impact, and revenue gain to see if the change is sustainable in your business.

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In our experience, quality of hire is dependent upon the recruiting and hiring process.  In simple terms, the more candidates you can generate for an open position, the more selective you can be.  Being more selective means that your new hires are more productive and stay longer.  Quality of hire improves. 

Business leaders looking to improve quality of hire should consider how their recruiting and hiring processes are organized.  Transforming these processes can help your organization achieve its hiring goals in the following ways: 

  • Provide agility in responding to hiring supply and demand 
  • Be adaptable to changing market conditions 
  • Drive alignment of all stakeholders in the hiring process 
  • Drive efficiency into the hiring process through planning and careful metric monitoring 
  • Drive effectiveness through relentless process execution 
  • Drive financial impact which benefits the business

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Implementing these changes can deliver significant financial gain.  The chart below shows results at one utility organization was able to reorganize its recruiting process to drive more candidates to the top of the funnel.  As a result, hiring managers could be more selective.  The results, measured by looking at the performance of employees in their customer service group responsible for collecting on delinquent accounts, shows a 30% improvement in dollars collected per hour. 
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Recently we had a discussion with an organization about where to place pre-employment assessments in the hiring process.  Some recruiters felt that the assessments should be at the end of the process after the candidate had been reviewed and interviewed by a recruiter.  Other recruiters felt that the assessments should be at the beginning of the process.  They are objective, calibrated against performance, and always available given online access.  In our experience, letting pre-employment assessments handle the “heavy lifting” to filter candidates at the beginning of the process has more value and better predictive results. 

An interesting article in the Harvard Business Review confirms this approach based on an analysis of 17 studies of applicant evaluations.  In the May 2014 issue of the Harvard Business Review, Nathan Kuncel, David Klieger, and Deniz Ones discuss their analysis and the results which show that an algorithm, or equation, outperforms human only decisions by at least 25% when it comes to candidate selection.  

The results below show the percentages of above-average employees hired through algorithmic systems versus human judgment.  The results below represent improvement over chance. 

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 The authors also discuss additional research they conducted with Brian Connelly of the University of Toronto. The authors concluded that “people are easily distracted by things that might be only marginally relevant, and they use information inconsistently. They can be thrown off course by such inconsequential bits of data as applicants’ compliments or remarks on arbitrary topics—thus inadvertently undoing a lot of the work that went into establishing parameters for the job and collecting applicants’ data. So they’d be better off leaving selection to the machines.” 

What do you think?   

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Pre-employment hiring or talent acquisitions consists of six functional processes that drive towards the goal of finding, hiring, and keeping new employees.   

Each function sets the stage for the next one.  Planning identifies who to recruit.  Recruiting puts a call to action message in front of those potential new hires.  Screening filters the candidate pool, while selection tests them to see if they “can” and “will” do the job.  The offer process attempts to gain the “sale” and the on boarding process welcomes them into the organization.  The most successful talent acquisition teams leverage data, process, and results tracking to drive quality of hire improvement.   

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But many talent acquisition professionals do not have a decision-based approach to help manage day to day responsibilities unlike other business functions.  For example, the science of finance helps the accounting function make better decisions.  The science of marketing helps drive better sales function results.  But many human resource groups do not have a science-based approach that helps drive better results by understanding how hiring links to performance improvements on the production floor.  That gap is painfully felt in the talent acquisition process.  To achieve great hiring results, the process needs to be managed and executed by setting metrics and then managing relentlessly towards those metrics to improve your quality of hire. 

One business process analogy to use is supply chain management.  Supply chain management is about getting the right materials and products to production and the point of sale at the right time. The talent acquisition process is similar but our experience shows that most supply chain process owners are more concerned about the inputs, rather than activities, into the process that impact the organizational outcomes like reliability and failure rates.  Supply chain managers constantly seek measurements of the process that provide feedback on how to improve the process.   

In our experience, many talent acquisition organizations don’t track the inputs, outcomes, and process improvement like this.  Many hiring organizations are only concerned with activities.  These activities may include sending out an employee referral flyer or posting jobs to the local unemployment office.  They are busy but the activities are not tracked or linked to performance outcomes.  Unlike the supply chain model which links inputs to quality outputs, accountability in the hiring process is limited.  Imagine what an automobile manufacturer would say if 65 percent of the sourced parts were defective. 

To improve your talent acquisition process, consider thinking like a supply chain manager and organize your pre-hire model around key processes, data, and results.  The chart below provides a comparison between supply chain models and talent acquisition models. 

July_7th_Blog_-_2nd_graphic

Source: Supply Chain Council. 

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Computational statistics and data analysis help companies understand hiring trends. The hiring process doesn’t necessarily end when someone accepts an offer. An employee’s performance and longevity in the company can inform front-end assessments and applicant screening tools. Through a continued analysis of quality of hire, companies can continue to see year-to-year improvements.

Reducing attrition and retaining high-quality employees is one area in which data from a quality of hire report quality-hire-section-05card can show a significant impact. In addition to being as productive as possible, companies want employees who will stick around. As previously pointed out, hiring is a time-consuming and expensive investment. The average cost to hire of a new employee is roughly $4,500. For every 10 people hired, companies shell out an additional $18,000 in attrition-related costs. The first 90 days of employment are critical. If an employee reaches the three-month mark, chances of them staying in the job for at least one year go up dramatically.

A company with quality of hire controls can also raise the talent bar. The tradeoff to higher standards is having to source more people for the job. But by casting a wider net, the employees your company does catch will perform better and likely stay longer.

The more companies learn about employees and their on-the-job performance, the better equipped they’ll be to develop recruiting and hiring strategies. Companies can forecast the impact employees have on their future and more easily adapt to changes in the labor market by making quality of hire evaluations a rule, not exception. In the end, high-performing employees lead to better business results. Assessing quality of hire could be the key to unlocking the potential for future company-wide success.

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A quality of hire report card is a snapshot in time that delivers a wealth of information about employee performance. That said, it shouldn’t be a static collection of data. A technology-based solution gives companies the power to gather and analyze data on how their employees are doing all the time.

Leveraging a combination of statistical modeling and technology can ultimately demystify the hiring process.quality-hire-section-04 Combining into one place all the intelligence a company knows about a person -- job-relevant skills, knowledge and abilities; personal information; and the job market they are navigating -- creates a robust model for analyzing performance at a much deeper level. Data can be sliced to understand trends. At the same time, companies can examine a specific individual’s performance and compare how employees stack up to begin to understand what qualities drive successful performance.

Using data analytics to evaluate quality of hire isn’t simply about capturing information and spitting back results. Rather, the best systems take data and reveal the company’s story. Answers to questions like “what does it takes to be successful in the job?” and “what type of qualities drive a particular level of performance?” become much clearer when employee progress is measured through the collection and analyzation of meaningful data.

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