Posts Tagged ‘HR Analytics’

January 30, 2011

Do We Need Internal Recruiting? Ask the CFO.

Kevin Wheeler posted an article on ERE that got the recruiting community fired up. He asked, “Do we need Internal Recruting at all?” His premise seems to rest with effectiveness, accountability and differentiation that a recruiting function may or may not deliver.

With 32 comments as of this post, it ranks near the top of the charts for getting folks riled up.

Here are my two cents, with a few more details than what I posted on ERE.

The dialogue is all good. It may be like the question about cars, is it better to buy or lease? And the answer is: It depends.

Kevin’s main point may really be rooted in economics. When an internal team has the same mandate to measure, track and report economic impact that an external provider does, there is most likely performance parity.

Unfortunately, the issue lies with the fact that many CFOs and CEOs do not hold internal recruiting teams accountable to document contribution and deliver continuous staffing process improvement. And without a mandate for economic accountability, the accounting infrastructure to document contribution is often lacking. A vice president of sales or manufacturing would never be allowed to operate with the poor economic reporting and accounting infrastructure that is deployed for the business process of recruiting. As such, it is common for internal recruiting teams to use ATS based reporting, thus relying on activity based measures instead of economic measures.

Henry David Thoreau gives us words to ponder for this situation: “It is not enough to be busy, so are the ants. The question is, what are we busy about?”

One gauge we use to explore the economic accountability of a recruiting team is how literate they are about job-specific performance metrics and how quickly they can access data sets of performance metrics. Ask a staffing professional, internal or external, if they measure and report on the cost of time to proficiency (total investment from sourcing to self-sufficient performance) for the position with the highest hiring volume. Ask who owns the budget for staffing waste. The answers to those questions reveal a great deal about the accountability expectations set by the CFO and CEO for recruiting.

Reporting on days to fill, requisitions open, requisitions per recruiter, and opinion-based quality of hire while good to know are a bit like busy ant metrics. Recruiters with economic accountability use HR analytics to document and report reductions in staffing waste and rework, increased yield in new hire productivity, reduced time to proficiency, increases in job family average performance metrics and the like.

From my experience, corporate resources flow to those who build a good business case and then document return on investment. Outside providers have to do this to earn repeat business. The best internal providers do so as well. Here is an example of how Key Bank documented high ROI from using pre-employment testing as a form of measurement rigor to reduce staffing waste.

Posted by: Joseph P. Murphy | January 30th, 2011
Permalink, 1 Comment »
, Tags: HR Analytics pre-employment testing Quality of Hire recruiting ROI staffing process improvement | Posted in General, HR Analytics, Recruiter Experience

November 15, 2010

All Referrals Are Not Created Equal – Quality of Hire = Quality of Referral

John Sullivan points out in his ERE post on referral programs that numbers are available for those who want to invest in measurement discipline and operate at the level of evidence versus opinion. There is plenty of data that suggests referrals work and make sound business sense. And, just like the issue of diversity, there are other dimensions of referral process effectiveness that can be quantified.

In a hiring environment using pre-employment assessment, it is possible to examine the relationship between quality of hire and quality of referral. In one client analysis, we were able to document that individuals scoring higher on the assessment tended to refer individuals who also performed well. And as one might also conclude, those who scored less well referred candidates with similar performance results. It is important to create referral behaviors from those more likely to generate high value candidates.

The anomaly, and there are always some, was the cluster of individuals with modest assessment results but with a high level of referring activity. There was no pattern to the quality of the candidates they put forth. We called these the ambassadors. They are just out piping a ‘follow me tune’ attracting all comers. Even a blind squirrel finds an acorn. It is important not to get referrals for referrals sake.

As such, using HR Analytics it is possible to target referral behaviors more selectively. But first, you need better candidate data.

Posted by: Joseph P. Murphy | November 15th, 2010
Permalink, Comments Off
, Tags: HR Analytics pre-employment assessment Quality of Hire | Posted in HR Analytics

November 9, 2010

Simulations and Selection Science: Interview with Mike Hudy, Ph.D. Part Two

In Part One of the Interview with Mike Hudy, he discussed the demands and opportunities I/O Psychologist face in developing simulation for pre-employment testing. In this conclusion, Mike offers a few suggestions on how to determine if a simulation may be appropriate for staffing process improvement in your organization.

What considerations should a company examine in deciding if a simulation would be appropriate for one of their jobs?

There are several factors to consider when examining if a simulation makes sense. If you have jobs with more than 100 incumbents, building a business case for simulations is typically pretty easy. Another factor is hiring volume. If you will hire more than 100 people into the same job in a year, simulations can make a significant contribution.

An additional factor would be the complexity of the job itself. This variable is often under-valued prior to a thorough job analysis. The more complex the job, the more complex the demands are on the pre-employment assessment.

The last and a very important factor to consider in the use of simulations is the candidate experience. As general rule, candidates find simulations engaging, a more valuable way of presenting their capabilities and companies who use simulations stand out in a positive way from other places the candidate may be applying.

In short, simulations such as the Virtual Job Tryout add selection science value across a range of factors that have a positive impact on staffing process improvement.

Part One

Posted by: Joseph P. Murphy | November 9th, 2010
Permalink, Comments Off
, Tags: HR Analytics ROI selection science Simulations staffing process improvement Virtual Job Tryout | Posted in HR Analytics, I/O Psychology

September 30, 2010

Social Media and Quality of Candidate | Candidate Competencies Vary by Source (Part 2)

A few months ago, I posted a blog on social media and quality of candidate. In the post, I suggested that we need to use HR analytics to evaluate this source of candidates not only by the volume of candidates generated but also by the quality of candidates produced. We conducted some preliminary analysis using assessment scores from the client’s Virtual Job Tryout and candidate conversion rate (what percentage of candidates that actually hired from a source) as quality of candidate measures. Results were somewhat mixed, but suggested that social media was generating a quality of candidate that was less than other sources used by the organization (e.g., referrals, job boards, etc.).

Candidate hiring rate varies by social media source

Well, we dug a little deeper into this data and a very interesting picture emerged. When we looked at the data by the various social media sites used by recruiters, two surfaced as being particularly effective: LinkedIn and Facebook. Candidates sourced via LinkedIn performed much better on the pre-employment assessment than candidates sourced through other channels. In addition, these candidates were hired at a higher rate than the typical candidate. This pattern held true for Facebook as well, but the results were not as impressive.

Candidate quality varies by social media source

We also compared pre-employment assessment results for candidates surfaced from LinkedIn versus Facebook and found some differences that at first glance seem to make sense. Candidates sourced through LinkedIn performed better on professionally oriented competencies such as Leads Courageously, Develops Others, and Achieves Results. Conversely, candidates sourced via Facebook performed better on more socially oriented competencies such as Customer Focus and Works Well with Others. Source can impact quality of hire.

While we have only scratched the surface here, these results from detailed HR analytics show that there is great promise and potential value to evaluating social media, as well as other recruiting sources, on the quality of its yield. Further, the data suggests that different social media channels generate different types of candidates with unique competencies and characteristics. Recruiters can use this kind of information to drive more strategic sourcing efforts by placing their bets on the channels that are best aligned with the type of candidate they’re looking to source.

Part 1

Posted by: Mike Hudy | September 30th, 2010
Permalink, 2 Comments »
, Tags: HR Analytics pre-employment assessment Quality of Hire social media Virtual Job Tryout | Posted in Client Case Studies, HR Analytics

September 29, 2010

Intuition or Intelligence: How Do You Hire?

Talent Intelligence was a big theme at TaleoWorld 2010. Taleo CEO Michael Gregoire, in his opening remarks stated 47% of new placements into management positions fail. I am not sure where that statistic came from, but it does not speak well about how companies are making decisions to hire or promote individuals. It makes me ask: Is the hiring decision based upon intuition or intelligence?

Where else in business would a 47% failure rate be tolerated? What Mr. Gregoire is referring to here is one form of staffing waste. This abysmal success rate seems to indicate a strong need for talent intelligence. Better candidate data for making more accurate hiring decisions.

Getting useful, meaningful data is the central challenge. Hiring managers and recruiters, while well intended, often place disproportionally high value on candidate data that is either not related to job performance or even worse, negatively related to job performance. And, one of the more common areas where we see this is the value placed on specific job experiences that while intuitively seemed to make sense, the evidence from HR analytics proved othewise. Here are a few examples. Previous cash handling experience negatively related to cash drawer accuracy, prior food service and hospitality experience negatively related to success in a food and beverage management position, previous sales experience with a competitor negatively related to sales success.

Thoughtful people in successful companies establish these screening criteria. However, in the majority of cases these criteria are assumptions. Assumptions that are never tested or proven. By not conducting the appropriate HR analytics, decisions get made based upon ego, not evidence. This is allowed because of the common and accepted assertion from recruiters and hiring managers: “I am a good judge of talent.” With a 47% failure rate, it would seem prudent to do some analysis. Just better than a coin toss does not seem like good odds for a critical and expensive business decision.

Employee selection is a process. The yield of the process can be measured and improved. Candidate evaluation with pre-employment assessments can be conducted in a manner that produces evidence in the form of data. This data can support HR analytics which in turn provides guidance to improve the objectivity and effectiveness of the hiring decision. If you are a Taleo user and want to make your ACE work better, we can help.

Check out a few of our case studies to see how HR analytics and pre-employment testing validation analysis have made a measurable difference in the yield of a business process called staffing. We can help you make the transition from ego to evidence, from talent intuition to talent intelligence.

Posted by: Joseph P. Murphy | September 29th, 2010
Permalink, Comments Off
, Tags: employee selection HR Analytics pre-employment assessment pre-employment testing staffing waste | Posted in Client Case Studies, Conferences, HR Analytics

August 25, 2010

Staffing Waste: Performance Variation in Employee Selection – Part VI of VII

No matter how similar candidates for a given job may appear on the surface, people are different. They are complex. They may behave in unpredictable ways once they’re hired. And that’s where the staffing waste problem known as performance variation comes in. You hired your best performer, and you hired your worst performer from the same hiring process. The range of productivity between these two extremes is a form of performance variation. See the ROI Calculators on our web page for an example of how performance variation can be documented.

If all employees perform at a very high level within a given role, then there is little opportunity for real strategic impact. But if differences persist, like they do in the majority of employee selection cases, it’s time to start developing methods which are better predictors of performance outcomes and close the performance gap. To use a manufacturing analogy, your goal is to reduce the number of faulty widgets coming off the assembly line. By implementing scientific employee selection methods you can more accurately hire people who perform like your top 80%, thereby reducing performance variation.

An essential tool for developing an employee selection process aimed at reducing performance variation is pre-employment testing. The chart below demonstrates the degree of performance variation within a group of employees. When you look at job performance and results from the Virtual Job Tryout, it is easy to see how hiring from those who score in the top 80% can make a significant contribution to the organization. This appraoch is quite conservative. Beginning your interviews with candidates who score in the top 50% can be transformative.

Contemporary pre-employment testing use multiple methods of candidate evaluation which provide a rich data set for analytical purposes, namely, validation analysis. A validation analysis is the method used to document which pre-employment data from candidate screening and evaluation actually adds value to how well the hiring decision predicts success on the job. For example, the outcome of a validation analysis can demonstrate the strength of relationships among variables such as work history and attendance, work style and time-to-proficiency, work samples and productivity, etc.

The result of approaching your staffing process with HR analytical tools is the ability to reduce performance variation. This means fewer hires that perform below average and a steady increase in your overall levels of productivity. By using this more calculated, data-centric approach, you can connect pre-employment assessment data to objective, on-the-job performance metrics. This places the full weight of statistical evidence over “gut feelings” on how well your employee selection process predicts better business outcomes.

Part I, Part II, Part III, Part IV, Part V, Part VII

Posted by: Joseph P. Murphy | August 25th, 2010
Permalink, Comments Off
, Tags: employee selection HR Analytics pre-employment testing staffing waste | Posted in HR Analytics

July 21, 2010

Staffing Waste: No Data Captured – Part III of VII

Not long ago, we at Shaker Consulting Group, along with the Society for Human Resources Management (SHRM), conducted a Quality of Hire Survey (see chart below) that asked 558 HR professionals if they have candidate evaluation information in a database. Sixty-six percent of respondents said they did not. That’s right, 66% with no candidate evaluation data (hence my call for analytical literacy in the previous section). But in addition to not collecting any data, another problem persists for many HR folks: collecting data that is insufficient or of little value.

Companies are not collecting or using candidate evaluation data

Without collecting adequate, quantifiable candidate data from your employee selection and evaluation processes, it will be virtually impossible for you to identify with any level of certainty the causes of staffing waste or to develop methods that reduce it. In other words, No Data = No Analysis = No Learning.

The good news is that better candidate data can be obtained and stored in a manner that lends itself to analysis with just a few tweaks to your staffing process. You can better leverage your existing Applicant Tracking System (ATS) by building scorable, objective candidate questionnaires. These structured approaches offered by most ATSs are efficient mechanisms for collecting job-relevant work experiences (biodata). Interview ratings can often be entered into the candidate record as well.

If you’re looking for an expert to advise and direct your efforts in this area, one suggestion is to meet with an Industrial-Organizational (I/O) Psychologist. Experts in the measurement of factors related to people at work, I/O Psychologists can help you explore a variety of methodologies for collecting candidate data. In essence, they’re scientists who can help you address issues in human resources with, well, more scientific resources.

For more information on our hiring assessment tools, contact us today.

Part I, Part II, Part IV, Part V, Part VI, Part VII

Posted by: Joseph P. Murphy | July 21st, 2010
Permalink, Comments Off
, Tags: HR Analytics Quality of Hire staffing waste | Posted in HR Analytics

July 1, 2010

Staffing Waste: Identify it, measure it, improve it – Part I of VII

Recently I had a candid conversation with the president of a building materials company about ways to improve the results of hiring new sales representatives. When I asked him about the cost of on-boarding a new hire, he said confidently, “The length of the sales cycle and the learning curve takes a new rep about two years to get up to speed. So I budget about $250,000 for each new hire.” “Unfortunately,” he added, “they all don’t work out. We make about 50 replacement hires per year.”

Who owns the budget for staffing waste?

“Where else do you budget for waste in units of $250,000?” I asked. After racking his brain for a brief and what appeared to be painful moment, the man replied that all of manufacturing at his company doesn’t waste that much in an entire year.

“However, I am getting better at firing new hires in the first year now,” he said with a bit of a chuckle, attempting to lighten the weight of his $12,500,000 realization.

I’ve included the above anecdote to illustrate the critical —yet often overlooked — concept of staffing waste. One form of staffing waste is the investment of recruiting and on-boarding new employees who quit or are terminated before they achieve proficiency or minimum performance standards. And, while the cost of acquiring front-line performers at your company may be far less than, say $250,000, early turnover at any organization can cause significant losses.

The purpose of this article is to help HR professionals and others in your organization see staffing as a process with yields and understand the scope and impact of staffing waste. I will identify and explore four specific methods that you can use to document staffing waste. And once I’ve armed you with several action steps to collect and explore this data, I’m confident you’ll be compelled to seek additional methods to improve your employee selection process to enhance your employment assessment test.

The hard truth is that you hired your best, and you hired your worst. And both outcomes came from the same hiring process. That’s why understanding how those decisions were made and how those candidates were evaluated is a critical component to reducing staffing waste.

Part II, Part III, Part IV, Part V, Part VI, Part VII

Posted by: Joseph P. Murphy | July 1st, 2010
Permalink, 1 Comment »
, Tags: employee selection HR Analytics staffing waste | Posted in HR Analytics

Categories

Recent Posts