3.4.3 Hiring Analytics Primer

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From the course by University of Minnesota

Recruiting, Hiring, and Onboarding Employees

989 ratings

University of Minnesota

Recruiting, Hiring, and Onboarding Employees

989 ratings

Course 2 of 5 in the Specialization Human Resource Management: HR for People Managers

Finding and hiring the right people is often cited as the number one concern of businesses today. It seems we are all competing for the best and brightest workers. As you will see in our time together in the second course, a critical component of the People Manager Value Proposition is to hire talented people who enable the organization to achieve its strategic goals. This course is an introduction into the topic of recruitment, selection and onboarding. At the outset of the course we will explore the importance of linking recruitment goals with overall company strategy. We then look at a number of options to recruit and select employees both effectively and legally. Throughout the course we will examine current issues in talent acquisition, such as how companies are now leveraging social media and hiring analytics to ensure better quality hires. At the conclusion of the course, we look at how to onboard employees to promote employee commitment and engagement.

From the lesson

Selection: Choosing the Best Candidates

In this module, we examine the selection process. Like the first two modules, we start from the big picture - what is selection and how do we tie it to strategic goals? We then explore the important legal considerations (U.S. laws) and other best practices in selection, such as avoiding biases and conducting background checks. Next we will see a number of different selection tools you can use to make a better hiring decisions. Lastly, we wrap up the third module by creating and presenting offers to candidates, and analyzing our hiring process for effectiveness.

3.4.1 Candidate Evaluation8:28

3.4.2 Creating and Presenting the Offer Package10:28

3.4.3 Hiring Analytics Primer7:32

3.4.4 Recruitment and Selection Summary4:13

Meet the Instructors

Amy Simon
Senior Lecturer
Department of Work and Organizations
Stacy Doepner-Hove
Director, Masters in Human Resources and Industrial Relations program
Carlson School of Management, Department of Work and Organizations

[MUSIC]

We are drowning in a sea of data.

We have so

much information at our fingertips, we're not exactly sure how to tap into it.

Up until now, a lot of companies have spent time looking at numbers,

tracking numbers, performance dashboards, and KPIs, and all these different things.

And in this video, we actually talk about taking that a step further, and

actually analyzing that data, and using it to make better decisions in the future.

>> Okay, so how does metrics differ from analytics?

Let's start off by focusing on metrics.

So what are metrics?

Metrics are measures of efficiency, performance,

sorry my word got cut off there, progress, or quality.

So let's look at some examples then, related to recruitment and hiring.

So cost of recruitment operations may be things like

travel expenses that are related to the recruiter,

and any travel expenses you pay for the candidate to come to your facility.

Maybe you fly the candidate to see you,

any advertising expenses, supplies, postage.

So just the cost of recruitment operations is an example.

So I'll put that on our list here, cost of recruitment.

Another metric would be the acceptance ratio.

So, how many offers did you make, and how many were accepted?

Another metric would be a yield ratio.

So a yield ratio thinks about how many people move from one step to

the next in a process.

So we had 100 applicants, 50 people had phone screens,

10 came in for interviews, and we ended up having 3 and

final interviews, and we made an offer to 1.

Another one would be how many resumes did we receive?

If we know how many resumes we received from each source,

then that maybe tells us one source is better than another.

But it really doesn't tell us anything.

It's just a number by itself.

So overall, metrics provide a snapshot in time.

And they're really good for things like dashboard numbers, KPIs,

we used to use something called the balanced score card which measures

the financial numbers and different performance numbers of the organization,

and so lots of different metrics there that we can take to look at and on going

basis that gives us some sort of snapshot again of the health of the organization.

Okay so lets do a quick recap here.

Metrics do provide a snapshot in time, and they are good for dashboards and KPIs.

So, a KPI would be something like, it depends on the industry and

the organization, but

it might be something like the number of safety accidents.

How many people were injured in the last year?

How many of those injuries were lost time injures where the person had to be away

from work because of the severity of the injury?

Another KPI would be customers that have not come back.

So the customer's lost, the customer you have not retained.

And the next part then is that metrics lack insight, so

we may know that we've lost customers, people have not come back,

we don't have their repeat business, but we don't necessarily know why.

And the same thing with the injuries.

We know more people are being injured, or getting injured at work, but

it doesn't tell us necessarily why.

And so because of those things,

we're also unable to predict what's going to happen in the future.

We can look at past numbers, and we can start to see trends.

But trends don't tell the whole story, and there a lot of environmental factors and

other components that may affect future outcomes.

All right, so let's talk then instead of metrics, moving to the ideas of analytics.

And remember, we live in this world now where we have so

much data, it's really just a sea of it.

And it can start to feel very overwhelming.

The numbers that we can collect, and

the amount of information at our fingertips, because of technology we're

able to collect a lot of data that we weren't able to do that before.

Sometimes it feels as though you're almost drowning in a sea of data.

Okay, so the first thing we need to think about in terms of analytics, and

being actually able to analyze data moving beyond metrics,

is making sure we ask the right questions, because if we don't ask the right

questions, we're not going to get meaningful answers.

So perhaps the questions we need to ask related to recruitment and

selection are some of these questions.

Where do we find the best people?

Where do we find leaders and innovators?

So because metrics alone don't give us the insight that we need,

we really need to move beyond metrics and start thinking about analytics.

Analytics will help us find meaningful answers to the right questions.

Metrics might tell us how many people joined the company last month, but

if we use data, a number of sources of data, it can help us with

insight into which sources actually yield the best performers.

And beyond that, how do we define best performers?

We won't have that information just with metrics alone.

Another type of metric would be how many offers were accepted last month.

But more interestingly, and more relevant would be

what management practices actually impact offer acceptance?

What recruiter traits impact offer acceptance?

Here's another one.

What is the cost per hire?

I mentioned that before.

Instead, insight would tell us based on data,

what selection factors determine job performance of new hires?

So in our short video together, I'm not able to give you specific information

on how to utilize data to find the best hire.

The point of the video, is for you to really start thinking about how do I move

beyond basic matrix into finding data, getting insight from data to drive

better decision making throughout the organization,

not just related to recruitment and selection.

There are a number of tools available to you.

I happen to notice a couple of coursera videos, or courses I should say,

that deal with analytics, people analytics, I would encourage you to check

those out along with any other resources that are available to you.

So with data, with the information we pull from data, that helps give us insight

into making better decisions, it helps us to develop a more forward looking mindset.

But remember, you need to be selective.

You can drown in data with all the things that you can collect and investigate.

And it's really hard to investigate all of it.

So think about, what's the business impact?

What are the things that we should really look into that have a critical impact

on our business future, and the future of our organization, and our strategy?

And then also, make sure that you're asking the right questions in order to get

meaningful answers.

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