This course is designed to provide you with an understanding of the role of data and technology in human capital management. Every topic in the course will be covered in the most practical way so that learners get hands-on experience. In the course we use the 4Ts principle: Task, Theory, Technique and Technology so that there is always a connection to organizational performance objectives, an overview of underlying theories and principles, and specific tools which help achieve business objectives. You will learn ● what combination of data, technologies, and tools can be used in people management processes to improve organization’s performance ● how to use some of these tools and how to select the ones that suit your objectives and budget ● to design individual and team development plans and measure its ROI for the organization ● how to figure out the qualities that lead employees to their best performance so you know what to encourage in current and look for in new employees ● how to identify the right channels to recruit your employees or team members ● what combination of monetary and non-monetary motivation tools work best for your organization ● how to predict what people will leave in the near future and how to make sure some of them stay ● how to measure engagement and make a strong organizational culture improve performance
Lecture 3 Benchmarking
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來自 Moscow Institute of Physics and Technology 的課程
人力资源分析概论
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從本節課中
Compensation
In this module we will talk about various compensation models and you will learn how to do compensation benchmarking. In your second peer-reviewed assignment you will apply the new knowledge to conduct your own benchmarking project.
與講師見面
Ilya BreymanAdjunct Professor
Center of Innovative Educational Technologies
Alexey DolinskiyAdjunct Professor
Center of Innovative Educational Technologies
Hi, welcome back.
Data can be extremely helpful in figuring out what would work.
If you're lucky to have a lot of internal compensation data and
performance data at individual, team and
organizational levels, you can conduct analyses of various complexity.
This analysis, as always, starts with the right questions.
For example, what is the relationship between the performance-based portion of
compensation, and performance?
How did the recent changes in the compensation model affect performance?
If we increased commissions, would our salespeople sell more?
What is the effect of stock option plans on employee retention and
performance, depending on vesting schedules, percentages relative to
employees' fixed compensation, levels of hierarchy, length of tenure, and so on.
And obviously you have to keep an eye for
confounding variables that may explain the correlations, and
above all you have to be careful not to interpret correlations as causation.
While your internal data can help you understand how your incentive systems
affect performance,
external compensation data can be quite useful to benchmark compensation levels.
Quantitative research can help here.
The good news,
there are many sources of data that you can use to benchmark salaries.
The bad news, you still have to calculate, or this is good news if you like math.
There are three major sources.
Traditional surveys is one.
They are published by the government, associations and consultants.
They are usually very general and give you an aggregate view and
may be somewhat stale depending on the location where you are at.
This website, for example, gives you a wealth of information on all sorts of jobs
tracked by the US Department of Labor.
Most of the information is dated 2014 and you can get state-level data.
Such sources are the cheapest and
arguably the fastest method to get salary benchmarking.
The second source is online data from sites like Payscale and Glassdoor.
In places with strong government data transparency practices,
you can find public sector employee salary data openly available.
Another source of data, particularly for traded companies, is their filings.
Websites like Bloomberg aggregate this data, mostly on executives.
The best part about this data is that it gives you information on specific people,
anonymous, identifiable, or identified, but not averages.
The third source of compensation data is custom surveys,
which you can conduct on your own or have a consultant do it for you.
Custom surveys entail contacting competitor or similar companies
to learn about compensation levels for the jobs you are interested in.
The strength of this method is being able to tailor it to your task.
Regarding of which source of data you will pick,
you will have to go through the following steps.
Identify the jobs that you are benchmarking.
Concentrate on job substance rather than titles.
A salesperson may be called client director in one company, a sales executive
in another and a grand master of customer happiness in yet another one.
It's critical to understand that you're comparing green apples with green apples.
Figure out which sources would fit your purposes best.
Do you need very specific data, or just ranges would do?
Are you analyzing particular job market which is very different from others?
Is this a unique role, or a very commoditized position?
Are your competitors big companies with publicly available data?
These questions can help you pick the right sources.
Weight your data.
If you're doing a salary survey and one of your responders provided information on
three employees and another on ten, taking simple averages may not be a good idea.
The same applies to different sources.
How many salary reviews was your Glassdoor figure based?
How much do you trust the numbers you got from salary.com?
By weighting, you are basically deciding how much influence a particular source
will have on your final results.
Compensation is a critical topic
which is hard to avoid when talking about human capital.
But as we've seen, it is very hard to generalize.
Eventually, it all boils down to your unique situation. But unfortunately,
reward systems are seemingly the easiest to manipulate.
In the world where conventional wisdom is highly influenced by the concept
of the rational economic man, consultants and managers often
resort to fixing compensation models to resolve problems that lie much deeper.
As Jeffrey Pfeffer put it, it's simpler for managers to
tinker with the compensation system than to change an organization's culture.
Today with so much internal data one has access to,
one could gather it's important to look at compensation and
performance relationship without forgetting a myriad of other variables.
