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In recent times, there have been massive investments by companies and
firms in internal databases and this has provided us the means to
provide an unprecedented look at the behavior of employees.
The interaction of those employees with the firm's customers and
a new way to think about and quantify employee productivity.
So my research is about taking these very large,
big data sets on the internal organization of firms.
To better understand employee productivity,
to better understand the design of intensive systems and
to better understand how these two aspects can be brought together to
improve the interaction of a company with its customers.
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There are two kinds of data that we're talking about here.
One is on the internal behavior of firms.
Off the type routinely collected by Human Resources Management Systems or
off the type collected routinely by ERP Systems or
Enterprise Resource Planning Software,
which collects data on what your employees are doing.
How they're spending time on their activities?
And combining this with compensation data.
For example, the salaries, quotas, commissions.
Putting these two aspects together, we can ask, which salaries work?
Which quotas work?
Which bonuses work for which sales agent?
And asked the more broader question, should we have bonuses at all?
Do quotas work well?
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Quotas and bonuses and commissions have very good incentive properties,
because they give you a lot of incentives to put in a lot of effort.
Because they form a goal that you have to work towards in order to earn commissions.
While that is true, the flip side is many people understand that quotas and things
like these also have some bad properties related to gaming or timing issues.
If sales people are gaming the quota in this manner,
it can actually create some really bad behavior and distortions.
In the past in theory, it was thought that these distortions are interesting,
but intellectually a curiosum.
Maybe not that important in the real world.
Some of the insights that we have been able to generate using real data from
real companies is that the magnitudes of these distortions can be so huge.
That the positive effects of quotas and bonuses may well be overwhelmed if they're
not designed correctly, because these distortions just have a huge magnitude.
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Different people are different.
There is lot of heterogeneity among salespeople, amongst your employees.
And it's not likely to be the case that there is a one size fit,
fits all approach.
The value of the data is that we can actually measure heterogeneity in
employees employee's skill.
And we can look at the data and
say that this employee in this particular territory.
For every time he called on a particular doctor,
his conversion of a call into sales volume is very high.
While some other employee when he called on a doctor for
a same number of calls, his conversion or production of sales is very low.
So we have the ability to use the data to fine tune contracts to
the individual employee level.
For example, certain kinds of employees who are quote unquote,
high powered might prefer that most of their compensation is
coming from commissions as opposed to straight salary.
So if you hi,
hire the right kind of employees, you could make a commission-based system work
much better than if you have employees who prefer straight salary.
On the other hand, if you are forced because of institutional or
within company considerations that you can have only a particular kind of
incentive scheme, you might think, well, I need to have the right kind of
employees in order to make this incentive scheme work.
What I'm trying to say here is that the hiring and firing of employees,
as well as the design of the incentives to incent them have to go hand and hand.
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