People Analytics Q and A with Greta Roberts

People Analytics Q and A with Greta Roberts

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In this question and answer session, I talk to Greater Roberts from the USA on People Analytics. Greta Roberts is an acknowledged influencer in the field of predictive workforce analytics.

MN: What is people analytics?

GR: (a) To me people analytics goes outside the domain of the workforce.  It can also include analytics that includes customers, patients, sports figures and the like.  What all of these have in common is that data about a human being is being analysed.  For clarity – sometimes we refer to this aspect as workforce analytics. 

(b) Since our firm is in the predictive space only, the most important people analytics data exists outside of HR in the line of business.  This is where business outcomes exist.

MN: Are there differences between HR reporting and people analytics; please explain the difference?

GR: (a) Big differences, yes.  Reporting tends to only show data that exists inside of HR databases i.e. # of hires, # of terminations, salaries, benefits, time to hire and the like.

(b) People analytics tends to include data from other departments i.e. Revenue per sales rep., Accidents per truck driver, sales per banking commercial lender, calls per day for a call centre representative.  This data exists in the software systems that are in the line of business.

MN: What value can a business get from people analytics?

GR: (a) Our suggestion `is to find a way to have insight into business outcomes.  To move beyond only HR data -- to connect HR data to business outcomes as stated above. 

(b) When HR cares about and is tracking things like “accidents per truck driver” then they are truly aligned with the business.

(i) If HR does not know the business outcomes of the individuals they help to hire – there is still massive misalignment.

MN: Which areas of people analytics is a business likely to benefit the most?

GR: (a) We like pointing people, to begin with, a specific role where there are obvious business problems – where the business is losing a lot of money or where there is a lot of employee turnover.  We specifically encourage our customers to look at turnover in higher volume roles.

(b) Examples include sales reps., call centre reps., insurance agents, people working in retail and the like.  In these roles, there is typically a lot of turnover and even a small reduction could save millions for the business.



MN:  How can a business put a people analytics function within their organisation?

GR: (a) We recommend that an analytics person is hired into either the line of business i.e. sales operations or into the workforce.  We like them being hired into the line of business because they can quickly access the data they need and they can quickly learn the actual business needs quickly.

(b) However, this can work well too if the analytics professional is hired into the HR group.

(c) One huge recommendation: have the analytics person be a different person than the reporting person.  If they are responsible for both, they will never have the time to do anything but reporting.

(d) We recommend that less emphasis is placed on their prior experience in HR and more emphasis is placed on their experience with data and analytics.

MN: I understand people analytics need people with average to above-average numerical reasoning; How easy is to implement taking into consideration that HR tends to attract people with low numerical ability?

GR: This is a great question.  Not all people (from any department) can be trained to do a great job at being the one doing the analytics.  I am a great example of this.  I can speak about analytics and understand it to a point – but I would not be a great analytics professional (despite any amount of training).  Again, we recommend that HR hires an analytics person who loves analytics and may have little or no background in HR.  HR is just a different dataset to them.  Trust me – experience in HR does not matter.

MN: One of the biggest challenges in doing people analytics is the lack of data on both HR and business?   There is also the issue of data integrity in this area. How can organisations deal with these challenges?

GR: (a) Data integrity is an issue in any department.  It’s a normal part of the analytics process.  A great data scientist should be familiar with how to process data so that it is ready for analytics and predictive analytics. 

(b)Many times the processing of data, in preparation for the analysing – is by far the longest part of the workflow.  But it is an extremely important – and again a very normal - part of the workflow.

(c) Perfect data simply doesn’t exist and likely never will.

MN: Can you give us from your experience some of the practical examples where you have applied people analytics and you were successful?

GR: Certainly.  Since we work specifically in the predictive and prescriptive domain my examples will be from these areas.

(a) Customer #1: This multi-national financial services organization had many thousands of financial services representatives who were available on the phones for customer inquiry.  Customers would call to discuss their financial portfolios.  There is a government law that these representatives need to study and pass a rigorous certification prior to having these discussions.  The training prior to the exam takes 12 weeks.  If the representative did not pass the exam they were fired – making this a costly process.

The business problem? Too many financial services representatives were failing the exam.  The organization had restructured their hiring processes, their training, their coaching and many other processes to try to reduce the failure rate with little success.  Using a predictive modeling approach we were able to identify attributes of top (and bottom) financial services representatives who had a high probability of passing the government exam. 

In their first year alone they had saved more than $4 million USD.

(b) Customer #2: Like many companies, too many sales reps at this US based, Fortune 500 organization were not meeting their revenue goals.  Much attention had been paid to training, recruiting and the like.  But the revenue challenges persisted. We used a predictive modeling approach to finally identify the attributes of the top (and bottom) revenue producers. Because of its success and the implications of this project to stockholders, this project has visibility with the CEO and CFO of the firm.

Greta Roberts is an acknowledged influencer in the field of predictive workforce analytics. Since co-founding Talent Analytics in 2001, she has established Talent Analytics, Corp. as the globally recognized leader in predicting an individual’s business performance, pre-hire and post-hire. She has led the firm to use predictive analytics to solve line of business challenges making Talent Analytics one of the only firms in the world predicting business outcomes. Examples include predicting someone’s probability of making their sales quota or being able to process a certain number of calls, or make errors, and the like.  Her strategy has led to the development of Talent Analytics’ award-winning predictive cloud platform Advisor. She is a contributing author to numerous predictive analytics books and is regularly invited to comment in the media and speak at high-end predictive analytics and business events around the world. Greta continues to be Chair of Predictive Analytics World for Workforce, an innovative, annual predictive analytics event dedicated to solving workforce challenges.  She is an Instructor on Predictive Analytics for HR and Workforce at UC Irvine; she is a faculty member with the International Institute for Analytics (IIA), is a member of the INFORMS Analytics Certification Board.

Follow Talent Analytics at twitter @talentanalytics.

Greta Roberts
This article was written by Greta a Guest at Industrial Psychology Consultants (Pvt) Ltd

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