The Chief Analytics Officer’s Guide to Employee Attrition

By Greta Roberts, May 23, 2016

Available to Research & Advisory Network Clients Only

Much has been written about customer churn - predicting who, when, and why customers will stop buying, and how (or whether) to intervene. Employee churn is quite similar. Businesses want to predict who, when, and why employees will terminate, and if intervening will deliver good uplift effects. In many ways, it is smarter for a company to focus inward on employees.

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Corporate recruiters have a very important and difficult job. They predict who will be a top performer in certain roles, and protect against non-performers getting inside the business ecosystem.

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Employee Engagement is a Tricky Predictive Metric

By Greta Roberts, Mar 15, 2016

In my day job, my work focuses on using predictive analytics to decrease employee turnover or increase employee performance. One topic that frequently comes up is employee engagement data, and whether it is meaningful to the analysis. There can often be years of employee engagement data, and the data is typically in HR’s control.

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When beginning a new predictive analytics project, the client often mentions the importance of a “quick win.” However, implementing a quick win for a predictive analytics project can be difficult. There are at least two challenges, which I’ll describe, when taking a traditional quick win approach to predictive analytics projects.

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How Credit Risk Relates to Hiring New Employees

By Greta Roberts, Jan 07, 2016

After an employee is hired, it is too late to find out if they are a good risk. Like lenders, businesses need to be able to predict – before extending an offer – candidates with a greater probability of being successful in the role they’re being hired for.

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Whether you’re new to the concept of Predictive Workforce Analytics, or just brushing up, here are 12 essential rules to help Human Resources derive the most value from analytics.

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My plea to HR (and any other department hiring a Data Scientist)? Stop hiring real Data Scientists until you’re ready to do real data science.

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Four Functional Clusters of Analytics Professionals

By Greta Roberts, Oct 15, 2013

Available to Research & Advisory Network Clients Only

In this IIA Client Phone Briefing, Greta Roberts takes a deeper dive into the four functional clusters of analytics professionals, identified in the 2012 Analytics Professionals Study.

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All In a Day’s Work: Four Functional Clusters of Analytics Professionals

By Greta Roberts, Pasha Roberts, Aug 06, 2013

Available to Research & Advisory Network Clients Only

The groundbreaking 2012 Analytics Professionals Study by Talent Analytics, Corp and the International Institute for Analytics utilized many measures to understand the characteristics of modern analytics professionals and data scientists. The study examined 302 active analytics professionals in a diverse sample of companies, industries, sizes and circumstances.

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Quantifying Analytical Talent

By Greta Roberts, Jan 16, 2013

Available to Research & Advisory Network Clients Only

Modern businesses have embraced analytics in a bigger, deeper, more sophisticated, and more specific way than in prior decades. This enhanced focus has led to many things: the measurement of everything, many new studies, new departments, new roles, and several educational initiatives. Glowing articles in the press and social media fuel this activity.

Though there is a great deal of actionable analysis coming out of this work, there is plenty of hype to go around. A new job category has been introduced, the “Data Scientist,” in deference to the new scope and tools involved, especially around Big Data. Under-standably, there is confusion in the job market about what this role entails. Hiring managers, scrambling to find the ideal analytics professional, share anecdotal tips such as “find those with creative degrees in music or art” along with the usual technical expertise.

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